Data-driven Decision Making: How-to Guide

Did you know that on average, we make about 35,000 decisions every day? But deciding what type of coffee to get for breakfast and which partner to choose for your business are completely different things.

All of our decisions have consequences, but in the realm of everyday life, it boils down to trivialities most of the time. In the realm of business, we need to guide ourselves with data-driven decision making. Each business decision can impact thousands of people and hundreds of thousands of dollars in revenue.

Today, we show you how to do data-driven decision making.

What is data-driven decision making (DDDM)?

Many decisions we make in our everyday lives are driven by data. Before we head outside for work, we take a look at the weather forecast. If it says there’s a high likelihood of rain, we bring an umbrella.

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In the business world, data-driven decision making means taking a look at data in the form of metrics and KPIs and then turning that data into actionable insights. Last but not least, you make a business decision as the final outcome.

For example, you notice through surveys and reviews that customers complain about the poor customer service in your business. You then take a look at which channels they most frequently complain about and which channels have the highest number of customer inquiries.

After taking a look at the data from an ad hoc report, you can see where the leaky bucket is and whether to hire new customer service managers, train your existing ones, or reassign your team to the busiest channels.

Top benefits of data-driven decision making

Using data to make more informed decisions sounds like the smart thing to do. And there are a ton of added benefits that you may not have even thought about. Here are some practical reasons to use data to guide your decisions.

More accurate decisions: stakeholders can rely more on relevant data and less on their intuition and gut feeling, leading to better-informed decisions as a result of ad hoc analysis

Less bias: instead of guiding your business strategy based on how someone feels about an important decision, you can get a more objective view of the truth

Identifying trends and patterns: through the use of business intelligence tools, various data sources, and AI-powered analysis, you can spot trends and patterns in your business, allowing you to do more accurate data forecasting

Better resource allocation: decision-makers can better allocate funds for campaigns, staff, day-to-day initiatives and more

Risk management: spot potential risks through data analysis ahead of time, make better decisions and avoid future risks

Uncovering customer insights: use collected data to find out what customers think about your product or service and how you can provide a better customer experience

Measuring and improving performance: through data collection and analysis, you can measure performance against your company goals

Competitive advantage: using data analytics in the decision-making process helps you stay ahead of the competition as you’ll know exactly what your next move should be

As you can see, data drives the world forward. But becoming data-driven seems easier said than done.

How to make more data-driven decisions, step by step

You don’t need a degree in data science to start using data and getting valuable insights for your business. Here is a simple process that can help any company become more data-driven and use this to reach their business goals.

Step 1: Define your goals and objectives

What is the main reason you want to become more data-driven? There are many to pick from, such as:

• Getting real-time insights about critical processes and KPIs
• Improving your bottom line by becoming more profitable and eliminating budget waste
• Collecting quantitative feedback from customers
• Improving operational efficiency
• And others

Of course, collecting data inputs is never a bad thing, but collecting data for the sake of it is not very practical.

Choose your key business objectives and based on that, set SMART goals: specific, measurable, achievable, realistic, and time-bound.

For example, improving customer lifetime value by 10% in a year by improving customer service through business phone app implementation.

Step 2: Define your data sources

One of the easiest ways to make more data-driven decisions is through data visualization. In other words, dashboards containing your most important information. But to go from data to dashboards requires a bit of thinking and planning.

You need to define your data sources, i.e. where your data comes from. For example, if you want to determine your customer lifetime value, this data can be pulled from a CRM app. To gain deeper insights into customer interactions and behaviors, you might integrate your CRM with advanced call center software, which can provide metrics like first call resolution and average handle time. Or if you need data from a large number of business apps, you most likely have to connect to a data warehouse that supports various types of data.

If you need data from a large number of business apps, you most likely have to connect to a data warehouse that supports various types of data.

The data sources and the amount of data you need to pull to get insights all depend on your overarching goals.

For example, if you want to create a dashboard for your SaaS app with a tool such as Luzmo, your data source might be a central data warehouse, your own API, or in some cases even a dedicated database per SaaS customer.. It all depends on your data infrastructure.

Once you collect your data, you may need to normalize and model it to prepare it for visualization and analysis. This will depend on your end goals and analytics tools in your tech stack. Ultimately, you need superior data quality for further analysis and visualization.

Step 3: Visualize your data

Data-driven organizations look at dashboards to understand data. Data literacy in your company won’t come from crunching numbers in Excel. It usually comes from looking at graphs, charts and other types of visualizations in data analytics tools.

Speaking of which, now that you have determined your data sources, you need to connect them to a data analytics/business intelligence tool and visualize the data for easier understanding. Some common choices for data-driven insights include Tableau, Microsoft’s Power BI, Qlik, and others.

These tools help you connect the data sources and turn them into dashboards that the end-user can explore and hopefully, understand. The end-user can be anyone, from employees in your business to the user of your product or app.

The visualizations allow for a data-driven decision-making process. Instead of looking at rows of numbers, the end-user can glance at their key performance indicators in the form of pie charts, bar graphs, Gantt charts, histograms, etc.

Nowadays, you can count on machine learning and artificial intelligence for the visualization of your data, provided that it’s properly cleaned, modeled, and prepared.

Step 4: Analyze and explore your data

Datasets visualized as beautiful dashboards are nothing more than fancy cartoons unless you do something with them. The end user should now look at the dashboard and get data-driven insights.

For example, they can take a look at customer data and determine that a certain feature in an app is underused. Or they can look at a sales dashboard to determine that a certain product category is not selling well in this quarter.

They can then make even more data-based insights by drilling further down into that data and exploring it.

The key part of the analysis and exploration process is that the end users don’t have to be data analysts. With good business intelligence tools, they can simply load up a dashboard, click around, and answer their specific questions.

Step 5: Draw conclusions and act on your data

With the data visualized and analyzed properly, the end user can make their own conclusions. For example, by reducing the advertising spend on the failing product category, they can save money and improve the company’s bottom line for the quarter.

Ask yourself the following questions:

• What did I learn from this data?
• How can this data help me achieve my decision-making goals?
• What is the biggest impact I can make by using this data?
• What information is already familiar to me?

Provided you’ve completed all the steps so far, you should know exactly what decisions to make to meet your current business goal.

Help your end-users make smarter decisions with Luzmo

We can’t tell you which decisions to make in your own business, but we can give you the tools to do so. Specifically, we help you unlock data insights for your end-users - whether a handful of customers or ten thousand app users.

With Luzmo, you can launch dashboards in your SaaS app in just a few days. Fully customizable, easy to connect with APIs, supports multi-tenancy, and has strong data security features - there is lots to like about Luzmo.

Don’t take our word for it - grab a free trial and create your first dashboard today.