Poor data quality can be a huge productivity sink for enterprise organizations. According to a study published by MIT Sloan, employees waste up to 50% of their time improving the quality of their data. This could be due to various factors like inaccurate data or inconsistencies across different sources.
The quality of data collected can not only bring down the efficiency of your workers but can also contribute to inaccuracies in output. This is especially true for business intelligence tasks that depend extensively on the quality and accuracy of the data presented.
How Do You Measure Data Quality?
Quality can be a subjective term. What’s poor quality to one person can be adequate for another. To remove these inherent biases, data quality is measured on a six-point scale.
- Accuracy
- Completeness
- Consistency
- Timeliness
- Validity
- Uniqueness
Here, accuracy refers to the factual correctness of data. Consistency is making sure this correctness exists across different overlapping data sources. Completeness is the availability of all necessary information across all your rows of data.
Validity is basically making sure that the data that you have conforms to the specific value parameters you have set (for example, the birthdays of individuals could be set in different formats), while uniqueness is making sure you only have one instance of a particular data across different platform.
Timeliness is ensuring that all of this data is present from the right timelines (for example, financial reports of the most recent quarter or birthdays as calculated by the current year).
Benchmarking data on these parameters will help organizations produce high-quality content for their BI projects.
How Poor Data Affect BI
Business Intelligence is an umbrella term that refers to all kinds of infrastructure used to collect and process data produced by a company’s activities. BI helps with decision-making across all organizational departments, including sales, marketing, human resources, and finance.
Business Intelligence adheres to the fundamental computational philosophy – Garbage In, Garbage Out (GIGO). In essence, the quality of input data determines the quality of output. Inaccurate or incomplete data can impact output quality, as much as invalid or outdated data.
Business Intelligence is not a cheap endeavor. The most sophisticated Business Intelligence tools typically cost as much as $5000 or more. This does not include the manhours put into processing data and deriving meaningful information to aid decision-making.
Poor quality data can hamper your business intelligence goals since it brings down the confidence level at which decisions are made. Poor quality data contributes to poor decisions made, which could be harmful to your business.
Let us take the example of a company using BI tools to decide on its product launch. A common way to do this is by understanding the most profitable demographic, competitive analysis of the market, and using customer surveys to identify gaps in the market.
A lot of data input in this exercise depends on the quality of data used. Asking the wrong questions in the surveys or not interpreting competitive analysis correctly could lead to faulty analysis, which will directly influence the decision made.
Depending on your industry, this could be a mistake that can cost thousands to hundreds of thousands of dollars.
How To Improve Data Quality for Business Intelligence
The most effective way to improve data quality for your BI projects is to establish solid protocols and SOPs for data aggregation. First off, do not rely on generic data sets. Identify the problem to be solved, and work on a dataset that is specifically curated for the specific problem. This way, you can remove any noise that can sway the results in unintended ways.
It is also good practice to identify the most optimal way to source data. This may be through traditional ETL methods, or through a data warehouse, or virtual data integration. Depending on the problem you are solving, the right strategy will ensure you have the most updated data needed to process information.
Hire dedicated data stewards whose job is to vet each source for the right data and make sure that the data fed to your BI operations comply with the predefined rules and guidelines.
Establish processes for continuous improvement. In some ways, Business Intelligence works on a trial-and-error method. Continuous Improvement (CI) enables processes to be incrementally tweaked towards perfection so that your quality of input, as well as output, is constantly on the way up.
Lastly, organizations need to realize that any tool is a weapon only when it is held right. Success with any Business Intelligence project is not derived just from the tools that are deployed but from the people using them. Hire the right specialists to execute your BI projects, so data quality issues are flagged at the right times so that any issues do not escalate into critical firefighting missions.
