Making a case for data quality of your brand is now more important than ever before. In this era of big data where every organization is hell-bent on laying its hands on as much data as it can, it becomes exceedingly important that you must be assured of the data quality you access.
If the report by Forbes that the amount of data produced every day is 2.5 quintillion bytes should be taken into cognizance, then data is surely the next “crude oil” and data quality can’t be overemphasized. According to WhatIs.com, data quality is a perception or an assessment of data’s fitness to serve its purpose in a given context.
On a daily basis, organizations depend more and more on data for their operations, data quality has, therefore, become a front-burner issue. SiriusDecisions reports that nearly 60 percent of marketers consider the overall health of their data unreliable.
Once your data quality is poor, expect an avalanche of problems. These problems range from inaccurate reporting to ill-conceived strategies.
Economic damages you have to shoulder due to poor data quality problems border on miscellaneous expenses such as demurrages and transportation fares when packages are wrongly shipped to other destinations right through to tough regulatory compliance fines for keeping inaccurate financial records.
Data quality is viewed in the realm of its accuracy, consistency, completeness, integrity, conformity, and timelessness. With the implementation of a data quality initiative, you can expect a 20-40 percent increase in sales for your brand.
It’s necessary we look at each of these to see how they enable you to make decisions fast based on the right information.
1. Completeness
The mistake you mustn’t make when considering the completeness of data is to assume that you must have every data that is available. For quality data to be complete all you need is to have the essential.
It must be comprehensive even if some optional data is missing. Once your data meets your expectations, it’s complete.
If for instance a record is required that must bear your customer’s first and last names, the middle name must not be there to make the data complete. Since no data value is missing, your data is in a usable state.
According to a research carried out by Experian, it was discovered that 77 percent of companies are of the view that their bottom line is affected by inaccurate and incomplete contact data and on average, respondents believe 12 percent of revenue is wasted.
2. Consistency
You need to enhance the sustenance of systems to report data from one segment to another. Data you collect across all systems must reflect the same information for it to be considered quality data and consistent.
Across the different sections of the brand, you must ensure that your data are in sync with each other. No distinct occurrences of the same data instances should provide any information that conflicts.
There must be alignment between the expected versions of data you are collecting and the types of data.
3. Conformity
Your data must follow the set of standards for data definitions like data type, size, and format. The global standard format for recording the date of birth, for instance, is “mm/dd/yy,” anything short of this does not conform with the standard format.
It’s absolutely important that your data maintain conformance to specific formats.
4. Accuracy
In order not to allow the renowned cases of garbage in/garbage out that confronted data quality efforts in the early days of computing to rear its ugly head with artificial intelligence (AI) and machine learning applications, accuracy must be your watchword. You have to ensure that your data to a very high degree correctly reflects the real-world object or the event being described.
The incorrect spellings of product, person names, and addresses could be viewed as little issues but can overall, greatly impact operational and advanced analytics applications.
5. Integrity
The integrity of your data determines how valid it is across the relationships. This ensures that the data in your database are both traceable and connectable.
If you have a situation where there is an address relationship data without the relevant customer then that data lacks integrity. No data should have any missing important relationship linkages.
Once you are unable to link related records together you may be heading for duplication crises across your systems.
6. Timeliness
Your data must be available in real-time when it’s expected and needed. Your data is of no use if the essence for which it’s needed has elapsed.
The timeliness of your data depends on the user’s expectations. You can use methodologies for such data quality projects like the Data Quality Assessment Framework (DQAF), which provides guidelines, for measuring data dimensions that include timeliness.
With DQAF, the actual times of data delivery are compared to anticipated data delivery schedules.
It’s not an overstatement to say that the expansion of data’s use in digital commerce, as well as the ever-ubiquitous online activity, have gone a long way to intensify data quality concerns. It has, therefore, become immensely important and very obvious that data quality is an angle you can’t afford to toy with if you actually want your brand to grow as well as grapple with the global competitive market.
