Data-Driven Decision-Making

Data-driven decision-making

Quick definition: Data-driven decision-making involves collecting and analyzing data to make more informed business decisions.

Key takeaways:

Term overview: Data-driven decision-making is an operating protocol whereby team members gather, analyze, and act upon data according to a company’s best interest.

The following questions were answered in an interview with Jeff Allen, Senior Director of Product Marketing for Adobe Analytics.

What is data-driven decision-making?
Why is data-driven decision-making important?
What are some common data-driven marketing tactics?
What are data-driven applications?
What are challenges in making data-driven decisions?
How has data-driven decision-making changed over time?
How does a company start incorporating data-driven decision-making?
What are data-driven decision-making best practices?

What is data-driven decision-making?

Data-driven decision-making is a protocol in which a company gathers data to analyze its best path toward its core goals. Companies can use data-driven methodologies to extract valuable business intelligence that enables them to excel—and it’s easy to understand why.

Relying on data and analytics enables a company to make fact-based decisions. A data-driven company increases efficiency by focusing on one or more targeted tasks with efficiency and flexibility that enables experimentation with extra headroom. This freedom is often what drives innovation.

Why is data-driven decision-making important?

Data-driven decision-making enables you to move faster and make fewer mistakes, inevitably leading to higher profitability. Collecting and analyzing data gives you the ability to deduce when your content does well and understand why it resonates with customers—or doesn’t. You can then use that information to predict how similar content will perform in the future.

On a strategic level, a company must decide to start making business decisions based on data to grow and enhance the customer experience. It becomes a matter of shifting a company’s culture from one reliant on gut feelings to one that revolves around making data-based decisions.

How data-driven decision-making benefits the customer

Data-driven decision-making is a great way to show customers that you're paying attention to them and respecting their time and interests. Remembering past interactions, both good and bad, helps you get the experience right the next time. Customers benefit from more personalized and tailored experiences—and those experiences can lead them to be more loyal and spend more money.

What are common data-driven marketing tactics?

Organizations use several data-driven tactics to collect information and predict or optimize an outcome. While the tactics an organization chooses to use depend on its goals and the information it needs to discover, one of the most common data-driven marketing tactic is split-testing (also known as an A/B test).

A split-test is a type of data-driven tactic that enables an organization to show two or more versions of a variable (e.g., a web page, a specific web page element, or another digital property) to different segments of website visitors at the same time. This process enables stakeholders to determine which version leaves the maximum impact and drives business metrics.

Some product development, marketing, sales execution, and sales operations programs utilize one or more data-driven tactics. When selecting a tactic, evaluate it against your use case to identify blind spots or an unacceptable level of false positives.

What is a false positive?

A false positive occurs when a data point indicates a defined state when they aren’t. For instance, in a home alarm system, a false positive would be a motion sensor detecting the wrong type of motion. If a plant is next to a fan, the moving leaves may set off the alarm. The alarm did its job of detecting motion, but it caught a plant, not a burglar.

False positives can be costly in digital marketing. They can skew the data in your system, which is why it’s key to evaluate any data-driven tactic against the use case you’re employing. It’s essential to determine if there will be noise in the data that will cause you to draw the wrong conclusions and make bad decisions.

The importance of inspecting data closely

Closely inspecting your data is one of the most vital components of the decision-making process. You must understand not just the information the data is telling you, but also its capabilities. There are often limitations to what information you can glean from a specific data source.

For example, when evaluating a customer’s journey, you might try to find which touchpoints impacted their decision to convert. With digital marketing campaigns, however, you can over-allocate credit to those interactions instead of some other point that was the ultimate conversion event.

What are data-driven applications?

A data-driven application is a system that autonomously executes a task. There are two main kinds of data-driven applications: Those driven by static data inputs and those driven by dynamic data inputs.

Static data inputs

A regular thermostat is an example of an application driven by static data inputs. It receives information to tell it what the room’s temperature should be, determines if it is at that temperature, and then runs the HVAC system if necessary. It only requires two data points to execute this task: the current and ideal temperatures. It can turn the HVAC system on or off, depending on which action completes the job.

Dynamic data inputs

A smart thermostat is an example of using dynamic data inputs. Smart thermostats use algorithms to learn about their environment and make increasingly complex decisions about operating within it. They examine the current temperature and determine if it’s suitable for the season and the time of day. If it isn’t, the thermostat will decide what action to take to achieve the right temperature, how long the process will take, and the steps required to reach its goal.

What sources feed data-driven applications?

The data for data-driven applications comes from several sources. You can automate some data sources, while others require manual input. Generally, for most systems,

any goal setting is either entered or refined by a user, but you can also pull it from a standard database.

For example, a thermostat could pull the current outdoor temperature from the internet and understand that 70 degrees Fahrenheit in the summer is experienced differently from 70 degrees in the winter.

If an application can acquire data from the internet or other machine-based sources, it can be pulled automatically. Otherwise, a user will have to input that data. But users might have different goals for different conditions. This could include different goals for winter versus summer, day versus night, and for going on vacation versus remaining at home.

If a user tells their thermostat they’re away, they will have a much different tolerance for how warm the house should get in the summer and how cool it can get in the winter. In other words, people are better suited for setting goals, while machines are best for extracting normalized data.

Challenges of taking a data-driven decision-making approach

For all the advantages of making decisions based on data, there are drawbacks. Data-driven approaches are not easy, and they’re not free. While the return on investment is exponential, being data-driven requires significant start-up capital.

There’s also a delicate balance between personalizationand invading customer privacy. You must be thoughtful about how you implement your data-collection practices to ensure that customers don’t feel like the privacy of their data is being violated.

In addition to a customer’s privacy preferences, there are many laws governing what kind of data you can collect and retain, some of which include:

These considerations, among others, make data-driven decision-making—as worthwhile as it is—time-consuming and expensive.

How has data-driven decision-making changed over time?

Data-driven decision-making continues to become more effective and accessible as big data and tech evolve. Some noteworthy improvements include:

How does a company start incorporating data-driven decision-making?

Once a company chooses to make the cultural shift to a data-driven operating model, they need the right people for the job. Such a large-scale, systemic shift requires professionals who know how to gather, process and analyze data, as well as how to present the information to help each member of the organization make the right decisions for their role.

With the right people in place, you can build a data-driven strategy based on something you're trying to accomplish—and then seek ways to measure whether you're achieving it. Next, inspect those measurements to determine that the data is clean and the signal is accurate. Once you meet these conditions, you can begin analysis and draw insights.

Breaking down data silos

When creating a data-driven strategy, it’s vital to ensure that everyone who needs the data to make decisions has access to it֫—which is otherwise known as data democratization. Silos can exist within an organization and prevent information from being easily disseminated. Data is often locked away in a system or owned by a team that won’t provide universal access. This limits how well the organization can make decisions and move forward.

Some people can associate the acquisition of large amounts of data with power, and they could believe that being the keeper of data helps them further their goals and advance their careers. Unfortunately, this can happen at the individual, team, department, or company level. Data can create a competitive advantage—and where there’s competition, there’s a risk that data won’t flow freely.

Data-driven decision-making best practices

Although there are various approaches to basing strategic business decisions on data, this framework is commonly practiced.

  1. Strategize. Know your purpose, where you’re going, and what you want to do. Understand your data-gathering plan, and plan to correct mistakes.

  2. Organize. Data scientists spend more time collecting and organizing data than analyzing it. They will need to clean and structure the data in order to correct inaccuracies and prepare it for database entry. In many cases, marketers and other business professionals rely on software to organize data for them.

  3. Analyze. You’ll have to know what your data says and what it means. There are three types of reports that can help:

    1. Descriptive (factual)
    2. Inferential (interpretive)
    3. Predictive (inference of future events based on analysis as performed)
  4. Interpret. Draw conclusions from your data reports and use them to develop a plan of action based on your goals.

  5. Share. Figure out how to present your data to others in an accessible fashion. What exactly did you do? Why is your purpose essential in the first place?

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