And so the IT team has to juggle millions of information that are critical to the business of their organization.
A reactive approach is not enough
Imagine playing a football match: if you’re constantly on defense, waiting for something to happen, you’ll hardly have the game in hand. If you answer only when they pull towards your door, jumping and running after the blows of others, the number of shots on goal will continue to grow. If you focus on proactivity, the story changes.
Choosing a proactive program to improve data quality allows you to avoid many common mistakes and identify others before they become a problem.
As you know, the quality of the data measures the state to identify any problems and evaluate their overall reliability. This allows you to see if the data we have is suitable for the purpose for which it is needed.
Why couldn’t data be reliable?
There are countless plausible reasons why data could be unreliable. Let us think, for example, of the banal errors of manual input of information. When an error is identified in these cases, it will then be corrected by activities such as profiling and data cleaning, taking any corrective measures to prevent it from happening again in the future.
Then there are the systemic weaknesses that translate into complexities that are not easy to solve. Which ones? For example, not treating data as a resource, underestimating its importance. Sometimes for stakeholders who use them, sometimes for lack of IT budget. Others, however, for the IT team that does not have the material time to implement and monitor best practices to ensure data quality. From creation, to archiving, to continuous analysis and governance, it becomes increasingly difficult to cope with large, growing volumes of data.
How can we be proactive?
Regardless of the size of an organization and the number of people dedicated to IT, data is a real business resource. Most data is not idle: it branches out into multiple stores and applications. You can easily compare an error on a data such as a computer virus: once entered, it spreads like wildfire and causes problems to the entire infrastructure.
To overcome these situations, the advice I would give you is to start your efforts from the beginning, when data is created and collected. If you want to achieve high quality sets, you need to consistently define and follow best practices for this purpose. This applies to conventional transaction data and big data, which includes a combination of structured, unstructured or semi-structured data.
Most databases provide a solid set of constraints to ensure information compliance, which can help make data more reliable. The structure of procedures and tools of master data management solutions is an excellent basis for a quality program, as it helps maintain a single point of view on data.
I want to point out that also (regardless of the maturity of the system), the execution of periodic reviews during the life cycle of the information, helps organizations to prevent incorrect or incomplete data from affecting the operations and business processes. Even if you tackle and resolve the complexities early on, it is important to evaluate them regularly over time, so that they continue to be suitable for the purpose for which they are collected. This is where proactivity comes into play, so you can determine which would have a heavy impact on your business and identify problems before they happen.
Like any company initiative, the creation of a mentality is an essential element, as is the investment in training.
Another activity in which you should be proactive, concerns metrics: identifying and documenting them thanks to the collaboration between all stakeholders, should be one of those points that update over time. Before using KPIs, always consider that they are applicable, both to the same sets on which they were used previously, and to new ones.
Proactivity leads to better decision making
