It is not hard to find that the word “big data” appears more and more frequently in our life, which is reflected in all aspects of our life. By harnessing the wealth of digital insights readily available and embracing the power of business intelligence, smarter decisions can be made that lead to business growth, development and economic strength.

By adopting the right reporting tools and understanding how to accurately analyze and measure data, you will be able to make data-driven decisions that drive your business.

While it is sometimes possible to follow your gut to make a decision, most business-based decisions should be backed by metrics, facts, or figures related to your goals or plans to ensure that management reporting, business operations, and decision deployments remain stable.

What is data-driven decision making?

Data-driven decision making (DDDM) is a process that involves collecting data based on measurable goals or KPIs and analyzing them to develop strategies and activities that enable the business in many areas.

At its core, data-driven decision making means using validated and analyzed data to achieve key business goals.

However, to extract real value from data, it must be accurate and relevant to your goal. Gathering, extracting, formatting, and analyzing insights to enhance data-driven decision making in the business was once a common pattern, which naturally delayed the entire data decision process.

But today, the development and democratization of smart software allows users to analyze and extract information from data without deep-rooted technical expertise. Less IT support is required to generate reports, trends, visualizations, and insights that contribute to the data decision process.

From these developments, data science was born (or at least developed in huge ways). This fairly new industry involves sifting through vast amounts of raw data to make intelligent data-driven business decisions.

There are two distinct types of gold that data scientists mine: qualitative and quantitative. ** Both are critical to making data-driven decisions.

  • 1. Qualitative analysis focuses on data that are not defined by numbers or indicators, such as interviews, videos and anecdotes. Qualitative data analysis is based on observation rather than measurement. Here, it is crucial to encode the data to ensure that projects are grouped together methodically and intelligently.
  • 2. Quantitative analysis focuses on numbers and statistics. Median, standard deviation and other descriptive statistics play a key role here. This type of analysis is measurement rather than observation. Both qualitative and quantitative data should be analyzed to make more informed data-driven business decisions.

Now that we’ve explored what decision making means in business, it’s time to consider why data-driven decision making (DDDM) is so important.

“Information is the oil of the 21st century, and analysis is the internal combustion engine.” “– Peter Sondergaard

Why is data-driven decision making important?

The importance of data in decision making lies in consistency and continuous growth. It enables companies to create new business opportunities, generate more revenue, anticipate future trends, optimize current operations and generate actionable insights. This allows you to grow your business over time, making your organization more adaptable. The digital world is constantly changing and evolving in tandem with the changing environment around it, requiring the use of data to make smarter, more powerful data-driven business decisions.

Data-driven business decisions determine the success or failure of a company, demonstrating the importance of online data visualization in decision-making.

MIT Sloan School of Management professors Andrew McAfee and Eric Brynjolfson once explained in a Wall Street Journal article that they conducted research with the MIT Center for Digital Business. In the study, they found that among the companies surveyed, data-driven companies benefited from a 4% increase in productivity and a 6% increase in profits.

Companies that take a collaborative approach to decision-making tend to view information more as a real asset than companies that take other, more nebulous approaches.

10 tips and Essentials for Enhanced data-driven decision strategies

Finally, here are 10 practical tips and tips to help you make better data-driven business decisions.

1) Avoid bias

Working with a team that knows what data you’re using will give you useful and insightful feedback. Often, this is done with innovative software that visualises what were once complex tables and graphs in a way that enables more people to initiate good data-driven business decisions.

As more people become aware of the actual data, there will be opportunities for more reliable feedback. A 2010 McKinsey study of more than 1, 000 business ventures showed that they made returns of up to 7% or more when reducing the amount of work their decision-making process did to affect the bias. When it comes to data-driven decision making (DDDM), it is critical to reduce bias and let the numbers speak for themselves.

By eliminating prejudice, you can discover more opportunities. Getting rid of preconceived notions and really studying the data can alert you to insights that can really change the bottom line. Business intelligence should not only avoid losses, but also win gains.

2) Identify goals

To get the most out of your data team, companies should identify their goals before starting analytics. Develop strategies to avoid following hype rather than business needs, and define clear key performance indicators (KPIs).

3) Now collect the data

Gathering the right data is as important as asking the right questions. For small businesses or startups, data collection should start on day one. Twitter co-founder and founder Jack Dorsey shared his experience with Stanford University. “For the first two years of Twitter, we ignored it… Instead of having a good balance between intuition and data, we base everything on intuition… So the first thing I wrote for Square was managing the dashboard. We have very strict discipline to document everything and measure everything.” That being said, implementing a business dashboard culture in your company is a critical component to properly managing the tide of data that will be collected.

4) Identify unresolved problems

With strategies and goals in place, asking the right data analysis questions helps the team focus on the right data, saving time and money. Both walmart and Google had very specific problems that greatly improved the results. That way, you can focus on the data you really need and move from “just in case” to “collect data to answer questions.”

5) Find the data needed to solve these problems

In the collected data, try to focus on the ideal data, which will help you answer the outstanding questions defined in the previous phase. Once you are sure, check whether this data is already collected internally or whether you need to set up a way to collect or retrieve data from outside.

6) Analyze and understand

This may seem simple enough, but it’s worth mentioning: After setting up all the questions to be answered and the data collection framework, read through it to extract meaningful insights and analysis that will enable you to make data-centric business decisions. In fact, user feedback is a useful tool for deeper analysis of the customer experience and for extracting actionable insights. In order to do this successfully, it’s important to have a background. For example, if you want to improve conversion in the purchase channel, it is crucial to understand why visitors choose to place orders. By analyzing the comments on the feedback form (in this channel), you will be able to understand why they are hesitant to pay and optimize your product accordingly

7) Re-examine and re-evaluate

When we reevaluate our decisions, most people resist, feeling that it would take too much time to start over. A friend who used to be a graphic designer once told me that he often found himself stuck in the final stages of a project. He is committed to his chosen direction and does not want to give it up so that he “slips one step at a time”. Indeed, when that happens, he will have to start all over again to look at the mistake that got him into trouble.

Validating data and making sure you’re tracking the right metrics can help you get out of decision mode. Relying on team members’ opinions and sharing them can help spot biases. But don’t be afraid to step back and rethink the decision. It may feel like a failure, but it’s a necessary step toward success. Knowing where we might go wrong and fixing the problem immediately will yield more positive results than waiting to see what happens.

8) Presenting data with visual products

Digging and gathering insights is great, but trying to get your message across is even better. With great data visualization software, you don’t need IT expertise to build and customize powerful online templates that tell your data story and help your team and managers make the right data-driven business decisions. For example, e-commerce manufacturers need to make action decisions based on users’ purchasing behaviors:

“Case from EasyV Data Visualization”

On the large screen of this case, information such as consumer portrait, consumer age, geographical distribution and type of goods purchased is displayed. Therefore, real-time data can be centralized while realizing rapid deployment of decisions.

Data visualization has evolved into an advanced solution for rendering and interacting with large numbers of graphics on a single screen, whether it focuses on developing sales charts or comprehensive interactive reports.

The point is that data discovery is the process that enables decision makers to uncover insights, and by using visualization, teams have the opportunity to discover trends and major outliers within minutes.

Data discovery trends will be one of the most important business intelligence trends in 2021, as humans can better process visual data.

9) Set measurable decision goals

Once the questions are asked, data and insights become difficult: you need to apply your findings to business decisions, and make sure that decisions are consistent with the company’s mission and vision, even if the data contradict each other. Set measurable goals to ensure you’re on the right path… And put the data into action!

10) Develop data-driven business decisions

This is often overlooked, but it is still very important. In our hyper-connected digital age, we have more access to data than ever before. To extract real value from these insights, it is critical to constantly refresh and evolve business goals in light of the surrounding environment.

At this point, the importance of data in decision making is obvious. But understanding the dynamics of data-driven business decision making and exploring real-world examples of data-driven decision making will steer you in the right direction.