- Recognize the importance of data quality
The primary purpose of data is to fuel business. Rather than making the IT department control the data quality, organizations must better equip the prime users to define the quality data’s parameters. If business intelligence is closely linked with the underlying data, there are better chances of adapting effective methodologies to help companies choose critical data on priority.
2. Avoid singular thinking
Accuracy will not be the same for all types of data. There is no one-size-fits-all policy for data quality. Data comes from different sources, and therefore, not all forms of data share the same quality or same metrics. For instance: While doing sentiment analysis on social media data, 80% of accuracy is sufficient, whereas it is not sufficient for industries like BFSI. Therefore it is preliminary for the data to be fine-tuned before analysis.
3. Focus on each stage of the data journey
Every organization wants to become data-driven with a holistic approach to adopting an enterprise-level data strategy. Moreover, they also want to optimize their technology investments and cut their costs. In such cases, the company should consider data as an asset to derive valuable insights.
4. Avoid unnecessary data
Organizations capture and use data every day across a variety of operations. The more data they have, the larger the margin for error will be. Organizations need to accept the reality that data isn’t always perfect. Understanding this will enable the businesses to spot the difficulties, build on their success, and locate the problems quickly — even before they happen.
5. Take responsibility
Data varies across organizations depending on their size, business model, fiscal health, and data strategy. Everyone in the organization is responsible for poor data quality. It’s a business problem, and the IT department alone can not be held accountable. By taking control of the data quality, companies can improve efficiency, cut costs and improve decision making.
6. Data pipeline design to avoid duplicate data
Duplicate data can be a whole or part of the data created from the same data source. Human errors cause most data duplications. This results in inaccurate reporting, lost productivity, and wasted marketing budget. A clear, logical data pipeline needs to be created at the enterprise level and shared across the organization to avoid duplication.
7. Enforcement of data governance strategy
The most effective way to improve data quality is to define the who, what, how, when, where, and why of the data. It is also essential to make sure everyone in the organization abides by these policies. The policies should be enforced by clearly documenting them so that they are accessible to the employees. This will not only improve security and compliance but also helps in improving business performance.
8. Invest in internal training
This could be a transformative approach. Attaining good data quality requires expertise and experience, which is far-fetched for an entry-level executive. This can be achieved through formal training. For competitive advantage, it’s critical to train their teams, manage data correctly, recognize its inherent value and encourage teams and executives to learn the basic concepts, principles, and quality management practices. This helps understand the benefits of good quality data and the costs incurred due to insufficient data quality.
Originally published at https://www.anblicks.com on March 31, 2021.