60% of companies around the world use data and analytics to drive process and cost-efficiency (MicroStrategy, 2020).


But are they making the best out of their data? Are they using it to its fullest potential?

Data-driven organizations are those that use data to guide their decision-making. Data is collected, processed, and analyzed to help inform business decisions. This data can come from a variety of sources, including customers, employees, financial information, and operational data.

Organizations that are not data-driven may still collect and use data, but they do not use it as a primary driver of decision-making. This can lead to suboptimal decision-making and inefficiency.

In fact, According to NewVantage Partners 2022 survey, fewer than one-in-three respondents thought of themselves as data-driven firms (26.5 percent).

Despite the fact that they have CDOs on staff.

So what type of barriers are preventing organizations from becoming data-driven? Let’s explore the top 10 reasons:

1. Lack of Buy-In From Top Management

Executive sponsorship and buy-in is critical for data initiatives to be successful. But it’s not just the top management that needs to be on board—the entire organization needs to be committed to becoming data-driven.

As the change management consultancy Prosci found, projects are much more likely to be successful when led by highly effective executive sponsors. In fact, their data shows that only 29% of projects meet or exceed objectives when led by very ineffective sponsors, compared to 72% for projects with extremely effective sponsors. This underscores the importance of having active and visible executive sponsorship for any project.

Thus, it is crucial for any organization looking to implement changes successfully to have an effective leader at the helm. Data driven organizations are no different–in fact, they may even require more careful and attentive leadership given the amount of data that needs to be managed.

In brief, widespread adoption is essential because it takes more than just a few analysts or data scientists to make an organization data-driven. Data needs to become part of the DNA of the organization, and that starts with buy-in from top management.

2.  Lack of alignment between business and IT

Man and Woman Working Together in the Office showing some data and graphs on a a white board table

There is a big disconnect between IT and business units when it comes to big data. While IT believes that access to data is less than adequate, only 46% of data users (analysts or business users) agree, according to NVP, Big Data Survey. This mismatch in perceived capabilities indicates that alignment is clearly an issue.

This is not surprising given that the roles and responsibilities of IT and the business are often seen as being in conflict with each other.

IT is typically responsible for ensuring data quality, while the business is focused on using data to drive decisions. This can lead to tension between the two groups, especially when things go wrong. For example, if poor data quality leads to a bad business decision, who’s to blame?

To be truly data-driven, organizations need to find ways to break down the silos between IT and the business. One way to do this is to create cross-functional teams that are responsible for both data quality and data-driven decision-making.

3. Poor data quality

If the data being fed into your organization’s decision-making processes is inaccurate, incomplete, or otherwise unreliable, it doesn’t matter how good your analytics are—the decisions made will be based on false assumptions. Data quality should be a top priority for any organization that wants to be data-driven.

According to Accenture, Closing the Data Value Gap Report, “only 32 percent of companies reported being able to realize tangible and measurable value from data, while only 27 percent said data and analytics projects produce insights and recommendations that are highly actionable.”

A lack of reliable data can have far-reaching implications that go well beyond just bad business decisions. It can also erode people’s confidence in the data and make them less likely to use it.

This is why it’s so important for organizations to have a clear understanding of their data—where it comes from, how it’s being used, and what its limitations are. Only then can they take steps to improve the quality of their data.

4. Poor cultural change

Adopting a data-driven culture is one of the most important—and yet challenging—aspects of becoming a data-driven organization.

Harvard Business Review’s 2021 survey shows that 92.2% of companies are still struggling with cultural issues that prevent them from achieving success. This is up from 80.9% of companies that cited cultural problems as their biggest obstacle to success just four years ago.

It seems that cultural challenges within organizations are becoming more and more prevalent, and are impeding success more than ever before. These challenges can manifest in a variety of ways, such as difficulty with change management or communication breakdowns. If left unchecked, these issues will only continue to cause problems for businesses.

It’s also important to note that cultural change cannot be imposed from the top down—it has to come from within the organization. To successfully change the culture, organizations need to find ways to engage and empower employees at all levels.

5. Lack of investment in data and analytics

Blue and Green Pie Chart

If an organization wants to be data-driven, it needs to make a significant investment in data and analytics. This includes hiring data scientists and analysts, as well as investing in the tools and technologies needed to support their work.

Not surprisingly, organizations that are not data-driven are less likely to invest in data and analytics. They may see it as a “nice to have” rather than a “must-have”, or they may simply not understand the value that data can bring to their business. As one executive respondent stated, “Our leadership team just doesn’t see the ROI in data and analytics, so they’re unwilling to invest the money needed to get us there.”

6. Misaligned incentives

In any organization, people are motivated by different things. For some, it’s the challenge of the work itself. For others, it’s the opportunity to advance their careers or earn a bonus.

In a data-driven organization, everyone needs to be aligned around the same goal—using data to drive decisions. This can be a challenge if people’s incentives are misaligned with that goal. One way to align incentives is to make data-driven decision-making part of everyone’s job description. 

This means that everyone in the organization, from the CEO to the front-line workers, is responsible for using data to drive decisions.

Another way to align incentives is to create bonus programs that reward people for making data-driven decisions. For example, you could give bonuses to employees who come up with ideas that lead to improved business performance

7. Data silos

If your organization has data spread across different departments, business units or geographies with little or no sharing taking place, it will be very difficult to develop a data-driven culture. Data silos can lead to duplication of effort, inconsistency in decision-making, and an overall lack of transparency.

According to a 2012 survey by the Economist Intelligence Unit, almost 60% of surveyed companies said that organizational silos were the biggest obstacle to using big data effectively. Executives in large firms were more likely to cite data silos as a problem than those in smaller firms.

 72% of executives in firms with annual revenues exceeding $10 billion said data silos were a problem, compared to only 43% of executives in firms with revenues of less than $500 million.

To overcome this challenge, you need to break down the barriers that prevent data from flowing freely throughout the organization. One way to do this is by establishing enterprise-wide data governance policies and processes. Data governance can help ensure data is accurate, consistent, and compliant with regulations. It can also promote collaboration and sharing of best practices across the organization.

 8. Lack of data literacy

In order for people to make effective use of data, they need to have a certain level of data literacy. Data literacy is the ability to read, work with, analyze and argue with data. This means that the majority of employees at these organizations are not able to fully leverage data to do their jobs effectively.

Lack of data literacy can lead to a number of problems, such as incorrect data being used to inform decision-making, incorrect conclusions being drawn from data analysis, and employees being reluctant to use data altogether. Data literacy is a key skill that all employees need in order to be successful in a data-driven organization.

9.  The Ownership & management of data

Data is often seen as a shared resource, which can lead to problems with who’s responsible for managing and governing it. This can be an especially big issue in large organizations with multiple business units.

In a data-driven organization, there needs to be a clear understanding of who owns the data and who is responsible for managing it. This includes things like ensuring its quality, security, and privacy.

Organizations also need to have clear policies and procedures in place for how data is accessed and used. Without these things, it’s very difficult to make sure that data is being used responsibly and effectively.

10. Inadequate data infrastructure

In order for data to be used effectively, organizations need to have a robust and reliable data infrastructure. This includes things like data warehouses, data lakes, data management platforms, and analytics tools.

A lack of data infrastructure can lead to a number of problems, such as data being spread across different silos and inconsistency in decision-making. It can also make it difficult to scale up data-driven initiatives.

In order to create an adequate data infrastructure, organizations need to have a robust and reliable data warehouse. 

To wrap up — “Culture eats strategy for breakfast.”

If there is anything that is clear, it’s that a Data-Driven organization requires a lot more than just data. Data by itself is not enough. Organizations need to have the right people, processes, and technology in place in order to be successful.

While it can be difficult to create a data-driven culture, it’s not impossible. The quote “culture eats strategy for breakfast” by Peter Drucker is very relevant here. Strategy is important, but culture is even more important.

In order to create a data-driven organization, you need to start by creating a data-friendly culture. Data needs to be seen as a valuable asset that can be used to improve decision-making. Employees need to be encouraged to use data in their work, and they need to be given the tools and training they need to do so effectively.

Therefore, kick off your Data-Driven initiative by creating a data-friendly culture. Only then will you be able to create a truly Data-Driven organization.

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