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The Democratization of Data

Industry Insights

Currently, the business world is progressively developing a datacentric culture. Changing a business culture is a difficult and long process that requires effort from both managers and employees. Low data availability hinders the development of such an analytical data-driven culture. Employees are less encouraged to use data when it requires a lot of time and effort to gain access to it. Companies today should strive for mass democratisation of data, ensuring data is available and easy to understand for all employees, not just to group of experts.

When data analytics leaders throughout Europe and the United States were asked about the top challenges when using data to drive business value at their companies, 41 percent agreed that the lack of analytical skills amongst employees was the biggest obstacle as of 2021. Other challenges that arise with the use of data in companies include data democratization and organizational silos.

What is Democratization of Data?

Democratization of data is the process of making data more accessible to a non-technical user without the involvement of IT specialists. In other words, it refers to self-service analytics where, for example, sales managers or customer support teams can access relevant, valuable information and easily derive insights.

It is important to note that democratization does not only include granting access to data, whether to raw data or service-ready dashboards. It is an ongoing process that facilitates everyone in the company to work with data comfortably, understand it and make data-driven decisions.

Therefore, it is crucial to maximize data accessibility in every business unit at every point in time.

 

Initial Data Culture Development

There are two common methods of data culture development.The traditional approach to a company’s structure development involves an independent IT department that performs and develops data analytics without a full scale understanding of the business unit’s needs. The business units, here, are the ones making regular business decisions and therefore, require data access from the IT team.

The second approach is when a company develops the "data culture" in isolation and chaotically within individual departments - but with an understanding of all business requirements.

Over time, the number of data streams, the complexity of processes and velocity of change from the external environment do not allow either approach to scale, giving rise to problems. Thus, both approaches to implement a data culture are ineffective. In the first centralized approach, IT becomes the only source of analytics, creating a bottleneck and locking up all the company's analytical needs. Consequently, IT struggles to deliver analysis and data in a timely manner, delaying the processes across the company. In a decentralized approach, users solve their problems in time, but each team creates their own 'version of truth'. For example, teams having different KPI calculations or definitions can lead to uncertainty in data interpretations.

An efficient solution combines both approaches: Systems are managed and maintained by the IT department, but the analysis carried out by other teams are embedded in the business units as outlined in the decentralized approach. The main benefit lies within the analysts’ ability to work independently with their own data for their own business purposes.

 

Democratization risks

There is a concern regarding data security: The more users can access data, the higher the risk of a data leakage. Another issue regards the inefficiency of democratization, as it involves the duplication of efforts across different teams, which could result in higher costs than that of a centralized data analytics department.

 

What can be done to grow towards data democratization?

     1. Create a Role-Based Access Model

In a democratized approach, companies need rules and policies that govern the automatic granting of access to employees depending on the context of the task. The model eases the attainment of the right permissions, and not lead to further complicated business processes.

 

The key to creating and maintaining a role-based access model is to create a system of rules that prevents any bottlenecks. For instance, OMMAX’s analytics team grants different levels of access to reporting systems (e.g., Tableau Server). Depending on the need and role in the company, access may vary from only viewing data, manipulating it or creating own reports and analyses. Data should move freely between different groups of users.

 

      2. Create a Data Catalogue

The creation of a data catalogue helps understand data and manage access rights. A data catalogue is a system that stores metadata (i.e., data about data) in combination with management and search tools. A data catalogue helps navigate the vast amount of information and understand usage scenarios. It makes it easy to search and find the right data sources, check their quality, and refer to the methodology for calculating indicators. The catalogue ensures transparency, consistency, and integrity of data. Most importantly, it simplifies administration.

 

    3. Data Literacy as the Key to Data Democratization

Data literacy is the ability to read, understand, create, and communicate data as information. For different stakeholders, different levels of data-related knowledge are relevant. For some, it might be enough to understand what data is gathered and what it implies. Others might find it important to know how and why certain data is tracked, where it is stored, and more. It is safe to say that data literacy has become a requirement for individuals to excel at their duties. For our clients, OMMAX provides deep-dive workshops about data potentials, covering what potential data can be gathered and used for different business purposes. This allows the company to boost their teams’ performance and gradually transform the corporate culture into data-driven one.

 

    4. Understand How Each Unit Works With Data

To choose the right tools and the right way of granting access to data, one must first understand how different teams typically engage with data. Each business unit may have extremely different goals and approaches to data usage. Some teams may use several tools on a daily basis, where each tool stores or generates different types of data, which is then used by the respective stakeholders.

 

    5. Prepare Data

To enable all the business teams to effectively work with the data needed, the IT team should first prepare it for convenient usage. This preparation requires a company data warehouse to make all the data available for analysis. Data integration (ETL/ELT) tools are essential to move data from and to external systems. Finally, business intelligence tools enable self-serve analytics, which is a central part of data democratization. All these steps can be supported by OMMAX: From developing a data strategy to the execution, including the identification of central data use cases, ETL setup, data storage, cleansing, and advanced analytics.

 

Conclusion

Data democratization is an ongoing process in a company that is progressively becoming data driven. There are different ways and solutions, some of which are outlined above, to approach to the process of democratization. However, none of these encompass “packaged” solutions that can be universally applied. Each company should find its unique way to move towards this goal.

OMMAX can help your business to keep up with current data trends and to thrive in the rising digital age. Reach out to us to discuss your potential!

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