Data-Driven Decision Making: A Handbook With Actionable Tips

Mar 23, 2020

We live in a data economy. Data is everywhere. Tech giants are collecting huge amounts of it about us. It’s scary how accurately they can predict our behavior and future actions. Experts can also use the data they are collecting to make smarter decisions and push their businesses forward. Data-driven decision making (DDDM) has become an integral part of an organization’s core activities. For example, a business might use feedback from user testing to decide if a feature needs further improvements or if it’s ready to be released to the market.

The recent growth and advancements in modern technology have enabled larger amounts of data to be captured, and technology is also able to process larger amounts to find interesting insights.

So, what is data-driven decision making exactly?

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What Is Data-Driven Decision Making?

DDDM is about making decisions backed up by hard data rather than gut feelings or intuition, which are often based on incorrect observations. That doesn’t mean your intuition is always wrong, but since we have access to data, why not use it for better decision making?

Here’s a definition from Techopedia: “Data-driven decision making (DDDM) involves making decisions that are backed up by hard data rather than making decisions that are intuitive or based on observation alone. As business technology has advanced exponentially in recent years, data-driven decision making has become a much more fundamental part of all sorts of industries, including important fields like medicine, transportation, and equipment manufacturing.”

Since an organization should attempt to leverage its data to make more informed decisions, domains such as data management, data processing, and data quality have become increasingly important for businesses. To get to DDDM, an organization needs to capture and store the right data. Therefore, it has to gatekeep the quality of its data and process it into an actionable dataset.

Many organizations think that any kind of dataset is suited for making decisions, but you may find yourself struggling to make better decisions for a variety of reasons. For example, your dataset might include a lot of unrelated or even missing data. Another reason might be a poor quality dataset, or perhaps your organization has been capturing the wrong data.

As you can see, DDDM requires much more than just looking at a dataset. Various domains are included, and you’ll likely have to consult data experts such as a data scientist. However, let’s not discourage you from getting started. The next section explains why it’s important.

Examples of Data-Driven Decision Making

Here are two simple examples that show how you can apply data-driven decision making in your organization. The first talks about finding the best strategy for launching a new feature or product on the market. The second tries to find the cheapest solution for hiring new employees.

#1: Market Launch Strategy

Let’s say you want to launch a new product and are looking for the best strategy. Instead of picking one and hoping for the best, you can use a data-driven strategy to find the optimal marketing strategy.

You can use data from previous market launches to predict the success of a particular strategy. Let’s say you have data about the number of sales per product. From this data, you can partially determine the effectiveness of each campaign and decide on the best one. Try to incorporate as many relevant data points as possible to make the smartest decisions.

#2: Cost-Efficient Hiring

Now let’s assume you want to hire a new employee. There are tons of options. As an organization, you can decide to outsource recruiting to an agency or do it in-house. In-house hiring would mean you carry out interviews yourself and you try to find a cost-effective method for reaching the right candidate.

To figure out the best approach, you need data to decide what method to use. First of all, you can access past hiring data and the costs associated with the methods you’ve already used. Next, you can look for official reports or studies that can reveal which is the best approach for your organization.

Data-Driven Decision Making: What’s the Importance?

Although the advantages of DDDM might be straightforward, there are many other advantages you might not be aware of. To give you an example, DDDM can enable your organization to make faster decisions, which will help your business progress more quickly.

1. Reduced Costs

You can reduce costs if you can make better decisions, such as deciding whether or not to develop a feature that your users really want instead of using intuition and guessing wrong. One good approach is using a questionnaire to learn what users want.

On the other hand, don’t neglect the extra hidden costs of implementing data-driven decision making. You’ll also need to do the following:

  • Buy storage for all your data

  • Purchase licenses for data processing, data management, and data analysis tools

  • Hire new employees such as data scientists

  • Teach employees how to capture data and how to use tools that might be new to them

In the end, the benefits will outweigh the costs and help you stay ahead of your competition in a competitive, fast-moving market.

2. Increased Speed of Decision Making

Everyone has their own opinions, so important decisions often require many iterations and conversations. When a decision is backed by hard data, there is less room for disputing the likely outcome. Being able to swiftly make decisions is a big advantage for an organization, especially in a competitive market.

3. Stimulate Continuous Improvement

Another benefit of data-based decision making is that it indirectly leads to continuous improvement. In order to implement data-driven decision making, your organization needs to capture data. Data capturing can be done by measuring your application using various metrics, giving you much better insights. These increased insights help you improve your product faster, increasing the overall efficiency and performance of your teams.

4. Shift How Teams Make Decisions

Most companies rely on senior, knowledgeable, experienced leaders. However, these people are only human and don’t always make the best decisions. Data-driven decision making shifts the way teams make decisions since they rely less on skilled people and more on data analysis and metrics.

In other words, teams shift from hierarchical decision making to a more open, collaborative form of decision making where data is central.

5. More Confident Decision Making

Data-based decision making helps your organization make more accurate, measured decisions. As a side effect, your organization can also feel more confident. For example, if a user survey shows that more than half of your users want a certain feature, there will be no doubt that you should implement it and no fear that users won’t like it.

How to Implement Data-Driven Decision Making

Next, let’s take a look at a five-step process for bringing data-based decision making to your organization.

Step 1: Define Clear Objectives

First, define clear objectives! It’s important to know what kind of decision you want to make and what your objective is.

For example, let’s say your objective is increasing sales by 50 percent. However, you are selling multiple products, some of which perform better than others. Try to be more concise and focus your efforts on one product. A clearer objective would be increasing sales for Product Y by 50 percent using online advertising.

This means we have to find the best method to advertise the product online. Next, we need to find relevant data that can help us make the right decision.

Step 2: Collect Relevant Data

Collect data consciously! You don’t want to capture tons of data, as that will make it much harder to find patterns or to process it into an actionable dataset. It’s important to first figure out what kind of data you need.

When you know what you need, you can use the tools needed to capture it. Make sure to verify that your organization is ready to adopt new tools because you might want to spend some time educating your employees.

Next is a short overview of data collection methods you can use to capture relevant data:

  • Surveys or questionnaires can help you gather insights into your audience’s needs.

  • User testing is an excellent way to test how customers interact with a product and what problems they encounter. It’s a great way to receive feedback about your product. User testing is also often used to find defects or other issues before releasing it.

  • Launching a product in a smaller market or in a test market allows you to gauge interest and gives you the data you need to make a decision about its viability. Often, large tech organizations prefer to use a dark launch to gauge the interest for a new feature. This means that a product is only released to a subset of the active user base so they can try out a new feature or product. Of course, your organization needs to measure how people use the product to gather metrics for further decision making.

Step 3: Data Management and Data Processing

Many organizations forget about the steps after data collection. Data management and data processing are two important tasks you need to consider as well.

Data management is mostly concerned with the tools you use and the way you store your data, while data processing is focused on creating a high-quality dataset. You don’t want to end up with data silos or unusable, low-quality data. Therefore, data processing can help you leverage your data or even combine multiple data sources into one high-quality dataset.

Now that your data has been processed, it’s time to analyze it.

Step 4: Analyze the Data

During the analysis step, the goal is to draw insightful conclusions from the data. Therefore, try to spot patterns and find trends. It’s not easy to find meaningful insights.

For example, your data might reveal that most users of your product are male developers between the ages of 25 and 35. This can help your organization target advertisements to the right audience.

Step 5: Measure Accuracy and Repeat

As a final step, you want to measure the success and accuracy of your data-based conclusions. Reflect on your objectives and the data you’ve collected. Ask yourself the following questions:

  • Did I define the right objectives?

  • Did I capture the right data?

  • What is the quality of the captured data?

  • Which steps can be further improved?

Of course, data-driven decision making is a continuous process. This means you’ll get better at each step, and it should become easier to spot patterns and trends as you go.

Pitfalls of Data-Driven Decision Making

Data-based decision making is a strategy every organization should use to improve the accuracy of its decisions. However, DDDM comes with a couple of pitfalls you should watch out for. Here are five common ones you can expect to run into.

1. Different Types of Data

Many organizations want to collect many types of data to support their DDDM. However, it’s not easy to process data when you hold multiple different types. Let’s say, for example, that you hold XML, JSON, and CSV data. You’ll likely need scripts or other tools to convert the data to one format. Also, you can make use of data management tools that can help you collect and correctly format it according to one standard.

2. Low-Quality Data

It’s tempting to collect as much data as possible, but don’t fall for the low-quality data pitfall. Although data processing techniques can help even if your data is poor, it’s better to aim for high-quality data, as processing it will require less effort.

You also don’t want to spend hours processing it when you need to make time-sensitive decisions, so try to collect the best quality data you can.

3. Not Using Visualization Techniques

Be sure to make use of visualization techniques. Also, experiment with different visualization methods to see what works best for your dataset. Visualizing your data will help you find patterns much more easily than if you’re just looking at raw numbers. It’s a crucial part of the data analysis step.

4. Lack of Education

Remember that it’s important to educate your employees. Your employees might need to learn to use new tools that your organization uses to collect data. Don’t neglect this, as poor education can lead to poor data quality.

In addition, create a culture of data-driven decision making. It’s important to involve everyone in this shift toward DDDM. Every employee handles data in some way. Therefore, they are more likely to embrace a data-driven culture if you show them the benefits of making data-driven decisions.

5. Aiming Big

Many organizations starting out with DDDM aim to solve a big problem during their first iteration. Instead, start small with a simple objective to get used to the process. You will have plenty of time to fine-tune the process and prepare for more important, bigger decisions.

In addition, starting with a small objective introduces your employees to smaller changes. As change is often hard, it’s best to move slowly. Don’t change every process in your organization all at once, as that will lead to chaos and resistance.

Learning About Data-Driven Decision Making

Data-driven decision making is a strategy every organization should try. It will help you make more accurate decisions by eliminating gut feelings. However, this doesn’t mean you have to change your whole approach at once. Start off small with a simple objective. The more you practice, the better you’ll become at detecting patterns and spotting trends in your data.

When starting with DDDM, document all steps and define new processes where needed. Documentation will help you fine-tune and adjust your strategy as you go.

Also, make sure your organization is ready. Educate your employees on the new tools you’ll need. Your DDDM strategy’s success depends on your employees. Besides that, the quality of your data is important since low-quality data is not well-suited for DDDM. Therefore, only capture relevant data that can be processed via data processing techniques.

In short, it’s always better to rely on data for making decisions. If you don’t have any data that relates to the decisions you need to make, look for official sources such as reports or studies that can help you make the best decisions.You can take it to an even higher level with Plutora and its predictive analytics that help you make more informed decisions. It’s the most complete value stream management platform on the market, and it can help you automatically process data to produce the right insights at the right time so you can make important strategic decisions. In addition, Plutora provides tools that make software delivery and all its processes more efficient.

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