Organizational silos weigh heavier than data silos – overcoming them is a cultural journey

Effective sharing of data and insights requires uniform business terminology Cross-functional sharing of data and data-based insights appears to be widespread in corporate practice. At least, that’s what a large proportion of respondents to our survey reported. 75 percent strongly or mostly agree with the statement “I frequently share my data and insights with users in other departments”. Among best-in-class companies, the level of agreement is as high as 92 percent. The level of agreement with the statement “Data and insights from users in other departments are frequently shared with me” is not quite as high, but still clear at 62 percent (90 percent among best-in-class). To what extent do you agree with the following statements in relation to the current state of your company’s data landscape? (n=314) In contrast, 37 percent of respondents chose lack of transparency and understanding of data across the organization as the second most common challenge in the use of data caused by their current data landscape. Not unexpectedly, this challenge is related to the lack of uniform business terminology. For companies lacking uniform business terminology, agreement rates are much higher (48 percent). What are the main challenges in the use of data caused by the current data landscape?Excerpt, by maturity (n=318) But then what benefit does sharing data and insights deliver at all? Do colleagues in neighboring departments even understand the insights I share? Are they at all relevant to their work? In practice, there are obviously justified doubts. An equal 37 percent confirm that cross-divisional collaboration and exchange of data and data-driven insights is difficult. Among business users, the rate is even slightly higher (39 percent). Here again, the rates are much higher for non-uniform (48 percent) data landscapes respectively. The data-driven enterprise doesn’t just happen – it must be deliberately shaped There is still a lot to do to become a data-driven company. The participants in this study are clearly aware of this. Which strategic measures do they consider relevant for this change? We already elaborated on raising awareness within the company regarding existing data silos and their consequences, which is the most popular strategic approach being taken. The following top 5 strategic approaches being taken or planned are listed on the left. Top 5 approaches to dealing with the challenges caused by data silos currently being implemented or planned (n=166) All companies face their own particular challenges over time with their data landscapes. For example, it may not be possible or desired to replace data silos for valid reasons. It may be cumbersome to make them easier to combine or to improve data quality. On the other hand, the list of data requirements is probably very long. Accordingly, the evolution of the enterprise data landscape becomes an ongoing process. Over time, new requirements will be added, and new use cases will be requested. Mechanisms are needed to help prioritize these tasks. This should be done across the entire enterprise and not from the isolated perspective of a single department. This is especially important for data domains that are relevant to many different business units. A good example of such a data domain is customer data, for which different departments have their own set of requirements. Therefore, it is also important to determine clear responsibilities for data and analytics and to define appropriate contact persons in the business for each data domain. The data-driven enterprise doesn’t just happen – it must be deliberately shaped There are also measures that, while considered relevant, are not currently planned by a surprisingly large proportion of companies. Best-in-class companies have recognized that understanding the value data has for the business is an important key to investing in the right areas and are taking appropriate action. Understanding the value data has for the business is an important key to investing in the right areas. It will not be possible to solve 100 percent of the company’s data problems. So, you need to determine which issues are most relevant to the business. If key personas are tied up with elementary data problems instead of working on the digital future of the company, you will not evolve into a data-driven, digital enterprise. Here, the causes must be identified and made relentlessly transparent. And finally, the creative collection and evaluation of new use cases (e.g., through design thinking) is an important measure for thinking outside the box. The aim is to develop innovative ideas for the company and to test their feasibility at an early stage using a “fail fast” approach to avoid misplaced investments. Top 3 approaches identified as relevant but not planned (n=118) It is interesting to observe that it is mainly the companies with the most data-related challenges due to non-uniform terminology who regard these strategic approaches as relevant but have no plans to implement them. By contrast, most best-in-class companies have implemented these approaches or are planning to do so. Shaping the digital future entails a cultural change that requires leadership In principle, all companies strive for digital transformation. However, they face several business and cultural challenges in this process. The highest rated concern, lack of communication (56 percent), is in stark contrast to the earlier statement that data and information is shared extensively between departments. The fact that this apparently does not really work in practice is also underpinned by the second-highest rated problem: a lack of motivation to share knowledge with others (42 percent). Further challenges include a lack of clear strategic goals and a lack of understanding of what these mean in practice. Top 8 selection on “What business and culture-related challenges have you experienced in implementing approaches to deal with the challenges caused by data silos?” (n=312) Some learning processes can be supported bottom-up. However, when it comes to strategy and goals, as well as the behavior of the people in the company, it is clearly the managers who are called upon. Lacking management support was ranked as the third most important challenge in the implementation of strategic measures, at 38 percent. For companies with decentralized (41 and 44 percent) and hybrid (43 percent) data landscapes, as well as for laggards (58 percent), this applies even more frequently. But even best-in-class companies struggle with similar challenges, although a lack of management support applies slightly less to them (31 percent) than on average. Evidently, the executives of best-in-class companies have understood that they must actively lead the digital transformation. This is certainly one of the reasons why best-in-class companies are ahead of others in their handling of data.