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. Additionally, the fields of data science, data analytics, and artificial intelligence (AI) are unlocking insights we never knew existed. Businesses now have the capability for data-driven decision-making, enabling them to respond swiftly to evolving market trends, consumer preferences, and competitive dynamics.
In this article, I’ll highlight the essentials of data science and data analytics as well as look at the steps needed for data-driven decision-making. Further, I’ll examine both the advantages and challenges for optimizing business decision-making in this era dominated by artificial intelligence and large data sets.
1. Data Science 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. This is the science behind data-driven decision-making. Below are two key definitions:
a. Data Science Definition.
“Data science is the discipline of making data useful.”
Cassie Kozyrkov, Chief Decision Scientist, Google
b. 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.
2. 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.
a. Data-Driven Decision-Making Explained.
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. Moreover, 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.
b. Example of How We Can Make Data-Driven Decisions.
To detail, 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
- Humans set goals.
- Humans identify data-collection opportunities (at least initially).
- Gather and collate all the data.
- Examine, interpret, and summarize the data, as necessary.
- Create a list of potential data-driven actions.
- 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 my article, Scientific Method Example: Data Scientists Use An Unique Way To Achieve The Best Results for more information.
3. 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.
a. 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. For more on this topic, see my article, How Data And AI Work Together To Better Empower Analytics.
b. Better Able to Measure KPIs and Align 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. Moreover with data-driven decision-making, organizations can track progress and measure success, which ultimately leads to improved outcomes and increased profitability.
c. 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, see my article, Predictive Analytics Types: The Best Opportunities For Supply Chains.
d. Reduced Human Bias And Error in Decision-Making.
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, see Unvarnished Facts’ article, Bias With Examples – Everything You Need To Know.
e. 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.
f. Better Risk Management and Mitigation.
Lastly, 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.
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.
- Using an Application-Centric Approach that Treats Data as a By-Product. Increasingly, businesses are struggling to manage the flood of data available. In many cases, it’s their legacy applications that are restricting access to their data. This results in data silos, duplication, and inaccuracies. What is needed is a data-centric approach, treating data as a valuable, permanent asset. For more details, see my article, A Data Centric Business: The Best Way to Agility, One Truth, Simplicity, Technology Innovation.
“There are three types of lies — lies, damn lies, and statistics.”
Benjamin Disraeli
More References on Data Analytics and Decision-Making.
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.
Greetings! As a supply chain tech advisor with 30+ years of hands-on experience, I take great pleasure in providing actionable insights and solutions to logistics leaders. My focus is to drive transformation within the logistics industry by leveraging emerging LogTech, applying data-centric solutions, and increasing interoperability within supply chains. I have a wide range of experience to include successfully leading the development of 100s of innovative software solutions across supply chains and delivering business intelligence (BI) solutions to 1,000s of shippers. Click here for more info.