Data mature organizations know how to leverage data at all levels of the organization to make decisions and allocate resources.
Becoming a data decisive company is the dream, but getting there can feel like a nightmare.
It seems every bit of progress is met with resistance. There are some common challenges to data maturity (meaning you’re not alone). So, we’ve outlined five of them. You’ll also find a bit of context as to why these elements are critical building blocks of a data mature organization and some ways to push through the problems and keep moving forward.
1. Insufficient Leadership Support
Support from leadership is crucial if an organization wants to be considered data mature. Leaders need to model behaviors around using data to make decisions.
They also need to recognize and reinforce employee behaviors reflective of data use. So when they see employees using data to make good decisions, these kinds of things should be celebrated and highlighted.
Leadership should also believe that employees are capable and can be trusted with the data they need to drive the organization forward. This means that when employees use data to generate insights, they’re empowered to make decisions relevant to their role rather than having to stop and ask permission to act.
It’s also essential for leadership to be concerned with data from many areas of the organization, not just maybe revenue or sales. There are many different types of data in any organization that can be important in learning how the organization can be improved or more productive.
You want the leadership to be concerned not just with the revenue data but also with human resource data, marketing data and data from other areas that can contribute to the performance of the business.
2. Organizational Culture
The culture also has to support the work of utilizing data. The organization should value agility and experimentation. More than anything, the organization should be aware that failure is necessary before real innovation or growth is possible.
Failure is essential to working with data because when data is used or analyzed, multiple experiments or analyses often need to be run before a valuable insight is gained, and change or decisions can be made.
Your organization also needs to be open to learning and adapting based on the data. And they shouldn’t be threatened by hearing unflattering findings or suggestions that changes need to be made to the status quo.
Another organizational value that needs to be present in a data mature organization is being results-oriented and focusing on outcomes, as opposed to the process or outputs.
The business should encourage staff to generate and test their data-supported hypotheses and question the status quo without fear of reprisal or criticism.
A certain mindset needs to be present in a data mature organization.
Data should be viewed as a core business asset that’s essential to business growth and success–not just something that’s nice to have or something that’s been imposed on us.
By the same token, data use should be a requirement and a priority for everyone in the business, not just the analytics team. Data should be regarded as a primary tool for decision-making in the entire organization.
And employees at all levels should be encouraged to be curious and willing to challenge assumptions with data. This also means questioning what the data might tell us, even if it leads to some uncomfortable conversations.
3. Siloed or Inaccessible Data
The data might be locked away somewhere or unavailable in some critical way, so data access is another important pillar of data maturity. And, it might be the most technical of the data maturity requirements.
Staff can access clean, high-quality data from a central source in a data mature organization. They can easily access the deep data they need to perform their work. This also allows people to collaborate across teams and share data from different functional areas. Additionally, processes need to be in place to prevent and mitigate data-related ethical and privacy concerns.
4. Misalignment Between Leaders and Data Professionals
Stakeholders in the organization might not know how to use data to reach their goals.
In less data mature organizations, the data and the business teams don’t always speak the same language. They can also be frustrated by each other.
Data teams can be frustrated because their reports and findings are never used. Business people might be frustrated because they don’t understand data analytics frameworks or methodologies. Leaders might also receive analytics reports that they can’t use.
To combat these common challenges to data maturity, data teams need to share a common language and framework around data, and business leaders need to take an active role in designing, monitoring and sharing insights from data projects.
We don’t want the business leaders to kind of step back and wait till the data project is over. They need to be involved in every step to add their business-related perspective.
Finally, data teams and business leaders need to understand their respective roles and data projects and how best to support each other.
5. Inadequate Data Literacy
People in general in the organization might not be skilled at using data and communicating with data.
The final pillar of data maturity, which is extremely important, is data literacy. It is a skill everyone who works with or interacts with data must have.
Being data literate means knowing how to read, work with, analyze and communicate with data. Each employee should have the appropriate level of data literacy for their role.
Data mature organizations also offer training and support to help employees enhance their skills.
That helps data-literate employees know what data is relevant to their role while at the same time having a comprehensive understanding of the data available within their organization.
How to Boost Your Organization’s Data Maturity
You are familiar with the common challenges to data maturity on this journey. So how do you figure out where your business is on the scale of data maturity? And once you find out, what do you do?
Step 1: Assess the Current Level
The first step is to assess your organization’s current level of data maturity. Pragmatic Institute has created a Data Maturity Assessment that allows you to quickly and easily do this work.
Ideally, an assessment like this should be taken by many people within the organization to get a full and reliable view of what’s going on in your organization.
And then, it can be helpful to break that data down to see if there are different perceptions or levels of data maturity in different business areas.
Step 2: Identify Gaps
Once you have the results from the data maturity assessment, the next step is to identify the gaps.
These gaps would be areas or pillars of data maturity that are not present in your organization or maybe could use some improvement.
And once you’ve identified the gaps, you would need to reflect on your organization’s data maturity level. Then, explore what can be done to raise that level.
Step 3: Work Toward Incremental Improvement
Work toward incremental improvement in the areas where you see a gap, and I say incremental because it’s important to remember that you won’t transition from one data category to the other overnight. And, you’ll face these common challenges to data maturity every step along the way.
It might take a few years to realize a net positive return on investment in your data maturity work, especially if significant change initiatives, employee training or data systems need to be developed.
Step 4: Reassess
And then, after you’ve been working to improve your data maturity incrementally, it’s essential to reassess to see where your organization has experienced growth. Then continue that cycle of improvement again. It’s a continuous journey toward data maturity.
Enroll in Data Science for Business Leaders
By enrolling in Data Science for Business Leaders, you’ll learn how to partner with data professionals to uncover business value, make informed decisions and solve problems. You learn more about common challenges to data maturity and ways to influence
This 7-hour course teaches how to:
- Focus Data Projects on Business Impact
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- Champion Data-Driven Decision Making