Digital transformation emerged decades ago, but COVID-19 highlighted the need for and accelerated digital transformation across organizations. The most common aspect of every digital transformation strategy is – analyzing the massive amount of businesses data through advanced analytics for smarter and more efficient decision making.

Gartner says that “By 2022, 90% of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency”.

The Current Situation and Challenges

Today, Chief Data Officer (CDO) is considered as a critical function comparable to IT, business operations, HR, and finance in 75% of the large enterprises. But unfortunately, less than 50% documented corporate strategies include data & analytics as fundamental components, and 69% of companies have failed to create a data-driven organization. It’s concerning but not surprising.

Companies are experimenting with new data technologies only to deal with the “Fear of Missing Out” (FOMO) without knowing how to execute a successful data strategy that drives business value.

Data Strategy: What Do You Need One?

Although almost all organizations today claim to be data-driven, most of them remain way behind the curve. And, the torrents of data generated every day need to be treated like a business asset.

Arguably, many enterprises, out of excitement to leverage data, begin to use the latest technologies and analytics solutions, which aren’t apt for their businesses. Hence, such enterprises fail to unlock the business value of such data. This is one of the key reasons why data strategy needs to be in place as it solves the inaction or inability of the business to solve its critical challenges. Creating a data strategy isn’t a standalone activity and is driven by the business’s strategic objectives.

Earlier, data was considered only as a byproduct of business activity or process. But today, having a data strategy helps you:

  • Identify core business needs.
  • Identify capabilities required to achieve your business goals like cost optimization, increasing revenues, accelerate the decision-making process, etc., and
  • Create an achievable roadmap for achieving those capabilities and set clear expectations in terms of timeframes, costs, etc.
The Business without a Data Strategy

Businesses can’t have a sustainable competitive edge without a data strategy in place and face multiple challenges like:

  • Duplicate, redundant, inconsistent, and multiple versions of “the truth”.
  • Incorrect data may lead to wrong business decisions and hurt the company’s bottom line.
  • Time and costs involved in data movement and development increase in such cases.
  • Ambiguous business priorities, vague business plans to achieve goals.
  • Slow and inefficient business processes become the bottleneck.
Define your Desired Outcome — Before You Start Building

Experts advise starting a data strategy with a right-to-left approach, focusing on the desired business outcomes first, not the data. An outcome-focused data strategy starts with answering questions like the organizational business goals, analytics use cases, framework to use, and other critical business questions.

Five Components of a Data Strategy

Here’s a comprehensive five-step approach to help an organization define their data strategy and improve all the ways to acquire, store, manage, share, and use data.

Data Identification

Identify what data is available to solve a particular business problem and structure it to have a data glossary with a defined name, format, representation, and metadata. It will help define the business outcomes, maintain data consistency, and metadata will help to locate the data when required.

Data Storage

It’s one of the most basic but complex aspects of any business organization. An organization should ensure that they are not focusing only on “data creation”, but also on data sharing and usage across its different functions. Lack of any system for data sharing will lead to duplication of each data segment, which wastes resources when data needs to be changed.

Data Provisioning

It’s not enough to make the data shareable. Gone are the days when only application developers and technical experts used data in the organization. Today, business analysts and other key internal and external stakeholders use data immensely, and they should be able to comprehend data well to analyze and use it for decision making. This step is also termed “data packaging” and helps make data more of a business asset.

Data Processing

Data is a raw ingredient. It has to be prepared, processed, transformed, and corrected to use efficiently. Data preparation involves data cleansing, standardization, transformation, and integration of data warehouses. Data prioritization is a simple but crucial component for an outcome-focused data strategy. Organizations can create a transparent and straightforward prioritization framework that should reflect business values, should align with the business strategy and the value or impact the business wants to make.

Data Governance

Finally, there should be systems governing the standards and maintaining the quality of data. These systems should be consistent and agile to adapt with time. Governance often broadens its scope with time, like establishing information policies, rules, and uniform data usage and management methods.

Defining Data Strategy is the Key

Creating an outcome-focused data strategy helps map the investments made and the business outcomes and value by using data. It helps in systemically achieving existing business objectives and identifying new analytical opportunities to grow the business.

The Power of Data Strategy

The mindset and cultural shift that comes with creating an outcome-focused data strategy is extremely powerful. Data is known, accurate, consistent, and reliable; can be seen as a key enabler to business outcomes. Business functions within the organization can now work on strategic initiatives that matter the most and drive business value.