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Essentials for Implementing Data-Driven Decision Making

Business analyst at a desk with an overlay of charts and graphs floating above desk
November 3, 2023

The impact of data on the modern business world cannot be overstated. As a recent article from Forbes explains, "it is critical for any organization looking to succeed in today's business landscape to prioritize the quality of data used to make important business decisions.1" What is data-driven decision making (DDDM), and how can an effective DDDM process yield better results for your company? In this article, we explore a five-step DDDM strategy and examine prime examples of how data analytics can guide strategic decision making.

What is Data-Driven Decision Making?

Data-driven decision making is just that – the use of data to evaluate options and make strategic business decisions. As humans, we are often tempted to make choices based on emotion, intuition or a "gut" feeling. However, when it comes to making business decisions, analytics and data can help provide more reliable, measurable and actionable results.

Over the last few decades, increased access to technology and the internet has driven a sharp rise in the amount of data available to decision-makers. For this reason, DDDM has quickly become a top skill for anyone entering the world of business. And that's just where new business professionals have the opportunity to set themselves apart – anyone with access to data can say they're using it to make decisions, but professionals need a solid grasp on analytics and the ability to identify the most valuable data in order to produce the results top employers are looking for.

With a seemingly endless supply of consumer, supply chain, marketing and other types of data, DDDM can quickly become overwhelming. It's up to skilled professionals to block out the noise, identify the most valuable data available and lead teams in making the right decisions. Utilized correctly, data can help a company improve customer satisfaction, increase profits, engage in effective problem solving and increase efficiency.2

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Five Steps for Implementing Data-Driven Decision Making

Data is essential to effective decision making in today's business world. However, even the best data is useless to business professionals who don't know how to use it. The DDDM process below can help you utilize analytics and make decisions that yield high outcomes.

Determine Business Questions or Issues

The first step in effective DDDM is to determine the appropriate questions you want to answer or issues you want to solve with data. Begin by discussing the following with your team:

  • What does the company want to accomplish?
  • Is the company trying to assess an opportunity or diagnose a problem?
  • Which areas are most important to achieving the company's overall strategy?

Strategize and Identify Goals

Based on the questions or issues you've identified, establish a clear analytical objective for your DDDM process. Consider the following:

  • What can you realistically accomplish with data?
  • Who will oversee the collection and data analysis?
  • What personnel will you need for the project?
  • What is the ultimate end goal for this project?
  • How will you measure the success of this project?

Target Data

Once you identify the strategic goals of your project, it's time to identify the data that will be most useful for you in this DDDM process. Consider the following questions in deciding which data to collect and how to do so:

  • Will qualitative or quantitative data be most useful to this project?
  • Is it feasible to convene focus groups to collect data?
  • Would online tracking and social media monitoring reveal meaningful data analytics?
  • Of the data you can access, which is most valuable to your goals?

Collect and Analyze Data

Now that you know the type of data you will target in this DDDM process, you need to identify the correct processes and personnel to gather and manage the data. Consider the following:

  • Does your company already have some of the data it needs in-house?
  • Will you need to purchase access to an existing data set?
  • Which routes will you need to utilize to collect data (computer software, online software, personnel, cameras, etc.)?
  • Of the data you can access, which is most valuable to your goals?

After you have collected the necessary data, it needs to be analyzed. Consider:

  • What insights can you gain from this data?
  • Is it what you expected? What stands out?
  • What big data platforms or tools can you access to assist you in this analysis?

Make Decisions Regarding Findings

The last step in the DDDM process is, of course, to make the data-driven decision. Leaders and other key decision makers can use the analytical insights gained from the process to take actionable steps needed for the success of a project.

How Data-Driven Decisions Shape Business

Data-driven decisions shape businesses in many ways. Big data drives most of the top tech giants, including Google, Amazon and Meta. However, DDDM isn't unique to the world of technology. The aviation company Lufthansa, for example, centralized its data collection and analysis practices in a recent attempt to improve business across its more than 550 subsidiaries. This move led to more strategic, data-driven decisions and a 30% increase in company-wide efficiency.3, 4

Many retail companies also use data-driven decision making to produce more positive business outcomes. One example is Nike, which has used valuable data to improve its supply chain efficiency. By understanding data regarding demand, material availability and distribution, Nike was able to both reduce costs and cut delivery times to increase customer satisfaction and loyalty.5

What Positions Utilize Data-Driven Decision Making?

Sound, ethical and innovative data governance is essential, no matter the business sector, company or goal. DDDM requires leaders who can strike a balance between using analytics and managerial instincts, and who can set the standard for best practices. Below are a few positions that utilize DDDM in their everyday tasks.

  • Data Analyst: As their title suggests, data analysts process large amounts of data to glean important findings that can guide impactful decisions. Data analysts also maintain databases, use visualization to communicate their findings and forecast trends based on their collected data.6
  • Data Engineer: Data engineers work with businesses to build stable, optimized systems that aid in the collection and analysis of data. Professionals in this role also conduct research and look for patterns in data to help forecast trends.6
  • Database Administrator: Database administrators are responsible for keeping data safe and correctly stored. People who work as database administrators manage data, implement security measures and often design the databases themselves.6
  • Business Analyst: Business analysts use data to evaluate businesses and make recommendations for improving a company's performance. Those who work in this position may use the DDDM process to make high-impact actionable recommendations to decision-makers.6
  • Data Scientist: Data scientists analyze data in order to offer business solutions and valuable insights based on their findings. They collect and process data using various data science techniques in order to offer well-founded advice to major decision-makers. Professionals in this role design data collection processes, look for trends using statistical methodologies and communicate their findings to stakeholders.6

Effective data-driven decision making begins with the right education. The Ohio University Online Master of Business Analytics is designed to prepare students for data-related careers and teach them how to implement data-driven decisions in different types of businesses. For an introduction to data analytics and DDDM, Ohio University also offers ten Online Graduate Business Certificates, including the Business Analytics Certificate. These certificates can also be applied to a graduate business degree.

Sources

  1. White, Jeff. "Why High-Quality And Relevant Data Is Essential In Today's Business Landscape." Forbes. Apr. 17, 2023. Retrieved Nov. 2, 2023 from https://www.forbes.com/sites/forbestechcouncil/2023/04/17/why-high-quality-and-relevant-data-is-essential-in-todays-business-landscape/

  2. Indeed Editorial Team. "What Is Data in Business? (Plus Importance and Examples)." Indeed. Mar. 10, 2023. Retrieved Nov. 2, 2023 from https://www.indeed.com/career-advice/career-development/data-in-business#

  3. Tableau. "A Guide To Data Driven Decision Making: What It Is, Its Importance, & How To Implement It." Retrieved Nov. 2, 2023 from https://www.tableau.com/learn/articles/data-driven-decision-making

  4. Grant, Devin. "What is Data-Driven Decision Making? (And Why It's So Important)." Drive Research. Mar. 27, 2023. Retrieved Nov. 2, 2023 from https://www.driveresearch.com/market-research-company-blog/data-driven-decision-making-ddm/

  5. Thought Spot. "6 Retail Big Data analytics use cases and examples." Retrieved Nov. 2, 2023 from https://www.thoughtspot.com/solutions/retail-analytics/retail-big-data-analytics-examples-and-use-cases

  6. Chatterjee, Maria. "Top 9 Job Roles in the World of Data Science for 2024." My Great Learning. Nov. 8, 2023. Retrieved Nov. 9, 2023 from https://www.mygreatlearning.com/blog/different-data-science-jobs-roles-industry/

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