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Why Is Data Strategy Important To Drive Data Science And AI Initiatives

Why Is Data Strategy Important To Drive Data Science And AI Initiatives

The importance of data in today’s world is well known. While it is extremely crucial to strategise data to be used for AI, ML or other applications, there are still many businesses that do not realise its importance. Every enterprise generates a huge amount of data which oftentimes is not leveraged to derive the best result out of it. Sateesh Rai, head of analytics at Orient Electric takes us through the importance of data strategy and why storing data and planning to use it in an efficient manner can bring about tremendous business transformation. It is an important framework for companies to derive useful insights. 

Why Data Strategy Is Important

While businesses earlier considered data to be just a byproduct of the various task performed, they are now giving importance to data monetisation and adopting data strategy. There are also many companies that think that having properly implemented systems in place, meeting software costs, storing data is sufficient to run the business, but they do not realise that having data strategy can help in deciding how this information will be moved, processed and shared. Many studies shared that the companies that adopted data strategy showed more than 50% growth. 

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Rai shared that it is important to have a business strategy in place to be able to drive the benefits of data strategy. He said that data is just one aspect of the business. “It is important to first have the business strategy in place as, without it, the right use of data cannot be made. Data should be treated as a corporate asset and be aligned with the business strategy,” he said. 

He further added that businesses are doing so many things today and CXO’s need to understand why and how data strategy would make a difference. “One of the best ways to do this is to consider how data was created and used in the past compared to how it’s created and used today. 

Challenges of Adopting Data Strategy

There are serval challenges that come in way of adopting data strategy — the biggest of which is to have a business strategy in place. It may happen due to:

  • Lack of clearly articulated business strategy
  • Absence of business and IT senior leadership in strategy formulation and execution
  • Lack of cross-business and IT collaborations
  • Complexity and lack of priority
  • Poor change management and controls
  • Lack of skills and expertise to realise the strategy

Once the challenges of business strategy are addressed, there may be challenges in terms of:

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  • Inconsistent data and outdated systems
  • Data may be stored in several different places and different formats
  • Data compliance
  • Incompatible data structures

How Can Data Strategy Be Accomplished

It involves 5 crucial steps:

  • Identify data and understand its meaning regardless of structure and location
  • Store data in a structure and location that supports easy, shared access and processing
  • Package data so that it can be reused and shared and provide rules and access guidelines for data
  • Process data which includes moving and combining data in disparate systems to provide unified, consistent data view
  • Govern data which involves establishing, managing, and communicating information policies and mechanisms for effective data storage. 

As Rai shared earlier, the business strategy must be aligned with data strategy. It is also important to manage people, processes and policies and culture around data to get maximum benefits. 

“Some of the crucial enterprise data architecture components are data modelling, data security, data management, data governance, document and content management, among others,” he said on a concluding note. 

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