Importance of Data Quality and Meta Data in Data Science

Introduction

The more we study everyday data, the more we keep on understanding that data is involved everything we do in life; not only in our art of decision making but also in how we get things done in terms of our daily activities. Let’s put that into perspective! If you have ever missed a flight traveling to a destination due to you forgetting that the flight was to move by 12:00pm and you got to the airport by 2pm missing out on the number 1 in front of the 2, the quality of the data you recorded in relation to the flight time was wrong and as a result you forfeited the trip.

Data Quality

Thus, as we keep on talking about data, we need to also learn about the quality of the data we are acquiring, storing and sharing on a day-to-day basis. Data quality can be referred to the suitability of either the qualitative or quantitative sets of information for the planned usage (Redman, 2013). And, there are of course, many purposes for data collection from conducting business operations, making accurate decisions and even forecasting future profit margins, customer relations or generally company conditions.

Metrics of Data Quality

With the constant application of data, the need for high data quality has necessitated the science of measuring and quantifying data for effective and profitable data application in the economy (Heinrich, Kaiser and Klier, 2007). In fact, companies are now utilizing the metrics of data quality to detect high-quality data for departments in their businesses like customer relationship management (CRM) and multichannel management (Cappiello, Francalanci and Pernici, 2004; Heinrich and Helfert 2003). For instance, raw data gotten from customers through surveys or opinion polls are not just acted on immediately but little metrics that aid the decision making process will be taken into consideration like the popularity of such suggestion or complaint raised by clients, the cohesiveness in putting forward the idea, the ratio of information to noise as regards the true state of affairs in the company and the bias or falsehood behind some utterances and the profitability in relation to undergoing that change process or solving the problem. Indeed, the burden of such task rests on the decision makers of that organization, however, the nuances of analysis lies on the data scientists to employ the metrics in analyzing the data received through the channels of communication between the business and its customers.

Meta Data

Since it is a common knowledge now that metadata is data about data, it becomes rather imperative to talk about how metadata can improve data in such a way that it adds credence to the data quality spectrum. For data users not to misrepresent data or fall into the trap of forgetting the sole purpose of collecting the data in the first place, it is now necessary to relate metadata to data not only in data science or web development like in the case of title formats in the HTML pages of website but also in the data collection and decision making processes of companies.

Conclusion

In essence, the quality of data requires decision makers to use metadata in this new era of data-centric lifestyle to better understand data as there is of course an abundance of data now like the Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data and so on (Sarker, 2021). Without qualifying and measuring these arrays of data, one can be lost not knowing how to make meaning out of them all.

Reference List

Cappiello, C., Francalanci, C. and Pernici, B. (2004). Time-Related Factors of Data Quality in Multichannel Information Systems. In Journal of Management Information Systems. 3(20), pp. 71-91.

Heinrich, B. and Helfert, H. (2003). Analyzing Data Quality Investments in CRM – a model based approach. In Proceedings of the 8th International Conference on Information Quality. Cambridge, 2003.

Heinrich, B., Kaiser, M. and Klier, M. (2007). How to Measure Data Quality? – A Metric Based Approach. In Rivard, S.J. Webster, eds., Proceedings of the 28th International Conference on Information Systems (ICIS). December, 2007. Montreal, Canada.

Redman, T.C. (2013). Data Driven: Profiting from Your Most Important Business Asset. US: Harvard Business Press.

Sarker, I.H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN computer science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x