The C-level of firms is now committing to investing in data democratization demands.

Data analytics assists businesses to understand customers at the granular level to meet their needs. Firms also identify data anomalies to mitigate risks and targeted frauds. Though firms have been collecting and analyzing data for decades, complex traditional data require months of work by highly specialized teams. But, today, markets change so quickly that organizations can no longer afford to wait for fear of information becoming obsolete and outdated very fast, even before the reports are published.

The amount of data for analysis is also growing at a rapid pace, and traditional data scientists are struggling to keep up with the pace. According to IDC’s report, worldwide data is expected to increase by 61% to 175 zettabytes by 2025. This is a critical alert that modern businesses cannot afford to ignore.

Legacy analytic models are too narrow in scope as they generally address one phase of a given process for a particular type of user. This results in the creation of ‘operational silos’ that were cut off from other people, business processes, and technologies, resulting in insulated decision-making to the detriment of the firm. Modern data analytics platforms seek to replace the time-consuming, insulating operations of legacy processes with a platform leveraging centralized data management and analytics. It breaks down the silos, increasing collaboration to enhance efficiency. This is the primary rationale behind self-service data analytics.

With start-to-end self-service analytics and data science platform, organizations can democratize advanced data analysis. This will enable users to create accurate and meaningful business intelligence with minimal support from the data scientists. As information gets more accessible for users to access and understand, there is room for creativity and collaboration, allowing firms to come together and make well-informed data-driven decisions.

Business users with varied specializations and responsibilities also utilize data to resolve problems in unique ways based on their knowledge and objectives within the company. This empowers them as a more significant asset to the company.

Equipping business users will also improve efficiency. Early theories can be tested cost-effectively and quickly to facilitate dynamic decision-making and efficient problem-solving. In this continually evolving market, this provides businesses more lead-time to identify existing trends and capitalize on opportunities to allow better accuracy in business forecasting. This finally translates to greater organizational agility having a shorter time to market.

Self-service analytics and data democratization are no new concepts. They have been empowering firms to create insights and drive change for years. However, diagnostic and descriptive analytics—finding out why and what happened—is just the beginning.

Investing in data democratization demands C-level commitment. Time and capital are needed to phase out legacy systems to introduce new processes in anticipation of industry developments. More than that, there exists a need for a cultural shift, requiring buy-in from all the lines of business.

As businesses progress and their analytic journeys will expand, executive managers will need to drive the adoption of self-service analytics and data science from the top-down. It will help to create a culture of ‘citizen data scientists’ across the organization as the employees learn to become well-trained data users. This will enable them to harness more sophisticated business intelligence adapting better to machine learning.

Breaking away from the legacy systems and entrenched thinking is critical for creating this new culture. Employees who possess innovative thinking and embrace new technologies should be encouraged, trained, and championed to advance these technologies across their teams. Firms that can modernize their analytic journey to unify their data landscape will stand a better chance to succeed in achieving a real digital transformation.