Data-Minded Management

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Data-minded managers need a basic understanding of the data analysis preparation process.

 

In today’s business world, more data is available than ever before on every aspect of operations, from equipment function and production efficiency to sales figures, market composition, and just about any other metric one can imagine.

The process of collecting and evaluating this information is called data analytics. “Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information,” Investopedia explains. “This information can then be used to optimize processes to increase the overall efficiency of a business or system.”

The degree to which different organizations use data analytics varies widely. Some companies, such as Amazon and Google, seem to have mastered the use of data and have enjoyed incredible market benefits as a result. Most organizations, however, are nowhere near their level. McKinsey Digital nods to this reality, saying, “That’s starting to happen in a few companies—typically ones that are reaping major rewards from their data—but it’s far from the norm.”

This situation needs to change. To compete at the top level in today’s increasingly data-driven markets, organizations must keep up in this crucial area. As the key drivers in an organization, managers are responsible for leading the charge—and programs such as Washington State University’s online Master of Business Administration can help. An online MBA degree can provide the MBA resources, background, and skills necessary for data-minded management, positioning graduates for success in the practice of data analytics and other business management areas.

Data Analysis Preparation

Becoming a data-minded manager requires a basic understanding of the data analysis preparation process. Investopedia explains that the process involves 4 steps:

  1. Determine the data requirements and how the data is grouped. Data may be separated by age, demographic, income, or gender. Values may be numerical or be divided by category.
  1. Collect the data. Collection can be done through a variety of sources such as computers, online sources, cameras, environmental sources, or personnel.
  1. Organize the data. Organization can be done on a spreadsheet or other software that can accommodate statistical data.
  1. Clean up the data. Cleaning means the data is scrubbed and checked to be sure it has no duplications or errors, and that it is not incomplete. This step helps correct any errors before it goes on to a data analyst to be analyzed.

Data Can Be Intimidating

These steps seem simple enough in theory. In practice, though, data can arrive in a hard-to-understand technical flood that is too much for some managers to handle confidently. As a result, they may dodge this responsibility.

“CEOs and other top executives, the only people who can drive the broader business changes needed to fully exploit advanced analytics, tend to avoid getting dragged into the esoteric ‘weeds,’” explains McKinsey Digital. “The complexity of the methodologies, the increasing importance of machine learning, and the sheer scale of the data sets make it tempting for senior leaders to ‘leave it to the experts.’”

This type of avoidance, unfortunately, robs an organization of critical information, so data analytics must be tackled. To make the job less intimidating, technical guru Manu Jeevan reminds managers that it is not their job to be data scientists. Their responsibility is to find and hire technical experts, then learn to speak just enough of their language to foster a useful collaboration. He suggests the following ways a data-minded manager can support data analytics even without a deep technical background:

  • Understand the fundamental principles well enough to be able to provide the appropriate resources to data science teams.
  • Have a clear vision of the implications of these projects.
  • Be willing to take calculated risks in investing in data and experimentation.
  • Steer the data team to make sure they stay focused on practical, applicable, and commercial solutions.
  • Ask probing questions and steer data scientists away from technical jargon.
  • Appreciate the importance of data analytics enough to take action on the results and invest capital as appropriate.

Data Visualization

Collecting and analyzing data is a great start, but the manager’s role rarely ends there. The manager usually must take the findings and act as an “information ambassador,” communicating the results and implications to other concerned colleagues so that high-level decisions can be made.

To perform this task effectively, many managers turn to a presentation technique called data visualization. This term simply means translating data into visual media such as charts, graphs, and other sight-centric formats. According to computing giant Oracle, in the past few years data visualization has gone from being a nice-to-have skill to an absolute must for managers who wish to understand and convey the significance of a data set. “Data visualization helps…communicate these analytic insights to the broader organization. Consider that 65% of people are visual learners, according to several studies; providing decision makers with visual illustrations of data increases understanding and can ultimately lead to better decisions,” the organization says.

Oracle points out that all data visualization is not created equal. Done right, the process makes complicated concepts easier to understand. Done poorly, it can actually confuse your audience or misrepresent your data. To reap the benefits of data visualization, managers should keep several tips in mind:

  • Invest in the right tech. Good technology tools make it easy to create compelling visualizations. The right programs allow anyone on your team to create visuals through features such as drag-and-drop assets, charts, and graphs; search functions; and guided navigation to help answer questions.
  • Know your purpose. Is the data you wish to share declarative (conveying facts) or exploratory (looking at data to try to solve problems)? The answer to this question will help you to identify the types of tools and formats you’ll need.
  • Keep your audience in mind. The level of detail that your data visualizations demand depends on who is viewing them. C-suite presentations require high-level information that helps leaders make strategic decisions. Lower-level employees may need finer details that concern their daily operations.
  • Enhance your team’s data visualization skills. Find ways to train users on data visualization tools so that your team can maximize your technology. Also, when making new hires, seek out people with data analytics expertise and experience in deep data visualization.

Benefits of Data Analytics

All of these skills come together to answer 4 main types of questions. According to Investopedia, data analytics can be broken down into these categories:

  • Descriptive analytics: Describes what has happened over a given period of time. Has the number of views gone up? Are sales stronger this month than last?
  • Diagnostic analytics: Focuses on why something happened. Did the weather affect beer sales? Did that latest marketing campaign impact sales?
  • Predictive analytics: Guesses what is likely to happen in the near term. What happened to sales the last time we had a hot summer? How many weather models predict a hot summer this year?
  • Prescriptive analytics: Suggests a course of action. If a hot summer looks likely based on historical data, we should add an evening shift to the brewery and rent an additional tank to increase output.

All of these types of analysis offer useful information that can steer an organization’s immediate and long-term course, resulting in significant market advantage. By understanding and using data analytics, data-minded managers play a vital role in this process—and make an important contribution to their organization’s bottom line as a result.

About WSU’s Online Master of Business Administration Program

Advanced education can provide the tools you need to become a data-minded manager. Washington State University’s Carson College of Business offers one of the top-ranked MBA programs in the nation. Its MBA curriculum is designed to equip students with the tactics, knowledge, skills, strategies, and other MBA resources necessary for today’s high-profile business leaders.

WSU’s Online MBA degree program offers several MBA concentrations—marketing, finance, hospitality business management, international business, and a general MBA. For more information, visit WSU’s Online MBA website.

 

Recommended Reading:

Building Analytical Systems for Monitoring and Analyzing Performance

Business Intelligence and Analytics: Increasing Efficiency Through Technology

What Are Information Systems, and How Do They Benefit Business?

 

Sources:

Benefits of data analytics – Investopedia

Degree of digital analytics use – McKinsey Digital

Data analysis process – Investopedia

Data intimidation – McKinsey Digital

Non-technical ways to support data analytics – Edvancer Eduventures

Data visualization tips – Oracle

Four types of data analysis – Investopedia

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