Data Driven to Data Mature
The road to recovery is paved with data
In uncertain times, neither blind activism nor resigned numbness will lead to success.
Data-driven decision-making principles need a C-level perspective to define the right questions, drive collaboration across the company and ensure the right measures are taken. Instead of taking decisions on a gut level, it is key to make data-based, smart decisions that allow you so successfully cope with COVID-19, ensure business continuity in the short run, and also become resilient and enable excellence in the long run. However, a foundational element to achieving that is what we refer to as data maturity of an organization.
Data driven implies fact-based decisions using metrics and data to guide strategic business decisions to drive business outcome rather than observation or intuition alone.
Data mature implies the ease by which an organization can arrive at a data driven decision.
A few metrics to measure an organization’s data maturity center around an organization’s speed to insight, ease of data discoverability, data and insight democratization, and business impact quantification of an insight.
While the term “data-driven” has been branded and re-branded for a number of years now, it is an aspiration that each organization in their sincerest effort strive to be. However, organizations often find it an intractable goal due to its dependency on data maturity.
What COVID-19 forced organizations to recognize rather bluntly is that the time for marking data-driven in bold on strategy decks is table stakes now and its impact to their bottom line is directly proportional to their Data Maturity.
Impactful data solves a problem and spurs action
Accessing your data maturity requires you to start by being honest, honest about your organization’s time to generate insight, very simply how much time it takes an employee with sound analytical skills to produce one business relevant insight. Does this question pop up a graph or a series of intake forms? Now consider the new normal, remote working conditions and whether your investments in human capital are spending time discovering organizational processes or generating insights.
A quick way to measure your organization’s data maturity is to map where you sit on the following Data Maturity Matrix¹ (brilliantly illustrated by Alex Bratton). If you find yourself indexed on the left, do not get disheartened. Industry research and our professional experience indicates nearly 2 out of 3 organizations³ index towards the left to middle of the matrix.
Trusted and connected data is the backbone of creating intelligence.
Even in organizations indexed to the right, user confidence that the data used is trustworthy, catalogued and traceable are common concerns. The value of data is directly proportional to the number of people who can connect to and utilize it in meaningful ways. Data is the most important asset and in the centre of an organization’s transformation journey. However, organizations tag less than 3% of their data and analyze less than 1% of it².
The end goal has to be turning data into intelligence and infusing this intelligence on what we truly want to impact.
Organizations generate Petabytes of data, but they don’t know where the data is. This data often sits in silos and is difficult to discover it. Even if we discover it, we are not sure if we can trust it. Inconsistent governance of data makes it difficult to connect data in an efficient and durable way. This makes it difficult to generate insights and organizational value from this data.
More than 85% of ML and AI created by created by organizations today don’t make it into production as they are either not built on the right data or they are not related to a strategic business impact².
Strategy and Analytics need to go hand in hand to generate meaningful results — with the need for an overarching approach from asking the right questions to gathering the right information and ensuring the right actions. Ultimately, not every organization will need to transform into a Data-Driven Organization — but any organization can adapt the necessary principles to drive their operating model towards insights excellence.
Next up.. Hardest part of ML and AI is not ML and AI.
This is a real struggle for many organizations. We will cover this in more detail in our next posts. Stay tuned!
Sources:
- Alex Bratton, How to Measure Your Organization’s Data Maturity
- Microsoft, Turning data into intelligent experiences
- McKinsey, Designing a data transformation that delivers value right from the start
LinkedIn Article: Click Here
Co-authored with @VikramSandhu
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Disclaimer: Views, thoughts, and opinions expressed in this article belong solely to the authors in their personal capacity, and do not represent the author’s current or past employers or any other groups or individuals.
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