In the age of Big Data and Industry 4.0, everything is driven by data. Whether in finance, industry or research, data is constantly being created, analyzed and used. This data therefore plays an essential role in the proper functioning of companies and their value creation process. In order for this data to continue to be used over time, it is necessary and vital to have good data quality.
Data Quality is an important element for any company, regardless of its sector of activity, since it is about maintaining the consistency of its data over time. It is therefore necessary to ensure that over the years, the data will not lose information and will remain viable and exploitable. As everyone knows, data tends to degrade over the course of its use. But in concrete terms, what are the criteria that qualify the data? Depending on the field and the interlocutor, the criteria may differ a little, but the main lines will always be the same. In its report on data quality, Forbes Insightsidentified four main criteria:
The data must correctly reflect what it represents. A location must allow the equipment or building to be located.The data must also be up-to-date to play its role and convey the right information. Of course, the accuracy depends on the context but also on the end use. The location of industrial equipment, for example, will need more accuracy than an entire building.
The data must be complete. A lack of information can lead to a misunderstanding of the circumstances and result in serious consequences in any field. Whether you are in finance, maintenance or IT, having incomplete data can prevent you from accurately identifying the situation, creating misguided strategies or directives.
The format of the data depends on the source or region of the data, so it is necessary to find a way to compare them. The date format changes depending on whether you are in the USA (Month – Day – Year) or in Europe (Day – Month – Year or Year – Month – Day). It is also important to manage the duplicates and errors that may be present. By standardizing formats, comparable and consistent data can be obtained and misinterpretations can be reduced.
The data must be reliable and in perfect adequacy with its use. Without credibility, the data will yield results that are of little use because they have no real basis. The data, from both internal and external sources, must therefore be authoritative.
Having poor quality data can indeed have serious repercussions. This means bad or lack of information. This results in distorted knowledge and therefore potential risks and problems in the company’s business or accounts. According to some studies, the impact could cost up to 25% of the turnover. More concretely, here are some of the consequences of poor data quality:
When the data available does not inspire confidence, the results that are derived from it quickly do not either. According to KPMG’s 2016 Global CEO Outlook, 84% of CEOs are concerned about the quality of the data they rely on. A lack of confidence may cause executives to turn to more traditional means and temper their willingness to invest in data quality improvement solutions. Instinct, experience and trusted opinions are essential, but basing decisions on these alone puts the company at greater risk than analyzing quality data.
With better data you get more information. If a company and its competitor target the same customer and the competitor has more information, it will have a more mature understanding of the need and will have an advantage thanks to its data which it will be able to take advantage of. Data is an asset. They must be handled correctly in order to gain and maintain a competitive advantage.
Poor data quality can result in significant revenue losses, such as incorrect customer data resulting in lost business. Like an insurance company with bad information about the assets of its policyholders, this would have a strong impact on revenues and a real loss of income. Estimating the location of your policyholders, rather than accurately surveying them, could, in the case of a flood zone, for example, create a large loss of revenue.
The reputation of a company is vital to it, so it is essential to prevent anything from reaching it. It doesn’t always have a big impact, and you may not even realize it, but it can be very damaging. Without complete and up-to-date data, unintentional errors are possible, such as misspellings in a customer’s name, or sending communications to a deceased person. Mistakes with media impact are, unfortunately, also possible, such as the catastrophic launch of Apple Maps in 2012. Because of bad data, both incomplete and inaccurate, the services competing with Google Maps were unusable, and caused a lot of ink to flow. By correcting this aspect in particular, Apple Maps now offers a quality service that is completely consistent.