Data Driven Decision Making: Process and Model

Today, the whole management world talks about how to create successful data driven decision making process and models in business to improve results.

Using data to make decisions is not just about the number of data scientists or technological components you have.

Data based business decisions arise from a complex process that involves people, relationships, analytics, culture, software, and problem-solving.

No matter where you are operating: in marketing, finance, banking or another area, you must build a data culture to succeed in our information era.

On this page:

  • What is data driven decision making? Definition.
  • The steps of data driven decision making process and model.
  • Characteristics of data driven companies.
  • Infographic in PDF.

Why data driven decisions?

Data hold valuable insights.

Managers can transform those insights into decisions and actions that improve every aspect of the business.

That’s why data possess a great power.

So, let’s see the definition.

Data-driven decision making is a process that includes making decisions based on data insights rather than making decisions based on intuition, belief or observation alone.

Some of the key benefits that data driven companies gain are better understandings of the market, greater customer acquisition and retention, and higher profitability.

But what are the characteristics that data driven companies possess? What business model they follow in order to maximize the value of the data?

Here are the key features that they have:

Characteristics of data driven companies

  • They understand data very well. It means they know where the data came from, what is the quality of it, which are the best data analysis methods, how reliable is data, how to measure its reliability and etc.
  • They stimulate the ongoing sharing of information and collaboration. Everyone in the organization has access to the appropriate data.
  • Keep their data clean. The data must be well organized, documented, and error free. With the help of the right data cleansing software tools, companies make a good basis for decision making.
  • Data driven companies are aware of confirmation bias, so they avoid it.
  • They have the right set of tools and skills to make insights and sense of structured and unstructured data and thus to come up with business decisions and make predictions.
  • They work with real-time insights.
  • They pay a serious attention to data collection tools and process as primary elements of the whole data environment.
  • They constantly update and improve their skill sets.
  • Data-driven businesses are able to apply the insights in a manner that support business goals.

Companies are using data to make better decisions about everything you can imagine – from product creation and marketing to hiring.

So, how the data driven decision making process looks like. How to implement a data driven model?

1. Set clear goals

Before starting to play with data for decision making, you must have a clear idea about what you want to get.

What are your targets and objectives?

Do you want to make the market segmentation more efficient? Do you want to reduce financial risk? What kind of a new product to offer? How to add more value to the current products. Do you need to optimize your supply chain?

A certain level of clarity about your goals will help you to convince others to support the project for achieving them.

2. Choose the right data sources

Now, you need to decide which data sources to use in order to answer all those questions.

This requires checking the data that you already have and defining outside data sets with valuable information related to your problems and goals.

Consider primary and secondary data channels. (To know what they mean see our post about primary vs secondary data). The different sources have different pros and cons.

You have a plenty of options for collecting information and raw data such as your internal data (reports, emails, customer feedback, and etc.), government data, non-governmental organizations, statistics, your local library, individual researchers, journal articles, blog posts and so on.

Also, don’t forget for costs. Some data sources can be very expensive. Actually, the majority of the primary data sources are at a high cost.

3. Set clear metrics

Stating the clear goals and defining data sources are great things. However, how do you know when you are close to achieving these goals?

With the help of the right metrics, of course.

Tracking some key strategic metrics is a crucial step toward a more data based decision making.

Metrics are numerical values to help you find out whether your efforts are making a difference. If yes, in what direction.

For example, if your goals relate to improving sales, you might want to look at metrics such as average time needed to close a deal, conversion rates, or revenue expectations.

Choosing the appropriate metrics is not a single and simple action. It is a process that involves choosing and refining initial metrics, defining the metrics that will have the most impact, choosing who will track the metrics and etc.

4. Monitor your metrics on a regular basis

It is a vital moment. Monitoring metrics frequently makes the data useful.

Be able to access the metrics on every device: mobile, desktop, tv, and etc.

Share it with your team members and the people involved in the data management process so they know how they’re progressing towards the targets and goals.

Aim to monitor real-time data not only summarized values.

5. Choose the right dashboard

Data-driven organizations start to look at their data from the morning. It’s a habitual practice.

They look the data in dashboards that describe key metrics.

These dashboards are most commonly implemented by a business intelligence software application with access via the Web.

Dashboard is a crucial tool in the data driven decision making process. It is the foundation of your data analysis.

There are two typical complaints about dashboards.

The first is that they don’t contain sufficient amount of data; the second is that there is overly much data.

Make sure the dashboard you are going to work with, covers the basic BI dashboard best practices.

6. Get the key people

As you are going to make decisions and achieve goals, you need to find out who is playing a role in each particular problem and goal.

Each project has involved people in it.

For instance, if it’s a marketing project, you need to draw in the marketing head and the key members of the marketing team.

But you might also need to include members of the IT team or sales department.

Also, you might need to create roles for people with specialized skill and expertise who will take care of specific issues.

7. Analyze your data

To analyze the data effectively, you may need to choose the right data scientists or business intelligence specialists and to pick the right data systems.

Ther is a variety of integrated software systems that connect all the different data sources.

The central to a data driven decision making process and model is the role of the data scientist.

Data scientists have a vital set of skills for analyzing and operating with data: statistical, mathematical skills, programming knowledge, infrastructure design and etc.

The level of skills and software features will vary according to what you want to analyze.

Download the following infographic in PDF for free

The steps of data driven decision making process - infographic


Today businesses are constantly gathering more and more data on every dimension of their operations.

Knowing how to manage these data and using it for decision making is what distinguish winners from losers.

With the help of well-organized data driven decision making process and model, businesses can leverage their resources to provide relevant insights when and where they are needed.

They are also much more likely to use insights to identify new business opportunities, predict future trends and thus improve profitability and performance.

Here is a great article about “34 business intelligence and marketing pros reveal their top tips for creating a data driven culture within an organization” from NGDATA.

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