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Business Intelligence (BI0- Delivering insights for enterprise users

Srinivas Krovvidy, Director and Head of Advanced Analytics Enablement, Fannie Mae
Srinivas Krovvidy, Director and Head of Advanced Analytics Enablement, Fannie Mae

Srinivas Krovvidy, Director and Head of Advanced Analytics Enablement, Fannie Mae

As enterprises collect more and more data, theyoften experience an immediate need to deliver business insights from that data to various business units. The number of usersof these data insights is very large, and they need a support system that can help them discover the insights they are interested in via a self-serve model. In the past, in order to meet that demand, business groups would bring in business intelligence (BI)tools and place themin silos with no central coordination. However, it is important to rationalize various BI tools so that they can be well managed. Enterprises also need to make sure that users are provided with resources, training, best practices, etc. so that they can leverage the tools to garner relevant business insights from the data. Considering the advancements in data analytics technologies, it is important to be involved in researching and evaluating new tools and features to meet emerging needs. These factors drove us to stand up a BI CoE (Center of Excellence) more than ten years ago.

CoE Operating Model

The vision for BI CoE was to make sure a small number of resources are focused on supporting the enterprise BI user community while continuously researching and staying ahead of our customers.

In a few years, the CoE matured into an enterprise shared service.

CoE services werefocused primarily on four areas:

• Tool Selection Guidance

- Tool capability comparisons, use case driven consulting and tooldemos,etc.

• Customer Engagement

- Front-End Tool Expertise, Prototyping & POCs Troubleshooting, Design Reviews, Best Practices

• Enablement

- Quarterly User Groups, Brown Bags, Hands-on Workshops, Networking & Collaboration

• Research & Innovation

- Research & evaluate new tools, Evaluate & test new features in supported tools, Monitor BI market trends & products

 BI empowers an organization with more actionable data, providing greater insights and facilitating more strategic decision making 

Key Observations

The following are a few observations based on the experiences of the BI CoE overmore than 10 years:

• During the last 5-10 years we saw a big shift from IT dependent BI (canned reporting) to business-enabled self-service tool access for analysts. This led to large use of tools such as Tableau that support these capabilities.

• In the past 2-3 years, we have observed demand for Natural Language Query (NLQ), which drove the use of tools such as ThoughtSpot and Narrative Science. Many BI Tools continue to evolve by releasing features that help them play in the NLQ space.

• In the past 1-2 years, we have seen increased interest in mobile platforms,and we have noticed that major BI vendors are releasing mobile apps with automatic mobile rendering capabilities.

• In the past 1-2 years, we have seen demand for non-technical users to prep their own data for use in BI tools without traditional ETL and dependence on IT.

• In the past year, wealso arenoticing increased interest in integrated data science and analytics tools like R and Python with the standard BI tools and desire to call models and use the result within BI tools.

• In the past few months, we see users who need a low-cost BI tool that integrates withotherdesktop productivity software such as Power BI.

• Currently we see BI being recognized as a tool for many. This mindset shift will provide a strategic advantage for the enterprise.

Today, a data analyst could be an underwriter, loan officer, or a mortgage broker. Browser-based analytics now enable business users themselves to answer impromptu questions that are relevant to their area of expertise, and then create sophisticated visualizations to share with others.

BI Trends

Looking forward, some of the upcoming BI trends we are seeing include: Data Quality Management to ensure business insights are derived from high-quality data; Augmented Analytics that uses machine learning automation and other techniques to augment human intelligence and contextual awareness (to tell users what they need to know before they even ask); and Embedded Analytics that include various BI solutions such as KPI dashboards or reports into applications,thus improving their decision-making processes and increasing productivity. 

 

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