Data is no longer a residual of Business Processes; it is the key ingredient behind Strategy Formulation and Strategic Governance.

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.

The need for Data Strategy

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.

5 pillars of Data Strategy

Before formulating a data strategy, an enterprise can consider to think on the following questions-

• WHAT – Is a Data Strategy

• 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?

• HOW- To implement a 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.

Decoding Data Synergies

Data Identification

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.

Data Accumulation

After an enterprise has identified data, it needs to accumulate and store in a structure that supports easy, shared access and data processing. Data accumulation and storage is a complex discipline and one of the basic capabilities of a technology portfolio. With modern data warehousing capabilities, most IT organizations have devised mature methods to identify and manage the storage needs of individual application systems.

Data is indispensable, whether it is to create transactional processing applications or develop complex analytical systems, data storage is extremely critical. The crux is to move away from data isolation by planning capacity addition and storage allocation into various systems.

Data Processing

Data is very much a raw ingredient until it is effectively processed. In most business, data originates from multiple sources, generated from a multitude of application systems including cloud applications, stakeholders, business partners, data providers etc. While this data may be a stockpile of information, it may not come in the same format as used in an enterprise. Every business has a way to accumulate and store data. Thus, to make data ready for use, it requires a series of steps to transform, clean and format the data as per the requirements of the enterprise.

Data Processing makes possible for homogeneous data sets to be integrated or merged by data users to match the individual requirements of the organisation like data sharing, transaction, data analytics etc.

Data Sharing

As data moves to the cloud, courtesy IoT data sharing is no longer restricted for exclusive access by architects and programmers alone. The rise of citizen data scientists makes data democratization possible to be shared and distributed to support both operational and analytical needs. Data is identified and accumulated so that this vital piece of information can be shared and reused across inter disciplinaries.

To treat data as a corporate asset, it must be packaged and prepared for sharing addressing data provisioning as a standard business process.

Data Governance

As data strategy broadens is horizons, data governance offers an imperative vigour over the data content, encapsulating changes concerning technology, processing and methodology associated with data management efforts.

Data strategy is incomplete without a scalable data governance structure. In the world of data parlance, most data governance initiatives begin by addressing specific tactical issues, encompassing data accuracy, or defining business rules. As data sharing and data usage gain visibility and awareness around data grows manifold, the need for data governance initiatives is increasingly felt. This leads to organizations formulating a curated set of information policies, rules and methods to ensure data is handled in an effective and efficient manner across the organisation silos.

A data strategy initiative is not a one-time effort but as the word defines it needs to be flexible enough to identify a shorter-term set of delivery milestones and blend in the enterprise's long-term growth objectives.