Before Thinking Artificial Intelligence and Big Data, Think Data Strategy

See AMP Version

According to Harvard Business Review, before thinking about AI and Big Data, companies should think Data Strategy because cross-sectoral studies show that, on average, less than half of an organization’s structured data is actively used to make decisions and less than 1% of its unstructured data is analyzed or used.

More than 70% of employees have access to data that they should not, 80% of the time of analysts is devoted to the discovery and preparation of data. Data breaches are common, unreliable datasets are spreading in silos, and enterprise data technology often does not live up to demand.

Having a Chief Data Officer (CDO) and a data management function is excellent but not necessarily possible in all organizations. On the other hand, each organization should, at its level, define a coherent strategy for the governance, analysis and deployment of information assets (data).

In short, a Data Strategy!

You use your business data to attack or defend yourself

There are two main approaches to the Data Management Strategy, Attack and Defense, between which any company should find a balance, which is not always obvious in reality.

A business sector with strict regulations (financial services or healthcare, for example) will guide the company on defence in terms of data management; a sector with strong competition for customers will instead put the company on the offensive.

Offensive data management activities tend to be more relevant to customer-centric business functions such as sales and marketing, and they run more often in real time whereas defensive data management activities are more relevant to legal, financial and compliance concerns.

Organizations that need to manage defensive activities are usually already equipped with internal data management mechanisms. But beware, if you do business with Europe, the General Data Protection Regulation (RGPD) is in force since May 25, 2018. This European Union general data protection regulation is the reference text for the protection of personal data. Failure to comply could be hazardous. For the purposes of this article, we are interested in highly competitive, customer-oriented business sectors.


When does data become information?

It is important to distinguish information from data and differentiate information architecture from data architecture. According to Peter Drucker, information is “data with relevance and utility”.

Raw data, such as customer retention rates, sales figures, and supply costs are of limited value until they are integrated, associated, or compared with other data. Then, they become revealing about the state of things and they can guide us in the decision making.

For example, sales figures placed in a historical or market context suddenly make sense. They may be up or down compared to benchmarks or in response to a specific strategy.

An enterprise data architecture describes how data is collected, stored, transformed, distributed, and consumed. It includes the rules governing structured formats, such as databases and file systems, as well as systems feeding processes with data.

The information architecture governs processes and rules that convert data into useful information.

Data and Analysis, your most powerful assets

A recent survey by New Vantage Partners reveals that in today’s high-stakes business environment, companies that differentiate, outperform and adapt to customer needs faster than their competitors rely primarily on their data analysis.

They find that the systematic exploitation of their data, coupled with analysis, offers opportunities to improve their business performance.

Moreover, for mature companies, Big Data analysis combined with Artificial Intelligence and Machine Learning can solve very complex problems.

This continued increase in the importance and challenges of Big Data is one of the most important features of the contemporary economy and society. The results of the survey over time provide interesting and useful documentation of this revolution.

However, the keys to success in meeting Big Data and AI challenges and reaping their benefits against the competition clearly identify some of the following prerequisites:

  • determine how your business should respond, define a Data Management Strategy
  • assign clear responsibilities for the Data Management Strategy and expected results (a Chief Data Officer, maybe!)
  • move towards implementing the necessary changes in a systematic way (Strategic Planning)



And you, what should you put in place in your organization to take advantage of that hidden gold mine; your data?

Leave a Reply

Your email address will not be published. Required fields are marked *