How Analytics by Design Tackles The Yin and Yang of Metrics and Data
Written by Dr. Kirk Borne
We hear a lot of hype that says organizations should be “Data-first”, or “AI-first, or “Data-driven”, or “Technology-driven”. A better prescription for business success is for an organization to be analytics-driven and thus analytics-first, while being data-informed and technology-empowered. Analytics are the products, the outcomes, and the ROI of our Big Data, Data Science, AI, and Machine Learning investments!
AI strategies and data strategies should therefore focus on outcomes first. Such a focus explicitly induces the corporate messaging, strategy, and culture to be better aligned with what matters the most: business outcomes! In a previous article here on Data Makes Possible, I identified some of the top recent trends in AI and data science that amplify our analytics ROI and that lead to big value from our data and technology investments.
So What is Analytics by Design (AbD)?
When considering the importance of an analytics-first strategy in business, we should be thinking about our outcomes first, then our data. This is called Analytics by Design, which is derived from similar principles in education: Understanding by Design, sometimes referred to simply as UbD. Analytics are the outcomes of the organization’s data activities, including data science, machine learning and AI. The outcomes of programs and activities should meet business goals and objectives, and metrics are necessarily derived from those. The metrics therefore measure performance against desired outcomes.
What we are seeing here are two complementary roles of data — “the yin and the yang” — in which data are collected at the front end (from business activities, customer interactions, marketing reports, and more), while data points are also collected at the back end as metrics to verify performance and compliance with stated goals and objectives.
Consequently, this is why we hear some organizations say that they are data-driven and we hear some that say that they are data-led: push versus pull. Similarly, some will say that they are metrics-driven. This definitely starts to become confusing and murky, maybe even a bit theatrical as practitioners rise to defend one perspective over the other.
But, as an old joke once stated, “Some individuals use statistics as a drunk man uses lamp-posts — for support rather than for illumination.”
This same aphorism can be applied to our use of data and metrics.
We can avoid these terminology debates by keeping our eye on the prize — maintaining our focus on the business outcomes (the analytics), which are data-fueled, technology-enabled, and metrics-verified. That’s the essence of Analytics by Design (AbD).
The AbD of Analytics Success
Analytics are the products of the data science, AI, and machine learning activities. These products can include models, enriched data sets, curated open data portals, APIs, applications, models, cloud services, data science notebooks, open source tools, and models — did I mention models more than once? Yes, I did. I mention models more than once because of their importance — models are the tools, instruments, and means of decision support, and of generating insights that lead to actions!
We have essentially just described the data analytics value chain, in which data flows through organizational processes to support innovation and to deliver value from the analytics products.
By applying an agile methodology, we are able to adopt a culture of experimentation that permits us to fail fast in order to learn fast and that delivers both the minimum viable product and the minimum lovable product. When analytics products deliver ROI (Return on Innovation) and value from our data assets, consequently this ignites, grows, and nurtures the organizational culture change that will be needed for the larger analytics implementations that will come. The key is to focus on the AbD of analytics success rather than on the ABC’s of data management.
Metrics and Data to Deliver Value-Packed Products
The analytics-first posture avoids the vicious cycle of debating the yin and the yang of metrics and data. The virtuous cycle of analytics-first focuses on delivering value-packed products. This mindset explicitly induces the organization’s message, culture, and strategy to be better aligned with what matters: business outcomes. Data are simply the input, albeit a lot of input. Data are not necessarily a differentiator — we all have tons of data. On the other hand, analytics are our uniquely valuable output.
Therefore, in sequential order, the four principles of Analytics by Design are:
- Adopt a culture of experimentation — “test or get fired” is the mantra within one successful analytics-driven organization.
- Identify desired results: outcomes, priorities, purpose, strategic objectives.
- Determine acceptable proofs, evidence, and metrics of success: data, KPIs, measurement instruments.
- Plan and design analytic activities: machine learning applications, data experiences, data products, analytic team projects, data products, areas for automation and AI implementations.
Data makes possible enormous business opportunities, if organizations can sustain focus on their mission, their “north star”, and their business goals. Analytics by Design steers organizations in that direction and avoids the two biggest productivity killers that can get organizations tangled up in the yin and yang of data and metrics. Those two productivity killers are: (a) following the big data hype due to fear of missing out; and (b) being activity-oriented and focused on “busy work” instead of being focused on value-producing business outcomes.
In summary, here are five take-away messages for organizations that have lots of data and that want to win with Analytics by Design:
- Nurture your analytics talent within a culture of experimentation. Having a data-fueled experimental orientation is the essence of data science and is an essential “innovation best practice”.
- Embrace the fact that the business cultural change required to adopt data science as a way of doing things (and not just a thing to do) is perhaps a greater challenge than the technological challenges.
- Demonstrating value and ROI from small implementations and Proofs of Value will inspire the culture change needed for the larger projects to come.
- Adopting a culture of experimentation is good data science, and adopting an analytics-first strategy is good business.
- If you want good data scientists to come and to stay, then rally them with this offer: Come for the data. Stay for the science!
Now, take the conversation to Twitter! Agree or disagree with this perspective on big data and Analytics by Design? Want to ask Kirk a question? Tweet @KirkDBorne using the hashtag #datamakespossible right now!
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