The Role of Big Data in Decision-Making
- Arend Pryor
- Mar 18, 2022
- 5 min read
Updated: Mar 22, 2022
Author: Arend Pryor | Created: 09/16/2021
Details: Sharing content created as part of pursuing my MBA degree
Assignment Details: Utilizing statistical and data analytic frameworks, you will be able to evaluate and analyze the role big data plays in business decision-making. You will also demonstrate the ability to analyze and graphically describe key business data in Excel, allowing business leaders to achieve competitive advantage.
Consider the following:
How is data analytics different from statistics?
What are the main differences between descriptive, predictive, and prescriptive analytics tools?
How do businesses use analytics to convert raw operational data into actionable information?
Think about the organization you work for (or any other organization you are familiar with). Does the organization use data analytics?
If so, how is it used? How can the organization improve the way it uses data analytics? What opportunities is the organization missing out on?
If not, how could data analytics be used to improve the organizations performance?
It was Johnathan Rosenberg, former Senior Vice President at Google who said,
“Data is the sword of the twenty-first century, those who wield it well, the Samurai” (Dykes, 2018).
This is a great quote illustrating not only the importance of collecting and analyzing data, but also in the power of its use. For example, data analytics is one such grouping of tools that can allow a company to obtain information, analyze their findings, and present recommendations and predictions on actions to be taken to gain a competitive advantage. A process also made possible via the use of statistical methodologies. The differences between these two items will be discussed further in the sections that following below. Also included will be a review of the main differences between analytics tools such as descriptive, predictive and prescriptive and the questions they seek to answer. Next, we will look at examples of how organizations use these tools to take action on the data being analyzed. Lastly, we will review an example of how the company I work for utilizes data analytics, how it can be improved, and identify opportunities that exist.
Let’s first take a look at how statistics differs from data analytics. Statistics can be thought of as a grouping of tools that the user to gather information, organize it, perform an analysis on this data, and lastly, present the results of the findings. Keying in on the process of analyzing the data, this is one of the most important parts of the puzzle as there are several approaches used to obtain data, perform calculations, and make predictions that are then shared. Analytics on the other hand utilizes statistics as well as several other methodologies to extract the data required. There are three categories of analytical tools, each of which looks to answer specific questions. These areas of tools include descriptive, predictive, and prescriptive analytics (Doane & Seward, 2021). Some of the most common data analytics tools include the popular programming language Python, a spreadsheet program you may have heard of called Microsoft Excel, and Tableau, which provides a visual representation of data analytics (Vohra, 2021).
For a real-world example, check out the Tableau dashboard shown below using the following link. It shows a graphical representation of Healthcare Supply and Demand (Click here).
Taking a deeper dive into analytical tools, we are now going to break down the differences between the three main types mentioned above. For instance, descriptive analytics is focused on identifying and presenting data that tells us, what happened. This is typically based on historical data and can provide details on previous sales or past website traffic. Next up is predictive analytics, which looks to answer the question, “what do we think will happen now?”. Performing this type of analysis requires the right know-how and an excellent data source with the intention of producing future forecasts, predictive modeling, and other statistical information aimed at providing guidance for the future. Lastly we have prescriptive analytics. This is considered to be the most advanced form of analytics and looks to tell us, what we should do next. Techniques used to help answer this question include machine learning, graph analysis, and complex event processing, all of which require accuracy of the previously mentioned analytics tools (Kalsbeek, 2020).
Google Analytics is a great example of descriptive analytics as it helps companies answer the question mentioned above, “What happened?”. It does this by providing a wealth of information related to website traffic such as identifying the most popular web pages within a company’s website, helping identify web pages that take the longest to load, and the average time users spend browsing the website. This is the type of information used by managers to pinpoint areas of the website that require additional focus to help improve performance or possibly increase engagement (Doane & Seward, 2021). With football season starting, some companies look to use predictive analytics to forecast the purchasing habits of their customers prior to game day and teams use past performance data to predict the value of their players for future seasons. In each case, these companies can stock up on certain food products or determine how much they will be spending on renewing a top player’s contract (Pickell, 2019).
Changing gears, we are now going to look at an example from the company where I work, which operates in the healthcare industry. This organization manages Medi-Cal benefits for individuals and families within 14 different Northern California counties. Data analytics plays a huge role for companies of this type, specifically data collected for tracking and reporting what’s known as the Healthcare Effectiveness Data and Information Set (HEDIS). Scoring above a certain level in six specific areas not only results in high rankings, but also allows for a company to meet accreditation standards. This is an ongoing process that requires organizations to continually monitor these areas to track performance levels as well as identify opportunities for improvement. Analyzing current and historical data allows the company to spot trends in the services they offer, member satisfaction, and quality of care (What Is HEDIS?, 2021). Although not directly involved in this area, one of the ways the company might improve their data analytics process would be to include stakeholders within the company who have direct interaction with our members and providers. This would allow additional subject matter experts to weigh-in on issues being faced and share their experiences as part of coming up with the most effective solutions and improving HEDIS measures and involvement.
While being a master of data analytics and statistics may not make you feel like a samurai master, these are important skills to have in today’s business environment. For instance, knowing the difference between statistics and data analytics is a good place to start as this provides a foundation to build upon and goes hand-in-hand. We also reviewed details for the most widely used analytics tools such as descriptive, predictive, and prescriptive, each of which looks to answer a different type of question. These are important questions to ask as the answers can provide guidance on actionable next steps and strategies. An example of using data analytics was also provided for the healthcare industry in the collecting and analyzing of HEDIS data, which is used to measure the effectiveness of the services being provided and levels of member satisfaction with the goal of continually identifying opportunities for improvement. Following this information, we reviewed my recommendation for improving the way in which the company uses these data analytics and the opportunity it offers.
References
Doane, D. P., & Seward, L. W. (2021). Applied statistics in business and economics (7th ed.). McGraw-Hill Education.
Dykes, B. (2018, April 26). Data samurai: Why your business needs a new breed of data analyst. Forbes. https://www.forbes.com/sites/brentdykes/2018/04/26/data-samurai-why-your-business-needs-a-new-breed-of-data-analyst/?sh=7da4ea9d4612
Kalsbeek, R. (2020). Where to start with the 4 types of analytics. Iteration Insights.
Pickell, D. (2019). 8 examples of industries using predictive analytics today. G2.
Vohra, G. (2021, March 8). 25 most popular data analytics tools to know in 2021. Jigsaw Academy. https://www.jigsawacademy.com/10-most-popular-analytic-tools-in-business/
What is HEDIS? (2021). WebMD. https://www.webmd.com/health-insurance/terms/hedis
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