Are we seeing the new age of democracy? Data-based democracy. I am certainly not the only one who has been witnessing data democratization across enterprises over the years. In fact, back in 2015, an MIT Sloan Management Review study found that 77% of respondents could access useful business data whenever needed. 

For the past five decades, data was primarily "owned" by IT teams and used by business analysts and decision-makers. This is no longer the case. More organizations are opening up to the idea of democratizing data analytics. As data increases exponentially, data democratization is necessary for employees and stakeholders to make accurate decisions.

McKinsey has previously reported how data-driven organizations are 23 times more likely to acquire customers and 19 times more likely to generate profits. However, in 2023, data engineering teams are likely to spend 30% more time optimizing data costs on the cloud. Moreover, despite all the latest developments, 60-73% of data goes unutilized for analytics.

So, how can enterprises democratize data analytics in 2023? Here are my 2 cents.

How Should Enterprises Democratize Data Analytics?

For organizations democratizing data analytics, it's not sufficient to simply invest in the right data analytics tools and hire the best data analysts & scientists. While technology and technical resources are essential, they are insufficient to drive data democratization.

Here's how the democratization effort should proceed:

1. Focus on Business Problems with the Maximum Strategic Value

Most organizations tend to focus their data engineering teams on business functions with the most data. This may seem logical as data analysts can leverage maximum data to generate their insights. 

However, instead of this approach, companies must focus their data analytics initiatives on business problems with the most long-term strategic importance. For instance, developing a KPI-driven dashboard may not have an immediate business impact – but can serve as a foundation for making data-driven business decisions.

Secondly, organizations must consider the "success" probability of their data analytics project. Focus on projects that have a high "success" chance and are quick to complete. Once completed, your data team is ready to take on more complex business problems in the future.

2. Understand Your Existing IT Setup

Before democratizing data, you should also take stock of your existing system. This means knowing the existing technology tools, data sources that you plan to work with, and how data systems are set up for different teams. 

As your organization grows to the next level, it's important to know how your existing infrastructure can manage the higher data volume. For instance, if most of the data is siloed, your organization cannot maximize the value of data analytics. Hence, you must ensure that the existing infrastructure can scale up with increasing data demand.

Additionally, for effective data democratization, organizations must also unlock their legacy data within existing systems. This requires them to budget for data integration services to modernize legacy platforms.

3. Make Data Accessible to all Employees

Data democratization is all about making data accessible to all employees. At the same time, organizations must recognize the data-related challenges faced by different employees and business teams. Also, not every user (or employee) requires high-level data insights.

If the organizational data is spread across various data warehouses or SaaS applications, a centralized data platform can serve to provide data access to both technical and non-technical users. Similarly, data dashboards or visualization tools provide a user-friendly way for business users to analyze and recognize data patterns.

4. Invest in Employee Training and Self-Service.

Providing data access to employees is just one part of the data democratization process. To maximize its benefits, users must learn to use data analytics effectively in their daily work routines. With the right data framework, data analytics teams can help business teams make data-driven decisions in the future (without their assistance). This also frees up the analytics team to lend more time to advanced analytics.

For effective self-service, employees can use data analysis dashboards to leverage their enterprise data. Similarly, training programs in data analytics, SQL, and statistics can empower employees across business functions to make data-driven decisions.

5. Develop a Business Vision for Data Analytics and Engineering

Last but not least, enterprises must develop a long-term business vision for data analytics and engineering. Depending on the industry, every enterprise can have a different vision of how best to leverage data tools in the long run. 

To develop a clear vision, enterprises must diligently implement the previous steps (outlined in this blog). Without a business vision, business leaders cannot motivate their users to remain invested in data technologies.

The Bottom Line

With the right tools and approach, data democratization is a "game changer" for enterprises across industries. However, despite the benefits, many businesses face challenges in democratizing data, such as siloed data, lack of data integrity, and lack of a data-driven organizational culture.

As a technology services company, Recode Solutions has succeeded in accelerating digital transformation for its global clients. Our data-related services include data engineering, data integration, and cloud-powered data analytics. That said, our technology consultants can advise you on the best way to democratize your data analytics initiatives. Get in touch with us today!