google_plustwitterFacebooklinkedin

In today’s world, data is the central component driving commercial successes of organizations. From better customer service to faster decision making, data analytics provides the right insights to gain efficiency across business functions. In the financial sector, an added layer of security, quality, and regulatory constraints data control.

Despite the ubiquity of data, the financial services industry is plagued by two major challenges for BI – data silos and a shortage of data science talent. For a business analyst or data scientist, the legacy systems hinder data analysis with time-consuming data extraction protocols and traditional BI tools. These legacy systems are limiting in their features with a rigid architecture, complex IT infrastructure and a lack of scalability and mobility.  Analysts are able to access the system only through reports and dashboards; preventing free exploration of data and slowing down operations.

The Power of Self-Service Data Analytics

The complexity of traditional BI tools has created a dependency on data experts for data insights. In order to increase efficiency, financial services companies are moving to advanced self-service analytical tools. As per Gartner’s predictions, “self-service analytics and BI users will produce more analysis than data scientists will by 2019”.

Self-service data analytics are user-driven solutions, leveraging application of newer technologies such as artificial intelligence (AI). One of the main functions of self-service data analysis is breaking down data silos by integrating disparate data from multiple systems. These agile systems are created for business users who can access data in real-time wherein, data is prepped, analyzed and shared as reports quickly, requiring no technical expertise or coding experience. Gartner’s report also states the implementation of a formal onboarding plan to ease the scalability of BI tools in an organization.

Most companies still have the “80/20 rule” of data analytics, wherein, data scientists spend 80% of their time on cleaning and prepping of data, while only 20% of the time is left for analysis. The ease of access to data and analytics through a self-service BI tool helps in reducing the time spent on data prep, letting the user focus more on data analysis and gaining insights. For example, business users can track investment portfolio performance through effective visualization techniques. A clear dashboard highlights areas of resource allocation to optimize profits, under-performing assets and provide insights to make better re-investment decisions across diversified investments.

From C-level executives to social media managers, various self-service BI tools are available for organizations of all sizes. Self-service BI tools provide users with an interactive dashboard. The dashboard gives a real-time overview of business parameters in the form of graphical charts and information reports. Business users can create ad-hoc reports on the fly with quick search queries and visualization tools. The intuitive platform also makes advanced and predictive analytics easily accessible to small and large organizations. Tools such as SiSense, IBM i2 Analyze, Tableau Prep, IBM Watson, Domo and many more cater to the data analytics and visualization requirements of organizations. SiSense helps startups and small organizations collate, analyze and view data without heavy investments in building IT infrastructure.

Self-service BI has gained significant momentum with the introduction of AI and machine learning. While BI tools are everywhere, the successful implementation of self-service capabilities calls for the adoption of a data-driven culture. For organizations with BI tools, a flexible and robust data governance policy is essential to enable and support the growth of self-service analytics. The use of information at every level helps in driving innovation while increasing operational efficiency at an organization. With automation insights at scale and real-time, business users can gain a deeper understanding of their customers, create efficient campaigns and improve customer service and experience.

 

About the author

Vignesh Venugopalan

More articles by this author