Category: Data-driven

What is data-driven decision-making?

Data-driven decision-making (also abbreviated as DDDM), is the process of using data to inform your decision-making process and validate a course of action before committing to it.

58% of respondents in a DDDM survey said that their companies base at least half of their regular business decisions on gut feel or experience rather than being driven by data and information. This means that these businesses largely make inconsistent and spontaneous decisions. What you should do, instead of going with a strategy you think is best, is use DDDM as a strategy that uses data to inform business decisions.

DDDM about grouping together historical information to analyze trends and make decisions for the future based on what’s worked in the past – rather than make decisions based on gut feelings, opinion, or experience.

Companies that embrace DDDM position data at the core of every decision they make, and how exactly you can incorporate your data into the decision-making process will depend on a number of factors, such as your business goals and the types and quality of data you have access to.

Why you should become more data-driven?

Now you know how you can benefit from data-driven decision-making, the next step is to identify how your organization can use data to make decisions for how to grow your business.

DDDM enables companies to create new business opportunities, generate more revenue, predict future trends, optimize current operational efforts, and produce actionable insights. That way, you stand to grow and evolve your business over time, and as a result, making it more adaptable. The digital world is ever-changing, and to keep moving with it, you must leverage data to make more informed and powerful data-driven business decisions.

What kind of business decisions can it be used for?

You can use data to find out about:

1. Finance – How will an investment in specialized software impact your business?
2. HR – What’s the most cost-effective way to hire new staff?
3. Growth – What activities can you do to prevent churn? How do you improve customer loyalty? Are the new features you’re planning likely to impact your business goals?
4. Marketing – Which advertising channel gets the best ROI, or what’s the cheapest way to promote a new product?
5. Sales – Which sales activities generate the most leads Customer service – What’s the most cost-effective way to handle support tickets? Which channels improve response times?

How to effectively use the data in 5 steps

1. Set your goals

Start by asking yourself: “What goals do I want to improve?” To get the most out of your data, companies should define their objectives before beginning their analysis. Set a strategy to avoid following the hype instead of the needs of your business and define clear Key Performance Indicators (KPIs). Although there are various KPI examples you could choose from, don’t overdo them. Concentrate on the most important ones within your industry.

2. Collect the data

Gathering relevant data is as crucial as asking the right questions. For smaller businesses or start-ups, data collection should begin on day one.

It’s important to stress that the word “relevant” is key here. You don’t want to spend hours analyzing data that won’t have any impact on your final decision. So, keep the data relevant, and only collect the data that relates to your objective.

You can find relevant data in sources such as: Website analytics CRM software Business intelligence platforms Social listening tools Feedback from customers.

3. Clean the data

Surprisingly, 80 percent of a data analyst’s time is devoted to cleaning and organizing data, and only 20 percent is spent actually performing analysis. This so-called “80/20 rule” illustrates the importance of having clean, orderly information before you can attempt to interpret what it might mean for your organization.

“Data cleaning” is the process of preparing raw data for analysis by removing or correcting data that is incorrect, incomplete, or irrelevant. To do so, start by building tables to organize and catalog what you’ve found. Create a data dictionary—a table that catalogs each of your variables and translates them into what they mean to you in the context of this particular project. This information could include data type and other processing factors, as well.

4. Analyze the data

Data analysis is, at its heart, an attempt to find a pattern within, or correlation between, different data points. It’s from these patterns and correlations that insights and conclusions can be drawn.

Data visualization is a huge part of the data analysis process. It’s much harder to derive meaning from a table of numbers. By creating engaging visuals in the form of charts and graphs, you’ll be able to quickly identify trends and make conclusions about the data. Services such as Google Analytics and Google Data Studio are very useful when visualizing data.

5. Draw conclusions

Now it’s time to draw some conclusions. Ask yourself, “What new information did you learn from the collection of statistics?” Despite pressure to discover something entirely new, a great place to start is by asking yourself questions to which you already know—or think you know—the answer.

The conclusions drawn from your analysis will ultimately help your organization make more informed decisions and drive strategy moving forward. It is important to remember, though, that these findings can be virtually useless if they are not presented effectively. Thus, data analysts must become skilled in the art of data storytelling to communicate their findings with key stakeholders as effectively as possible. Here we can use the data visualization services mentioned above.

You’ll also need to create a plan of action to put your decision into practice. The key at this stage is to make clearly defined goals on what needs to be done and when, by whom, why you’re doing it, and what is the outcome you expect – rather than creating vague goals that “need to be done before the end of the year”.

Still not sure where to begin?

Are you still not sure where to start? The Color Club is ready to help you out! Data-driven decision-making is one of our core competencies, and we have many years of experience when it comes to collecting and analyzing data to help drive better decisions. We can help you interpret the data or consult you on how to handle it all on your own.