The light at the end of the error tunnel is called Data Quality. This is the trump card that companies will have to play with ever greater determination to break through the doors of the future. It’s easy to see why: working in a context full of incorrect data can cost the company’s budget and the management of the ordinary. On the contrary, being sure to operate with correct, precise and reliable information means carving out a leading role in terms of credibility. It means avoiding misunderstandings, mistakes, problems, lack of contact with customers and raising your risk profile. It means equipping yourself for the tools to compete. And, in a word, it means winning.
Data Quality: an essential lever for the development of the company
Remember the explosion of the Space Shuttle Challenger, the tragedy of space that struck the emotions of the whole world? The investigations (Criticality of data quality as exemplified in two disasters, Fisher & Kingma, 2001) have shown that behind the drama there was not an inevitable fatality, but ten different categories of Data Quality problems. That is, trivializing, of bad data management. This is, of course, the extreme example, not the only one, but the case is exemplary. And it clarifies how essential it is to rely on correct data to avoid irreparable consequences. Extending the reasoning to business logic of less “human impact”, it is clear that taking care of the company’s Data Quality is an essential task, able to prevent wrong decisions and damage to the economy of the company. A task that requires commitment and costs, but much less than those that would involve not dealing with it.
How to care for data reliability
But what does it mean in practice to take care of the company’s Data Quality? Let’s imagine the amount of data that the company has to manage on a daily basis. VAT numbers, addresses, tax codes, telephone numbers, dates: sending documents with an error in one of these elements can mean causing damage – even significant – to the company. An invoice that does not arrive at its destination. A customer with whom you lose contact. The risk profile that changes in a worsening sense. Avoid all this you can, by implementing a process of analysis and improvement of the data itself in terms of Quality:
- completeness (do I have all the data necessary to complete the process?)
- accuracy (do I have accurate data to represent the information?)
- timeliness (how do the production times of the process data affect it?)
- coherence (are the data contradictory?)
- univocality (is the same information always represented by the same data?)
- integrity (have the data changed over time?)
- formal compliance (do the data entered meet formal standards?)
Benefits of accurate company data quality
The evaluation, according to these criteria, of the data present in the information system translates into an opportunity for the company to make correct data-driven choices. Companies able to move along these lines acquire great ability to react flexibly and dynamically to market demands, avoiding to miss important business opportunities. The latter are, in fact, for the most part now characterized by requests based on interdepartmental analysis of cross-referenced and integrated data, and respond to volumes of information, from different sources, in continuous growth.
In this context, the recent introduction of data management requirements has raised awareness of the benefits of data quality. But the task of taking care of these aspects cannot be exhausted with intentions. And it must necessarily translate into codified procedures capable of founding new concrete values for the business, through a rigorous metadata management system (business, technical and operational). The company must then first clean up the data in the information system, checking the regularity of its processes to prevent errors, even in the presence of correct data, may occur or repeat in the future.