According to the Global Data Management Community (DAMA), Data Quality is the planning, implementation and control of activities that apply data quality management techniques, in order to ensure that they are fit for purpose and meet the needs of users.

Today, most companies profess themselves Data-Driven because their main strategic decisions are addressed by data analysis. However, often the model used is not correct or not adequate for business objectives;

the tool that plays a key role in choosing the right business strategies is the quality of information. The data must be reliable, relevant, correct, accurate and above all suitable for the analysis function to be performed.

Why is data quality so important for business and innovation development?

Recent research has shown that dealing with poor quality data is the main cause of exceeding the budget costs of a project and contributes to destroying the value of the business. Moreover, this problem is likely to become more serious as businesses become more digitalised and complex.

Most companies base their choices on data that does not represent reality, in fact they are obsolete, polarized, derived from outdated sources, located in non-communicating databases and can’t describe the scenario that is being analysed. One of the main consequences is the wrong budget allocation on operational activities, just think that according to Forrester (world leader in market research) 20% of online adv spending is wasted due to poor data accuracy, that translates into tens of billions of dollars.

Other aspects that should be reflected in the choice of a Data-Driven approach are the rapid changes of scenario due to global events such as pandemic, climate change and global geopolitics, they have made obsolete the historical data on which companies have always based their decisions.

For example, if we consider urban mobility, in recent years services, usage dynamics and habits of citizens have changed rapidly, making it necessary to produce new data describing the current situation. This has put many companies in crisis, because the application of Data Intelligence cannot be of help if the starting point is anachronistic.

Moreover, in order to correctly describe the phenomena, it is increasingly important to integrate data internally generated with data from third parties, such as those coming from social networks through the use of Sentiment Analysis (Click to read the dedicated article). This path is more complex and requires activities of Data Cleansing and Artificial Intelligence to reach an all-around understanding.

Criteria for Data Quality

Ensuring data quality is the basis for all Data Intelligence actions.

It is essential to start from a clear definition of business objectives, in fact no data can be defined “correct” but only adequate for the analysis to be performed.

The main criteria to be taken into account for measuring quality are:

  • Comprehensibility: determine how easy data is to understand
  • Accuracy: refers to the difference between an estimate of how an attribute should be valued and the actual value reported by the data
  • Reliability: indicates the degree of credibility and reliability, closely related to the source
  • Completeness: is a measure of correspondence between the real world and the specific dataset. Indicates how many and which data are missing in the dataset to offer a 100% complete representation of the real context
  • Correctness: the degree of accuracy
  • Interpretability: refers to the availability of documentation that indicates to users what types of data are contained in the database and how to analyse and interpret them
  • Objectivity: indicates impartiality/objectivity
  • Quantity: indicates how appropriate the volume of data held in relation to a given asset is.
  • Relevance: refers to database adequacy in relation to a given application context

Based on in-depth consideration of the criteria mentioned, an organization must define specific metrics to determine Data Quality in its business context.

Data Quality as the basis of innovation

At Pragma Etimos we are convinced that Data Quality is a fundamental element for all companies and today, in an increasingly competitive and complicated market, it takes on a strategic role.

We believe that there is a direct connection between data quality and the innovative capacity of a company, in fact, Data Quality not only serves to make strategic decisions but also to understand hidden problems and inefficiencies, directing towards new solutions. Thanks to Machine Learning algorithms, a good database can highlight signals that are a symptom of something important to consider in a timely manner.

Our Data-Driven approach allows you to create a high-quality data collection method on which to base effective and sustainable economic, social, environmental and digital strategies.


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