Data-driven decision-making: A simple guide for modern businesses

No matter the industry, if you run a business, you’ll have to make tough decisions that can sway success in one direction or the other. When the stakes are high, you need to make decisions based on facts and evidence. This is why a data-driven decision-making (DDDM) model is ideal for driving the direction of your business. 

But what exactly do we mean by data-driven, and why is it so important for business owners and stakeholders?

What is data-driven decision-making?

Data-driven decision-making (DDDM) is the process of examining data once it’s converted into meaningful metrics and making practical, day-to-day business decisions based on the analysis of that data. DDDM has become an integral part of data-driven marketing and business oversight in almost every industry, and certainly for organizations with an online presence.

Why is data-driven decision-making important to modern brands?

With DDDM, decisions are the byproducts of what the data shows. There’s no personal bias or other subjective elements like our intuitions, past experiences, and desires involved. A team adopting a data-driven decision-making model significantly reduces disagreements or conflicts due to differing opinions or egos. It’s all about following the evidence to achieve company benchmarks. Data, after all, is factual and unbiased.

The right data can lead to the decisions that transform your business. Or you can lean on bad data, and subsequently make some ill-informed decisions.

A recently released white paper by IDC showed the power of truly data-driven organizations. Such organizations saw two and a half times better results across multiple business metrics.

• For companies that were leaders in data and analytics, there was a three-times-higher growth in revenue.
• They were also three times more likely to have smaller time-to-market times.
• In addition, they were twice as likely to report improvements in profits, customer satisfaction and operational efficiency.

For many organizations, IT teams are the drivers allowing them to transform their business with data.

Data-driven decision-making examples

So, how do companies use data to make decisions? 

Here’s an example scenario: Let’s say a SaaS company is trying to improve the user experience of its cloud storage network. Among the many data points collected is the usage volume of each built-in feature. Beta testing metrics show customers use the file-share feature the most. Developers can press further with surveys to discern what changes users would like to see in the file-share feature. If customers indicate a desire for offline sharing capability, then an update may be something like instant file sharing via Bluetooth. 

As you can see, the decision to add an offline share feature was based on data and surveys. At no point was there any mention of the developers’ own judgments and subjective thoughts.

For real-life data-driven decision-making examples, look to Amazon, which relies heavily on DDDM. The items you see on your recommendations list are based on data like your past purchases, browsing history, and items sitting in your shopping cart. The same goes for social media sites like Facebook. The ads you see are based on data pertaining to your profile information, affiliated groups, etc. 

What are the 5 steps involved in data-driven decision-making?

DDDM is a company-specific process. Your organization will need to find a process that works best for your internal requirements. However, the process can generally be broken down into these five stages.

1. Identify your vision

Identify the “what” and “why” of your company’s vision. What is the greater goal and what are the smaller steps along the journey that can help you get there? Data alone can only get you so far if there’s no context behind your objective. We recommend establishing a year-end vision at the start of every calendar year. 

2. Find data sources

Use a data management tool to accrue data points in real time. There are a number of data sets to collect, such as:

• Profile data
• Behavioral data
• Predictive data
• Demographic data
• Employee data
• A/B testing data

In addition, customer data can also be first- or third-party. The former refers to data collected directly from your customers and is the data you want to prioritize. Third-party data comes from external sources, such as competitors and subsidiaries, and can be useful as supplemental information.

3. Organize the data

Omit data points that aren’t relevant to your company’s vision. Set your data management tool to present the data on your dashboard in real time, as it filters in. This is the stage where you identify the metrics and key performance indicators pertinent to your benchmarks. 

4. Conduct data analysis

Examine the data—what trends does it show? Use a modern data management tool to filter the data and translate the incoming data points into a readable graph. Customize your dashboard and have the information presented in your choice of graphs, charts, or even written summaries. For a more complete picture, combine this with qualitative data, such as written testimonials, reviews, or answers from open-ended questions.

5. Draw and act on the conclusion 

Look for patterns in the metrics. What does the data indicate? You should be able to identify a trend regarding customer behavioral patterns. Come up with a conclusion based on the analytics and formulate an action plan. For example, if data shows loyalty program customers have a significantly higher lifetime value, then your next company vision (established in step one) can include heightened efforts to get customers to sign up for the loyalty program.

Make data-driven decision-making intuitive with Lytics

We can’t overstate the importance of data for decision-making. With Lytic’s Decision Engine, activate data in real time and gain valuable insight regarding consumer trends. Our Decision Engine has been proven to be an effective data activation tool through numerous real-world use cases.