It’s beyond imagination. With the advent of the digital age, data are driving many decisions in our lives.
What if those decisions prove wrong?
Certainly, your data-driven decisions will lash loss, which can be massive. It is what the bad data cause. IBM has predicted that the redundant data costs the US economy nearly $3.1 trillion every year. Isn’t it a colossal loss?
You can recover such a hefty loss through data quality management. Let’s get through what it means.
Data Quality Management:
Data Quality Management (DQM) determines a virtuous cycle, wherein data analysis, observation and verification filter useful data from the database. In other words, the high granularity in the database is transformed into fine granularity for achieving a business goal.
The word ‘granularity’ here refers to the size of the database that could be split into subsets. Let’s say, a person’s profile determines high granularity. When it is segmented into name, gender, age and country, it will turn into useful information or fine granularity.
Objective:
The data stewards capture data to react to acute errors in the database of an organisation. They proactively manipulate or control errors in it for pulling out accurate business intelligence/decisions. Simply say, the DQM process aims at attaining fine granularity or useful information for putting into data analytics. This is how the business goals are set.
In short, organizational strategies or decisions in the digital era are slave to data researchers and analysts. They exploit granularity to derive ideas for improvising business operations.
Let’s understand how it works.
Data Quality Management Framework: Filtering oddity from a database is a challenge. These methods of data validation or quality management counter it to ultimately access the value out of data.
1. Identify Bad Data & Assess: This method determines the redundancy. Subsequently, it supports analysis of how erroneous data affect business operations. Recall the most recent US presidential elections. The exit polls on the New York Times estimated a landslide victory of Hillary Clinton. The referendum went to Donald Trump in reality. The pollsters in this case proved wrong, as the bad data covered the reality. This is one of the cases where assessment of redundancy is essential.
2. Evaluation: This method of the data quality management framework emphasizes on measuring quality. What the data analyst has assessed, it is compiled together to focus on the critical datasets. The critical data sets are churned through the funnel of business users’ requirement. Thereby, the reporting dashboard is prepared.
It begins with blueprinting of data quality rules. The analysts keep the effect of bad data into account. Then, the evaluation is carried out accordingly to achieve the quality.
3. Analysis of Data: This is the most puzzling method. It scales around data validation to ground up for the quality improvement. The most feasible technique, such as data type validation, range and constraint validation and structured validation, is deployed to check the validity.
If you have capital and resources, the machine learning algorithms could be deployed to train the AI for filtering valid data out of the whole database.
4. Executing Quality Improvement: The data governance models hold the center of the stage. They are built around data quality identifiers and rules. The data stewards look into technical and business process related modifications. They carry out a comprehensive change management plan to adapt all stakeholders according to changes.
5. Control: This step effectuates the review of the data validation. Simply say, it helps to measure the degree to which the data can or cannot meet the level of acceptability. Once the data are found consistent with the business goals, the data geeks communicate with the data quality metrics. It lets them check if or not the quality management is ongoing.
Data are a pool of intelligence. Like useful data, a database is a composition of bad data or anomalies. These anomalies can cast a negative impact on your business and society. This is why data quality is essential.