For more than twenty years, I’ve watched companies drown in their own data— adrift in spreadsheets and fragmented data silos. At the same time, some companies actually get it, using data-driven decision-making to sprint past their competition. Without a doubt, the business world has split into three camps: 1) those overwhelmed by data that they can’t interpret, 2) those hoarding data in useless silos, and 3) the rare few turning their data into unprecedented insights for competitive advantage. Here’s the uncomfortable truth: leveraging your data for effective business decision-making isn’t a nice-to-have anymore. It’s survival.

In this article, I’ll show you how Data Science and AI are generating insights that didn’t exist five years ago. Most importantly, I’ll share with you how to harness these decision-making tools without falling into the traps that turn data initiatives into expensive disasters. Indeed, I’ve seen mid-sized companies outmaneuver industry giants by spotting supply chain risks months earlier. At the same time, I’ve watched executives make catastrophic decisions by blindly trusting flawed data and following outdated analytical processes. Without a doubt, it is crucial to learn both the enormous advantages and the critical limitations of data-driven decision-making. Think about it – your competitors are mastering this right now. The question is: will you?

1. Data Science and Data Analytics Basics.

Data-driven decision-making stems from an ever-growing field known as Data Science. Basically, Data Science helps businesses employ Data Analytics to extract valuable insights from vast data sets. To better understand these two disciplines, Data Science and Data Analytics, see definitions below.

Definitions: Data Science and Data Analytics

To elaborate, the multi-discipline field of Data Science builds data-focused products such as the technical infrastructure, methodologies, models, and tools to make data useful. On the other hand, Data Analytics relies on Data Science’s tools to interpret data, past and present. Most importantly, Data Analytics answer specific operational questions that drive insights for better decision-making. Without a doubt, both of these disciplines are the key success factor for data-driven decision-making. For more information on the specifics of Data Science and Data Analytics, see article links below.

• Data Science: Data Science Definition – The Truth About This Discipline And Its Massive Growth.
• Data Analytics: A Data Analytics Perspective To Better Empower Supply Chain Managers

A Data Analytics Perspective To Better Empower Supply Chain Managers

“… both of these disciplines, Data Science and Data Analytics, are the key success factor for Data-Driven Decision-Making.”

2. Decision-Making Explained Leveraging Large Data Sets and Advanced Data Analytics Tools.

Today, affordable data storage, cloud computing, and increasingly powerful computing resources offer most businesses unparalleled access to vast datasets containing invaluable insights about their operations and processes. Furthermore, advanced information technologies, such as Artificial Intelligence (AI), now empower organizations to rapidly harness these extensive datasets, thereby unlocking unprecedented insights. Consequently, data scientists are leveraging these cutting-edge technologies to develop sophisticated analytic tools, enabling businesses to make sharper, data-driven decisions—truly maximizing the potential of “Big Data.” Without a doubt, this continuous technological advancement is actively transforming organizational decision-making processes. In fact, smart businesses are in the process of conducting a vital re-evaluation of how human decision-makers and advanced analytical tools can best collaborate for optimal outcomes.

a. Data-Driven Decision-Making Explained.

So, let’s look more closely at Data-Driven Decision-Making (DDDM). Specifically, what is it? In fact, there are many ways you can describe it, but I like this definition of data-driven decision-making.

“the process of collecting data based on your company’s key performance indicators (KPIs) and transforming that data into actionable insights.”

This Is What Decision Intelligence Technology Is And Know What Its Not

I like this definition because it specifically focuses on the need to leverage data for actionable insights tied to corporate outcomes. This helps to explain why it is a necessity for businesses to adopt a data-driven approach, discarding obsolete decision-making processes, to keep their business competitive.

Also, data-driven decision-making is not necessarily a “cookine-cutter approach” as the extent of data usage varies within industries. Also, each business has unique challenges in terms of the availability of data volume and type, its context and quality, and the analytical tools employed. Moreover, these analytical tools such as Decision Intelligence technology are increasingly automating analytics. Also, these analytical tools can directly augment decision-makers, provide recommendations, and even automating decision flows. For a detailed discussion on this topic, see my article, This Is What Decision Intelligence Technology Is And Know What Its Not.

b. Examples of How We Can Make Data-Driven Decisions.

Decision-making using data is not just for businesses, but also for any type of decision-making to include even for scientific research. Further, thanks to advanced analytics, many decision-making steps can be automated or streamlined. Also, there are many approaches and methodologies to make data-driven decisions. For example, below are the typical steps of a data-driven decision-making process for a business. Within these steps, I highlight the pivotal role of human input within each stage, even in highly automated environments.

Example Steps For Data-Driven Decision-Making

1. Humans set goals.
2. Humans identify data-collection opportunities (at least initially).
3. Gather and collate all the data.
4. Examine, interpret, and summarize the data, as necessary.
5. Create a list of potential data-driven actions.
6. Humans evaluate options and make decisions.

Also, as mentioned previously, scientists can use data-driven decision-making for their research projects. This includes Data Scientists doing research or building analytical software tools. For example, scientists use a decision-making approach called the scientific method. To detail, see my article, Scientific Method Example: Data Scientists Use An Unique Way To Achieve The Best Results for more information.

Scientific Method Example: Data Scientists Use An Unique Way To Achieve The Best Results

“… smart businesses are in the process of conducting a vital re-evaluation of how human decision-makers and advanced analytical tools can best collaborate for optimal outcomes.”

3. Major Impacts Of Data-Driven Decision-Making.

Indeed, businesses can greatly benefit from data-driven decision-making over traditional business decision-making. Further, advanced data analytics tools to include AI enable businesses to gain insights from their data like never before. To detail, see below for the top 6 benefits that are especially unique to data-driven decision-making.

How Data And AI Work Together To Better Empower Analytics

• Process Extremely Large Data Sets. Humans do not have the capability to manually process large data sets With the advent of advanced analytics and AI, businesses can now make sense of large sets of data and identify actionable insights. For more on this topic, click here.
• More Effectively Align Decision-Making with Corporate Goals Using Key Performance Indicators (KPI). Also by using data to measure the efficacy of business practices, organizations can gain a better understanding of what is working and what is not. Moreover, organizations can track progress and measure success, which ultimately leads to improved outcomes and increased profitability.
• Enables Increased Use of Predictive Analysis Through Data Modelling. Indeed, data-driven decision-making allows organizations to use historical data to develop predictive models. As a result, these models help them better forecast and plan for the future. For more information on predictive analytics, click here.
• Reduces Human Bias And Error. Without a doubt, human decision-making processes are often heavily influenced by an individual’s personal biases and subjective opinions. So with data-driven decision-making, there is the potential for reducing human error and increasing efficiencies. For examples of biases, click here.
• Improved Financial, Operational, And Customer Insights. Further, by analyzing data from multiple sources, businesses can identify trends and patterns that might have otherwise gone unnoticed.
• More Robust Risk Management and Mitigation. Lastly, by using data to identify potential risks, businesses can develop more effective risk mitigation strategies. As a result, businesses can rapidly take action before it’s too late. For more on risk mitigation, click here.

Risk Mitigation For Supply Chains: How To Best Identify, Make Assessment, Overcome

“… advanced data analytics tools to include AI enable businesses to gain insights from their data like never before.

4. Challenges With Data-Driven Decision-Making.

So data-driven decision-making has a marked improvement over human decision-making and traditional process automation technology. However there are still limitations. To list, below are limitations with using data-driven decision-making.

Data-Driven Decision-Making Limitations

• Not Leveraging All The Data. As part of the data-driven process, data is many times summarized or discarded. Thus, this may obscure insights, relationships or patterns that were in the “big data” set.
• Bias Is Still Present Or Even Elevated. Summarized data and data-driven recommendations can include hidden biases that even an experienced decision-maker may not be aware of.
• Garbage In, Garbage Out (GIGO): Treating Data as a By-Product of Business Software. Increasingly, businesses are struggling to manage the flood of data available. In many cases, it’s their legacy applications producing fragmented data with limited insights for making better decisions. As a result, corporate data is stuck in silos, duplicated, out-of-date, and inaccurate. To assure effective data-driven decision-making, businesses need to start putting their data first, treating data as a valuable, permanent asset. This is called having a data-centric approach, treating data as a valuable, permanent asset. For more on being a data-centric business, click here.

A Data-Centric Business: The Best Way To Agility, One Truth, Simplicity, Technology Innovation

“To assure effective data-driven decision-making, businesses need to start putting their data first, treating data as a valuable, permanent asset.”

More References on Data Analytics and Decision-Making.

Data Analytics vs Data Science – Know the Most Important Differences

For more on data-driven decision-making, see asana’s article, Data-driven decision making: A step-by-step guide.

Also, this article is part of a series, AI Impact On Business Decisions. See links below:

• The Human Problem-Solving Process – Part 1
• Process Automation – Part 2
• Data-Driven Decision-Making (This Article) – Part 3
• AI Impact on Business Decisions – Opportunities – Part 4
• AI Impact on Business Decisions – Limitations – Part 5

Need help with an innovative solution to make your supply chain analytics actionable? I’m Randy McClure, and I’ve spent many years solving data analytics and visibility problems. As a supply chain tech advisor, I’ve implemented hundreds of successful projects across all transportation modes, working with the data of thousands of shippers, carriers, and 3rd party logistics (3PL) providers. I specialize in launching new analytics-based strategies, proof-of-concepts and operational pilot projects using emerging technologies and methodologies. If you’re ready to supercharge your analytics or if you are a solution provider, let’s talk. To reach me, click here to access my contact form or you can find me on LinkedIn.