What is Data Strategy?

The corporate data is growing in complexity and size with every passing day. Despite heavy investments in infrastructure and data management, the data problems continue to trouble companies. Many companies still do not realize the importance of a well-defined Data Strategy.

Over the past 20 years, the importance of data has grown. It is recognized as an asset and not an application by-product anymore. Organizations need to develop Data Strategies that match today’s realities. Data strategy is a well-defined approach to improve acquisition, storage, management, and sharing and usage of data. It allows data to be managed and used like an asset.

Many companies have data-management functions and Chief Data Officer (CDO), but they are ineffective in the absence of clear cut laid down Data Strategy. Traditionally, data strategies were focused on storage & retention of data. Strategy revolved around estimating data growth, managing storage capacity and methods of handling data retention. The strategy did not address, how to improve the ways you acquire, store, manage, share & use data.

A good Data Strategy consists of following components.

Data Identification

One of the key elements in data strategy is identifying the data irrespective of its origin, structure or location. It is important to have data element naming and value conventions to use and share data.

In addition to data, it is also important to have means of storing and accessing metadata or data dictionary. Just as you have a catalogue system in a library to locate any book, metadata helps you in locating any piece of data. Without metadata, company is likely to overlook its prized data because they would not know it exists.

Data Storage

Data Storage is a very important piece of data strategy. Most organizations calculate& provide for data-storage requirements of individual systems.  This approach overlooks the data sharing and usage. Data sharing mostly happens at a transactional level but bulk data sharing is often overlooked and considered as one-off activity. A good data strategy ensures that once the data is created, it is easily accessible and shared by everyone without requiring anyone to make their own copies.

Data Provisioning

Data should be packaged so that it can be reused and shared by various applications. The rules and access guidelines must be defined. Data sharing cannot be left to whims of application developers as a onetime requirement. It has become a business need and should be dealt with accordingly. If company’s data is an asset, it should be properly packaged and prepared for sharing.

Data Integration

Integration is much more than traditional Data Warehousing approach. It includes all data (structured, semi-structured and unstructured) and its movement across systems. Each development team builds its own logic to link data across various applications. No attempt is made for storing and reusing the logic. A proper integration strategy would ensure that data is cleansed and merged in final data set in a consistent and repeatable manner for everyone to use.

Data Governance

Governance is about establishing, managing and communicating information policies for effective data usage. The purpose of governance is to decouple the data from its applications and make its details available to all other data constituents. It plays an important role in overall data strategy to ensure that data is managed consistently across the company.

A well-defined data strategy helps an organization address its current and future business needs in a more structured manner as it grows and evolves ensuring its success.