A strikingly high number of controllers find the data quality within their organizations insufficient, according to research by Finext. Only 32% of the surveyed controllers are satisfied with data quality. Controllers are also critical about the level of data integration - a topic closely related to data quality. What are the reasons for this? And more importantly, how do you solve this?
Previous research by Finext already showed that controllers experience blockages in digital transformation. The main blockades were insufficient integration of systems & data and limited data quality. Enough reason to devote a follow-up study to this and to delve deeper into data quality and data integration. What do controllers run into? And how do they solve them?
Controllers dissatisfied with data quality
To start with data quality: only 32% of the respondents indicated that they have good data quality. A shockingly low number, we think, given that data has an increasingly important place within the (financial) governance of organizations. Data-driven steering is necessary for the longer-term viability of the organization, but is not sufficiently enabled in the current situation.
Strikingly, 80% of the respondents indicated that they do have processes in place to maintain good data quality. Respondents themselves indicate that one of the solutions to improve data quality is to standardize these processes. Human error also plays a role in poor data quality: the majority of respondents experience problems with data entry.
Data integration also still insufficient
Controllers' assessment of data integration is also negative: only 37% think data integration is good. Of these, half indicate that they have very good data integration. So it can be done! The secret of data integration? That varies from organization to organization. There are many different factors that influence it, from knowledge and expertise to legal obligations.
One factor that plays an important role in many organizations is organizing clear internal agreements, such as standardized processes and clear data definitions. The majority report having problems with poor data definitions, making it impossible to link different data sources.
The number of digital solutions used may also be a factor: almost half of the respondents indicated that they use many digital solutions, which means they have to work with many data sources. This shouldn't cause any problems if the solutions are properly linked, but the in-depth interviews revealed that many organizations experience considerable struggles because of this.
Technical solutions to improve data quality and data integration
Many respondents want to eliminate these bottlenecks and are already deploying solutions that can address the data problems. In doing so, the main focus appears to be on technical solutions. For example, many organizations are using Enterprise Resource Planning and Enterprise Performance Management systems. ERP systems integrate data coming from operations (sales, HR, logistics) for other parts of the operation and the organization. EPM systems do the same, but link different data sources for more insights and better direction.
In addition, Extraction, Transformation and Load, or ETL for short, is already used quite frequently. In many organizations, ETL is used to unify data from different structured databases into another database. What is striking is that while ETL is widely deployed, it is not perceived by controllers as being sufficiently effective.
Organizational data solutions
As for the effectiveness of these solutions, the majority of controllers believe that organizational solutions, such as data-ownership and a Data Management Office, add much value to improving data quality.
It is striking that the value of organizational solutions is estimated to be high, but their implementation lags behind. So there are still plenty of opportunities for improvement here. Setting up a Data Management Office (DMO) has the greatest potential here. Changing the organizational culture also offers perspective. A greater understanding of what data can mean provides opportunities for more data-drivenness and the development of knowledge and experience around data.
Roadmap for data quality and integration
The deployment of technical solutions alone does not provide sufficient guidance for improving data quality and data integration. A combination of organizational and technical solutions delivers the best results. But how do you know which combination is optimal for your organization? That requires a well-founded roadmap.
To come up with a good roadmap, first start by evaluating: how does your organization view data? Then think about where you want to be in 2 years when it comes to data. Then outline the goals and wishes to achieve that vision. Finally, weigh the possible solutions against the goals and wishes: which solutions will help you reach your goal? You can then implement the resulting roadmap - with technical and organizational solutions that are effective for your situation - in a targeted, step-by-step manner.
Added value of data quality and data integration is high
Low satisfaction with data quality and data integration is at odds with what controllers want, the survey found. The majority of respondents believe they need to optimize processes, automation and collaboration on data quality and data integration, even if the investment in this is significant. In short, controllers clearly want to work on this. Time for action, then.
.webp)