In recent years, the rise of big data has created three types of businesses: 1) those overwhelmed by an influx of data, 2) those collecting useless, compartmentalized data, and 3) those utilizing data analytics to make informed decisions. Regardless of where your business falls on the data analytics spectrum, continuous improvement is essential to effectively leverage your data. Indeed, data-driven decision-making can now unlock countless business insights that were once hidden. Moreover, this approach empowers companies to react more precisely and swiftly to market shifts, customer tastes, competitor moves, and other outside influences.

In this third article in my 5-part series on AI Impact On Business Decisions, I’ll explore with you the impact and limitations of data-driven decision-making. Specifically, this article will touch on data science basics, describe the data-driven decision-making process, its benefits, and its challenges for improving business decision-making in the age of AI.

Data Science Basics.

Data-driven decision-making is a result of a still emerging scientific field called data science. Because of data science, businesses are able to use data analytics to extract value and insights out of very large data sets. To better explain, below are two key definitions when it comes to understanding  the science behind data-driven decision-making.

1. Data Science Definition.

“Data science is the discipline of making data useful.”

Cassie Kozyrkov, Chief Decision Scientist, Google

2. Data Analyst Definition.

“Examine large data sets to identify trends, develop charts, and create visual presentations (i.e. data analytics) to help businesses make more strategic decisions.”

Northeastern University

For more on the basics of data science, see my article, Data Science Definition – The Truth About This Discipline And Its Massive Growth.

Decision-Making Using Big Data.

Now thanks to cheap data storage, cloud computing, and faster computers, most businesses have access to large data sets about their business and computers.  In turn, data scientists have built data analytic tools to help businesses make decisions using this “Big Data”. As a result, a data analyst along with business stakeholders are enhancing their decision-making processes to be data-driven.

The Best On-Time Delivery KPIs To Make Your Customers Delighted

So what is data-driven decision-making (DDDM)? 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.”

Additionally, how are businesses transforming their decision-making processes to be data-driven? Indeed, most businesses are leveraging data to improve their decision-making processes. However, it is somewhat dependent on their business focus, the amount / type of data they have, and the data analytical software tools they are using. To illustrate, below are the steps for a data-driven decision-making process a business may use. In this scenario, I have highlighted where a human is definitely in the loop for each decision-making step. In fact for most data-driven processes, a human analyst is deeply involved in each step.

Example Steps For Data-Driven Decision-Making

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

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

Also, for more advanced projects where data scientists are actually doing research or building analytical software tools, they use a decision-making approach called the scientific method. To detail, see SC Tech Insights’ Scientific Method Example: Data Scientists Use An Unique Way To Achieve The Best Results for more information.

Major Impacts Of Data-Driven Decision-Making.

Businesses can greatly benefit from data-driven decision-making over traditional business decision-making. Further, data science and data analytics tools 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.

1. New Capability To Process Large Data Sets Effectively.

With the advent of big data, traditional business decision-making processes are no longer sufficient to handle the sheer amount of data being generated. Now, businesses can now make sense of large sets of data and identify actionable insights using data-driven decision-making. 

2. Aligning Business Goals And KPIs.

By using data to measure the efficacy of business practices, organizations can gain a better understanding of what is working and what is not. Also with data-driven decision-making, organizations can track progress and measure success, which ultimately leads to improved outcomes and increased profitability.

3. Increased Use of Predictive Analysis Through Data Modelling.

Data-driven decision-making allows organizations to use historical data to develop predictive models that help them forecast and plan for the future. 

4. Reduced Human Bias And Error in Decision-Making.

Traditional decision-making processes are often heavily influenced by an individual’s personal biases and subjective opinions. Also with data-driven decision-making, there is the potential for reducing human error and increasing efficiencies.

5. Improved Financial, Operational, And Customer Insights.

By analyzing data from multiple sources, businesses can identify trends and patterns that might have otherwise gone unnoticed. As a result, this allows them to make data-driven decisions that positively impact the bottom line. Also, this improves operational efficiencies, and enhances the customer experience.

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

6. Better Risk Management and Mitigation.

By using data to identify potential risks, businesses can develop more effective risk mitigation strategies. This allows them to proactively identify and address potential risks before they become major issues. By analyzing data, businesses can identify patterns and trends that could indicate potential areas of risk, allowing them to take action before it’s too late. For more on risk mitigation, see my article Risk Mitigation For Supply Chains: How To Best Identify, Make Assessment, Overcome.

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.

“Facts are stubborn things, but statistics are pliable.”

Mark Twain

• Not Leveraging All The Data. As part of the data-driven process, data is summarized or discarded. Thus, summarizing data, either by a data analyst or a machine, may obscure insights, relationships or patterns that were in the “big data” set.
• Bias Is Still Present Or Even Elevated. Summarized data can include hidden biases that even an experienced decision-maker may not be aware of. Thus, decision-makers are still influenced by their own bias, but also can be led astray with bias embedded in the summarized data.

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

For a simple example of the challenges with data science, see TowardsDataScience’s Don’t Ignore Bears: The Pitfalls of Summarizing Data with Medians. Note, this article just focuses on one minor aspect of the challenges with data analytics. Specifically, the pitfalls of using statistical medians in summarizing data. These types of issues and more can occur when doing data-driven decision-making. So beware.

“There are three types of lies — lies, damn lies, and statistics.”

Benjamin Disraeli

What Is Data Science? Its Focus, Applications, 6 Components Explained

For more on our Series on 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