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Data-Driven Decision-Making: Its Enormous Impact And The Truth On Limitations

The recent explosion of big data has led to three distinct business profiles: 1) those drowning in data, 2) those hoarding pointless data in silos, and 3) those harnessing data analytics for smarter decisions. Indeed, it’s critical for businesses in all three groups to continually refine their data analytics approach to fully reap its benefits. Further, the fields of data science, data analytics, and artificial intelligence (AI) are now opening doors to insights we never knew existed. Indeed, businesses have the ability for data-driven decision-making to respond with agility to changing market trends, consumer preferences, and competitive dynamics.

In this article, I’ll highlight the essentials of data science and data analytics as well as unpack the steps for data-driven decision-making. Further, I’ll explore both the advantages and challenges for optimizing business decision-making in this era dominated by artificial intelligence and large data sets.

Data Science Basics.

Data scientist at work - data-driven decision-making

Data-driven decision-making stems from an ever-growing field known as data science. With the help of data science, businesses employ data analytics to extract valuable insights from vast data sets. To better understand the science behind data-driven decision-making, here are two crucial definitions:

  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.

Data Science Definition – The Truth About This Discipline And Its Massive Growth.

Data science is an ever-growing field, but it can be a struggle to differentiate between what is hype and what is real. Click here to get a clear data science definition. Plus, you’ll get a comprehensive understanding of data science’s beginnings, a breakdown of what data science is and isn’t, and the scientific process utilized by data scientists to turn data into actionable insights.

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 operational processes.  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.

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.”

So, businesses are increasingly adopting data-driven approaches to transform their decision-making processes. Indeed, the extent of data used varies within industries, the availability of data volume and type, and the analytical tools employed. The typical steps of a data-driven decision-making process for a business are detailed below. Within these steps, I highlight the pivotal role of human input within each stage, even in highly automated environments. Concurrently, intelligent technology such as decision intelligence continues to advance. As a result, over time these advances in automation will decrease the amount of human involvement needed in business decision-making. For a detailed discussion on this topic, see my article, This Is What Decision Intelligence Technology Is And Know What Its Not.

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.
  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.

Indeed, 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. Better Alignment of KPIs with Business Goals.

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. 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. As a result, these models help them forecast and plan for the future. 

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

Indeed, traditional 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.

5. 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. 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.

6. Better Risk Management and Mitigation.

Also, by using data to identify potential risks, businesses can develop more effective risk mitigation strategies. As a result, this allows them to proactively identify and address potential risks before they become major issues. Further by analyzing data, businesses can identify patterns and trends that could indicate potential areas of risk. Hence, this allows 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.

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 some of the challenges with data analytics. For example, there are several pitfalls with using statistical medians in summarizing data. Thus, 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

For more on our Series on AI Impact On Business Decisions, see links below:

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