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How to be a Data-Driven Organization (And How to Tell if Yours Is One)

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By Taye Johnson

Senior Data Analyst

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The first article in this series discussed how data could help organizations make better business decisions. Companies that either know how to get the most out of their data or measure success are on the right path, but it doesn’t necessarily mean they’re data-driven.

Being data-driven means using data as the universal driver of internal and external decision-making.

Organizations often overlook data as a separate entity and not as a strategic component of success measurement: a nice-to-have to help bolster success, but not the standard or basis for strategic planning across the organization. 

Being data-driven is more than a tactic or an as-needed approach to strategy or development. It’s a foundation. A cultural mindset. It tells us everything we need to know about an organization’s standing and what can happen next. 

As companies become more tech-enabled, they see more value in data and its potential learnings. They throw thousands, millions, sometimes billions into supporting technology solutions to make data work better for them. But making investments in the right infrastructure is not enough. 

According to insight gathered from Harvard Business Review, the main challenge for organizations that want to use data more efficiently isn’t technical but cultural. 

Let’s look at what a data-driven organization looks like and the steps you can take to get there.

There’s a culture built around being data-driven, and it starts at the top. 

Think of the organizational decision-making process as building a staircase with the goals at the very top. Your data is your blueprint. It tells you how to assemble each step: what dimensions they should be, and the tools and players involved to get to the top.

Without that blueprint, yes, you may have the right tools, and you have the team to make it happen, but it may not come together very efficiently or quickly. You may waste a lot of time and resources along the way. The tools you invested in may not be helpful because you don’t understand how best to use them.

Creating a data-driven organization starts by establishing a culture around data–and has to happen from the top down. When senior leadership cultivates a universal understanding that a product, campaign, plan, or initiative’s success and future are based on data, this sets a precedent for everyone.

Data-driven organizations know their data landscape.

One of the biggest pitfalls for many organizations is the amount of data gatekeeping that occurs – whether intentional or not. Technology platforms and the accessibility of data have evolved quickly, and its ability to be easily accessed, managed, or shared has not always been a priority. As a result, the platforms used to host data are often owned by a select few, are duplicative or out-of-date, or contain so many filters that it’s difficult to determine if the data it provides is accurate.

When the steps to gathering and analyzing data are convoluted and indecipherable, it’s easy to ignore its merit – or not even bother to question it.

As with many aspects of an organization, policies and protocols are typically well-documented, and roles and responsibilities are understood. Data management is no exception to this rule.

Data-driven organizations often have well-documented and well-observed policies and strategies to know who owns and can access which data and know exactly how it’s shared across the enterprise. The data extracted doesn’t have to be questioned or decoded in ideal circumstances. Data-driven decisions can confidently be made because there is shared accountability in managing them.

Don’t have a data strategy in place? Answer these questions as a starting point.:

  • Where is all of my data coming from?
  • What tools and technologies are used for data management?
  • Who owns and has access to each product or system?
  • How is data stored?
  • How is information shared?
  • Can data be accessed easily with no or limited manipulation?
4 ways to get the most out of your data.

Data is easy to access and understand.

Let’s say you have your data strategy, and you’re going to focus on accessibility within that framework. As we mentioned data gatekeeping earlier, sometimes data can only be accessed or understood by certain people within the organization, making it difficult to get answers quickly. 

With universal alignment on goals and responsibilities also come universal practices on storing and saving data. A consistent approach eases the burden of tracking down the insights you need. 

One approach to this is to determine if your data is FAIR (Findable, Accessible, Interoperable and Reusable)

  • Findable: Standardized terminology within meta descriptions, naming conventions, and data formats make searching a far less arduous task. It takes some up-front work to ensure these conventions are in place, but it can decrease the amount of time used for compilation in the long run. 
  • Accessible: Once you find the data you need, you’re able to pull it with little difficulty. This doesn’t mean everyone in your organization needs access to every piece of data. Still, a documented governance strategy noting roles and procedures will make it easy to find out how to get the data you need.
  • Interoperable: One of the toughest steps. It means your data can connect to other platforms within the organization. Say a marketing analyst for an e-commerce company wanted to determine if a digital product issue is a cause for high cart abandonment rates. If the data tools are well-integrated, they would identify any correlations and outline next steps.
  • Reusable: When data is reusable, it is verifiable (the source is known and trusted) and non-restricted (can be used by multiple teams across the organization).

Nerdery partnered with a global MedTech organization that was so focused on their data and how to access it that it got in the way of supporting their customers. We helped alleviate some of those challenges by making it easier for different departments to access the same data sets and develop customer solutions quickly.

Data is used for decision-making AND should tie to your goals.

Though we’ve talked about data as the driver of strategic decision-making, it also has to serve and support organizational goals.

Many organizations see data as something siloed and separate in many ways. Often data is an afterthought or a fudgeable component of the product plan. If you do have data engineers or scientists at the ready, they may not even be part of the product plan until it’s too late.

As your organization works toward short- or long-term goals, the data you prioritize should always measure those goals. Let’s say an organization’s goal is to increase revenue by 40% and use digital products to help achieve those goals. Instead of looking at general metrics to understand how products are used (say, cart abandonment rate for shoppers or revenue based on platform referring channel), take a step back and consider how product performance may be helping (or hurting) revenue. What data can tell us this story, and identify the exact metrics that will inform the product’s progress towards increasing revenue.

Partnering with data scientists, agency partners, and stakeholders will make it easier to identify which metrics will show progress toward that goal.

It’s true that for many, this is a dramatic change in how organizations operate, and – depending on your organization – it won’t happen overnight, in a few months, or in the next year. It takes time, but the upfront work leads to more streamlined decision-making and more assured success.


Learn more about how to get started on your data journey.


Get the most out of your data by reading the first article of this series.


Sources: 

https://mitsloan.mit.edu/ideas-made-to-matter/how-to-build-a-data-driven-company

https://www.tableau.com/learn/articles/data-management-best-practices

https://blog.datumize.com/how-to-become-a-data-driven-company-5-practices-to-adopt

https://hbr.org/2020/02/10-steps-to-creating-a-data-driven-culture

https://www.nature.com/articles/sdata201618