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Creating a powerful data department with data science

Leon Gordon

22 June 2022

Effective data teams will be tasked with creating a program that not only measures success but also inspires employees to take ownership of the outcomes.

Introduction

Data can be immensely powerful for companies looking to use technology to improve their performance, when used correctly. Data specialists can help your organization make more informed decisions with the information you have, improving its efficiency and helping you avoid pitfalls that could prove disastrous for your business. But data science is about much more than just providing recommendations – it’s about creating a culture of data-driven decision making throughout your organization. In this article, we’ll look at how you can use data to inform decisions in your company, why having a data-driven culture is important for success, and how to build one of these cultures at your firm or organization.

 

What are the benefits of a data-driven culture?

A data-driven culture is a culture where data is used to make decisions, to measure success and inspire employees. While it’s easy to see the benefits of a data-driven culture from an internal perspective, let’s take a look at how it can benefit your customers too:

Using data for decision making allows you to find patterns in customer behavior that would have been hidden otherwise. You’ll be able to understand what motivates them and what doesn’t. This will help you create better products or services that match their needs and aspirations more closely.

Measuring success shows customers they are valued by you because they provide useful information about themselves when they interact with your business or organization. A good example of this is LinkedIn encouraging users who have added connections or liked content on its site over time by showing them posts from those people above others in their news feed; this means its content has been valued highly enough by other users so as not only makes itself visible but also encourages engagement amongst friends/likes/pages which keeps them coming back again and again!

This helps companies highlight key metrics around user activity which helps improve future iterations of product offerings based upon user input.

 

How to build a data-driven culture

As the data-driven culture is more and more important, the role of the data consultant is becoming increasingly critical. Without a strong team of data scientists to support their business, companies are leaving themselves vulnerable to mistakes that could be easily avoided with the right tools in place.

The first step in building a strong team and building an effective culture is understanding why it’s so critical:

  • Data drives decision making
  • Data drives business strategy
  • Data drives customer experience

Creating an environment where everyone understands the value of data and believes in its ability to drive decision making

To create a culture of data science, you need to teach people why data matters. You can do this by educating them about the importance of data in decision making. Emphasize how important it is to understand the context behind decisions and not rely solely on gut feelings.

You can also allow people to see how powerful your toolset is by showing them its capabilities through demos, workshops, and other interactive sessions. If you’re working with a smaller team that might not be able to attend these events (or if they aren’t available), make sure each member has access to all the resources they need online so they are able to keep up with their colleagues who are learning on-the-go.

 

Deciding on metrics

Once you’ve defined your goals and objectives, it’s time to identify the metrics that will help you achieve those goals. You want to make sure the metrics you choose are quantitative and measurable. In other words, they should be focused on outcomes rather than processes—for example: “We want to increase sales” versus “We want to increase inventory turnover.” They also need to be easy for everyone in your organization (not just data scientists) to understand; otherwise, they won’t have much value as a tool for communication or accountability. Furthermore, they must be easy for anyone in the company (including executives) who needs data-driven decision making tools at their fingertips—i.e., they need not require special training or knowledge of statistics in order for someone else within an organization’s hierarchy chain of command

 

How to use your metrics to make decisions

You’re probably familiar with metrics. They are a set of quantifiable numbers that provide useful information about your business. Metrics can help you understand what’s working, what isn’t, and how to fix it. You should definitely start using them!

That said, not all metrics are created equal; some are better than others at providing actionable insights into your business. Let’s take a look at two types of metrics that we recommend for data-driven decision making:

  • Key performance indicators (KPIs) – KPIs show progress from the past few months or quarters toward a specific goal (for example, sales). They’re easy to access and track continuously throughout the year because they’re often delivered in charts or graphs on dashboards that display historical trends over time. Additionally, they’ve been well researched by both academics and professionals alike so we know which ones work best when used together within certain contexts (for example marketing vs customer service).

 

  • Performance indicators – Performance indicators tell us how well we’re doing in specific areas but don’t give us much context as far as whether our efforts have been successful thus far—which makes them more difficult for decision makers since no actionable insights come from them unless there’s already been enough time invested into analyzing results first! However these can be very valuable when determining whether things need tweaking later down the line since many businesses tend towards perfectionism instead of experimentation (due largely because their “experiments” aren’t always considered successful before trying something else new again). These types include financial measures like ROI which show how much money was spent versus how much money was made during a given period/time frame etcetera.”

 

Where data is priority number one, the most important trait is trust

In any relationship, trust is an essential ingredient. It’s how people feel that you have their best interests at heart, and it’s what keeps them coming back for more. In a data-driven culture, trust is of the utmost importance—especially when your coworkers are all working on different projects and never actually meet face-to-face.

The key to building trust in this environment is transparency: if everyone knows what everyone else is doing, they can trust that everyone is working toward the same goals. For example: if one team member finds some new insight while conducting research on their own time, they should share it with the rest of their company so everyone can use it going forward (and maybe even incorporate it into their own work). Allowing employees this freedom creates a sense of ownership within each person’s role; they feel like they’re part of something bigger than themselves because they’re contributing toward something larger than themselves—which builds camaraderie among employees as well as brand loyalty among customers or clients who value such things!

 

Effective data teams will be tasked with creating a program that not only measures success but also inspires employees to take ownership of the outcomes.

To ensure that your data program is successful, you must also create a culture of data-driven decision making. This means that employees should be able to use data to make decisions and not just rely on the opinions of their managers or personal experience. It’s also important for your team members to understand how their work impacts the overall goals of the company.

When it comes to measuring success, effective teams will set goals and create programs with actionable metrics in mind. For example, if you have an email marketing campaign that you want people to sign up for (which would then lead them into becoming customers), then it would be important for your team members to know what percentage of recipients actually sign up after receiving an email from you instead of just sending emails blindly hoping something good happens.

 

Conclusion

To effectively create a data-driven culture, you must ensure that everyone understands and believes in the value of data. Trust should be at the core of your organization – if it is not present then you cannot hope to successfully implement a data program with success. Companies need to decide how they will use their metrics in order to make decisions about future actions – but always remember that these decisions should still be based on gut instincts as well as data analysis. Just like any other tool, there are limitations to what information can tell us about our world around us so never stop thinking for yourself!

Leon Gordon, is a leader in data analytics. A current Microsoft Data Platform MVP based in the UK and a Partner at Pomerol Partners. During the last decade, he has helped organizations improve their business performance, use data more intelligently, and understand the implications of new technologies such as artificial intelligence and big data.


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