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Data Science Maturity Model - Strategy Dimension (Part 2)

Mark Hornick
Director, Advanced Analytics and Machine Learning

In my previous post, I introduced this series on a Data Science Maturity Model and the dimensions we'll be discussing. The first dimension is 'strategy':

What is the enterprise business strategy for data science?

A strategy can be defined as "a high-level plan to achieve one or more goals under conditions of uncertainty." With respect to data science, goals may include making better business decisions, making new discoveries, improving customer acquisition / retention / satisfaction, reducing costs, optimizing processes, among others. Depending on the quantity and quality of data available and the way that data are used, the degree of uncertainty facing an enterprise can be significantly reduced or accentuated.

The 5 levels of the 'strategy' dimension are:

Level 1: Enterprise has no governing strategy for applying data science.

For enterprises at Level 1, the world of data science may be unfamiliar, but data certainly is not. Data analytics may be a routine part of enterprise activity but with no overall governing strategy or realization that data is a corporate asset. The enterprise has defined goals, but the extent to which data supports those goals is limited.

Level 2: Enterprise is exploring the value of data science as a core competency.

The Level 2 enterprise realizes the potential value of data and the need to leverage that data for greater business advantage. With all the hype and substance around machine learning, artificial intelligence, and advanced analytics, business leaders are investigating the value data science can offer and are actively conducting proofs-of-concept - exploring data science seriously as a core business competency.

Level 3: Enterprise recognizes data science as core competency for competitive advantage.

Having done due diligence, enterprises at Level 3 have committed to pursuing data science as a core competency and the benefits it can bring. Systematic efforts are underway to enhance data science capabilities along the other dimensions of this maturity model.

Level 4: Enterprise embraces a data-driven approach to decision making.

Once an enterprise establishes a competency in data science, enterprises at Level 4 feel confident to embrace the use of data-driven decision making - backing up or substituting business instincts with measured results and predictive analytics / machine learning. As data and skill sets are refined, business leaders have greater confidence to trust data science results when making key business decisions.

Level 5: Data are viewed as an essential corporate asset - data capital.

A capping strategy with respect to data science involves giving data the "reverence" it deserves - recognizing it as a valuable corporate asset - a form of capital. At Level 5, the enterprise allocates adequate resources to conduct data science projects supported by proper management, maintenance, assessment, security, and growth of data assets, and the human resources to systematically achieve strategic goals.

In my next post, we'll cover the 'roles' dimension of the Data Science Maturity Model.

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