Photo by Kampus Production via Pexels
Like any group of people who spend a significant amount of time together, businesses have their own culture. The nature of a company’s culture can have a huge impact on how certain practices are adopted or whether they are adopted at all.
For example, a company culture that encourages learning new skills and focuses on innovation will have an easier time adapting to new technologies compared to a company culture that sticks to the idea that “things have just always been done this way” or those that believe education stops after college.
At this point, most people know that data can be used to make better business decisions. And yet, the data shows that a lot of businesses still have trouble implementing useful data strategies. According to Wavestone/NVP’s annual data executive survey, it's the human factors—culture/people/process/organization—that remain a barrier to becoming a data-driven organization for 78% of their respondents. So, in many cases, the cultural aspects of a company appear to be a bigger issue than the technical difficulties surrounding data solutions.
In order to effectively create a data-driven culture, leaders must first recognize the barriers that exist within their organization. From there, they can find solutions for building up a culture that thrives on learning and making changes based on data.
Challenges to Creating a Data-Driven Organization
The challenges to creating company culture around data are not always easy to identify. Many are deeply ingrained attitudes and biases that we might not even realize we have until we look at the bigger picture. Here are some common barriers that block effective cross-departmental acceptance of data.
A Disconnect Between Data Teams and Business Teams
One major roadblock in the creation of a data culture is the lack of understanding, communication, and collaboration between data teams and those on the business side of the same company. It can seem, at times, like the two speak a different language altogether.
A recent report from Business Application Research Centre (BARC) exemplifies this disconnect. The report found that 70% of business users believe that implemented data and analytics applications do not cover their current or future requirements, while only 32% of central data and analytics teams agreed. If they are using the same data applications within the same company, why is there such a difference of opinion?
The problem mostly lies in the segregation of data teams and business teams. The data teams have the technical know-how and an understanding of how to find the patterns in the data, while the business teams have an understanding of the market, how to manage the company, and know how to come up with reasonable goals for the company. All of these things are important, but in order for them to be effective, they need to be weaved together. Data is not helpful if the business leaders don’t understand it and the data team doesn’t know how to communicate its usefulness properly.
Lack of Access to Useful Data
Many companies, especially larger ones, have a siloed-data problem. This occurs when one department or team has a useful dataset that is inaccessible to other departments or teams. Just because a team created a dataset does not mean that the data is now useful to others. Siloed data can impact a company in several ways:
- Wasting valuable time when multiple teams have to come up with the same data. This also wastes company money by increasing the number of servers and data storage devices an organization must buy to store duplicate datasets.
- Missing out on opportunities to compare datasets to ensure accuracy–leading to errors.
- Flawed decision making due to a lack of insight from data.
This might sound like more of a technical issue, but it stems from a cultural one: Siloed data is often a result of a mentality around data ownership. Some company cultures lean toward distrust or the guarding of work, in order to receive individual praise, instead of being more collaborative in nature. Since it takes work to create a dataset, some may feel the need to keep it siloed. This, however, is rarely helpful for the organization as a whole.
Poor Data Governance
Data Governance revolves around managing and controlling data to capitalize on it in the most comprehensive way possible, and to gain the greatest amount of knowledge and insight possible in the most accurate way. This involves really understanding what you have and where it can take you. Unfortunately, some organizations are simply not very good at doing this.
Poor data governance can lead to significant problems. This is particularly true with sensitive data, like patient data in the healthcare industry or financial data in the banking industry. The mishandling of this data will not only cause issues with decision making, but in the worst cases, can lead to compliance issues and security breaches.
A good company culture around data leads to better understanding of data and its impact on an organization. Without this, data governance suffers. Furthermore, poor data governance can lead to bad experiences with data, worsening the attitudes surrounding it.
Distrust of Data
In some cases, a distrust of data arises from bad experiences, like working with inaccurate data which led to bad results or even embarrassment. According to a report by SnapLogic, 82% of IT decision makers have had to rework data analytics projects due to poor data quality. This ties into the problems created by poor data governance. With good data governance, these situations became much less likely.
However, distrust in data has other sources too. Some people mistrust the source of the data or believe it to be biased or incomplete, while others worry about manipulation. In an ever-changing world, there is also a question of when data becomes outdated. Much of this distrust stems from a lack of transparency, data literacy, and clearly defined data practices.
It is true that data can be biased and manipulated. But, in a data-driven culture, data sources and collection methods are clearly laid out and the people involved have the data literacy to know whether or not data is trustworthy. Any data that is deemed untrustworthy can–and should–be thrown out.
Resistance to Change
People tend to stick to routines. When change comes about, there is often a sense of uncertainty that comes along with it. This can lead to a resistance to change. The Harvard Business Review put out a list of several reasons for resistance to change in business, many of which come from a place of uncertainty. This uncertainty might stem from a fear that the change will lead to failure, putting employee job security at risk, or a reluctance to work with new teams or management, due to a lack of trust. Other times, people simply like the way things are and they don’t want to change.
Whatever the reason, this resistance is often a huge obstacle for implementing shifts toward a data-driven culture. Often, it is a lack of communication and understanding that exacerbates these feelings of fear and uncertainty. To combat resistance to change, leaders often need to shift to a more open and communicative culture and encourage learning in the workplace.
Sound Familiar?
Building a data-driven organization can be hard, and the challenges are not always easy to identify. But there are plenty of organization who have done it successfully, and when we look at what they do differently, a pattern emerges. We will explore that pattern of best practices in our next post.
In the meantime, a good way to start your organization's data journey is by requesting an enterprise assessment. During this process, our tenured solution architects interview your team members, asking a broad range of questions about your use of technology, your overall data strategy, and how both align (or fail to align) with the organization’s long-range goals. This helps bring the business and technology sides of the organization into greater alignment. Contact us today if this sounds like the right option for you.
