Analytics Best Practices: A Business-driven Playbook for Creating Value through Data Analytics by Dr. Prashanth Southekal

Author:Dr. Prashanth Southekal [Dr. Prashanth Southekal]
Language: eng
Format: epub
Publisher: Technics Publications
Published: 2020-04-30T16:00:00+00:00

Populating any missing values

Standardizing and enriching the integrated data.

Removing duplicate records if any

Validating and verifying the data with key business stakeholders

Conclusion

Data is the fuel for running analytics algorithms or models. But getting good quality data for analytics is challenging for most business enterprises. A study from the Harvard Business Review discovered that data quality is far worse than most companies realize, saying that a mere 3% of the data quality scores in the study were rated as “acceptable” [Nagle et al., 2017]. So, should the analytic initiatives wait for the data quality to improve, or is there a workaround?

There are many options or workarounds for getting good quality like data sampling, feature engineering, and acquiring and blending new data from internal or external sources. The analytics team should strategically source data given that getting perfect high-quality data is nearly impossible in most scenarios. However, if quality population data is available, it is always recommended to use the population dataset. These three strategies, however, do not fix the data quality problem in the company; they are essentially best practice workarounds that allow one to move forward with your analytics initiatives. As civil rights movement leader, Martin Luther King Jr said, “If you can’t fly then run, if you can’t run then walk, if you can’t walk then crawl, but whatever you do you have to keep moving forward.” And according to tennis player, Arthur Ashe, “Start where you are. Use what you have. Do what you can.”

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