How to Answer the “Why” Behind a Data Strategy?
Critical success factors behind a modern analytics landscape lies from the fact that it is not restricted to technical excellence but comes from answering the trickier “why” questions. This includes understanding deep learning models behind business problems; trusting data model predictions and explaining outcomes in a simple yet comprehensive language.
Of late, many of the data scientists are more interested to sharpen their skills and unearth interesting nuggets buried in data than engaging themselves to this softer cause. Though this may sound natural with a narrow focus on data and the tools required to explore it, understanding the critical ‘why’ is more mainstream to reach out to more users across the value chain.
To understand the nuances of a Data Strategy, let us understand it from a consulting team’s point of view who is assisting a large MNC to develop its data strategy. This move may trigger questions from the top end management to understand why and how a data strategy may trigger a difference to the existing work processes. To answer why and how one must consider how this data was created and subsequently used in legacy systems then how it is deployed today.
The absence of a clearly defined data strategy may raise concerns to data duplicity, processing overlaps and chances of work being replicated. A data strategy effectively helps and ensures that this data is managed and deployed as an asset rather than a residual of business processes. Thus, establishing a common ground for practices and processes to be always analytics-ready, besides effectively managing and sharing data across the enterprise.
Before formulating a data strategy, an enterprise can consider to think on the following questions-
• WHY- Is the need for Data Strategy
• WHERE – Is the starting point of the Data Strategy?
• WHEN- Is the correct time to invest in a Data Strategy
• WHO – Is the driving factor in the Enterprise to pilot this Data Strategy?
In simple words, an effective data strategy must answer to the “What and Why” behind a data strategy first which we discussed in the beginning. Add to it, a data strategy must answer the way data is identified, accessed, shared, understood and deployed. To be successful in contributing to decision-making activitie,s a data strategy must bring the different disciplines within data management under a common platform.
The five core components of a data strategy we would discuss are- Data Identification, Data Accumulation, Data Processing, Data Sharing, and Data Governance.
The first step towards a dependable data strategy is to identify the origin of data, and understand whether it is unstructured or structured. Data processing and analysis is feasible, after enterprises come to a clarity on this process. Data identification extends to data defined in a specific format, and establishes consistent data element naming independent of how data is accumulated and stored which forms the second process of Data Strategy.
