When I was in middle school (quite a few years ago), I started to realize that I was pretty good at math. I had done okay before, but the problems and concepts were becoming more difficult. Surprisingly, I was really “getting it”, while some of my classmates encountered more challenges with the subject matter. The teacher motivated us with a special letter grade when our performance on a homework assignment or quiz was stellar — a large letter grade “A” on the top of our assignment page, which she called a “big bold A”. This grade was not simply recognition for getting a 90% score on the assignment, but was awarded for achieving 99% or 100%. Thus, it recognized stellar performance, a demonstrated mastery, and clear forward progress in the complex subject matter. This teacher’s practice inspired me to pursue a STEM career, and I consider her one of my favorite and best teachers ever in my 20+ years of education.
This school memory came back to me recently when I began considering the data indicators of stellar performance, demonstrated mastery, and clear forward progress in enterprise analytics projects. That connection was strengthened when I began thinking about how the opposite of an “A” grade in school is an “F”, and how analytics failures are often associated with three inhibitors to success: fear, fragility and friction.
Common Challenges for Data Scientists
So, what does this mean for us, as analytics practitioners, leaders, and stakeholders? We are digital (data) professionals who are entrusted with our organization’s massive (digital) data assets. Our mission is to discover insights and to deliver value from these data. We are tasked with doing that both efficiently (measured by time-to-solution) and effectively (measured by completeness and accuracy of solution). We can categorize the challenges that we encounter with this assignment into three categories:
- Finding competitive advantage for the organization in data analytics, including data science, machine learning, artificial intelligence (AI), and automation.
- Acquiring, nurturing, benefiting from, and retaining key data science and analytics talent.
- Avoiding the hype, shiny object distraction, and FOMO (Fear Of Missing Out) that can drive misguided motivations and “busy work” around those big data assets.
The Three F’s of Enterprise Analytics
Unfortunately, each one of those challenges is often accompanied by one of the following three myths and our corresponding natural reactions to them, respectively:
Myth #1: Machine learning and AI are big, scary topics.
“How do we get our people to agree that our organization needs to pivot into doing this work?” = Fear!
Myth #2: Data science and analytics are only for the data scientists.
“What if our experts leave?” = Fragility!
Myth #3: Data-first is the right strategic posture for success.
“How do we get value from data with so many diverse tools, techniques, technologies, talents, and technical debt?” = Friction!
Voilà (there it is) — the three F’s!
Adopting a “Data for All” Business Mindset
Fortunately, there are three better responses to the three challenges and three myths than those three Fs, respectively:
- Embed the data insights discovery technologies (analytics, machine learning, AI, and automation) within existing (already adopted) enterprise tools and systems.
- Adopt a culture of experimentation and a “data for all” (data literacy) mindset across the whole organization.
- Analytics-first is a better strategic posture for success. An “Analytics by Design” strategy directs the corporate focus onto the analytics: i.e., the products and outputs (not on the data, the input). The analytics focus explicitly induces the organization’s communications, culture, and corporate activities to align with what matters the most: mission objectives, business outcomes, value creation, and competitive advantage. After all, every organization now has tons of data. But your analytics are your unique, invaluable output.
By empowering all digital workers in your organization to innovate, deliver ROI, and create value from data assets, even on small projects, this will inspire greater digital transformation, data analytics adoption, and cultural change. Committing to a clear, analytics-first strategy prepares your entire organization for the larger enterprise implementations (machine learning, AI, and automation) that will come.
Data analytics “busy work” (motivated by hype and FOMO) without a mission outcomes focus can produce another “F” – “Fantom” (phantom) analytics – corresponding to lots of activity around data, with very little value to show for it.
A STELLAR Framework for Enterprise Analytics
STELLAR analytics can boost analytics performance from early-stage “sandbox” experiments to late-stage enterprise projects. The key is to get moving, keep moving, and to accelerate forward progress. If the inhibitors (fear, fragility, and friction) stand in the way of analytics progress, then the project will likely receive a failing grade of “F”. A better grade is not just an “A”, but a stellar “A” (like in school), which is enabled by STELLAR analytics.
My Definition of STELLAR Analytics
The areas associated with this analytics framework include: Streaming, Team, Edge, Location, Learning Business System, Agile, and Related-Entity Analytics. Let me take a moment to briefly cover each of these topics below:
- Streaming Data Analytics: real-time access to, interaction with, and discovery from data, such as detecting POI (persons, patterns, products, processes, or points of interest) and BOI (behaviors of interest for any “dynamic actor”).
- Team Analytics: a culture of experimentation that celebrates and validates the power in diversification, collaboration, data-sharing, data reuse, and data democratization.
- Edge Analytics: locality in time, at the moment of data collection (enabled by the Internet of Things [IoT]) – “What else is happening now?”
- Location Analytics: locality in geospace, within a given spatial context (also enabled by IoT) – “What else is happening at that place?”
- Learning Business System: A learning business system embodies data-driven knowledge-generation business practices, with performance measurement, continuous feedback, learning, and improvement, which are embedded in daily business practice (example: Learning Health Systems)
- Agile Analytics: outcomes-driven, iterative, builds proofs-of-value, fails fast to learn fast, thinks big, but starts small (with the Minimum Viable Product or Minimum Lovable Product) with continuous integration and delivery through DataOps (DevOps for data analytics).
- Related-Entity Analytics: locality in data feature space – “What else is like this entity / event?”
The data science objectives here are to detect existing, emerging, and actionable patterns in data: (a) segments (classes), (b) trends (correlations), (c) surprises (anomalies, outliers), and (d) linked entities and events (co-occurring associations).
Moving Business at the Speed of Data
In summary, the mission of data-rich organizations is this: to produce successful business outcomes and value from data through analytics. Since the rate at which data flows through organizations is lightning fast, users of analytics applications need strategies, tools, and techniques that quickly leverage those data to extract insights, to make data-driven decisions, and to take the next best actions – in other words, to help business move at the speed of data!
To achieve those goals, developer teams must be able to provide agile user-accessible analytics, data exploration dashboards for “any user”, and capabilities for user-generated actionable insights – all while avoiding the three F’s (fear, fragility, and friction) in the end user’s experience of their analytics applications.
STELLAR analytics boosts the ability of organizations to deliver big value from big data and to achieve straight A’s on their business outcomes scorecard — indicators of stellar performance and clear forward progress in enterprise analytics projects. Adopting STELLAR analytics is a good data practice and adopting an analytics-first strategy is a good business practice. With that, you can give your team a “big bold A” for analytics mastery.