Why Is Data So Hard?: Challenges, Solutions, and Future Directions

Why Is Data So Hard?: Challenges, Solutions, and Future Directions

Kristin Kennedy
Copyright: © 2019 |Pages: 23
DOI: 10.4018/978-1-5225-7769-0.ch011
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Why is it always so complicated to acquire data to assist with making decisions? This chapter will provide some background as to why data is so challenging, specifically with the intent to create empathy for what those who are responsible for data go through on a regular basis. By first understanding the current issues, one can begin to work on solutions. Many books and documentations deal with all of the latest trends in data, but this chapter will attempt to explain the most relevant terms and concepts of the day. After explaining the state of data and all of the intricacies involved, this chapter will propose recommendations for how to create a data-driven culture that will increase ease and efficiency of the use of data for any institution.
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In order to understand some of the complexities of the present, it is important to know the history of the past; this is significant because many of these challenges are cultural in nature and require an understanding of the current culture. In the early nineties, data was just plain hard to get. It required coding and a certain skill set to even understand where to start (Kennedy, 2018). Once one received the data, it was often limited in scope and distributed via spreadsheets or green bar printouts. The data often came from the office of Institutional Research (IR), or if there was an Information Technology Office (IT) it might have come from there. IR offices began popping up as early as the 1970s when the U.S. Department of Education began mandating reporting as well as enforcing financial penalties for non-compliance (Gagliardi, Parnell, & Carpenter-Hubin, 2018). The data produced was prescribed and well documented as to the required specifications but did not often match the way in which institutions viewed themselves. These compliance reports became crucial and as a result, were the primary focus of the IR offices, taking priority over anything else. This is still true today, as the compliance reporting required by the U. S. Department of Education, the state in which the institution operates, the accrediting body under which the institution resides, and any system or governing body that regulates the institution, continues to grow, and yet the IR offices are required to do more.

Key Terms in this Chapter

Institutional Research (IR): This office is responsible for various things that could include compliance reporting to the Federal Government, State Government, and any other regulatory body.

Data Lake: A conglomeration of data from many different sources that is easy to use and has all sorts of data in many different formats.

Artificial Intelligence (AI): The idea that some device can use machine learning to learn behaviors appropriate to the situation without human intervention.

Data Warehouse: A data source created for the purpose of making reporting and visualizations easier among an institution and contains cleaned and connected data from around the institution.

Business Intelligence: A group who usually works within the Information Technology area of an institution and is responsible for curated and enriched data, as well as reports and visualizations. This group is focused more around operational reporting.

Information technology (IT): This office is responsible for technology in any institution. This group generally supports all things technology related and is sometimes referred to as Central IT.

Analytics: The analysis and evaluation of data for the purposes of diagnosing, predicting, or prescribing actions.

Machine Learning: The concept that a computer learns something after being fed a great deal of data from which to acquire knowledge.

ETL: Extract, transform, and load is the process of taking data from one source, enriching it, and then placing it in another source, usually a data warehouse.

Big Data: A term that generally describes a large amount of data.

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