Insights

War Wounds and Wins: Machine Learning Stories from the Frontline

War Wounds and Wins: Machine Learning Stories from the Frontline

Machine Learning is quickly becoming a household term and everyone wants to build and use novel ML systems, but getting started harder looks different than you’d expect. 

We have invited some of the best of the best data science professionals to share their experiences with deploying machine learning solutions.  Join us as we learn from them what to do and not to do along with hearing about their projects and the impact they had.  

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Is Citizen Data Science A Good Thing?

Is Citizen Data Science A Good Thing?

Written by Mike Lampa, GDM Advisor
Image credit: Zelda Zinn, Walker Fine Art, Denver

Self-service Discovery Analytics established itself soundly in the mid-2010’s thanks to pioneers like Qlik, Tableau and Tibco Spotfire.  These vendors had a new sales model approaching the influencers and decision makers in the business and offering entry level licenses for a price that one could essentially put on a Purchase Card because that price was within their discretionary budget authority.  The User Experience of these platforms closed a huge and painful gap for the business users, but opened new concerns around data security and governance. The IT teams developing and supporting the enterprise analytics solutions were taken aback and tried to resist the movement to no avail.  Then the industry researchers published points of view on the value proposition of self-service and offered frameworks and approaches to enable the movement.  IT eventually came to the realization that it is best to embrace self-service analytics and collaborate with the business for the good of the enterprise. Once that acceptance caught on, the vendors enjoyed significant licensing revenue and account expansion.  Then those business analysts felt their next constraint. They were still dependent on IT to deliver the desired data for their self-service discovery analytics & visualization use-cases. 

On the heels of the incredible success and market disruption brought about by these self-service Discovery Analytics platforms came another wave of self-service enabling technologies; self-service data preparation / curation tools. The vendors followed the same commercial and user experience model as their self-service discovery analytics counterparts.  They enjoyed the same success and caused a similar market disruption in the data integration & transformation segment.  This time the research firms were out in front of the self-service data preparation products.  They offered adoption frameworks, product reviews, comparisons and ratings.  IT again struggled and resisted supporting this next self-service wave because it blurred the line for where and who should be building the data pipelines that extract, transform and load datasets with content considered fit for purpose by the business analysts producing and consuming those datasets. On the surface the self-service data prep platforms are functionally equivalent to the traditional IT-oriented ETL / ELT platforms. But they offer a significantly simpler user-experience. This helped reduce constraints and the dependency on IT to provide datasets. Business end-users building datasets also raised “data anarchy” concerns as these datasets proliferate across the organization.  Those organizations that intentionally embrace the value proposition of self-service simply extended the rules of engagement and governance guidelines to account for self-service data prep in addition to self-service discovery analytics.  Many of those same organizations also invested in modern data catalogs in order to automate the knowledge sharing, tracking and administration of those datasets throughout their entire life-cycle. 

Enter Self-Service Predictive Analytic Platforms and the Citizen Data Scientist.  Gartner defined the Citizen Data Scientist as “A person who creates or generates [machine learning] models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics.”  Glassdoor reports that Data Scientist is the number one job for the last four years. The U.S. Labor Department reports the demand for data science skills will drive a 27.9% rise in employment through 2026. There is a mounting huge demand for a short supply of qualified data scientists. Add the fact that organizations across all industries and all sizes are realizing Machine Learning (ML) and Augmented Intelligence (AI) is the new analytics table stakes. Increased demand, shortage of supply and mounting pressure to develop and operationalize ML models before the competition does.  A perfect storm! There is early evidence of software platforms coming out targeting the data savvy Business Analyst / Citizen Data Scientist persona once again.  The platforms provide a low-code or no-code user-experience offering pre-built ML models and algorithms targeted for pre-conceived classes of predictive analytics use-cases.  The stakes (rewards and risks) are orders of magnitude higher in ML and AI as compared to the descriptive / diagnostic analytics.   We need to scale but we need to keep our organizations out of trouble. How do we compensate for the unique expertise of a qualified data scientist to ensure we are using proven methods, selecting the right models for the right problems, using the right data to feed the models for training and validation purposes, ensuring the data being used and the outcomes being produced are accurate, ethical, legal and unbiased?  Start with leveraging and extending the self-service framework to include continuous innovation, exploration and vetting of new predictive analytics use-cases and hypotheses. Quickly determine whether to pivot to a different use-case or persevere.  In the event the decision is to persevere, it’s time to engage the qualified data scientist to perform a comprehensive Model Risk Management review prior to adding the use-case to the Analytics portfolio for continuous integration / deployment and operationalization release.   The value proposition here is we have the business domain expertise mashed up with the data science expertise.  We’re grooming the business analysts to be more self-sufficient even in ML / AI based analytics.  This helps scale the ideation of promising new ML / AI Analytic use-cases conserving the demand for the data scientist to focus on the mentoring of the business analysts and developing the next generation of more complex models to be published to the library of pre-built ML models.

Yes, it’s a good idea to extend self-service into the predictive, machine learning analytics space.  Extend the rules of engagement and governance frameworks to include [machine learning] Model Risk Management. Pair up the Citizen Data Scientists with qualified Data Scientists. Establish a continuous innovation / exploration discipline to incubate and validate new analytics-enabled use-cases. Then watch your competitors navigate your wake in the rear-view mirror.

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New Year’s Resolutions all Data Management Professionals Should Make

New Year’s Resolutions all Data Management Professionals Should Make

The Bulb: December 2020
Written by: Mike Lampa and Julie Burroughs, GDM Advisors

As we reflect on the trials and tribulations of 2020 and look towards the prospects and promise of 2021, leadership has a new opportunity to differentiate their enterprise through an effective and efficient analytics-driven transformation journey. Here are our thoughts for your consideration as your data and analytics 2021 resolutions…

Level up my delivery teams to be true lean agile.

If you want to innovate and deliver at scale, you need to adopt a lean agile product management operating mindset for your Analytics program. Yet most organizations tell us they are lean agile and after assessing their operating model against the seven core competencies of Business Agility (see November Bulb article) we see evidence of the Team & Technical Agility core competency, but limited or no adoption of Lean Portfolio Management, Agile Product Delivery and Organization Agility. These four core competencies are key to continuously delivering quality innovative analytic products at scale and more quickly than ever before. Adoption of the continuous innovation in cloud technologies further speeds time to market with value generating analytic products. The new game in town is co-development with these technology vendors while taking advantage of their IP and resources. If you aren’t exploring scaling your agile practice, you will likely continue to not meet the needs of your enterprise.

Augment standardized dashboards with data science augmented operational insights.
In the age of self-service analytics, the business needs more than standardized dashboards and visualizations. Business knowledge workers need to answer different questions and discover new insights without preconceived constraints yet, the average adoption rate of those dashboards is around 21% according to Gartner. This is a lot of wasted time and money. The latest breed of analytics reporting tools offer “Google like” search capabilities to ask a question, get the right answer AND receive machine learning enabled insights related to the question could very well show the cause for the answer results. We say “analytics for all” and tools like ThoughtSpot take that challenge on. This is a new breed combining search and AI-driven insights which needs to be considered as part of differentiating through analytics.

Stop delivering monolithic data governance.

Data Governance programs have a long and disappointing history of costing millions and taking years to implement and generate ZERO value. Let’s face it governance considerations are simply additional requirements that define a quality Data Product the business needs in order to build Analytic Products. Stop the madness. Adopt the Universal Principles of Data Ethics. Take a lean-agile product management mindset when implementing governance as you build data products. Focus on critical and common data elements that make up those data products and which will further the achievement of strategic objectives. Include governance, quality and regulator team members on your Agile Teams and start generating a continuous flow of value through improved data security controls, regulatory compliance and quality. This , taking years It’s OK, you can let go. You can do it. Sometimes you just have to do a reboot. Sunk cost fallacy comes into play here but as you take a hard look at these programs you see where and their annual cost to continue to support them, is not giving the results. Modern technologies and approaches to governing data will contribute significantly to the value produced by your data products.

Help my organization become more data literate.

Get them collaborating, ideating, discovering even arguing for a point of view armed with data. In an Accenture study, only 32% of companies reported they are able to realize tangible and measurable value from their data, partly due to poor Data Literacy. Data Literacy is a foundational organizational competency that will lead to increased data democratization and monetization. Perform Data Literacy Health Radar on a regular cadence to continuously gauge your current level and desired level of mastery and proficiency across the eighteen key traits of Data Literacy. With those gaps in hand, design and execute on a Data Literacy implementation journey. This isn’t a daunting activity. Consider an automated data catalog to springboard your literacy program. The benefits are never ending.

Automate my data and machine learning operations – keep my head above water.

Come on man. The data tsunami isn’t coming, it has arrived. And the increasing demand for the data, NOW, is off the charts. DataOps & Machine Learning Ops (MLOps), are the analytics domain adaptation of DevOps, a proven software delivery discipline. The Continuous Delivery Pipeline is the foundation that enables enterprises to release Analytic and Data Product value, in whole or in part, at the right time and when it’s needed. DataOps has a primary focus on delivering data pipelines that produce the Data Products which in turn enable the creation and delivery of Analytic Products. This is the Analytic Product demand-driven model for Data Products. Data that is fit for purpose for analysis and the supporting services to easily access the data for analysis. MLOps has a primary focus on delivering additional data pipelines and persisted data that is fit for purpose for Machine Learning (ML) analytic models, as well as the ML model code and execution telemetry metadata to enable ML model efficacy monitoring and alert triggering to refine those ML analytic models. Modern technologies for DataOps and MLOps automates the build of the pipeline code, the testing of the code, the management of the code, the bundling of the code into deployable builds, the enablement of provisioning infrastructure as code and the monitoring of the telemetry of the deployed pipeline’s execution cycles.

So raise your glass, toast the work you did in 2020 and make the resolution to modernize your analytics program.

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A Quarter Century of Analytics – Isn’t it about Time to Generate Economic Value?

The Tipping Point 

The Analytics industry has made incredible strides since it’s nascent days in the mid-1990’s. We have evolved the architectures, established program management disciplines,  improved on our techniques to define requirements, invested in tools to help automate the processes around data management, integration, quality, governance, visualization as well as the generation of predictions and prescription of insights. However, 25 years later the majority of organizations are not getting the economic value they expected and desperately need out of their Analytics Program investments. Time to market is still too long, the underlying data and analytic solutions still have too many issues so they are not operationalized, the expected economic value is still not being realized and everyone is frustrated.

The McKinsey Global Institute’s findings from the 2019 AI Frontier Summit found Augmented Intelligence (AI) techniques have the potential to create between $3.5 trillion and $5.8 trillion USD in measurable value annually across nine business functions in 19 industries.   This constitutes about 40 percent of the overall $9.5 trillion to $15.4 trillion USD annual economic impact that could potentially be enabled by all analytical techniques.  We need to morph all of that latent potential into realized value!

Most enterprises recognize the need to be data-driven, yet 60% of data projects fail to move past preliminary stages, and 87% of data science projects never make it to production. More surprisingly the number of data-driven companies has actually fallen from 37% to 31% since 2017, despite increased investment, according to Gartner.  We’re going the wrong way.

Gartner also predicts digital innovation timelines will double for the enterprise. Through 2021, digital transformation initiatives will take large traditional enterprises, on average, twice as long and cost twice as much as anticipated. Smaller, more agile organizations, by contrast will have an opportunity to be first to market as larger organizations exhibit lackluster immediate benefits.

We are at a tipping point! We need a new operating system for our enterprise analytics program. 3rd Generation Business Intelligence calls for more ubiquitous adoption of Self-Service Analytics capabilities balanced with pragmatic and appropriate governance.  We need to consistently demonstrate enterprise analytics program execution competency.  Analytics and data-driven cultures, processes, portfolio management disciplines and Data Literacy education all need focused attention.  

Let’s acknowledge it, continuous disruption at an increasingly accelerated rate is the new normal. It’s time to capitalize on that disruption through the establishment of a Lean-Agile Product Management mindset for our analytics programs. We need to cultivate continuous innovation, generate measurable economic value and relentlessly improve our portfolio of Analytic and Data Products. Enterprises that double-down on this new operating system will ultimately achieve Business Agility. 

Business Agility requires that everyone involved in the definition, delivery, adoption and oversight of Data and Analytic Products—including business and technology leaders, development, IT operations, legal, marketing, finance, support, compliance, security, and others—all use Lean and Agile practices to continually deliver innovative, high-quality, value generating and differentiating Analytic and Data Products faster than the competition.

Developing the seven core competencies of the Scaled Agile Framework (SAFe ®) provides a flexible operating system built upon the time tested Lean, Agile, and SAFe practices. Enterprises that achieve Business Agility can deftly organize and swiftly reorganize their analytics program to sense and respond to market rhythms and external events without completely disrupting the existing enterprise organizational hierarchy.

Organizing the SAFe ® operating system around the analytic program and value streams instead of departments or projects, offers a way for enterprises to focus on Analytic and Data products that create measurable value for its business stakeholders and end-customers, through product / service differentiation, innovation, and growth.

The following infographic published by ScaledAgile represents the seven core competencies of the Scaled Agile Framework SAFe ® which when mastered will elevate the business agility of the enterprise.  We have taken these core competencies and applied them to the execution of the enterprise analytic program.  The enterprise will experience a continuous delivery of value generating, innovation differentiating portfolio of Analytic and Data products that meet the need! The delivery cycles will be greatly compressed. The quality of those Analytic and Data Products will be dramatically improved. The deployment of the Analytic and Data Products will be executed on a predictable cadence at a sustainable scale. The adoption and utilization of those Analytic and Data Products to their fullest is assured. 

Business Agility is the ability to compete and thrive in the digital age by continuously sensing and quickly responding to market rhythms and emerging opportunities with innovative analytic-based business solutions. Business Agility also takes feedback from the organization, in the form of rip-tides or feed-back loops from the delivery teams and internal innovation events as opportunities to innovate.  Let’s take a closer look at each of the seven core competencies as applied to the enterprise analytics program.

Team and Technical Agility

Agile development is the cornerstone of Business Agility. The Team and Technical Agility competency describes the critical skills and Lean-Agile principles, practices and enabling tools that high-performing Agile teams and Teams of Agile teams use to create high-quality Analytic and Data Products for their internal business stakeholders and the enterprise customers. As we’ll see with each of the the seven core competencies, Team and Technical Agility consists of three dimensions:

• Agile Teams – High-performing, self-sufficient, cross-functional teams anchor the competency by applying effective Agile principles and practices. These teams are comprised of data professionals (Data Engineers, Data Scientists, Data Analysts, Data Quality Analysts, Enterprise Architects, Production Operations, Security, etc), business professionals (Business Process Owners, Business Analysts, Managers, Directors, Operators, Data Stewards, etc) and where possible and applicable the representative voice-of-the-customer for the end-customers who know their need and will experience their need has been met. The embedded analytic solutions within the products and services delivered to the customer being a contributor to that perceived differentiation and realized satisfaction.
• Team of Agile Teams – Agile teams operate within the context of a SAFe Agile Release Train (ART), a long-lived, team of Agile teams that provides a shared vision and direction and is ultimately responsible for the continuous delivery on the enterprise objectives through Analytic and Data products that optimize business outcomes and meet customer needs. 
• Built-in Quality – All Agile teams apply rigorously defined Agile practices and techniques to create a high-quality, well-designed portfolio of Analytic and Data Products that support current and future business needs.  Following proven quality assurance principles including Behavior Driven Development (BDD), Test Driven Development (TDD) and highly automated testing of data pipelines, analytic algorithms and calculations will contribute to the relentless pursuit of continuous improvement and quality of those Analytic and Data Products.

Agile Product Delivery

Business Agility for analytic programs demands that enterprises rapidly increase their ability to deliver innovative Analytic and Data Products which foster market differentiation. To ensure the enterprise is continuously creating the right solutions for the right customers at the right time; a balance must be  maintained between an internal execution focus to optimize enterprise performance  and  external customer focus to meet customer needs through analytics-enabled product and service differentiation. Agile Product Delivery is a customer-centric approach to ideating, defining, building, and releasing a continuous flow of value-generating Analytic and Data Products to enterprise stakeholders and end-customers. The three dimensions to Agile Product Delivery for Analytic and Data Product development are:

• Customer Centricity, Design Thinking and Behavioral Stories – Customer centricity puts the customer need at the very core of every analytic product and feature decision. Design Thinking techniques are used to ensure the Analytic and Data Product is desirable, feasible, viable, and sustainable. As new analytic ideas and hypotheses are flushed out through data discovery and analytic prototyping techniques, those that show promise of economic value are documented further with EPICS using behavioral storytelling techniques and placed into the Agile Product Delivery backlog.  Where the development of those Analytic and Data Products necessitate technical tools and platforms, ENABLERS are used to document the enabling technology needs thus ensuring the Architectural Runway stays in front of the Analytic and Data Product delivery.  The combination of Analytic and Data Product EPICS and ENABLERS are prioritized and loaded into the Product Increment (PI) objectives and delivery backlog to be worked upon during the next scheduled PI release.   
• Develop on Cadence; Release on Demand – Developing on cadence helps manage the variability inherent in product development. Unknowns are always being revealed. The cadence surfaces them quickly so the impact on the desired MVP can be assessed and decisions on the features to deploy can be made. Decoupling the release of value to customers from the deployment of value on cadence enables and ensures customers can get what they need when they are ready for it. 
• DataOps, Machine Learning Ops (MLOps) and the Continuous Delivery Pipeline – DataOps & MLOps, the analytics domain adaptation of DevOps,  and the Continuous Delivery Pipeline creates the foundation that enables enterprises to release Analytic and Data Product value, in whole or in part, at the right time and when it’s needed.  DataOps has a primary focus on delivering data pipelines that produce the Data Products which in turn enable the creation and delivery of Analytic Products.  This is the Analytic Product demand-driven model for Data Products.  Data that is fit for purpose for analysis and the supporting services to easily access the data for analysis. MLOps has a primary focus on delivering additional data pipelines and persisted data that is fit for purpose for Machine Learning (ML)  analytic models, as well as the ML model code and execution telemetry metadata to enable ML model efficacy monitoring and alert triggering to refine those ML analytic models.

Enterprise Solution Delivery

Building and evolving large, complex enterprise Analytic and Data Products is a monumental effort. They may be supporting a wide-range of interrelated internal enterprise analytics decision support systems. They may be implemented as embedded services in a wide-range of products, tools or platforms sold to thousands or millions of end-customers.   

Many such systems require hundreds or thousands of engineers. They demand sophisticated, rigorous practices for engineering, operations, and support. Moreover, over the decades, ensuring that these analytic systems are still operational, their purpose and mission evolve to continue to differentiate and produce value. That calls for continuous research on market rhythms, incubating  new capabilities and techniques, ensuring technology upgrades and security patches are applied seamlessly and proactively, and other enhancements proposed from the internal rip-tides are addressed. As true with ‘living organisms’, natural selection happens. Therefore, the activities above are never really ‘done.’ Instead, they are released earlier and continuously enhanced over time for the effective life-cycle of the portfolio of Analytic or Data Products that make up the large solution ‘organism’. 

The Enterprise Solution Delivery competency describes how to apply Lean-Agile principles and practices to the ideation, specification, development, deployment, operation, and evolution of the world’s largest and most sophisticated analytic software applications, networks, and cyber-physical data systems. It consists of three dimensions:

• Lean System and Solution Engineering applies Lean-Agile practices to align and coordinate all the activities necessary to specify, architect, design, implement, test, deploy, evolve, and ultimately decommission these systems. 
• Coordinating Trains and Suppliers coordinates and aligns the extended set of value streams to a shared business and technology mission. It uses the coordinated Vision, Backlogs, and Roadmaps with common Program Increments (PI) and synchronization points. Many times these large systems are reliant on a partner eco-system providing source data products and enabling tools and platforms. Their products are part of the ‘organism’.
• Continually Evolve Live Systems ensures both the Analytic and Data Product development pipeline and the large analytic systems themselves support continuous delivery of value, both during and after release into the field.

Lean Portfolio Management 

The three competencies above provide the technical practices needed to build, deploy, manage and support a meaningful portfolio of Analytic and Data products. But none of them directly address the more significant issue of why those Analytic and Data Products are required, how they are funded and governed, and what other analytic capabilities are necessary to deliver enterprise value. Traditional Program / Portfolio Management approaches must be modernized to support the new Lean-Agile Product Management way of thinking to deliver value through Analytic and Data Products. The Lean Portfolio Management competency aligns enterprise strategy and the analytic program execution by applying Lean and systems thinking.  The fundamental shift is to fund the Analytics Value Stream to continuously deliver Analytic and Data Products.  The size of the fund pool is based on Product Managers estimation of value generation balanced against the demonstrated amount of value generation velocity of the Agile Teams. The allocation of the funding pool is continuously facilitated through a participatory budgeting process. The prioritization of the budget allocation is based on the estimated amount of value and needed time to market to realize that value.  The net result is a fundamental shift.  Bringing a continuous flow of work to the people instead of bringing people to the work.  Again, comprised of three dimensions:

• Strategy and Investment Funding ensures that the entire analytic portfolio is aligned to the proper enterprise strategies and it is funded to create and maintain the Analytic and Data Products necessary to meet business targets across the functional organization and / or  to meet the needs of the customers that use the analytics-enabled products and services of the enterprise. It requires the cooperation of the internal business function owners, portfolio stakeholders, technologists, Voice of the Customer Evangelists (VoiCE) and Enterprise Architects. It also requires input from the customer for their needs to ensure customer centricity. 
• Agile Portfolio Operations (APO) coordinates and supports decentralized program execution across the Agile teams.  APO enables operational excellence through a relentless quest for improvement via continuous feedback loops and corrective action implementation. It requires the cooperation of the Agile Program Management Office/Lean-Agile Center of Excellence (APMO/LACE), Communities of Interest (CoI), Voice of the Customer Evangelists (VoiCE) providing input to Product Managers, Release Train Engineers (RTEs) and Scrum Masters. 
• Lean Governance manages spending, audit and compliance (guardrails), forecasting expenses, and measurement. It requires the engagement of the Agile PMO/LACE, Business Owners, and Enterprise Architects.

Organizational Agility 

Even with the technical and programmatic oriented competencies above, enterprises must be able to adapt quickly to respond to the challenges and opportunities that today’s rapidly evolving markets and environments present. This requires more flexibility and adaptability than the traditional hierarchical operating system is likely to be able to muster. Again, we turn to the second operating system for help. The SAFe approach to cultivating a Lean-Agile Product Management mindset helps businesses address these challenges. Enter Organizational Agility, which is too is comprised of three dimensions:

• Lean-Thinking People and Agile Teams – This state occurs when everyone involved in solution delivery is trained in Lean and Agile methods and embraces and embodies the values, principles, and practices.   Agile teams are not limited to technical teams.  Every organizational unit in the enterprise would benefit from these principles.
• Lean Business Operations – Teams apply Lean principles to understand, map, and continuously improve the portfolio of Analytic and Data Products to optimize business processes that produce and support the business’ internal execution and delivery of products / services. 
• Strategy Agility – This state occurs when the enterprise consistently demonstrates the ability and adaptability needed to continuously anticipate / sense shifts in market rhythms and quickly adapt strategy when necessary.

Continuous Learning Culture 

And even with mastery of the above, there will be no steady-state. Change is a constant. Startup companies continue to challenge the status quo. Juggernaut data-driven companies like Amazon and Google are entering entirely new markets such as banking and healthcare and they are quickly dominating. Expectations from new generations of workers, customers, and society as a whole challenge companies to think and act beyond the theater of balance sheets and quarterly earnings reports. To address the demand for continuous learning, growth of its people, and improvement in processes, the Continuous Learning Culture competency describes a set of values and practices that encourage individuals—and the enterprise as a whole—to continually improve their data literacy, market knowledge, analytic tool competence, personal performance, and innovation contribution. It is expressed in three dimensions:

Learning Organization – Employees at every level are learning and growing so that the organization can transform and adapt to an ever-changing world. 

Innovation Culture – Employees are encouraged and empowered to explore and implement creative analytic ideas that enable future value delivery.  High performing, data-driven organizations invest in various forums for their people to engage, ideate, validate and propose new innovations that meet customer needs through value-generating solutions for inclusion in the portfolio of Analytic and Data Products.  Self-Service oriented data and analytic products with embedded augmented intelligence machine-learning technologies enable the business knowledge workers to continuously ideate, validate and prototype new analytic capabilities at scale.

Relentless Improvement – Every person in the enterprise focuses on continuously improving its analytic solutions, products, and processes.  Analytic solutions will only generate value when they are adopted, operationalized and utilized by the organization’s decision-makers and the end-customers for whom the solutions were deployed. This critical last mile of success is ensured by continuously soliciting feedback to learn what is needed to achieve that success. Also, the Analytic and Data Products themselves are deliberately designed with machine-learning based capabilities to continuously generate the telemetry necessary to monitor efficacy and to continuously improve the performance of those deployed and operationalized Analytic and Data Products.

Lean-Agile Leadership 

The organization’s executives, managers, and other leaders provide the foundation ultimately responsible for the adoption and success of the Lean-Agile Product Management mindset and for the mastery of competencies that lead to Business Agility. Only they have the authority to effect change and continuously improve the systems that govern how work is performed. Only they can create an environment that encourages high-performing Agile teams to flourish and produce economic value from the analytics program. Leaders, therefore, must internalize and exemplify leaner ways of thinking and operating so that team members will learn from their example, coaching, and encouragement. Leaders develop along three dimensions:

• Leading by Example – Leaders gain earned authority by modeling the desired behaviors for others to follow, inspiring them to incorporate the leader’s example into their developmental journey.  
• Mindset and Principles – By embedding the Lean-Agile way of working in their beliefs, decisions, responses, and actions, leaders model the expected norm throughout the organization.  They drive the urgency to be a data-driven enterprise.
• Leading Change – Leaders lead and serve, not merely support, the data-driven,  analytics-driven transformation by creating the environment, preparing the people, and providing the necessary investment in education and enabling resources to realize the desired outcomes from analytics!

The Call to Action 

Ok, by now, you might be contemplating, “Doing the same thing in the same way within your analytics program and expecting a different / better outcome is folly, at best. However, this new operating system advocates a significant change in the way our Analytics program is run.”  Yes, you’re right. Here’s the beauty of the Lean-Agile Product Management Mindset approach for delivering continuously improved value out of your Analytics program. It is predicated on continuous learning, it is founded on a continuous flow of Minimum Viable Product (MVP) increments not only in the Analytic products produced but equally important through deliberate incremental implementation of changes in process, discipline and technique for running the analytic program itself.. Establish your current level of health across the seven core competencies of Business Agility. Decide which of those health readings you want to improve upon over the next 3, 6, 9, 12 months and layout the implementation details for the journey towards Business Agility tailored for your enterprise analytic program.  Measure the performance of the program’s ability to generate economic value for every Product Increment (PI) release (quarterly). Reassess your Business Agility health and establish the next set of desired improvements on a regular cadence (quarterly).  These results will inform the enterprise where the next wave of continuous improvements need attention and investment.   

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