Data quality is a somewhat relative concept, which will depend on different factors. In general, we can say that this should be a measure of precision, data validity and integrity. But fundamentally, must be of sufficient quality to support our purpose of business.
Its suitability for use within the company, In other words, for operational purposes or for use in Business Intelligence initiatives, will ultimately determine its quality. To that end, it is found that the most institutions have problems with the quality of their data– According to Gartner, more than 25 percent of critical data from Fortune companies 1000 they are defective, and in innumerable cases they do not know it.
The huge poor quality data costs they are well known, in the same way that, among other methods, data profiling is used to correct it and thus avoid its negative consequences. Fundamentally, since the current business trust data more than ever. Not in vain, one of the main challenges IT departments face is adapting the business to the new digital context to obtain competitive advantages.
Profiling to boost data quality
The information provided by data profiles is of great help in boosting data quality. Specifically, the profiling procedure assesses and improves the quality of data from a given source system, trying to correct and improve existing problems, as well as avoiding them in the future.
Since it is a business imperative to be at the forefront of this new digital era, where data quality takes on unprecedented relevance, meeting the challenge requires data profiling. Data quality projects can trust data profiling solutions that automate the procedure representing a qualitative evolution with respect to manual coding profiling.
Among other benefits, reduce effort and extend reach and improve consistency across data quality initiatives. By having automated discovery capabilities scan all logs unique data, from any source, Finds hidden anomalies and relationships with great efficiency.
Taking advantage of these cutting-edge tools that determine the quality of the data itself translates into more and faster comprehensive data evaluation. Therefore, by automating data profiling, we can deliver high-quality data profiles with unprecedented speed.
How Profiling Helps Improve BI
As long as the quality of the data is important to a certain initiative, profiling will prove to be a necessary solution, as it helps to improve data quality and, therefore, is an essential requirement to provide the data with the necessary reliability.
In summary, Profiling provides us with actionable data that makes a critical contribution to the success of data quality initiatives and in BI projects, since they involve collecting data from various sources for analysis and reports. In particular, can help ensure the success of a business intelligence project by quickly shedding light on the true structure and content of the data.
Let us remember that its main objective is to analyze, measure and monitor data quality before initiating a data quality procedure, whose ultimate objective is the creation of data quality compliance reports, always in the context of needs. business and trade rules. Despite this, its ability to provide rapid and comprehensive information on data quality provides a wealth of versatility to this tool.
Therefore, Data profiling can be very useful in situations where data quality is a key issue. As indicated on the top lines, its use will help us identify data quality issues, that can be corrected in the source system or in ETL processing. O, as an example, guides us on the convenience of creating new business rules, as well as the viability of the project as a whole.
In BI initiatives that require the conversion or migration data, the elaboration of data profiles will also be an important support for reduce risks. Depending on the results of your evaluations, they will guide us on the steps to follow to achieve to end the long-awaited trust in data, necessary and unavoidable requirement to take the project to the best port.
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