A data-driven culture is essential for improving performance and fostering continuous learning, with the goal being company-wide proficiency in leveraging data effectively to make informed decisions.
However, many businesses face significant pain points and frustrations on the path to developing a data-driven culture.
There is often a lack of understanding of how to utilize data, difficulties in aligning organizational culture with data-driven practices, and challenges in implementing these strategies effectively.
To overcome these hurdles, businesses need clear guidance on the steps required to create a data-driven culture. Understanding the importance of data-driven decision-making is crucial, as is having a structured approach to integrating data into organizational processes.
The importance of data privacy and security concerns cannot be overstated and should form a core part of any data literacy and training plan.
Why a Data-Driven Culture Matters
Many business leaders, particularly those who have been in an industry for a long time, have become accustomed to making decisions based on intuition and for many, this has worked in their favor for some time.
However, in a world of ever-increasing data availability and processing, organizations succeed when they make data driven decisions.
Benefits of Data-Driven Decision-Making
Karel Callens, CEO and Founder of Luzmo explains first and foremost, any individual will feel more confident when they have data available to back up an important business decision.
“Thanks to this confidence boost, businesses will be able to make decisions much faster, with less hesitation, and speed up their business growth,” he says.
Data-driven decisions are supported by facts, which helps create organizational buy-in: It’s much easier to explain and justify a decision when people understand the why behind it.
When a decision makes sense to more people in the organization there will be more internal consistency and logic in company decisions, which leads to outcomes that are immediately aligned to overarching strategic priorities.
Data-driven decision-making not only optimizes decisions made in the present, but with modern analytics tools, the outcomes of choices made can be monitored against the same KPIs.
“These impacts can form the basis of iterative decision-making which improves continually,” Callens says.
This is one of the reasons AI is so exciting for the analytics field: It excels at this kind of iterative improvement which calibrates and then optimizes the new tools that are designed to manage big data sets.
The Impact of Data-Driven Culture on Business Success
Data-driven culture directly impacts business success by making it easier and faster to deliver what customers and partners need and will need.
“You can anticipate demand, anticipate desired features, and build products and services quickly in response to patterns in data indicating trends in the market,” says Krishna Subramanian, co-founder and COO of Komprise.
It also allows the organization to fix problems faster, which makes customers happy and keeps them coming back.
“This is just a start,” she explains. “Data-driven cultures can also foster lower attrition by understanding what employees need to succeed and be productive or how to operate more efficiently.”
However, organizations often fall into the trap of getting “lucky”, a less than scientific long-term strategy and then trying to replicate that success without understanding which levers were being pulled to create the original outcome.
“A data-driven culture demystifies success for businesses,” she says. “It enables them to track how decisions impacted metrics and then replicate that success.”
This is doubly important as markets change and adapt, as what works today won’t work tomorrow.
Modern analytics tools can measure these changes in the background so you are aware of changing trends and can be proactive as an organization rather than reactive.
Steps to Establish a Data-Driven Culture
Assessing Current Data Practices
The first step organizations must take to assess their current data practices is to truly understand the data.
That requires figuring out what data the organization has, where it is, how fast it is growing, which data is needed and which isn’t, which data is lacking or misunderstood, and which data can be purged.
After that assessment, then you can begin to understand usage patterns and what needs to change to push the needle.
Employee surveys can help us understand what departments need, what they don’t have, and where their frustrations lie.
Gal Ringel, co-founder and CEO at Mine says that completing a data mapping exercise is “without a doubt” the most valuable thing you can do to assess an organization’s data practices.
“Knowing how many sources you’re using, who is using each source, and the data that lives within those sources is invaluable,” he says.
Traditionally data mapping was done manually, meaning the privacy officer or CISO would need to go survey every department and ask why they were using each tool.
“Although there are now tools to automate and drastically improve that process, if you want to truly assess the scope and difficulty in capturing an organization’s data governance, doing it manually will be an elucidating experience — even if the final data map will be incomplete,” Ringel says.
Aligning Organizational Goals with Data Objectives
The hardest part of aligning organizational goals with data objectives is deciding what to measure.
With businesses collecting more data than ever, for data analysts it can be more like scrounging through the bins than panning for gold.
“Hiring data scientists is outside the reach of most organizations but that doesn’t mean you can’t use the expertise of an AI agent,” Callens says.
Once a business has a handle on which metrics really matter, the rest falls into place, organizations can define objectives and then optimize data sources.
As the quality of the data improves the decisions are better informed and the outcomes can be monitored more effectively.
Rather than each decision acting in isolation it becomes a positive feedback loop where data and decisions are inextricably linked: At that point the organization is truly data driven.
Subramanian explains that changing the culture to become more data-driven requires top-down focus.
When making decisions stakeholders should be asked to provide data justification for their choices and managers should be asked to track and report on data metrics in their organizations.
“Have you established tracking of historical data metrics and some trend analysis?” she says. “Prioritizing data in decision making will help drive a more data-driven culture.”
Ringel says it’s important to have good data on hand and have a good read on the market.
“If your data aligns with what the market indicates is happening, or better yet, can predict market shifts, you will be able to set realistic goals,” he says.
Investing in Data Infrastructure and Tools
Investing in robust data infrastructure and tools is critical to fostering a data-driven culture. Organizations must consider scalability, ensuring that the infrastructure can grow with the organization and handle increasing data volumes.
Cloud-based solutions often provide flexible scalability options. Integration capabilities are vital; selecting tools that seamlessly integrate with existing systems and data sources reduces data silos and enhances accessibility across the organization.
Jared Coyle, head of AI at SAP North America, says that many organizations believe in the “one data lakehouse to rule them all” approach.
“There is something to be said about having a cohesive set of modeled information from across your enterprise software landscape accessible from one place,” he says.
However, such an aggressively monolithic approach typically becomes untenable in the long term, as software gets upgraded, data models change, and new technologies (e.g., front-end reporting tools or vector databases for generative AI) are introduced.
“A better approach is to have a more heterogeneous data fabric,” Coyle explains. TThis means that you allow the originating systems to define as much of the data structure and architecture as possible. Only transform what you truly need to.”
The benefit of this approach is that the organization is always working with near-real-time data in its original form, which protects you from major data model changes and future versioning challenges.
“It also creates less custom effort in the modeling process itself, if managed appropriately,” he adds.
This approach depends upon the business, industry, and customers — if the organization has petabytes of data, you’ll need a robust hybrid cloud strategy that gives you the flexibility to store data in different places and easily move it as needed depending upon its lifecycle and value to the organization.
With smaller data sets, a company might just choose to go all in on the cloud because that is more affordable.
If AI is going to be made central to the business, a different kind of infrastructure set will be required, as compared to a company still dabbling in it or where AI won’t be as important.
Regardless, every business will need a tool to analyze their data in storage and other tools for reporting, analytics and AI-based analysis and automation.
“This is not a time to be conservative; it’s time to take risks,” Subramanian says.
To help, there are a plethora of free and affordable commercial tools and services to help any size business in any industry take calculated risks and baby steps toward making data an asset that drives decisions every day.
Fostering Data Literacy and Training
The best thing an organization can do to foster data literacy is to make data fully available to everyone, particularly those in business making decisions.
“Make the data management and analytics group a shared service provider to those in the business,” Coyle says. “”ag your data and dimensions so that people know what it is in plain language.”
It is advisable to host regular, consultative calls that allow business teams to ask questions of your deep dive analytic talent, thus better democratizing data access and insight generation across the organization.
Employees must understand what’s in it for them and the company to change the way they work by leveraging data, metrics, and AI tools.
Oftentimes the average information worker doesn’t know where to start — data needs to be consumable and the tools to analyze it or use it need to also be easy to use without requiring specialized skills.
“The onboarding ramp should be simple and progressive,” Subramanian explains.
For instance, somebody who works in data all day might send out emails with an extensive list of metrics, graphics and charts that are not consumable by the average employee — data must be delivered to meet the needs of the individual worker and/or department.
Overcoming Challenges in Implementing Data-Driven Culture
Overcoming common challenges in implementing a data-driven culture requires targeted strategies.
Breaking through data silos: Data silos, where data is stored separately and is difficult to access and integrate, can be mitigated by implementing integrated data platforms and fostering a culture of data sharing. Ensuring data quality through robust data governance practices is essential for maintaining accuracy, consistency, and reliability.
Updating outdated tech: Outdated technology can impede data-driven initiatives, so investing in scalable, integrated, user-friendly data infrastructure and tools is crucial. Resource constraints can be managed by prioritizing initiatives with the highest impact and seeking executive support for necessary investments.
Addressing Resistance to Change
The digital economy today needs data to grow and thrive, and companies that don’t realize that and enact best data practices are going to be left behind.
Transitioning to a healthy data-driven culture does not mean people lose their agency, it just means having more information on whether a choice will be right or wrong.
Coyle advises implementing top down and bottom up at the same time, starting at the top.
“Make sure your leaders are open to the change, and that they are encouraging everyone to make decisions based on data, which means they have to show how they are making decisions,” he says.
Next, empower your people to make data-driven decisions at the grassroots level and reward data-driven decisions, even if they are not the decisions you as a leader would have intuitively made.
“Make sure the data is available to all, and that you’re referring to it regularly,” Coyle says. “Lastly, you want to encourage everyone who has a role in creating great data, to be part of the movement.”
This means putting in place recognition and reward mechanisms for those creating great data.
For example, a shipping organization could spotlight the dock worker who consistently scans the incoming shipping manifest.
In this way businesses can raise up the critically important actions that build a data culture.
Handling Data Privacy and Security Concerns
A governance strategy around data privacy and security is critical.
Always ensure on a data governance committee that the chief information security officer (CISO) plays a prominent role.
“It’s important to segment domains,” Coyle says. “HR data is different from sales and operational data domains.”
Where possible, leverage the security already in place in the systems where data is generated.
Always assume (outside of HR and personally identifiable information) that a person who can see aggregate data, can see granular drill-down data as well.
All information security must be handled at the field- or row-level to minimize risks — it is essential that your data strategy and your security policies are fully in line.
From Ringel’s perspective, it all starts with product development and ensuring privacy by design and privacy by default principles are understood and enacted by the entire team.
The risk of data-driven organizations is data collection creep, so being able to resist that and prioritize customer security and privacy while delivering a great product is half the battle.
“Asking yourself if you should do something is much more important than asking if you could do something” he says.
Navigating Cultural Shifts and Organizational Dynamics
Department heads and leadership must play a pivotal role in communicating to the workforce what are the best use cases for leveraging data for decision-making, content creation, process automation and other purposes.
This will vary by industry, company, and unique needs of the customer base.
Once those core use cases are agreed upon, then it’s up to IT, data scientists and other data stakeholders to determine what data is available and what tools are available (or still needed) to deliver the data outcomes.
That could include reports or AI-driven processes such as an AI tool that analyzes clinical notes for trends and outliers.
From Coyle’s perspective, “overcommunication” is a key element to any successful navigation to the new world of a data-driven culture.
“Reward those whose actions are reflective of the culture you want to build,” he says.
He recommends starting the hardest part, the human part of transformation, by living at the top of the organization the ethos you wish to drive more broadly.
“Lastly, be very vocal about the rewards you as a business, and a team, are reaping, through this data-forward approach,” he says. “With these steps, in time, you will transform.”
