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Data Strategy

It is hardly arguable, that we live in the age of exponential growth. Besides many other things, the growth of the data volume generated by us is rocketed too (e.g. according to researches of IDC mankind doubles the data generated in every 24 months), thereby the business value potential hidden in this data is also continuously growing. If you want to manage and utilize your corporate data assets, you should design a comprehensive, enterprise-level data strategy, and you should continuously implement and update it.

What is the enterprise data strategy?

There are many different definitions of the enterprise data strategy, but all agree that the basic goal of data strategy is to create and maintain an enterprise-wide strategy that ensures the adequate protection, quality, value, and utilization of corporate data assets available through harnessing data-related and data-dependent capabilities. An effective data strategy (similarly to other corporate strategies) has the following properties:

  • Actionable
  • Relevant (contextual to the organization, not generic)
  • Evolutionary (needs continuous revisiting and update)
  • Integrated / connected
How does the data asset generate business value?

Corporate data assets can generate value for the company in many different areas:

Improving Business Decisions

In order to make good business decisions, we need to know better our customers, products, services, competitors and markets, monitor better financial and other internal processes, and effectively leverage human resources. Enterprise data assets can help to achieve these objectives.

Improving Your Operations

Proper utilization of data assets can also provide you the opportunity of optimizing your company’s processes, products and services. This could include developing or improving a more competitive product/service, but data can also help to optimize the process of product manufacturing. Some examples of utilization in different areas:

  • Manufacturing: monitoring equipment and machines for early identification of wear and tear, for intelligent planning and reduction of downtime (predictive maintenance), for reduction of defective products or for improvement of product quality
  • Warehousing and Distribution: automated warehouse; intelligent supply chain management; delivery route optimization
  • Business Process Improvement: fraud detection, data-driven risk estimation, automatic content generation with artificial intelligence
  • Sales and Marketing: churn / fading / attrition prediction, personalized offers and product recommendation systems, dynamic pricing, customer satisfaction prediction, channel optimization

Data Monetization

Well-maintained data assets with appropriate quality can directly increase your company’s market value, but it is also possible to sell data assets to individuals and other companies.

Why do we need enterprise data strategy?

As we saw previously, the company’s data asset can create business values in many different areas. However, in the absence of comprehensive enterprise data strategy the various business and professional departments:

  • Can not utilize the corporate data asset (as they do not know that it exists; they do not recognize the application of it; they do not have the proper knowledge to exploit it); or
  • Begin to utilize the data assets on their own, but the solution will not be complete or effective (as they are not able to include all the necessary other business departments/experts; they do not use all the data available at company level; they take into consideration only their business goals, but not the enterprise goals); or
  • Use inaccurate, inconsistent data or data with inadequate quality/content that leads to poor business decisions.

Who in your organization should drive the data strategy

According to our experience, building of data strategy is not primarily an IT, but a business task. This does not mean that IT managers and professionals can be left out of data strategy creation, but data assets are primarily business assets, therefore the business side should drive the building, implementation, and continuous review of the data strategy. Forbes estimates that by 2019, 90% of large corporations will employ a Chief Data Officer (CDO), whose primary task and responsibility will be the proper management of corporate data assets.

Where do you start?

  • The objectives of the data strategy should be derived from the company’s business strategy and the following issues should be addressed in the process of building the data strategy:
  • Available and potentially obtainable internal data sources, and accessible external databases (the goal is to identify the combination of data, that provides the highest business value during the exploiting of corporate data asset),
  • Data utilization (in decision making; in business operations; in direct monetization),
  • Technology and data infrastructure to be used (data collection, storing data, analytics and data processing, access to data, visualization and communication),
  • Building data competencies (building and developing internal skills and competencies; using external competencies),
  • Data governance (data ownership and privacy, integrity and security).



Antal Kerekes

Antal Kerekes

Partner, PwC Hungary

Gábor Oltyán

Gábor Oltyán

Senior Manager, PwC Hungary

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