Data as an Enabler of Corporate Value

Data Quality Management’s Break

The accuracy and speed of execution in many corporate processes rely on the quality of the data at hand. Traditional Master Data Management efforts have failed to deliver accurate data, and a better approach is required. Ensuring accurate data is hard work and requires resources. The key in raising data quality is in expending those resources where they have the highest impact: on data with high value.

Valuating Data Keys

Valuating data requires an understanding of the relationship between that data and the corporate value drivers: key value indicators, organization, and business requirements. When we link data with those entities, the value of data can be determined and prioritized.

How does it work in practice? For a given corporate activity such risk management, audit & compliance, purchasing and others – we need to determine the following:

  • Key Value Indicators: How do we measure success?
  • Organization: Who is accountable and responsible? 

  • Business Requirements: How do we define the rules for success?

With these in place we can link the underlying data and determine how it impacts the key value indicators. Any data that is not required to be linked to these keys by implication does not add value to this corporate activity. If we disagree, then we need to rethink and expand our key value indicators, organization, and business requirements.

Note that we look at the impact of data on the key value indicators i.e. not at the value of data itself. We can, however, say that the value of data is determined by its impact on the corporate value expressed through key value indicators.

With this tool in place, we can now determine which data we should focus on. This concerns specific records, not just classes of data. When we identify a data record that impacts corporate value more than another record, then our priority is with the first.

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Value Versus Performance

In management we are used to be concerned with performance. Better performance means better results. Today, this thinking is flawed and we need to shift our attention to value. Better value means better business.

Performance is concerned with how well we carry out a task. It isn’t concerned with the value of the output of that task. It assumes that there are other mechanisms such as strategy and innovation that look after the value. When this is not the case, the business is headed for trouble, regardless of how good its performance is. elabed_jan02

When we shift our management focus to value, not only are we directly driving the success of the business, we will also detect performance issues that impact that value. We will pick up activities whose performance makes no difference at all and we can eliminate them. More importantly, we will identify all available and missing elements that create corporate value.

Processes and Quality

In our current performance oriented management approach, we naturally focus on process and quality. Processes streamline production and quality ensures that the processes are well carried out. This focus on processes and quality assumes that we are in a production environment. Most of the time, this is not the case. In the information corporation we are driven by information about our business environment and outlook. Trying to force real time decision making-and creativity into strait jackets of process and quality will hamper progress.

In the information corporation we need to measure value as our output and use it as the main management driver. If there are processes and quality systems in place, then we should determine how they impact value and manage them accordingly.

This doesn’t mean that there are no production lines in our businesses. There are, and processes and quality will still be required there. However, they should be linked to a value management system.

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Dr. Walid el Abed

Dr. Walid el Abed

Dr. Walid el Abed is a French linguist and computer scientist, and is known for the proposition of a new model called the Semantic Meta Model (SMM) (Meta Modèle Sémantique - MMS). He is the founder and CEO of Global Data Excellence. He is a worldwide renowned expert and visionary in AI and data-related topics, with 30 years of on-field experience across various sectors.

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