“Data culture eats data strategy for breakfast” has become a popular saying among data and analytics managers and executives. Even the best data strategy cannot fulfill its potential if the data culture in the company does not match it. Ultimately, it is the people in the company who have to change their behavior and mindset in order to benefit from the ever-increasing amount of data available to them.
In last year’s BARC Data Culture Survey, participants named ‘data access’ as the most frequently implemented of the six elements in BARC’s Data Culture Framework. For this reason, this year we focused on questions about the implementation of data access. The results provide interesting insights into prevailing views on the ‘right to know’ and ‘need to know’ principles of data access, the technologies used and the importance of data knowledge (i.e., the information available on data and the skills required to use it).
In this year’s survey, ‘data access’ is now considered the most relevant initiative to positively influence data culture while ‘data strategy’ took first place from ‘data access’ as the most commonly implemented initiative. This confirms that the opening statement has reached the top of organizations and that the consideration and development of a data culture should be anchored in the data strategy.
This study was based on the findings of a worldwide online survey conducted in July and August 2022. The survey was promoted within the BARC panel, as well as via websites and newsletter distribution lists. A total of 384 people took part, representing a variety of different roles, industries and company sizes.
- The majority relies on a mixture of data and gut feeling for decision-making
Following an increase in 2021, the proportion of companies making primarily data-driven decisions has remained stable this year at around one third. 50 percent state that they generally base their decisions on a combination of data and gut feeling.
- Data knowledge is essential to data and analytics
There is a clear understanding that data is an asset to the organization. Almost three quarters of respondents also state that they have recognized the need to invest in ways to access, link and understand metadata. However, when asked about the specific technologies used, it is apparent that the most appropriate tools are not very widespread yet. There is still work to be done to implement and disseminate data knowledge.
- Data culture pays off
Almost half of the companies surveyed count improved decision-making among the goals they have achieved, and more than a third have achieved continuous process improvements and cost reductions through the use of data. However, expectations are much higher and more diverse. Respondents are looking for further improvements in their decision-making and processes, revenue growth and, ultimately, to gain competitive advantage.
- Data literacy, leadership and communication need a boost
Data strategy, data governance and data access are the three data culture initiatives that survey participants consider to be the most relevant and commonly implemented. On the other hand, data leadership, data communication and data literacy initiatives have only been launched by around 20 percent. It is interesting to note that the CxO perspective is quite different: 81 percent of CxOs claim that data literacy is already in place or planned, and 78 percent say the same for data communication.
- Companies seem to focus on the wrong actions
The biggest reported obstacles to implementing a data culture are a lack of resources, a lack of knowledge, a lack of roles and responsibilities, and inadequate communication. This is where companies should take action and seize the initiative if they are serious about becoming data-driven. Unfortunately, it is precisely these obstacles that are the least frequently addressed in concrete initiatives.
- Most companies believe in the value of liberalizing data access
Companies today still predominantly follow the ‘need to know’ principle, which means data access is only granted on request. 59 percent of respondents see greater advantages in the more liberal ‘right to know’ approach. 37 percent have already adopted this principle and believe they are more successful with it.
- The conditions for data access for all are not yet in place
The biggest challenges to liberalizing data access are a lack of data knowledge on the part of users and enabling simple access methods. Many of the conditions for better data access must therefore be created first. More than half of all respondents would like their data to be more transparent and want to be able to connect to it more easily.
- Best-in-class companies increasingly rely on modern concepts, technologies and metadata
Best-in-class companies use technologies and concepts beyond ‘classic’ business intelligence tools significantly more frequently than laggards. These include tools for metadata management (e.g., data catalogs, data intelligence platforms), tools for data virtualization, organizational concepts (e.g., data mesh) and architectural concepts and principles such as data fabric. Best-in-class companies have extracted significantly greater added value than laggards from using these technologies and concepts. For these approaches to be successful, the ability to apply them properly is essential. Knowledge about data, technologies and concepts is a key competency that often needs to be developed in the workforce.
BARC Data Culture Framework
The BARC Data Culture Framework identifies the six most important action areas, thus giving companies a guideline on where to focus their attention. There is no hierarchy among the six action areas. Strategy, leadership and governance typically specify goals and parameters in which a data culture is promoted or can be restricted. Data access and data literacy are important enablers, while data communication serves as a facilitator for data culture.
- Take a holistic and long-term approach to data culture
Changing the data culture in a company takes time – a change of mindset and a shift in behavior cannot come overnight. This is not a sprint but a marathon. However, it also demands a holistic approach that addresses a number of areas at different levels. BARC’s model for systematizing the starting points for changing a data culture – the BARC Data Culture Framework – has become well established.
- Track your progress
In each of the six pillars of the BARC Data Culture Framework, it is worthwhile to regularly measure progress. It makes sense to obtain the broadest possible amount of feedback from within the company. This survey clearly shows that executives, employees in operational functions and data and analytics leaders can have completely different views on how initiatives are progressing in key areas. The difference is particularly striking in relation to data literacy and data communication.
- Rethink the openness in handling data
Opening up the use of data for employees starts with making data available as widely and openly as possible. Consider to what extent a ‘right to know’ data access principle can be implemented and driven forward. Concerns about data security should be taken seriously, but must not block a transformation to data access that is open in principle and has as few hurdles as possible.
- Better data access demands data knowledge
Knowledge about data creates transparency and helps people to find, understand, evaluate and use data. A lack of documentation is one of the main challenges to data access. Knowledge about data already exists explicitly in the form of metadata. Learn how to extract metadata, expose knowledge from it and promote transparency with data intelligence. It is important to keep the following in mind:
• Create clear structures and responsibilities for data so that it can be clearly defined and described.
• Enable the generation and collection of metadata to help users find and understand data. Get a picture of what information needs to exist about data.
• Establish a space for knowledge sharing and encourage employees to share their experiences and results with data in communities or on knowledge platforms.
• Build trust in data through data governance, such as data quality monitoring and reporting.
• Encapsulate the complexity of distributed data landscapes and promote easy access to data and data knowledge through metadata technologies such as data catalogs
- Build competence – invest in data literacy
Lack of knowledge and competence reduces success in data and analytics and lowers the chances of leveraging the full potential of data. Invest specifically in the development of competencies through education, training, communities, etc. This should reach as many employees as possible, and can only succeed if the offer of competence development is targeted to different roles, backgrounds and tasks. Furthermore, data literacy should be understood broadly. It is not just a matter of competencies for understanding data, but also of competencies for establishing business context, mastering technologies for accessing and preparing data, and analyzing and communicating with data using the appropriate methods.
- Consider modern technologies – the data warehouse does not solve all problems
The increasing complexity of system and data landscapes and technologies used requires a rethinking of technology support for access to data. Requirements and scopes have changed. For example, it is not always necessary or useful to physically integrate data. New concepts such as data fabric and data virtualization can help provide data more flexibly. Data knowledge in the form of metadata is also scattered across numerous applications. Tool support (e.g., in the form of modern data catalogs) helps to organize integration more efficiently and simplify access.
Infographic of the key findings