In the previous post we talked about how important data is for companies and how it is necessary to define a Data Strategy effective that can relate business objectives and the strategic use of data as a business asset. Defining an effective Data Strategy delivers a fundamental weapon into the hands of companies: decision-making.
We are called to make decisions continuously. Being “helped” by data in making decisions (Data-Driven Decision Making or DDDM) allows us to rely on objective elements such as reading and interpreting numbers, minimizing the influence of personal impressions. More rationality and less emotionality.
So the volume of data is so important, but we can guess how important it is to manage quality data to support the analyses on which we base not only our business decisions but also our operational and planning activities.
Failure to see the importance of data quality to support effective decisions contributes to losing track with a consequent deterioration in business volume. Gartner estimates the losses incurred by companies that do not pay attention to data quality at 12.9 million dollars.[1]
To increase your business, you need to be able to have correct information at all times, with minimum margins of error and fast analysis times. In addition to the time factor, it is also necessary to consider the context in which the data is generated.
In this regard, the ISO organization defines Data Quality as”Degree to which the characteristics of data satisfy stated and implied needs when used under specified conditions” (ISO 25012) and again:”The totality of characteristics of an entity that bears on its ability to satisfy stated or implied needs” (ISO 8402).
Therefore, the same information in a given context could be considered of low quality while, in a different context, of high quality. Evaluation closely related to the objective you want to achieve.
It is clear, therefore, that companies must, in order to compete in the market, orient themselves to a more conscious management of their data, combining their functions with that of managing data quality: the Data Quality Management.
DQM is a structured and continuous process that involves understanding, analyzing and improving information with a proactive approach.
A process that begins with the analysis and evaluation of data in order to identify anomalies that may have a negative impact on business performance. The analysis leads to the definition of metrics that will guarantee more accurate data. For the assessment of the qualitative level of the data, it is possible to refer to the most commonly used evaluation measures:
. Accuracy: the data precisely describe a given event.
. Completeness: the set of data is sufficiently exhaustive to produce meaningful analyses.
. Consistency: information from different sets is not conflicting or redundant.
. Integrity: the data must comply with the procedures, must not contain errors and must belong to consistent data types.
. Accessibility: the data are understandable and up to date.
. Actuality: the data must refer to a finite time context in which the events that are the subject of analysis occurred.
The measures identified to increase data quality must be disclosed to business functions that will help to maintain the adequate quality level of the data through the application and monitoring process.
To improve their capacity for action and business development, it is good for companies to invest in DQM strategies that will provide concrete benefits as well as a more conscious use of data assets.
In conclusion, the more correct and contextualized the information in our possession, the better the analysis will be that will help us generate value for our business. They represent our North Star.
Credits
1 Gartner”How to Improve Your Data Quality”
Author
Davide Loffredo
CEO — Value S.r.l.
Contacts: amministrazione-value@we-plus.eu