Despite all of the technological advancements, working with data is still difficult. Data volume increases continuously as all aspects of an organization become digitized. Considering our several decades of experience in data management and the overall progress in technology, it would seem logical that everything should be improving. Reality, however, shows that the opposite is true.
As the number of data sources, data collection and data application spectrum grows, data infrastructure becomes extremely complex, heavy and expensive. Those working with data have to not only be knowledgeable in their direct duties, but also make sure that life is easier for the end users, thus encouraging data-driven decision making. Exactly the navigation through the increasing data complexity is where the idea of data democratization comes into play.
There is a famous quote from Peter Drucker: “if you can’t measure it, you can’t manage it.” While this idea is important, it doesn’t always work in real life. Organizations can manage processes that can’t be measured, but that can be done much easier, more predictably and less risky when using data insights. For example, multiple insurance companies working in the same market will, most likely, have access to very similar customer data, but will come up with different conclusions that will affect their decision making.
What is democracy in regards to data?
It always means participation. It also means awareness and responsibility. In this case, participation includes the participation of different types of employees. Awareness and responsibility includes responsibility for one’s own actions, because, in order for the rest of the organization to trust the data analysis insights, they have to trust the person doing the analysis. Thus, data democratization requires a certain organizational culture as well as equipping its employees with the right tools, so that they can operate with data in a meaningful way. It is important that these tools are available to those who don’t have extensive technical knowledge, therefore it is important to create a comfortable environment where the end-user can play with the data according to their wishes. This eventually leads to better business decisions, because it helps overcome many typical organizational challenges when working with data – access, access speed and data reliability.
One of the most important technologies in data democratization is a data catalogue.
A data catalogue is much like a catalogue in a library, where the librarian can help anyone find the book they are interested in. The data catalogue “knows” everything about data in the system. The main questions for the catalogue are:
The data catalogue that we understand and use today entered the market about eight years ago. As the main knowledge center in the data management system, the catalogue consists of three parts:
The data quality section features information on how the data is collected; it is also where the data can be summarized by using profiling and its quality can be assessed; it is where business terms can be added to the data to make it easier to understand for non-specialists. The key for success for this quality section is the cooperation and collaboration between data scientists and the business department. The other sections of the catalogue are similarly responsible for management and consumption of data.
From the solutions available on the market, one of the best tools for data democratization implementation is IBM Cloud Pak for Data or CP4D. It is a hybrid-cloud-based data analytics solution that tackles such challenges as data complexity, data accessibility, user collaboration, etc. CP4D includes several data analytics and management services that can be accessed from any place in the world. This system can be operated on IBM Cloud, as well as many public clouds.
CP4D provides a single interface for all users in the organization – data specialists, business analysts, data managers and quality analysts. Tailored options for each user type are provided in order to efficiently address their main needs while also not complicating the process itself.
If you wish to learn more about IBM CP4D after reading this article,feel free to contact us at: https://www.datigroup.com/en/contacts