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Data Analytics

Explore the fundamentals of data analytics and how to apply them in the workplace

By: Aeron Zentner, Editor-in-Chief

Over the past decades, data has evolved from a specialized commodity for few to access and utilize into a vital component that is blending into the fabric of our professional and personal lives. Data is ubiquitous and constantly being created, collected, and analyzed. Knowing even the basics will empower you to make more informed decisions and be an engaged—and essential—member of your organization or business. Armed with the ability to harness data, businesses and individuals can better understand how to operate and function effectively.

Applied Data Analytics

Data is considered the fuel of the future, as business strategies thrive on an ever-growing and diversifying universe of data that flows into and is created by an organization. Understanding how data can strengthen workplace operations, planning, and overall effectiveness is essential across all occupational roles and industries. Developing the knowledge and skills to engage data throughout its lifecycle can empower you to uncover new information, validate ideas, and build upon current insights that can help facilitate innovation, create positive change, or increase performance.

Upon entering the workplace, we are guided by standards, best practices, and principles to ensure we have a safe, healthy, and productive work environment. Data handling also has rules to abide by. As a commodity, data is valuable—and in certain instances, highly sensitive. Therefore, it is strongly encouraged and often legally mandated that companies adopt a set of standardized data protection and governance practices to ensure data security and integrity. Having a basic understanding of these practices and regulations will help you use data more responsibly, undoubtedly to you and your organization’s benefit.

It is undeniable that data is ubiquitous and is collected through a plethora of ways on a massive scale from a vast array of sources. Oftentimes it is difficult to know where to start and what types of data you need for a particular project—and where to go to find them. By building a framework to understand the business objective and question(s) you need answered, you will be able to determine the most appropriate method(s) to collect data. This can range from collecting relatively small amounts of secondary data from the internet to personally planning and executing a large-scale survey.

Once you have data, you may think that you are ready to transform it into information. However, prior to your analysis and synthesis of the data, you will need to determine the quality of the data in your possession. By interrogating and examining data, you can better understand the nuances of what is included and excluded from the dataset(s). Working in tandem with this practice is the cleaning process to ensure that the data is clearly structured, organized, and coded in a way that it can be analyzed.

Data can be analyzed using many different data-processing and analysis applications. Microsoft Excel is the current industry standard and learning the basics will save you an incredible amount of time (and make you an invaluable member of your organization). Excel provides a broad set of tools to clean, organize, sort, calculate, analyze, and display data—and is intuitive and easy to use. Spend some time familiarizing yourself with its functionality and you will be amazed at its versality.

Now that you have data and tools to clean and analyze the information, it is essential to effectively and accurately interpret the information to clearly tell the story or describe/infer how the data relates to your business objective(s). While data analysis can be used to uncover new insights or understand speculations, numerical information may not provide the full story of the data or situation. Supporting quantitative (numerical) data with qualitative (non-numerical) data can provide additional depth to strengthen understanding and provide context not shown by the numbers.

As your transformation of data into information is nearing the end, an important factor to consider is how to share your data story to build knowledge. Data visualization is an effective way to share information through graphical representations. Visualizations can be used to aggregate data findings in a way that is clear, concise, accessible, and palatable to a broad audience. But creating compelling visualizations alone are not enough to tell your story. By understanding your audience, setting, and information, you can decide which kind of presentation will most effectively foster learning, understanding, and knowledge building.

Lastly, data is not only a part of the business world but can definitely be found in your everyday life. As society continues to increase its use of new technologies, data will serve as the vehicle to help solve challenges and provide opportunities for people to function more effectively. Recognizing the application of readily available sources of data, whether that be exercise data from your smartphone or financial tools from your bank, will empower you to make more informed decisions in your life.

Further Reading

Throughout this module you will find suggested resources specific to a data analytics skill. Here are some good resources to help you gain a better appreciation for and understanding of data analytics.

  • Albright, S. C., & Winston, W. L. (2020). Business analytics: Data analysis and decision making (7th ed.). Cengage.
  • Devlin, B. (2013). Business unintelligence: Insight and innovation beyond analytics and big data first edition. Technics.
  • Foreman, J. W. (2014). Data smart: Using data science to transform information into insight. Wiley.
  • Harvard Business Review, Porter, M. E., Davenport, T. H., Daugherty, P., & Wilson, H. J. (2019). HBR's 10 must reads on AI, analytics, and the new machine age. Harvard Business Review Press.
  • Harvard Business Review. (2018). HBR guide to data analytics basics for managers. Harvard Business Review Press.
  • Page, S. E. (2018). The model thinker: What you need to know to make data work for you. Basic Books.
  • Maheshwari, A. (2021). Data analytics made accessible. Kindle edition.
  • Nussbaumer Knflic, C. (2015). Storytelling with data: A data visualization guide for business professionals. Wiley.
  • Provost, F., & Fawcett, T. (2013). Data science for business: What you need to know about data mining and data-analytic thinking. O’Reilly Media.
Page citation: Zentner, A. (2021). Data analytics. SAGE Skills: Business. https://sk.sagepub.com/skills/business/data-analytics