Design success into the office of the CDO

Peter Nichol
-
0
Design success into the office of the CDO

Every obstacle, hurdle and misstep raises awareness and decreases the likelihood of a recurring event. Use experience and wisdom to avoid the mistakes of others and find success when designing and implementing an office of the CDO.

Your office of the CDO needs a vision. Success won’t sprout from a rock. Leveraging data as a strategic asset can’t be done without defining a strategic approach to that data. Building a data strategy doesn’t prevent you from being agile in your approach. At the beginning, your vision of the organization’s data strategy might be fuzzy. That’s okay.

As you develop your data vision, provide guidance on how to unify business and IT perspectives, and promote value metrics from a data-driven culture, remember that change is welcome. If you’re headed down a path and not garnering the necessary organizational buy-in, change your course. There’s a better path forward. You just need to discover it—without going through the difficult journey that Milton Hershey did.

4 failures to success

The sweetest city in the world is Hershey, Pennsylvania. However, it didn’t start out sweet for Milton Hershey, an American chocolatier, businessman, and philanthropist. Milton didn’t see much use for school and only had a fourth-grade education. At the age of 14, he started an apprenticeship for a local printer. That was short-lived, and he was later fired for dropping his straw hat into a machine. He quickly was paired in another apprenticeship with a confectioner named Joseph Royer, based in Lancaster, Pennsylvania.

During his four years with Royer, Hershey learned everything he could about the candy business. Then, at the age of nineteen, he moved to Philadelphia to start his confectionery company. Unfortunately, he couldn’t make it in Philadelphia because of heavy supplier debts, so he moved to Denver, New York, Chicago, and eventually New Orleans, but never made a success of his business in any of them.

Throughout his journey from location to location and with each failure, Hershey was learning. He discovered that fresh milk is vital to good candy. In 1886, at the age of twenty-nine, he was penniless. Eight years later, he sold Lancaster Caramel Company for $1 million and turned to chocolate, where he soon founded the Hershey Chocolate Company in what would become Hershey, Pennsylvania.

Hershey became a successful businessman. When he died, he signed over all his company shares of the Hershey Chocolate Company, via a trust, to the Hershey Industrial School, which was an orphanage he founded. The shares were valued at $60 million. For reference, that same year, Coca-Cola sold for $25 million.

It’s fascinating what an individual can do with a vision and passion. Milton was famous for saying, “The caramel business is a fad.” At the time, he sold Lancaster Caramel Company, profits were at all-time highs. Yet, he sold his business and went into chocolate. Understand what makes your business successful.

4 pillars of success

To discover your company’s data-strategy vision, build around the four core aspects of the office of the CDO. These require careful set-up to enable your successful data office initiative:

  1. Governance
  2. Standards
  3. Architecture
  4. Talent and culture

Governance defines the process, establishes forums, and promotes strategic communication. Architecture creates the guardrails for reference architecture, identifies common lexicons, and develops an edge for the data platform. Standards elaborates operating procedures, specifies technical standards, and identifies design precepts. Talent and culture span education and training, skills, roles, and responsibilities of agile teams—which are the human aspect of change.

These four areas are the pillars of a successful office of the CDO.

Adjusting how we look at opportunities—including data—doesn’t happen overnight. We’re transforming an organization. To do this, we must lean on these four core pillars of a successful office of the CDO, which we’ll now discuss in detail.

Data governance: discovering better insights

Governance offers accountability for data, business agility, better compliance, IT agility, and stronger insights. Setting up seamless data governance facilitates stakeholder interactions and makes the decision-making process easy. Without this framework, decisions will spin, and participants will become frustrated that engagement isn’t consistent or uniform across business areas or divisions.

There are six areas of interest when we’re talking about data governance and the role of the office of the CDO:

  1. Enterprise data governance
  2. Data-quality management
  3. Master-data management
  4. Metadata management
  5. Data-protection management
  6. Data strategy and diagnostics

Enterprise data governance is the management of business data. This includes the overall management of the availability, usability, integrity, and security of enterprise data. Data quality management is a set of practices that aims at generating and maintaining high-quality data used for decision-making. The quality measures ensure that, throughout its lifecycle—from acquisition through distribution—the data is fit for use. Master-data management is a method for an enterprise to link critical data to a common point of reference. This discipline converges IT and business partners to ensure the uniformity, accuracy, stewardship, semantic consistency, and accountability of master-data assets. Metadata management is the administration of data that describes other data. Metadata involves any information that can be integrated, accessed, shared, linked, analyzed, and maintained. This organizational agreement describes enterprise information assets. Data-protection management enables data security across the enterprise—including automation, orchestration, and document management—to control the many data-protection activities required to run an enterprise. Data strategy and diagnostics is a guide for optimizing data, removing redundant data, and simplifying the lifecycle management of data.

There are many more areas we could add to this mix including data modeling and design, data integration and interoperability, documents and control, data storage and operations… the list goes on. However, we want to start with the basics.

Together, these six elements build the foundation of a robust, data-governance program.

Data standards: sharing data we trust

Data standards communicates enterprise data-sharing frameworks. The objective is to improve trust. We can measure the trust of the data by measuring the credibility, reliability, intimacy, and self-orientation of data.

Addressing trust gets us to the primary value of establishing a standard for data sharing. Who should participate? How transparent is the data? Who can share data? How do we remove misaligned interests?

Producers and consumers of enterprise data must meet baseline standards. Additionally, trust frameworks must be tailored to producer and consumer needs. Combined, this approach raises the level of trust and decreases the risk associated with data production and consumption.

Standards for data sharing describes the ways data can be shared. It also highlights how data can be restricted or access to the data can be increased by removing restrictions in the following sequence:

  1. No awareness of the data set
  2. Awareness of the data set
  3. Awareness of data scope and data dictionary
  4. Query highly aggregated, obfuscated, or perturbed data
  5. Query lightly aggregated, obfuscated, or perturbed data
  6. Access aggregated, obfuscated, or perturbed data
  7. Access to data
  8. Ability to share data

No awareness of the data set means the existence of the data set isn’t known. Awareness of the data set makes knowledge of the data set known. Awareness of data scope and data dictionary increases knowledge to include the scope and parameters of a data set—for example, knowledge or access to the data dictionary. Query highly aggregated, obfuscated, or perturbed data enables queries on data sets, but these are highly restricted—in this case, access to a division’s or a department’s data might be removed. Query lightly aggregated, obfuscated, or perturbed data slightly widens the endpoint of data access. For example, in this case, previous access may have been to a state-based population, and this opens access to multi-state searching. Access aggregated, obfuscated, or perturbed data enables the ability to run defined logical operations and pull de-identified data. This level of access provides access to an aggregated data set; however, access to the raw data set isn’t permitted. Access to data unlocks the technical restrictions of operations that may be performed with the data, although specific access rights are usually restricted to certain individuals. The ability to share data may allow sharing to one consumer but may restrict that consumer from sharing the data with another consumer.

Data trust is validated by the enterprise standards in place to share data.

Data architecture: integrating data investments with business strategy

Data architecture guides the integration of enterprise data assets. Architecture is focused on the abstraction of the system, not the system itself. As complexity within your enterprise increases, the need and value provided by data architecture become ever more valuable.

Data architecture has a broad reach and includes many components to build a single version of the corporate “truth.” These ten pieces of enterprise data architecture will ensure the following enterprise data assets are integrated:

  1. Business entities
  2. Business relationships
  3. Data attributes
  4. Business definitions
  5. Taxonomies
  6. Conceptual and logical views
  7. Business glossary
  8. Entity lifecycle and states
  9. Reference-data values
  10. Data-quality rules

Business entities refer to the various components of the business. Business relationships identify how those entities interact and ultimately share data. Data attributes tag and identify data elements using known classifications. Business definitions clarify the intent behind the data sets. Taxonomies establish schemes and classification system for groups or attributes with similarities. Conceptual and logical views identify a high-level relationship between entities and how the data is physically represented in the database. The business glossary defines terms across domains and serves as the authoritative source for the data dictionary. The entity lifecycle and states specify where the entity is in its lifecycle from acquisition to destruction. Reference-data values are standards that can be used by other data fields. Data-quality rules are the requirements that the business sets on its data.

It’s also useful to conduct an information value-chain analysis. This process identifies matrix relationships among data, processes, organizations, roles, locations, objectives, applications, projects, and data platforms.

Talent and culture: moving people not data

When we think of data, we often think of the bits and bytes. We should be thinking about people. Talent and culture are the most difficult aspect to get right when developing a successful office of the CDO.

There are seven main areas in driving culture and getting the right folks in the right roles:

  1. Talent acquisition
  2. Performance management
  3. Competency management
  4. Learning and development
  5. Leadership development
  6. Career management
  7. Succession management

Talent acquisition is the process of finding and onboarding skilled data talent. Performance management ensures that individual activities and outputs meet organizational goals. Competency management is the process of developing the skill sets of individuals. Learning and development attempt to enhance individual performance by tuning and honing skills and knowledge. Leadership development helps to expand an individual’s capability to grow within the organization. Career management is the deliberate planning and coaching of an individual’s activities, engagements, and jobs over a lifetime. Succession management is the systematic process of developing, identifying, and grooming high-potential individuals for more aspirational roles.

By developing a talent management plan, we improve our odds of attracting, developing, motivating, and retaining high-performing employees.

Designing a world-class office of the CDO

Designing a world-class office of the CDO begins with a vision and a data strategy. We establish a strong organizational base for scalability and growth by using the pillars of governance, standards, architecture, and talent and culture.

Take time to understand the intrinsic value of data. Is your data accurate and complete? Determine the cost value of data. If you lost your data, what would it cost to replace it fully? Evaluate the business value of the data. How fit-for-purpose is this data to make data-driven decisions? Measure the performance value of your data. What parts of the data fuel key business drivers?

Designing the office of the CDO is an exciting process. Let’s hope your venture into the data business is less bumpy than Milton Hershey’s entrance into the chocolate business.

Assembling the right resources for the office of the chief data officer

Peter Nichol
-
0
Assembling the right resources for the office of the chief data officer

Creating an office of the chief data officer is the first step in developing a data-driven culture and maximum business value.

We’ve come a long way from the first website, which was published on August 6, 1991. The Internet has over 1.94 billion websites. Over seven billion search queries a day are conducted worldwide, and over 15% of those are entered into a search box for the first time. Data is transforming how we do business and, more importantly, how we make business decisions. However, 51.8% of the traffic is solely from machine bots; the remaining 48.2% is from human traffic.

From this ongoing surge of data has emerged the chief data officer role—and, more recently, to support that role, the office of the chief data officer.

Establishing the right structure can have a positive impact on organizational transformations to drive a data-driven culture. Let’s address four questions that clarify the value of the office of the CDO:

  • What’s its purpose?
  • What are the primary office functions?
  • What resources and skills are required?
  • What are the major duties of the office?

The purpose

The CDO is an executive responsible for enabling and championing value creation for the organization through the use of data assets internally and externally. This includes governance, planning, definition, capture, usage of, and access to data and information. Generally, the CDO has accountability in three areas: data management, analytics and technology.

  • Data management captures the care protection and governance of data from establishing a strategy for designing the implementation policies for governance.
  • Analytics includes any capabilities required to analyze data to transform it into useful insights.
  • Technology covers the data architecture, infrastructure, and services for the ingestion, movement, monitoring, and storage of data.

The CDO is accountable for capturing high-quality and timely data and leveraging data assets to stakeholders. To fulfill this mission, we need to understand the purpose of this role. The role of the office of the CDO is simple: create value from data. To frame the context of the role, we’ll dive into its functions.

The functions

There are 100 ways to build a good data office, but there are only a handful of ways to build a great team. The office of the CDO needs to envision, prototype, evangelize, implement, and support existing and new data platforms. There are two broad paths that organizations can take here.

The first path is to have the office of the CDO run IT data operations. This means the CDO assumes responsibility for all database administrators and any resources that support the creation or maintenance of data assets. This could be in the form of custom systems, SaaS solutions or off-the-shelf solutions. The benefit of this approach is that the data increases in value while redundancy and cost decrease. The flip side is that day-to-day operational activities limit the focus to approaches geared to developing strategic data assets.
 
The second path is to have the office of the CDO run the IT “asset” operation. Here, we’re specifically talking about managing existing data assets and leveraging new ones. The benefit of this approach is it facilitates greater collaboration and the ability to share data assets. The disadvantage is the lack of raw-data ownership, budget limitations, and the requirement of additional, cross-functional buy-in before significant transformation can occur. Sometimes this buy-in doesn’t occur, which stifles progressive ideas that push the boundary of normal.

The resources

The resource makeup of the office of the CDO varies greatly based on employees and annual revenue so that this approach can take a number of forms. However, some common themes are observed. The variability is that one company might need one of a particular resource and another might need 100. Use your judgment to scale the primary functions based on your business demand.

Next, we’ll cover the following primary roles and the skills required:

  • Chief data officer
  • Data scientist
  • Data modeler
  • Data architect
  • Data analyst
  • Front-end designer/developer
  • Database administrator
  • Portfolio manager
  • Project manager
  • Business relationship manager

Chief data officers provide leadership on maximizing the value of data assets enterprise-wide. This role is responsible for leading the transformational change to position the organization so it’s data-driven. Driving the use of the right data at the right time, creating a data-driven culture, and leading analytics are vital. However, the most important aspect of the role is establishing and fostering organizational buy-in for the office of the CDO function as well as the future role data will have in the organization. Few leaders will argue that data is transforming business decisions and that business models are changing; the challenge is that those same leaders might not believe that your office of the CDO is the right team to do that. This is why establishing collaborations and building trust outside of IT is essential.

Data scientists help to identify opportunities to improve organizational outcomes by utilizing data, developing predictive models, and sharing stories that present new insights. There are seven major areas of significance to data scientists: data collection (web scraping, HTML, CSS), data ingestion (SQL APIs, JSON, XML), data cleansing (multiple data types), data visualizations (D3, Tableau, Spotfire), basic analysis (R, Python), data mining (variance analysis, measuring bias, feature normalization, feature selection, feature extraction, clustering analysis, association analysis) and predictive modeling (data modeler+, graph analysis, bootstrap or bagging modeling, ensemble models, Bayesian analysis, neural networks, deep learning). An effective data scientist can apply sample and survey methods, determine statistical significance, conduct outlier analysis and make data-driven decisions to identify new data-science opportunities previously undiscovered.

Data modelers use a variety of data types to build and design predictive models. To understand sampling methods and measure statistical significance, data modelers need to have much of the experience of data scientists. For example, data visualization, basic analysis, data mining and predicting models are key skills for this role.

Data architects develop linkages between systems. They need to have experience with multi-architectures and implementing complex database policies and standards. This background allows them to develop complete solutions to validate, clean-up and map data. Ensuring end-to-end data quality requires integrating data from unrelated sources. Having internal knowledge of the organization’s domains is a crucial element.

Data analysts facilitate data collection and aid in data cleansing with primitive analysis skills. Often this role is the initial drafter of organizational policies, standards, and procedures before more experienced resources assume ownership. These resources likely are familiar with R, Excel, and SQL at a high level but hit limits quickly when applying this to SQL APIs, JSON or XML applications.

Front-end designers and developers mainly focus on client-side development using technologies such as HTML, CSS, JavaScript, jQuery and RESTful service APIs. This code is executed inside the user’s browser and can extend into the UI/UX experience for users.

Database administrators specialize in software to store and organize data. Usually, this role includes capacity planning, installations, configuration, database design, data migration, performance monitoring of data, security, backup and recovery, and basic troubleshooting. This role is hands-on regarding data and, as a result, needs to be carefully managed with segregation of duties.

Portfolio managers focus on value realization from products, services, interactions, assets, and capabilities. This includes making investment decisions to balance objectives, asset allocation, and risk for optimal performance. This role aligns strategy with the bottom line to optimize delivery orchestration across the data portfolio of investments, projects, programs or activities.

Project managers lead data-related project initiatives and provide contract support to align with corporate policies. These resources work with multidisciplinary teams like legal, cloud, finance, operations and various business functions to lead projects and get them over the finish line.

Business relationship managers stimulate, surface, and shape business demand to define the full business value envisioned. This involves building credibility for the office of the CDO, establishing partnerships outside of IT to increase awareness of existing capabilities in house, and introducing new data capabilities that have force-multiplier effects for business partners.

Likely there are dozens of resources that could be pulled into a CDO team to align to organizational needs. The foremost that comes to mind are subject-matter data experts that have specific and deep domain knowledge of how your business operates.

Now that you know the critical roles to establish the office of the CDO, spend your time finding the best resources to staff your office. These resources are in high demand, so you must assume it will take longer than planned to recruit the team.

The duties

The primary responsibilities of the office of the CDO used to be focused on data governance, data quality, and compliance drivers. Today, the focus of this office is to enable a data-driven culture and maximum business value.

To exploit data to achieve a competitive advantage and establish the office as a strategic advisor, the responsibilities need to be communicated across the organization.

Leading change and championing a data-driven culture can be enabled with the following defined responsibilities:

  • Envision, design, and communicate a collaborative, enterprise-wide data strategy.
  • Establish a governance structure for managing data assets using a repeatable process and standardized frameworks.
  • Define, implement, and manage organizational data principles, data policies, data standards, and data guidelines.
  • Decrease the cost of collecting, managing, and sharing data while increasing the value.
  • Enable data-as-a-service for enterprise-wide adoption using a data-service strategy.
  • Develop data-quality measures and practices to improve organizational trust in data.
  • Manage the data portfolio to coordinate the investment prioritization of enterprise-wide data initiatives.
  • Identify opportunities for the organization to more fully leverage data for a strategic advantage.
  • Champion organizational change management for a data-driven culture.
  • Advance how enterprise-wide data assets are managed to provide deeper insights.
  • Establish policies and programs for data stewardship and custodianship for stakeholder engagement.
The office of the CDO gets the business of data onto the minds of your organization’s executives. It’s the first major step toward developing a data-driven culture. Data enablement is a change that requires shifting organizational strategies, processes, procedures, technologies and culture. Use these four tips when introducing organization-wide change, transformational change, personnel change, unplanned change or remedial change: Make it clear. Make it real. Make it happen. Make it stick.

Why RPA is a CIO priority

Peter Nichol
-
0
Why RPA is a CIO priority

Cognitive automation technologies are changing our business. RPA is the first step in that evolution. Be part of the business-value realization with RPA.

Robotic process automation (RPA) is the game-changer your organization doesn’t know about. There are only a few leaders in your organization who fully appreciate the potential of RPA. The hype about RPA reminds me of the hype about the Internet in the mid-1990s. We know it’s going to take off, but we don’t know where or how this idea of knowledge-sharing will be adopted.

RPA applies AI and machine-learning capabilities to perform a repeatable task that previously required humans to perform.

Similar to the concept of a blockchain, a large part of the slow adoption of this technology is related to education. Once you’ve internalized the power of RPA, you’ll quickly apply RPA-type concepts throughout your organization.

RPA isn’t a physical robot. It won’t deliver your FedEx package with a smile. It’s also not going to delivery your Amazon package in an air taxi on Sunday. The beauty of RPA is that it can automate activities based on rules and relieve your team of the burden of performing manual processes. Processes that are manual, repetitive, and have high error rates are where RPA excels.

RPA does three things well. It reduces cost, improves quality, and improves operational controls. It doesn’t matter whether you’re using Blue Prism, WorkFusion, Kryon Systems, UiPath, Automation Anywhere, or NICE. Each of these tools can help you realize better business outcomes.

Let me guess. You need to improve business outcomes quantifiably. You’re searching for that 10x game-changer for next year. You already promised business leaders some magic, and you have no idea where that magic powder will come from. Not to fear.

RPA has some fascinating applications for the next-generation CIO.

RPA for advanced analytics

Building a data lake? RPA can help. Starting a data-enablement initiative? RPA can help. RPA streamlines and automates time-consuming, high-volume, and repetitive activities. Big data requires data aggregation, curation, data cleansing, normalization, data wrangling, and tagging of metadata.

RPA offers amazing benefits to enable advanced analytics:

  • Removing the need to rekey data sets manually
  • Migration of data
  • Data validation
  • Producing accurate reports from your data
  • Foundation for an action framework: “good to know” (within tolerances), “interesting” (better than expected) or “need to act” (action required)
  • Aggregate social media statistics
  • Process mining technology to visualize the actual process
  • Ingestion from acquired sources
  • Link systems to systems (APIs)
  • Data rules for accuracy, consistency, validity, timeliness, and accessibility
  • Data deduplication
  • Scrape data from websites
  • Performing vendor master file updates
  • Data extraction
  • Advanced-processing algorithms
  • Formatting

RPA can handle even the most complex environments. If you’re able to record and play the activities, RPA can be a welcome operational improvement.

RPA for business-process waste removal

Data integration is the initiative that never gets finished. Somewhere along that last mile to fully automating the integration of those systems, there’s either no budget available or no interest. RPA can pick up and connect that last mile, removing waste in the process. RPA will be leading the next wave of increased productivity, and it can help tackle the eight major types of transaction-processing waste:

  • Defects; e.g., highlighting missed deadlines or overspend
  • Overproduction; e.g., extending reporting based on the severity
  • Waiting; e.g., waiting for approvals
  • Non-utilized talent; e.g., issuing and tagging training to employees when necessary based on events
  • Transportation; e.g., facilitating handoff between functions—like when an approval system isn’t talking to the PO and invoicing system
  • Inventory; e.g., processing data for entry into a larger system
  • Motion; e.g., removing repetitive keystrokes when switching between applications
  • Extra processing; e.g., formatting reports, adding details.

RPA is disrupting digital transformation and operational excellence. RPA’s fast and inexpensive approach to automation saves labor, extends capacity, increases speed, and improves accuracy.

RPA for project, program, and portfolio management

Haven’t heard of RPA and project management in the same sentence? News flash: RPA isn’t going to replace the human need for project managers.

Managing project budgets, monitoring risks, and balancing resource capacity all are functions central to the role of program and project managers. RPA can freshen up the definition of the standard of what’s deemed “good” when it comes to IT portfolio management. There are multiple ways in which automation minimizes risks and can streamline portfolio management activities. Here are the big hitters:

  • Create multi-thread, digital approvals for statements of work
  • Generate contracts using the company’s “gold standard”
  • Automate the creation and distribution of portfolio reports
  • Generate documents
  • Push communication of project variances
  • Balance resources; e.g., reporting on utilization and reallocating resources
  • Reduce the dependence on spreadsheets to manage information
  • Answer the question, Are we on track?
  • Collect and disseminate project-specific information
  • Screen, filter, and track candidates for the recruitment process
  • Create financial-scenario modeling based on thresholds
  • Automate data ingestion for dashboards; e.g., PowerBI or Clarity PPM
  • Provide sensors to continuously identify progress wins and capture value delivered
  • Forecast based on historical data
  • Assure PMO policy adherence; e.g., process documentation and project audits
  • Automate project and program SDLC process-step progression

RPA can play an important role in your IT portfolio ecosystem. The short duration (1-2 months) and low investment cost ($50-100k) makes an RPA pilot an easy win for your organization. RPA makes quantifying improvements easy. This metric-driven approach simplifies business-partner discussions when outcomes are immediate and visible.

RPA for IT asset management

Do you know when your licenses are due for renewal?  RPA and AI are going to transform IT asset management (ITAM). The nature of IT asset management is repetitive and standard. This taps directly into the sweet spot for RPA. Several applications exist for RPA within IT asset management. Here are the most impactful:

  • Automate software audits
  • Compare licenses purchased to licenses contracted
  • Manage source-code control
  • Oversee vendor and resource on-boarding and off-boarding; e.g., delete domain users or modify distribution lists
  • Provide reporting and analysis
  • Manage incident resolution; e.g., server restarts, password resets, etc.
  • Self-heal; e.g., system health checks, automating backups, and event monitoring
  • Automate fulfillment processes; e.g., IT asset requests

RPA won’t fix your broken workflows, but it can help automate them to ensure human errors are removed and process-cycle time is reduced. You’ll still need to spend time fixing the gaps in the process, but intelligent automation can integrate data extremely well from disparate data systems.

RPA for financial management (procurement-to-payment)

Financial processes are riddled with searching, transferring, sweeping, copying, pasting, sorting, and filtering. Financial-process automation will improve relationships with your suppliers and internal partners as well as improve efficiencies within the finance department. RPA can be used to validate contract terms against invoices and validate that standard data such as address and billing information wasn’t changed in recent invoices.

While the most obvious benefits are around financial risk and controls, cutting down on manual processes often presents even more positive organizational impacts. Freeing up overworked and overcapacity financial leaders can improve morale. By shifting resources from mundane, tactical activities to strategic, high-value-added activities like performing analysis and predictive modeling, RPA can become a force multiplier for financial-management teams.  Let’s look at a few of the many use cases for RPA for financial management:

  • Supporting the quarterly close
  • Calculating and anticipating accruals based on real, invoiced (and what’s not invoiced) data
  • Moving data from Excel to readable reports
  • Uploading transaction data from various financial systems
  • Generating standard journal entries
  • Identifying atypical and exception spending
  • Calculating and processing annual vendor rebates
  • Communicating to vendors missing or late invoices
  • Tracking vendor adherence to billing policies and best practices
  • Autoloading quarterly forecasts into the financial system of record
  • Reconciling forecast to actuals for departmental category spend
  • Monitoring CapEx and OpEx forecast to actuals variance

Operational financial and accounting processes are great examples of where RPA can shine. These processes often are repetitive and typically result in human error of some kind. Financial review prep, interdepartmental reconciliation, and financial planning and analysis all present opportunities for automation.

RPA for security

Protecting an organization from cyberattacks is a 24/7 job. The problem is that humans need sleep. RPA can humanize the role of the CISO and, almost more importantly, the role of cybersecurity managers. By leveraging RPA, we allow the function of the CISO to “get to human” by being visible in projects, developing organizational relationships, and inspiring new leadership. This isn’t possible when resources are consumed by cyber-threat prevention and mitigation at all hours of the night. RPA can strengthen and simplify your security operations in multiple ways:

  • Deploy security orchestration, automation, and response to improve security management
  • Shut down unauthorized privileged access
  • Robotic security and password configurations are encrypted and can’t be accessed by company personnel
  • Identify and prevent zero-day attacks
  • Cyber antispam (non-threat driven spam)
  • Cyber-threat identification, bot creation, and threat cleansing
  • Filter out false-positive threats
  • Issue consistent credentials enterprise-wide
  • Automate password rotation
  • Review 100% of access violations in near real-time
  • Improve security and auditing of data
  • Implement intelligent automation using artificial intelligence; e.g., creating tickets in ServiceNow based on threat analysis and immediately shutting down that risk
  • Identify atypical user and machine actions based on behavioral analysis
  • Lower the cost to detect and respond to breaches
  • Rapidly detect, analyze, and defend against cyber attacks
  • Identify behaviors that are unlikely to represent human actions

AI-enabled cybersecurity is increasingly necessary. The volumes of end-point data are exploding, and our budgets are not. Organizations need to turn to RPA as threats overwhelm security analysts. Detection, prediction, and response can all benefit from applying RPA to transform your organization’s cyber defense.

Intelligent automation shares the workload

Becoming more strategic starts when you stop spending all your time on tactical activities. That’s a difficult process when these tactical activities are required to keep our businesses running day-to-day.

Step beyond simply establishing an RPA center of excellence (COE). First, find the early adopters (the believers). These are the folks that push normal off the table and continually challenge what worked okay yesterday. Second, be collaborative. Seek out cross-functional leaders that you can educate to be champions in the pilot. Third, identify a problem. Focus in on a specific business challenge and articulate a business case where RPA is fit-for-purpose. Quickly move from a proof-of-concept (POC) that takes 2-3 weeks to a proof-of-value (POV) that takes 6 weeks.

Blockchain, cognitive analytics, augmented reality, and robotics all present huge and largely untapped opportunities for organizations. Your business model is changing. Be part of the change. Ideate together. Adopt quickly. Embrace the total value of ownership and apply RPA to accelerate business value.

How analytics help justify the role of the CIO

Peter Nichol
-
0
How analytics help justify the role of the CIO

CIOs require unified intelligence for data-driven insights leading to actionable organizational decision making.

Executive attitudes, application portfolios, and suppliers each affect the perception of the value that the CIO brings to the table.

Initially, the CIO was a functional unit head, making promises of rapid delivery. Next, the role progressed to the CIO as a strategic partner that enabled business partner convergence. Subsequently, it advanced into a business visionary role that drove strategy. Today, the role of the CIO is centered around business transformation and building a data-driven culture.

The CIO is a change agent. All the IT functions that support business operations must work smoothly, or not much transformational change can be realized.

To change much, you need information and intelligence. Here’s how we CIOs get that intel.

A long way from there

Our role as the VP of IT in the mid-1980s has come a long way. The role was focused on IT infrastructure and getting technology deployed into the business. Then we had the international push in the late 1990s, where our global business knowledge regarding expansion strategies was tested. We started decentralizing and introducing business-process standardization. The technology was aligned with business models.

By the 2000s, CIOs were technology integrators. We had global system integrations, and it was vital that technology integrated with business solutions horizontally across our organizations. As we moved into the 2010s, we shifted to designing the architecture of business. Relationship orchestration and solution integration dominated most meeting discussions. Our focus was getting technology to provide on-demand business services. As we open the door to the 2020s, the role of the CIO is all of those responsibilities and more. Today, we’re conductors of value and leaders of transformational change.

Before you, as a CIO, can change much, you need intel—insights, analytics, and predictions of what will be and a reminder of what was. How do we obtain those insights to change the course of organizational analytics?

A simple and powerful method to gain actionable intel is an organizational analytics dashboard. Why? It provides self-service access to real-time information on the health and well-being of your organization.

Separate the data from the visualization of that data. This could be presented from the same system or not. It’s less important that they’re the same and more important that data presented as information is clean and actionable. From my experience, often the entry of data and the visualization of data require different systems.

Here are the six essential components of an actionable CIO dashboard:

1. Business capabilities

Business capabilities articulate how an organization plans to integrate and standardize to enable the organizational business model.

By aligning all projects, initiatives, and work to a business capability, it becomes clear how that element further enables the organizational goals and, more specifically, the business strategy.

There are three general views that help illustrate business capabilities:

First, Gartner’s Run, Grow, and Transform model quickly helps to identify if you’re maintaining core systems; e.g., just keeping the lights on or truly transforming the business model of your organization. This model can be useful to build the classic images of technical debt increasing over time and spend on innovation decreasing. This is visualized with the typical chevrons left to right with dollars of spend listed annually by category.

Second, the Center for Information Systems Research (CISR) out of MIT looks at four different investment classes: strategic, informational, transactional, and infrastructure. This is portrayed in the visual form of a triangle. Strategic focuses on innovation, major change, facilitation, high-value adds, and deep customer interaction. Informational concentrates on increased control, better information, better integration, improved quality, and overall faster cycle times. Transactional is simply cost out and increased throughput. Infrastructure is foundational and covers business integration, business flexibility, reduced marginal cost of IT business units, reduced business cost and IT standardization.

Third, we have the classes of business capabilities and enabling technical capabilities. Business capabilities highlight major functions that enable the organizational mission and advance initiatives e.g., in healthcare, this could be claims processing or revenue cycle. Technical capabilities enable those business capabilities, i.e., analytics or cloud computing.

These three views provide CIOs actionable intel on organizational initiatives and their linkage to the business strategy.

Questions this component of the dashboard can answer:

  1. Is the technical debt impacting our ability to innovate?
  2. Do we have the right balance between foundational and transformational initiatives?
  3. Which are the top business capabilities we’re accelerating this year?

2. Balanced scorecard

This is a concept we’ve all heard about, but no one has seen. Well, maybe we’ve seen it, but it’s more like a sighting of a Dodo bird—more myth than reality.

Regardless, the balanced scorecard concept is sound. The idea is that a balanced scorecard connects the business strategy to key elements such as mission (our purpose), vision (our aspirations), core values (our beliefs), strategic focus (our goals), objectives (our activities), measures (our KPIs or strategic performance), targets (our desired performance), and initiatives (our projects).

This connection between strategic objectives, high-level strategy, measures, and strategic initiatives provides insights into how effectively we’re executing on the organizational mission.

Key areas to cover in a scorecard that focuses on portfolio delivery might include the following:

  • Forecast vs. budget variance
  • Schedule confidence for active vs. committed projects
  • Data validation issues per active project
  • Statements of work in progress for active projects
  • Average project size per project manager or business relationship manager (BRM)
  • Average spend per project (for the current year)

Together, these elements provide you, as CIO, with information to better guide the organization.

Questions this view can answer:

  1. How accurate is our ability to forecast spend?
  2. Are projects rolled up into programs for maximum cost and management efficiency?
  3. How confident are we that we’ll meet our business commitments (risk in delivery)?

3. Executive portfolio summary

The executive portfolio summary is the heart of portfolio management—how we manage strategic organizational investments.

Generally, there are three components to portfolio management: application portfolio, infrastructure portfolio, and project portfolio. For simplicity, we’ll wrap these into a single and unified view in our dashboard.

There’s an endless amount of information that can be included in this view. However, these are some of the most important elements:

  • Projects – active workstreams
  • Project managers – who’re leading what?
  • Project health – cost, schedule, scope, benefits, and quality
  • Value – are we improving outcomes quantifiably?

Usually, this component of the dashboard includes a lower, more detailed view with specific project information including business case, sponsor, status, budget, weekly status comments, etc.

Questions this view can answer:

  1. Are we spending wisely?
  2. Are projects moving through the pipeline smoothly, or do we have bottlenecks?
  3. Are we on target to deliver initiatives for the current year?

This dashboard component is often the most referenced view by leaders and team members alike.

4. Resource management and capability planning

The objective of resource management is to improve the on-time execution of initiatives, view resource availability, and analyze utilization.

The capability planning of resources isn’t only about accurately forecasting true resource demand and accounting for future resource needs. Oh sure, that’s part of it, but we’re talking about building the credibility of IT: Say what you’ll do, and do what you say. It’s expectation management. Taking the time to develop a resource-management and capability program that can be visualized as a dashboard for insights and action is one of the more significant business transformations a CIO can make.

There’s a lot to present, but let’s highlight the most useful areas:

  • Allocations and stage
  • Allocations and status
  • Named resource utilization
  • Allocations by project
  • Allocations by resource role
  • Allocations by resource team
  • View by business relationship manager (BRM)
  • View by functional manager
  • View by project manager
  • Dimensional pivots by committed work, year, project name, resource, cost, or utilization percentage.

This information empowers your organizational leaders and enables intelligence decisions. It also helps to shift into a data-driven mindset and ensure information floating up is accurate and grounded by hard data.

Questions this view can answer:

  1. Do we have a justification for headcount increases?
  2. Are organizational roles continually overallocated?
  3. Which functional managers require assistance in managing their business demands?

As a CIO, I find the resource view one of the most powerful. We know we’re only as good as our worst organizational link. This view helps us identify where we have weak links in the chain.

5. Financials

Financial management is integral to responsibly managing portfolio investments. Financial planning is often a quarterly cycle that’s all-consuming for an organization. Project managers are scrambling to true-up project forecasts. Functional managers are balancing demand with capacity. Leadership is anticipating delivery roadblocks.

Financials are a lagging indicator, but they still offer huge insights for a CIO. If financial spend is trailing forecast, it’s a good indicator that we have influences pushing against our ability to deliver organizationally. These influences could be political, environmental, social, technological, economic or legal.

When presenting financials, less is more. However, we do need the ability to drill down deep. The Six Sigma 5-Whys come to mind here. Why is the variance off? Why is that project the major contributor to our variance? Why is that vendor not responsive? Why are we paying when we’re not receiving delivery? Why do we have a contract that doesn’t tie deliverables to payments?

Here are key areas of concern for visualizations:

  • Budget
  • Forecast
  • Actuals and Accruals
  • Variance and absolute variance
  • Variance by project
  • Variance by project manager
  • Variance by functional manager
  • Spend by vendor
  • Projects by vendors impacted
  • Vendors with projects impacted
  • Contracts by status
  • Contracts by vendor
  • Contracts by functional manager
  • Contracts by business unit by vendor

Accurate financials can be a game-changer when it comes to managing business expectations. Financial accuracy is a discipline and a mindset that needs to be cultivated and rewarded internally.

Questions this view can answer:

  1. Which projects have a greater financial risk?
  2. Do we have contracts about to expire?
  3. Has our CapEx-to-OpEx ratio changed from our initial estimates?

Solid financial management is required to scale or grow any business. A variance of 5% on $50 million is $2.5 million. However, a variance of 5% on $5 billion is $250 million. The cost of lost opportunity could be the innovation game-changer your business partners are looking for. Take the time to put the people in place, establish the processes, and invest in technology to improve financial accuracy.

6. Investments by area (total cost of ownership)

“What have you done for me lately?” Investments by area help to immediately quiet those business partners that feel they never get a fair shake when it comes to funding. Who was actually allocated what dollars has no relevance in their reality. These difficult business partners find a way to consume time that would be better spent on other areas. In their defense, they’re often the most steadfast defenders of value for their business. We, as CIOs, need to meet them halfway.

The investment-by-area view tracks financial performance for their business unit. Project-cost accounting tracks financial performance of resources (employees, non-staff augmented resources and vendors), software, hardware, projects, licensing, infrastructure, vendors, run (keeping the lights on), product development (small enhancements), equipment, professional services, and more. Everything the business consumes financially is tracked in investments by area view.

Ideally, this is project-cost accounting for each business unit rolled up to its functional head or executive leader. Not all organizations are ready for project-cost accounting. In that case, a “show back” approach is useful and serves as a step in the direction of adopting project-cost accounting. This approach shows business partners their investments by area. Investments by area sound more strategic—like we’re investing in innovation—whereas total cost of ownership sounds like we’re paying off debt.

Questions this view can answer:

  1. How much value is IT providing to the business?
  2. What’s the financial commitment from IT to our business partners?
  3. Is spending on each category reasonable based on our strategy?

This approach should illustrate the end-to-end cost. This includes the cost from inception (plan, build, run) to destruction (sunsetting the technology). This complete, multiple-year view helps business partners to understand how their decisions have a direct impact on committed spend in future years.

Is your role secure? Do you feel you have organizational and business-partner support to drive your agenda and enable the strategic mission? You might. You might not. Establishing a CIO dashboard can provide hyper transparency in your IT’s ability to meet expectations. A CIO dashboard offers insightful and actionable intelligence that’s vital to justify the role of the CIO. Are you justifying your role?

First, define “good” to scale your organization

Peter Nichol
-
0
First, define “good” to scale your organization

Reposition your organization for success by connecting your vision to behaviors for organizational growth.

Next year will be here soon. As the new year begins, your leadership will be shouldered with the same challenges. Team gaps that slowed progress this year will still be present unless the team structure, department, and organization change.

Have you recently been asked to step in and help transform an underperforming team? What’s the first thing you do? Maybe you quickly identify low-hanging fruit (immediate gaps) that could be solved in under 30 days. Next, you might explore tactical areas (short-term wins) that could be addressed in 30-90 days. Finally, you may look at strategic areas that would take more than 90 days to solve (game changers). There’s just one problem. You’re taking the same approach and applying the same tools as your predecessor.

Stop asking your team what activities they’re doing. That approach doesn’t work. What does work is defining “good” up front.

Use influence to define “good”

Constructed for the 1889 World’s Fair, the Eiffel Tower today remains one of the most-visited monuments in the world that visitors have to pay to see. The tower spans 324 meters—about the height of an 81-story building. Maurice Koechlin and Émile Nouguier were co-designers of the Eiffel Tower and published the first designs of the tower in 1884. However, credit for the tower’s final design and engineering is usually attributed to Gustave Eiffel.

Eiffel had a vision of what good looked like, which was based on the influence of existing architectural excellence. Eiffel could have modeled the tower’s structure to be similar to the copper Statue of Liberty designed by French sculptor Frédéric Auguste Bartholdi. His vision could have mirrored the Washington Monument, designed originally by the architect Robert Mills and constructed of marble, granite, and bluestone gneiss. The Washington Monument was also the tallest structure in the world at the time. Eiffel might have leveraged the design of the Cologne Cathedral as a model of architectural brilliance, illuminating the original Medieval plan. The cathedral was completed in 1880 and is constructed of stone with wooden internal features.

Despite the more than 5,300 plans and drawings that had been created for the tower, Eiffel took an alternative approach. He first defined good and then took advantage of progressive materials available at the time—specifically, puddling iron, a precursor to construction steel.

Focus on the right stuff

You’re not alone in your struggle. Each day in the office, you’re putting out fires. Business relationship managers (BRMs) are fighting to move from tactical order takers to trusted strategic partners. You strive to have that strategic discussion, pontificating upon a mythical day in which there’s team stability and consistent value delivery nine months down the road.

Inevitably, the harsh reality of today’s organizational dysfunction brings you back to address recurring problems—problems thought to have been previously solved. Do any of these sound familiar?

  • The license for one of your development products expired.
  • A vendor was working at risk after a statement-of-work expired.
  • Resources had been working hard—just on the wrong stuff.
  • Work appeared to be happening, but the outcomes weren’t being realized.
  • The team size was perceived as bloated, yet delivery continued to waffle.
  • A hero complex was embraced when issues arose, and root-cause analysis was an afterthought.
  • Operational urgencies had bled out strategic energy.

Often, leaders fail to define good before taking action. When absorbing a new team that’s struggling to perform or recharging an existing team, don’t ask everyone on the team what activities they perform. At best, you’ll document the current state of activities that defined low performance; at worst, you’ll miss the real activities not being performed that directly contribute to the dysfunction of the team. You must first define good.

A blueprint for organizational redesign

Let’s take a lesson from Eiffel about his approach to building the Eiffel Tower. A similar approach can be used to shift organizations from low-performing to high-performing.

The Applied Convergence for Organizational Excellence approach (ACOE, pronounced as “ace”) is a technique I’ve modeled over the years. The process has 10 steps to help you define good for your organization or team.

  1. Apply frameworks for influence
  2. Elaborate specialized roles
  3. Align capabilities to roles
  4. Assess the capabilities
  5. Clarify the roles
  6. Define good
  7. Map individuals to roles
  8. Interview for role misalignment
  9. Reset job responsibilities
  10. Share the vision

You now have defined good.

Illuminating the journey to good

Apply frameworks for influence means basing your capabilities on one or more frameworks such as Control Objectives for Information and Related Technology (COBIT), Information Technology Infrastructure Library (ITIL), IT Service Management (ITSM), Skills Framework for the Information Age (SFIA), or IT Capability Maturity Framework (IT-CMF).

Elaborate specialized roles means you expand each capability or function into roles that would be applicable for the most mature and specialized organization.

Align capabilities to roles refers to defining twelve core roles and aligns all capabilities into one of these twelve roles.

Assess the capabilities involves reviewing each capability to determine the maturity level of that capability (0% non-existent, 25% developing, 50% defined, 75% managed, or 100% optimized).

Clarify the roles means capturing individual feedback and ensuring that resources participate and provide feedback on the roles.

Define good is a step that sets the stage for what good looks like in terms of organizational capabilities and roles.

Map individuals to roles refers to linking existing individuals to the newly defined roles.

Interview for role misalignment involves asking each individual in a role today to identify responsibilities they believe are within their job description. It doesn’t matter if the resource says, “yes, it’s in my role” or “no, that’s outside my responsibility.” Your goal is to identify the gaps not being addressed today; e.g., the gap between poor performance and your definition of good. A simple application of this step is to put the role responsibilities in a two-column, PowerPoint slide and then highlight any responsibility the resource feels is outside the scope of their work in blue. The responsibilities in blue highlight the organizational gaps by role to get to good.

Reset job responsibilities means creating a new job baseline for the resources and establishing clear ownership for all capabilities and functions.

Finally, Share the vision is a process that communicates the converged vision of the new capabilities and roles across your organization.

At this point, you’ve defined good. The current organization’s responsibilities are clear, and the gaps to advance the organization to good are identified by role.

Champagne at the top

It’s important to consider the influence of great organizations when designing organizational structures to foster high-performance teams. However, like Eiffel, be sure to apply new materials, ideas, tools, and techniques in your approach. The creators of existing organizations didn’t have the luxury of incorporating these new patterns and designs into their models. If they did, maybe they’d be on top of the Eiffel Tower, toasting at the champagne bar. It’s hard to beat a good glass of bubbly.

Your peers are going to suggest you survey individuals to understand organizational gaps by taking an inventory to capture all the aspects of poor performance. Think differently—play chess while your competitors are playing checkers—and begin by defining good.

Portfolio velocity: the new measure of business value realized

Peter Nichol
-
0
Portfolio velocity: the new measure of business value realized

Welcome to the last time you report on the number of projects your team delivered.

Are you still talking about how many projects your team delivered last year? Please don’t. You’re misrepresenting what your team, department, and organization delivered. Stop talking about projects completed and start talking about portfolio velocity.

At the beginning of each year, CIOs need to commit to deliver work. This could refer to a group of critical projects, features, buckets of work, or even just finishing certain carryover projects. Each year, we make these commitments, and each year we tell ourselves there must be a better way.

The fallacy of using projects as a metric for capacity

How does your team estimate their capacity for the year? The most common measure is the simplest: the number of projects completed. On the surface, this sounds reasonable. We have critical projects. If those projects are completed, we declare victory. There’s just one minor problem: this method is flawed.

Organizational growth presents huge opportunities, and it carries equal challenges. Living and dying by the number of projects your department delivers doesn’t work. Here’s an example of why:

Year One went great. There were 100 projects completed. The team is perceived as really delivering. In Year Two, the team struggled to deliver 50 projects. And, by Year Three, the team delivered only 25 projects. What happened? The budgets for the projects were doubled each year. Staff was added. There’s no excuse. The peppering of reasons—ranging from too much work to not enough time—fall on deaf ears.

This isn’t the full story. We need to add context. When the team was delivering 100 projects, they weren’t complex, averaging less than two months with budgets of under $50K. Companies mature as they grow. Year Two required additional roles beyond the original technical lead and developer. An architect modeled the solution. The security lead addressed authentication and authorization. The quality lead tested for external interfaces, which didn’t exist in Year One. A project manager managed the vendor relationship and procurement process.

In Year Three, complexity, effort and the budget increased. Projects required more rigor to ensure success working across time zones, engaging third parties, and with more business and technology complexity. Projects involved multiple departments, which required shared services—e.g., legal, procurement, infrastructure, service desk, etc.—and demanded an average of four to nine months of effort to deliver. Projects also required more funding for delivery given the increased number of participants.

We know that going from 100 to 50 to 25 projects delivered per year isn’t a story we want to tell. Our team has worked harder each year. We’re building a team of more competent staff. The organization has provided greater funding each year. We need to rewrite this story.

Agile velocity

Velocity is a great tool to measure the performance of a team. A team’s velocity is the amount of work a team delivers over a fixed period. In Scrum, velocity is commonly measured in “points,” also known as story points. The points measure the effort for a given story, i.e. a specific piece of work.

Consider three factors when estimating points: complexity, effort, and doubt.

Using a scale of 1 to 5, 1 is the least complex and 5 is the most complex. What matters is the relative value of the points. We could have chosen 1,000 to 5,000 or 100,000 to 500,000. What matters is that a 2 is twice the value of a 1. Agile teams can have trouble with absolute values. The concept of triangulation helps ensure consistency. Periodically during the estimating process, compare a 1-point and a 2-point story. Discuss whether the 2-point story is twice as complicated.

The estimation fallacy

In his book, The Black Swan: The Impact of the Highly Improbable, Nassim Taleb describes several lessons that impact how we estimate:

  1. Epistemic arrogance: people claim they saw it coming, e.g., we estimate well in general except for ‘’
  2. Narrative fallacy: missing cause and effect, people bend stories to fit current understanding, e.g., this project should be like that one
  3. Asymmetry: an unequal upside and downside, e.g., missing by a factor of 2 is easy

Michael Bolton provides a great example of project estimation fallacies with Black Swan-like events. In his example, a project is estimated to take 100 hours with each task requiring 1 hour. He added in some basic assumptions:

  • 50 percent of tasks complete in 30 minutes, not 1 hour, i.e. the task is completed early
  • 35 percent of tasks complete on time
  • 15 percent of tasks experience bad luck.

Our 15 percent of “bad luck” tasks is broken out into the following assumptions:

  • Little slips: 8 tasks slip to 2 hours
  • Wasted mornings: 4 tasks slip to 4 hours
  • Lost days: 2 tasks (one in 50) slips to 8 hours
  • Black cygnets (baby black swans): 1 task slips to 16 hours, i.e. 1 in 100 tasks takes two days

Using this simple example, the probabilistically average project would come in 24 percent late. A project that was committed for Q4 would be delivered in Q1.

Introducing portfolio velocity

Our goal is to estimate the departmental capacity to deliver value. How do we measure this? Portfolio velocity.

Accuracy doesn’t matter, but consistency does. We can apply this new measure to estimate our departmental capacity. To calculate your portfolio velocity, perform the following steps:

  1. Select a project
  2. Estimate the complexity on a scale of 1 to 5, with 5 being the most complex
  3. Estimate the effort by estimating the number of months to complete the project ranging from 1 to 12
  4. Calculate the product by multiplying the complexity by the effort to determine the project velocity
  5. Sum the velocity for all projects to determine the portfolio velocity
  6. Calculate historical portfolio velocity for past years
  7. Determine reasonable growth rates on top of last year’s velocity
  8. Rank the projects in the portfolio 1 through n
  9. Draw an above and below the line based on your new forecasted velocity

By completing through step 5, you now have, in your hands, the forecasted demand. To determine if this velocity is reasonable, we need to conduct a brief exercise to discover the historical portfolio velocity by looking at prior years and conducting a similar exercise for projects from these years.

Let’s assume we determined that the velocity for Year One was 200 points, Year Two was 300 points, and Year Three is forecasted at 1200 points. Already our story is changing for the better. However, it’s clear our capacity isn’t going to increase four-fold.

Assuming moderate growth of 10 percent, we can forecast our Year Three velocity: 330 = 100 percent + (10 percent x 300). Using the ranked list, we can determine which projects will be delivered in Year Three and keep us at or slightly below our target portfolio velocity.

Estimation exploratory options

The 1-to-5 complexity scale works well. Alternatively, a base-2 sequence (0, 1, 2, 4, 8, 16, 32, 64, 128) or a Fibonacci-like sequence (0, 1, 2, 3, 5, 8, 13, 20, 40, 100) can help make the difference between projects more distinct.

However, humans are notoriously poor estimators, and I’ve found that teams more effectively estimate based on common orders of magnitude such as 1 to 5 or 1 to 10. It’s easier for a team to estimate the size of a mouse at 1 and an elephant at 5 versus a mouse at 1 and an elephant at 8 or a cargo ship at 64.

Measure what’s valuable

Too often, we get swept away by maintaining historical measures. Being accountable is good. Ensure you’re holding your team, department, and organization accountable for the right measures.

Make this year different—with hard commitments—by determining your portfolio velocity.

Do you have a data strategy to achieve better organizational analytics?

Peter Nichol
-
0
Do you have a data strategy to achieve better organizational analytics?

Every company is talking about analytics, but only a handful have a simple data analytics strategy.

Big-data analytics, actionable insights, and powerful outcomes are the de facto expectations for data-analytics programs. Is your data strategy aligned to deliver those results?

Organizations are seeking sophisticated analytical techniques and tools to gain more profound insights into how they can capitalize on the blue ocean of data analytics. Listen this week at your office and you’ll undoubtedly hear whisperings about harnessing the power of analytics. It might not be called data management or big-data analytics, and the questions might be more subtle, such as:

  • How do we discover new insights into our products?
  • Which operational capabilities will deliver the highest ROI?
  • How do we leverage our data to generate better strategies and execute with improved confidence?

Managers and leaders alike are searching for approaches to tap into the value of big-data analytics. What exactly is a big-data analytics strategy?

A comprehensive data analysis foundation

Set the frame mentally of the building blocks of a world-class data-analytics program. It doesn’t need to be perfect. Identify the critical components that make up a data-analytics foundation:

  • Presentation layer: where the dashboards and workflows live
  • Big–data processing and analytics layer: the base for pattern matching, mining, predictive modeling, classification engines, and optimization
  • Data-storage and management layer: relational data systems, scalable NoSQL data storage, and cloud-based storage
  • Data-connection layer: data sensing, data extraction, and data integration

The analytics framework can also be segmented into four phases: descriptive, diagnostic, predictive, and prescriptive. The descriptive phase defines what happened. The diagnostic phase determines why it happened. The predictive phase forecasts what will happen. The prescriptive phase identifies what action to take. Together, these phases help leaders classify the types of questions they’re receiving. These can also highlight capability deficiencies.

Big-data analytics frameworks

Frameworks—in data analytics—provide an essential supporting structure for building ideas and delivering the full value of big-data analytics.

Does it matter whether your framework is bulletproof? No, it doesn’t. It’s important that the framework provide a set of guiding principles to ground thinking. Establishing common principles prevents revisiting the same topics.

Think of a data-analytics framework as an ontological approach to big-data analytics. There’s one framework that’s particularly useful—the annual Big Data Analytics World Championships for Business and Enterprise—which stresses the following:

  • Practical concepts: predict future outcomes, understand risk and uncertainty, embrace complexity, identify the unusual, think big
  • Functions: decide, acquire, analyze, organize, create, and communicate
  • Analytics applications: business insights, sentiment analysis, risk modeling, marketing-campaign analysis, cross-selling, data integration, price optimization, performance optimization, recommendation engines, fraud detection, customer-experience analytics, customer-churn analytics, stratified sampling, geo/location-based analysis, inventory management, and network analysis
  • Skills and technical understanding: data mining, statistics, machine learning, software engineering, Hadoop, MapReduce, HBase, Hive, Pig, Python, C/C+, SQL, computational linear algebra, metrics analysis, and analytics tools (SAS, R, MATLAB)
  • Machine learning: machine-learning tools, supervised learning, Monte Carlo techniques, text mining, NLP, text analysis, clustering techniques, tagging, and regression analysis
  • Programming: Python basics, R basics, R setup, vectors, variables, factors, expressions, arrays, lists, and IBM SPSS
  • Data visualization: histogram, treemap, scatter plot, list charts, spatial charters, survey plots, decision trees, data exploration in R, and multivariate and bivariate analyses
  • Fundamentals: matrices and linear algebra, relationship algebra, DB basics, OLAP, CAP theorem, tabular data, data frames and series, multidimensional data models, ETL, and reporting vs. BI vs. analytics
  • Data techniques: data fusion, data integration, transformation and enrichment, data discovery, data formats, data sources and acquisition, unbiased estimators, data scrubbing, normalization, and handling missing values
  • Big data: Setup Hadoop (IBM, Cloudera, Hortonworks), data replication principles, name and data nodes, Hadoop components, MapReduce fundamentals, Cassandra, and MongoDB
  • Statistics: ANOVA, Skewness, continuous distributions (normal, Poisson, Gaussian), random variables, Bayes theorem, probability distributions, percentiles and outliers, histograms, and exploratory data analysis

Use these eleven lenses to define your data-analytics strategy. Unfortunately, the framework won’t replace a great leader who understands how to execute these programs successfully. It will, however, help steer the conversations in the right direction.

If your team is less familiar with the principles of big-data analytics, use these questions as a guide:

  1. Practical concepts: What future outcomes do we want to predict?
  2. Functions: Do we have a methodology or process to mature data-analytical requests?
  3. Analytics applications: Which insights are we seeking to generate?
  4. Skills and technical understanding: What skills and competencies are critical for producing new organizational insights?
  5. Machine learning: Which business capabilities would benefit from enhanced machine-learned capabilities?
  6. Programming: What are the most important technical programming skills to mature within the organization?
  7. Data visualization: Which visual representations lead to the best decisions?
  8. Fundamentals: What layer has the greatest potential for transformation—how we make decisions involving presentation, big-data processing, data storage, or the data-connection layer?
  9. Data techniques: Which data transformation techniques are essential to move us from data to information?
  10. Big data: Based on our business architecture, which technology components are foundational to providing intelligent data analytics?
  11. Statistics: How do we envision data being categorized and analyzed?

Making your data strategy actionable

There are thousands of ways to develop a big-data program but only one method to measure success: Did we achieve the outcomes desired?

Leveraging top-down and bottom-up interaction models helps to lock in value and prevent leakage. Use the below categories to group ideas in your process of forming an actionable plan. Once this exercise is complete, place each interaction on the y-axis.

  1. Overarching strategy: defines the value and categories of results
  2. Tactics: articulates how value will be created
  3. Measurement plan: identifies program success metrics, KPIs, and tracking mechanisms for tracking to the plan
  4. Analytics: captures predictive modeling to forecast experiments—largely to perform correlation analysis—leading to specific actions
  5. Optimization opportunities: maximizes investments for the agenda with the highest probability to achieve the greatest outcome

hen list the three-tiered approach against the x-axis:

  • Quick wins: under 30 days
  • Intermediate wins: 31 to 90 days
  • Long-term wins: greater than 90 days

The result is a graphical view of your data strategy. This approach will help your team generate ideas and determine a general sequence of delivery, weighted by the idea that will most significantly impact the organization.

The secret of successful big-data analytics programs

Different stakeholders will be using your organization’s data for different reasons. Perspectives matter. Data analytics are changing the way company decisions are being made. Data engineering, domain expertise, and statistics each can play a role in the discipline of data science for your organization. Understanding concepts such as mathematical techniques is increasingly more important to extract the maximum information from large data sets. Roles we hired for—even two years ago—don’t have the raw skills required to communicate the salient features of data succinctly.

Using a combination of “big” data and “little” data creates the foundation for quick wins. Sure, after reading an entire book on a particular subject you’d gain more insights, but often even reading a chapter or two can offer substantial perspectives. Start small with little data and build strategically to achieve big-data analytics success.

The capabilities and roles of world-class, master data management

Peter Nichol
-
0
The capabilities and roles of world-class, master data management

Business strategy achievement requires data management capabilities. Define these first.

Data management enables the storing of everything from genomic data to Xbox scores to your Pandora playlists. If the data were unified, we’d have the beginning of master data management.

Organizations have data throughout their environment that provide single views of key data entities common across their organization. Data management provides a single view of data. Master data management provides a complete view of your organization’s data.

Broadly speaking, civilization has witnessed five generations of data management following manual processing using paper and pencil:

  1. Mechanical punched card: data processing
  2. Stored program: sequential record processing
  3. Online network: navigational set processing
  4. Nonprocedural: relational databases and client-service computing
  5. Multimedia databases: object-relational databases with relationships

Data models, scaling, automation, integration, and workflows increase the complexity of generating usable information from data.

Technology leaders who are thinking ahead must answer three questions to stay competitive:

  1. Why is master data management the backbone of an organization?
  2. What capabilities are required for business-strategy achievement?
  3. How do these capabilities translate into tangible roles within my organization?

The business case for master data management

Master data management maximizes business outcomes with improved data integrity, visibility, and accuracy. The result is better decision-making. The efficiency and effectiveness of decisions are at the heart of every organization. Are you deciding on the best location for that off-site meeting? You need data. A list of the top 1,000 venues is interesting, but a cross-section of the top ten sites—as ranked by attendees over the last three years—is more useful. Are you developing your business strategy? A summary of 100 business cases with corresponding business strategies is useful, but a revised view of only business strategies that were successful provides more meaningful information.

We collect data. We assemble information. We create knowledge. It’s knowledge that we’re striving to generate. To get there, we need people, processes, and tools to enable the best decision-making possible.

Better decision-making, reduced operational friction, and repeatable processes all benefit from understanding how your organization values and utilizes information. Achievement requires a master data-management program.

We’re talking about capabilities

Competencies and capabilities are different. Competencies measure how a company can deploy resources and use them to achieve business strategies. Capabilities, on the other hand, are the abilities, resources, activities, routines, and processes to build a competitive advantage. Competencies are skills. Capabilities are abilities.

Here’s another way to delineate between competencies and capabilities. Competencies are individual characteristics and capabilities are organizational. Let’s address the organizational elements.

An organization’s capabilities are core functions or the secret ingredients for success.

Master data management has three, high-level capabilities: business capabilities, information-services capabilities, and data-management capabilities.

Business capabilities

  • Governance: the political process of changing organizational behavior by an established system of who has the right to make decisions
  • Stewardship: business ownership of data quality for one or more subject areas; deduplication; maintaining hierarchies; and developing business rules
  • Platform and architecture: technology and data-management assets including data modeling, data architecture, and metadata management (data dictionaries, glossaries, and data lineage)
  • Security: data availability, protection, disaster recovery, and data redundancy

Information-management capabilities

  • Intelligence: ad-hoc query and real-time dashboard capabilities; making the data usable
  • Analytics and visualization: core reporting, advanced analytics and risk management, regulatory and statutory reporting
  • Workflow: process-model data flows
  • Quality: dimensions of data quality
  • Integration: model connection interfaces to entities

Data-management capabilities

  • Operations: operational transactions and business processes of the enterprise
  • Data acquisition: ELT, audit, balance and control, and testing
  • Curation: the active, ongoing management of data throughout its lifecycle from creation to archiving or deletion.
  • Science: data mining; establishment of methods, processes, algorithms, and systems to extract knowledge or insights from data in various forms, either structured or unstructured
  • Performance: enterprise performance management of thresholds and tolerances

Design of progressive data management programs accounts for the social, business, and technological changes that can affect how data is managed throughout an organization. Stay focused on which specific organizational capabilities will be required for your master data-management program to provide better insights into your data.

The roles of data management

Despite your best efforts, eventually the conversation will shift to who’s doing what to support the necessary data activities. The roles below are included as illustrative models of potential general role descriptions that address the majority of organizational data-management activities. Roles can be compacted if teams are lean or expanded if organizational needs are large.

  • Data architect: identifies objects and data elements to be managed, specifies the policies and business rules for how master data is created and maintained, describes any hierarchies, taxonomies, or other relationships important to organizing or classifying objects, and explicitly assigns data-stewardship responsibility to individuals and organizations
  • Data custodian: has ownership of the data, maintains accuracy and currency of the assigned data, and determines the security classification level of the data
  • Data steward: implements data policies, standards, procedures, and guidelines concerning data access and management
  • Data business analyst: collects, manipulates, and analyzes data
  • Project manager: appoints and supports data stewards in their areas of responsibility
  • Business relationship manager: determines which data requests will be queued and executed
  • Business intelligence specialist: serves as the business and technical subject-matter expert on data or information assets
  • Database administrator: is responsible for storage, organization, capacity planning, installation, configuration, database design, migration, performance monitoring, security, and troubleshooting as well as backup and data recovery
  • Data scientist: applies knowledge and skills to conduct sophisticated and systematic analyses of data to produce insights
  • Data engineer: develops, constructs, tests, and maintains architectures such as databases and large-scale data-processing systems; integrates, consolidates, and cleanses data
  • Data developer: develops, tests, improves, and maintains new and existing databases to help users retrieve data effectively

Don’t assume personnel are clear on their responsibilities. First, create each job description. Second, validate these job descriptions within the organization to ensure that gaps and overlap are addressed. Third, develop a RACI-accountable and responsible metric to assign ownership. Fourth, develop job postings. This is the job description jazzed up to represent the flavor of the organization and the team where the role resides.

Lastly, many roles require training. Data stewards and data custodians immediately come to mind. These roles have specific functions to perform. However, it’s not sufficient to only train folks in new roles. The organization as a whole must be educated to drive the change collectively. Master data-management isn’t a separate movement; the change needs to be organizational.

GDPR: Are you ready for the new face of data privacy?

Peter Nichol
-
0
GDPR: Are you ready for the new face of data privacy?

The CIO’s guide to the breadth and depth of GDPR.

The right to privacy is a long-standing concept that goes back to English Common Law. The Castle Doctrine gives us the familiar phrase, “A man’s home is his castle.” The castle can be generalized as any site that’s private and shouldn’t be accessible without permission of the owner. The idea of privacy quickly expanded to include recognition of a person’s spiritual nature, feelings, and intellect. It’s the right to be left alone.

The European Union (EU) General Data Protection Regulation (GDPR) replaced the Data Protection Directive 95/46/EC to strengthen and unify data protection for individuals within the EU and address the export of personal data outside the EU. The EU parliament passed the Regulation—after four years of debate—on April 14, 2016, with an effective date of May 25, 2018.

Modern U.S. tort law

There are four categories of modern tort law in which the concept of “invasion of privacy” is used in legal pleadings. These four concepts are remarkably similar to the revisions of GDPR:

  1. Intrusion of solitude: intrusion into one’s private quarters
  2. Public disclosure of private facts: the dissemination of truthful, private information
  3. False light: the publication of facts that place a person in a false light
  4. Appropriation: the unauthorized use of a person’s name or likeness

The intrusion of solitude refers to a person intentionally intruding—either physically or electronically—into the private space of another. Typical examples include hacking into someone else’s email or setting up a video camera to secretly view a person unknowingly.

The public disclosure of private facts is an act of publishing information that wasn’t meant for public consumption. This concept is different than libel or slander, where truth isn’t a defense for invasion of privacy.

False light specifically refers to the tort of defamation. Communication of false statements or information that hart the reputation of an individual person, business, product, group, government, religion, or nation all fall within this definition.

Appropriation of name or likeness prevents—often at a state level—the use of a person’s name or image, without consent, for the commercial benefit of another person. This protects a person’s name from commercialization in a similar fashion to how a trademark action protects a trademark.

Modern tort law extends beyond the protection of the individual. However, there’s one grey area: how information is shared. GDPR directly addresses the need to protect personal information, outside the borders of a country, for the safety of its citizens.

The threat is here

There were 1,579 data breaches and over 179 million records exposed in 2017 according to the Identity Theft Resource Center’s 2017 year-end report—a dramatic 44.7 percent increase over 2016 data breaches. The breaches and records lost were spread across industries:

  • Banking: 134 breaches, 3.1 million records
  • Business: 870 breaches, 163 million records
  • Education: 127 breaches, 1.4 million records
  • Government: 74 breaches, 6 million records
  • Healthcare: 374 breaches, 5 million records

The threat to citizens’ privacy isn’t coming. This threat has already arrived.

GDPR policy in a data-driven world

Since the original 1995 directive, GDPR has established key principles that govern data usage, storage, and dissemination. The Regulation expands four core areas:

  1. Territorial scope: this extends the jurisdiction of GDPR to all companies processing the personal data of subjects residing in the EU
  2. Penalties: an organization can be fined up to 4 percent of annual global turnover or €20 Million (whichever is greater)
  3. Consent: long, complex terms and conditions and data requests must be intelligible
  4. Data-subject rights: breach notification, right to access, right to be forgotten, data portability, privacy by design, and data-protection officers (DPOs) have been clarified, often increasing the scope of GDPR

Territorial scope states that if the data includes subjects from the EU, the company must comply with the Regulation. This area also clarified the processing of personal data by controllers or processes—regardless of whether the data processing happens in the EU. If EU personal data is touched, your organization is impacted. The penalties are severe, and companies are taking notice. In addition to the 4 percent penalty, there’s a tiered approach to fine companies’ 2 percent for not having their records in order (EU article 28). Additionally, not fully and promptly notifying the supervising authority of a data breach will be costly. It’s interesting to note that the “controllers and processors” make it clear that cloud and SaaS providers aren’t exempt from GDPR enforcement. Consent, although previously technically available, was often buried within unintelligible terms and conditions. Consent now must be in clear and plain language, including easy-to-grant or withdraw consent.

The data-subject rights cover six areas in more depth:

  1. Breach notification: inform the supervising authority within 72 hours of the breach
  2. Right to access: notify individuals if their personal information is being processed and for what purpose
  3. Right to be forgotten: withdraw consent and erase all data traces (EU article 17)
  4. Data portability: provide data in common-use and machine-readable form
  5. Privacy by design: design data protections into systems—versus a system addition
  6. Data-protection officers: appointment of DPOs is mandatory for processing operations that require regular and systematic monitoring of data-subjects

Processing and using personal data

These onerous obligations replace the old Directive and apply to all twenty-eight Member States of the EU—from the UK to Estonia. GDPR encourages companies to re-examine organizational policies, standards, guidelines, procedures, and processes.

As your organization assesses GDPR impact, there are 10 questions to keep in mind:

  1. How does expanded territorial reach impact your customers, providers, and partners?
  2. Do you have sufficient DPOs in place with the appropriate programs?
  3. Are data accountability and privacy included in the business process and system design?
  4. Are the tasks of data processors defined into organizational roles with appropriate accountability and responsibilities?
  5. Has your organization revisited corporate policies and procedures while taking into consideration the broad-reaching scope of GDPR?
  6. Is consent to access the array of products, services, and interactions written in clear and plain language?
  7. Do customers understand how to clearly grant or withdraw consent?
  8. Have risk assessments been performed to quantify the economic and financial risk or non-compliance that could result in fines?
  9. Is the process for data-breach notification streamlined to ensure compliance within the 72-hour guideline?
  10. Does the organization have clear guidelines on the definition of a “serious” breach?

Companies have a lot to do before GDPR becomes effective on May 25, 2018. Stay on top of the latest GDPR developments by following the Article 29 Data Protection Working Party (WP29). This working group is an independent European Union Advisory Body on Data Protection and Privacy and includes representatives from each of the EU member states. Together, we can improve how big data is processed while limiting the financial risk to our organizations.

Breaking down artificial intelligence to form a starting point for adoption

Peter Nichol
-
0
Breaking down artificial intelligence to form a starting point for adoption

To leverage, communicate and sell the power of artificial intelligence, we first must capture its essence.

Artificial intelligence will humanize recommendation engines, improve the accuracy of logistics engines, and represent a monumental change in the friendliness of chatbot engines. Learning new languages (Duolingo), finding new dinner plans (Replika) and making photography exciting again (Prisma) is how our business partners will be introduced to the potential of artificial intelligence.

How we plan for AI

If I asked you how to build a house, you’d have a series of steps in mind. When asked how to validate a company’s technology security perimeter, other action steps come immediately to the forefront. And when booking a vacation to Brazil, a clear approach to get you on the beach fast rushes to the mind.

We’re of course not talking about building houses, creating security resilience, or booking vacations. We’re talking about how to introduce business leaders, scientists and medical professionals to the power of artificial intelligence. So where do we start? What’s our first step?

Three steps toward AI enlightenment

We start with a framework for all intelligence agents. Artificial intelligence can be separated into two categories: (1) thought processes and reasoning and (2) behavior. Whether you lean more toward the mathematics and engineering side (rationalist) or closer to the human-centered approach (behavior), the heart of AI is trying to understand how we think.

The first step: Decide which of the four categories of artificial intelligence the enterprise will explore.

  1. Thinking humanly: systems that think like humans
  2. Acting humanly: systems that act like humans
  3. Thinking rationally: systems that think rationally
  4. Acting rationally: systems that act rationally

The second step: determine the intent of our artificial intelligence initiative.

Thinking humanly (cognitive modeling) blends artificial intelligence with models—as in the case of neurophysiological experiments. Actual experiments in the cognitive sciences depend on human or animal observations and investigations. Acting humanly (Turning Test) attempts to establish a line between non-intelligence and satisfactory intelligence. Thinking rationally captures “right thinking” in computer language. Coding logic is fraught with challenges, since informal knowledge doesn’t translate well to formal notation. Acting rationally is about acting. Agents perform acts, and “rational agents” can autonomously maneuver, adapt to change and evolve (learned intelligence).

The third step: identify the capabilities required.

Thinking humanly capabilities:

  1. Observation
  2. Matching human behavior
  3. Reasoning approach to solving problems
  4. Solve problems
  5. Computer models to simulate the human mind

Acting humanly capabilities:

  1. Natural language processing
  2. Automated reasoning
  3. Machine learning
  4. Knowledge representation
  5. Computer vision and robotics

Thinking rationally capabilities:

  1. Codify thinking
  2. Pattern argument structures
  3. Codify facts and logic (knowledge)
  4. Solve problems in practice (not principle)
  5. Solve problems with logical notation

Acting rationally:

  1. Thought inferences
  2. Adapt to change (agents, chatbots)
  3. Analyze multiple correct outcomes
  4. Operate autonomously
  5. Create and pursue objectives

Step beyond

Artificial intelligence, since the mid-1940s, has moved across the plane of learning from philosophy to control theory. The philosophy of logic and reason established the foundations of learning, language and rationality. Mathematics formally represented computations and probabilities. Psychology illuminated the phenomena of motion and psychophysics (experimental techniques). Linguistics studied morphology, syntax, phonetics and semantics. Neuroscience poked at the function of the nervous system and brain. Control theory combines the complexities of dynamic systems and how behavior is modified by feedback.

Navigation, neural networks, gene expression, climate modeling and production theory all stem from control systems engineering.

It’s easy to become tangled up in the possibilities of artificial intelligence. First, we must decide which of the four categories of artificial intelligence we will explore. Second, we must determine the intent of our artificial intelligence initiative. Third, we must identify the capabilities required. In sum: Start with a plan and clarify your first three steps for your organization to realize the potential of artificial intelligence.

Peter B. Nichol, empowers organizations to think different for different results. You can follow Peter on Twitter or his personal blog Leaders Need Pancakes or CIO.com. Peter can be reached at pnichol [dot] spamarrest.com.

Peter is the author of Learning Intelligence: Expand Thinking. Absorb Alternative. Unlock Possibilities (2017), which Marshall Goldsmith, author of the New York Times No. 1 bestseller Triggers, calls “a must-read for any leader wanting to compete in the innovation-powered landscape of today.”

Peter also authored The Power of Blockchain for Healthcare: How Blockchain Will Ignite The Future of Healthcare (2017), the first book to explore the vast opportunities for blockchain to transform the patient experience.

1...789...12Page 8 of 12