As a follow-up to the organizational component - namely governance actors (see part 2 of this series) – let's move on to the third pillar of corporate data governance : information asset management.
Over the years, companies have accumulated data on their clients, their activities, their experiences – happy or unhappy – creating and enriching on a daily basis the information asset which is increasingly considered by international organizations (OECD, EC, IFRS…) as a major « intangible asset » for enterprises, having to appear at the top of the balance sheets with rules of appreciation or depreciation.
In this context, the management of information asset is comparable to the management of a financial portfolio comprising stocks, bonds, real estate securities, etc. The goal is to minimize the costs and to maximize the value of the capital by respecting the rules that were established both internally and externally, and by minimizing risks. It's the same thing with data : the administrators have to minimize costs and increase the value by respecting the governance rules - internally and externally - that come with constraints.
Inside the costs of these intangible assets can be counted :
- The direct costs : acquisition, storage, processing… in other words, operating budgets.
- The indirect costs : lack of quality, loss of productivity...
- The hidden costs : billing errors in favor of customers, for e-commerce lost parcels and re-shipments at a loss, delays in projects, loss of credibility with customers…
When seriously estimated, these costs will make you dizzy. As an example, it has been calculated that for a simple database of about 200 tables of 5000 lines each, a rate of 4% of data errors - considered by many specialists as a very low error rate - amounts to more than € 200,000 per year in direct, indirect and hidden costs.
And what about value ?
The other important aspect of information asset management is data valuation.
While it is naturally accepted that data is vital to the business, how can it be valued? And furthermore, in the long run ? How to calculate the cost of their performances ? The goal is to first adapt the offers regarding customers and their changing needs, and secondly to develop new services, that is to say new sources of income.
In order to do this, data discovery and predictive analytics are key tools to use and leverage the value of internal business data. The last thing an enterprise wishes is to miss out on substantial profits because they would not have been able to predict the evolution of the market by analyzing the data of customers and products.
In the same train of thought, data analysis continuously improves product quality and business process performance.
Finally, the third aspect of valuation is the increase in business knowledge through the use of external available data. Big data and artificial intelligence techniques, relying both on internal and external data, help broaden the scope of business knowledge, whether it is in the field of customer and behavioral knowledge, in the field of know-how awareness or in other fields.
This type of valuation gives competitive edge and important flexibility to the enterprise that is more and more confronted to constant competition coming from all sides.
In short, a smart governance practice will allow cost rationalization (applications and others), performance optimization and prediction of the business needs. All this without slowing the smooth running of the company.
In the fourth part of our series on data governance, we will address the important subject of control (or the establishment of mechanisms to ensure that the defined rules are correctly applied).