Managerial Decision-Making and the Role of Data

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Decisions drive the business trajectory. The right decisions can increase profitability, promote growth, and maintain stability. The wrong decisions can have the opposite effect, possibly jeopardizing an organization’s health. Because of this, managerial choices in companies carry significant weight.

Fortunately, data can help managers make well-informed decisions that can lead to growth and stability. Thanks to concepts like cloud computing, the prospects of data usage have become more powerful with each passing year. According to the statistics site Statista, the global big data analytics market will grow to more than $68 billion by 2025.

This provides fertile ground for managers equipped with a Master of Business Administration to plant the seeds of smart managerial decision-making. However, it’s also important that they have a sturdy understanding of what all this data can do for their business — and what could happen if it isn’t properly utilized.

Managerial Decision-Making Process

Unfortunately, artificial intelligence algorithms are only as smart as the information they’re fed. A “garbage in, garbage out” situation is becoming more of an issue as we collectively amass an unfathomable amount of data on transaction records, social media activity, inventory logs, location data, surveys, and just about anything that can be digitized.

Competent, effective managers know how to weed through garbage data and find the few nuggets of truly valuable, insightful information. They also know how to make decisions based on that data. They don’t do this by merely collecting and scrutinizing information from a bunch of different sources. Each year, data volumes grow exponentially. Statista projects that the amount of created, captured, copied, and consumed data will increase from 64.2 zettabytes to over 180 zettabytes between 2020 and 2025.

Unfortunately, much of the data collected is of poor quality or difficult to convert to a format that’s usable alongside other data for comparison. What’s more, cognitive biases still affect managers when data is presented and the time for a human decision is at hand.

An effective managerial decision-making process can involve understanding data and helping companies to make sense of it all and put it to use. Leadership and management author Art Petty highlights some of the ways top companies are effectively utilizing data right now in his article for The Balance Careers titled “6 Challenges Managers and Organizations Face with Data”:

  • Marketing departments collect useful data from event attendees and people who download content.
  • Executives use data to support and define new strategies.
  • Sales departments collect data on customers throughout every stage of transactions.
  • Customer support departments collect data from calls and chat interfaces.
  • Management teams use data and key metrics for scorecards.
  • Accounting departments use customer data for billing and quality assurance teams use customer data to measure customer satisfaction.

Petty also points out that the problem of incomplete and incompatible data is beginning to be addressed through transformation and integration technologies. These solutions convert unusable data into a format that can be analyzed to gain useful insight. Managers entering the job market today can expect to be heavily involved in this process.

Data Analysis and Decision-Making

When managers attempt to scale the mountain of data they have at their disposal to make better-informed business decisions, they need to ask the right questions. It’s important to acknowledge how data analysis and decision-making overlap and help one another.

While data scientists and analytics professionals will bear the brunt of the data responsibility, managers and executives play an important role both at the beginning and at the end of the process. These decisions can have substantial ramifications on business strategy, so it’s important that they approach the concept of utilizing data intelligently and proactively to ensure that the data optimally shapes their strategies. This can be a daunting task, one built on terminology that may be previously unknown, and data that could potentially be subpar. It’s important for managers to approach this task carefully.

Decision-Making Tips

There are a few useful strategies to use for greater success in managerial decision-making. These decision-making tips can help managers control and focus data’s power with greater success, which can ultimately yield optimal results that can pay dividends for a business.

Build an Understanding

There’s a robust vocabulary concerning data management and analytics. Getting familiar with this vocabulary, as well as the methodologies used in data collection and analysis, can help individuals approach data analysis with a greater sense of confidence.

Know What Is Needed

There’s a lot of data out there. As such, it’s imperative that individuals develop a proactive plan that focuses on what specific data is needed to make a fully informed decision. Doing so will help any data analysis project run with greater efficiency.

Use Trusted Sources

Not all sources are quality sources. It’s important to do a little homework on sources to ensure that the data they create is accurate. If any red flags develop, it’s advisable to move on from the source.

Quality Control

Even when using a quality data source, it’s still important to periodically check the data to confirm its accuracy and to spot outliers that may deviate from data patterns.

Guard Against Bias

Managers should keep track of any assumptions or biases that they may have going into data analysis. They should never assume their assumptions are correct, particularly if the data generated proves them to be incorrect.

Understand Correlation vs. Causation

Decisions should only be made when the data reveals a high frequency of correlation. The higher the frequency, the more likely those correlations suggest causation.

Advocate for Optimal Data Strategies

Managers should encourage a strong data management presence in their firm. This can include pushing for hiring data-savvy employees for their team that can constantly review data for quality.

Make a Big Difference in Business

While managers aren’t necessarily data experts, they should at least be proficient in data analytics, so they can recognize which insights are relevant and which may be less than useful. In a data-based world, effective managerial decision-making requires a basic understanding of data management.

Ohio University’s Online Master of Business Administration program and its Business Analytics track can help get you ready to dive into data and dig up key business insights. Our curriculum features focused courses, such as Prescriptive Analytics, Predictive Analytics, and Business Intelligence, to help you gain expertise in the fundamental components of data analytics. Learn how OHIO can help you advance your career in business today.

Recommended Readings

Four Ways Predictive Analytics Is Changing the Course of the Future

The Growing Field of Data Science

How Data Driven Marketing Is Changing the Shape of Business


Australian Bureau of Statistics, Statistical Language – Correlation and Causation

Datapine, “Why Data Driven Decision Making Is Your Path To Business Success”

Forbes, “Bringing Data Into Decision-Making at All Levels of the Organization”

Forbes, “How to Harness a New Wave of Data-Driven Decision Making”

Forbes, “Why Business Need Data to Make Better Decisions”

Houston Chronicle, “What Are Two Important Managerial Decision-Making Techniques?”

Investopedia, “Data Analytics”

LinkedIn, “7 Ways How Data Analysts Can Overcome Bias in Decision-Making”

Statista, Big Data Analytics Market Revenue Worldwide in 2019 and 2025

Statista, Volume of Data/Information Created, Captured, Copied, and Consumed Worldwide from 2010 to 2025

Tableau, A Guide to Data Driven Decision Making: What It Is, Its Importance, & How to Implement It

The Balance Careers, “6 Data Challenges Managers and Organizations Face”

WordStream, “5 Killer Examples of Data-Driven Marketing