The saying that "data is the gold of the 21st century" has been encountered by almost everyone in recent years. But there is a crucial difference between data and gold. Data only have value when you use them. In order to enable usage, something has to change in many cases – the data have to be democratized.
Where does the hype about democratized data come from?
Digitization is advancing inexorably, and expectations of it are justifiably high. Process optimization through integration and automation, provision of new data-driven products and services, as well as faster adaptation to changes are only a small excerpt of the goals arising and achieved through digitization. The fuel for all this is data generated and collected in increasing quantities, and therefore increasingly a subject of strategy and governance. However, collecting data alone does not add value, which is obtained only through a comprehensive use of the collected data. Many of us are likely to be very familiar with a concrete obstacle – data silos.
At many companies, data are occasionally stored in what are known as silos which can only be accessed by limited user groups via partly outdated and rigid access concepts whose maintenance is complex. Furthermore, granted access to data is often only possible via standardized queries and reports. This opposes an extensive use of data.
In times when central data platforms are increasingly in demand, whether on-premises or in the cloud, the topic of data democratization is also increasingly at the focus. The objective: All enterprise staff who can and want to use data should be given the opportunity to do so.
What actually is data democratization?
Any mention of democratized data probably spontaneously invokes the idea of full data access for all employees. This is also reflected in the introduction of this blog post, data access truly being a fundamental component of data democratization. But that doesn't cover everything.
Data tools and literacy are just as important as data access. Consequently, democratization also means availability of the tools needed by everyone to work with data at the various skill levels involved. This should also enable users with relatively little data know-how to work with data via intuitive visualization tools with simple interfaces. A data catalogue is a tool which supports data consumers regardless of their level of experience. A metadata directory helps keep track of a company's database and enables "shopping for data". Users can inform themselves about existing data and options for accessing them, and are also supported in interpretation and use of the data.
Another important factor is data literacy, which must go hand-in-hand with a change of mindset. Employees must be enabled and trained as to how, when and why they can use data. Skills needed to understand data and their possible derivations must be imparted and reinforced.
Reservations about data democratization
The understandable reservations which companies currently have about data democratization can be eliminated by means of suitable solutions. Two reservations, in particular, are encountered frequently in everyday business life.
The first one concerns potentially incorrect integration and combination of data by technically untrained employees, in turn possibly leading to wrong decisions. The answer to this is that data democratization involves not only mere data access for employees, but also their training and advancement (data literacy). This is intended to increase technical and professional skills in dealing with data. It is also a popular means of certifying data sets for reporting tools. These sets already contain harmonized attributes, key figures and relations which ensure data integrity and quality. Advisable furthermore is a data catalogue in which users at departments can look up descriptions of attributes and key figures, together with the correct calculation formulas.
The second, widespread criticism of data democratization is that access by everyone to all data poses a threat to data privacy and security. That would indeed be problematic. However, it is essential to note that democratization does NOT mean that ALL data should be unconditionally accessible by ALL employees. Explicitly excluded here are personal and business-critical data. These must be protected by means of access management. Applicable to sensitive data is the need-to-know principle, according to which any person should only see what is absolutely necessary. Here, a data catalogue can also help identify personal and critical data, and track data usage via data lineages.
Benefits of democratized data
Adherence in real life to the principle of democratized data provides numerous advantages. Some of these have already been mentioned in previous chapters, but will be summarized again here.
Data democratization gives employees comprehensive and uncomplicated access to the data and analyses of relevance to their daily work. It also increases the speed and quality of processes heavily reliant on data.
In addition, the number of employees possessing data competence generally rises. From complex data analyses to preparation in visualization tools, each employee possesses the individually necessary data know-how and can thus support development of new data-driven products and services, for example, to uphold the pursuit of the objectives of digitization mentioned earlier.
A high level of data competence also causes business decisions to be based on data instead of isolated expertise or multiple truths. This also heightens awareness of data quality. Problems are identified and resolved more quickly, thus also increasing the quality of data-based decisions.
The value of existent day-to-day business can be raised not only through data utilization, but also further through new data-driven use cases. Data make it possible to optimize the business model, expand it and even open up further markets through new data-driven services and products.
Would you like to advance data democratization with all its facets at your company? Just contact us and we will assist you, from an initial exchange of experiences, through development of a concept, right up to its operationalization.