Solving the Last Mile Challenge in Big Data

As we ease into the Big Data economy, business intelligence needs to undergo big changes to make it effective till the last mile - where results matter the most: the end user. While end user is the last mile where the most important business decisions are made, the current business intelligence poses two huge obstacles: cost and complexity. Let us understand why businesses aren’t getting the most out of BI and what can be done to overcome the last mile challenges.

What Businesses are Not Getting Right with Current BI

In any regular enterprise, the BI projects are mammoth undertakings that require huge price tags, armies of implementation consultants, and specialized, full-time staff for ongoing support. According to Gartner, over 60% of enterprises state they have a BI strategy, but despite the many years of experience that most enterprises have with BI, they aren’t doing better in addressing the fundamental challenges of BI from an end-user's perspective.

BI technology needs to refocus on the following points:

• Adopt Search-based Simple Interfaces: If you had to take a training course or rely on outside support to use Google, Whatsapp, or Facebook, would they have claimed billions of users? Of course not! The simplicity of User Experience is critical and paramount. A simple, intuitive end-user experience will be an integral element because it is an already common and effective user experience model to search and access consumer information (just the way you use Facebook, Amazon, or LinkedIn). Following this model, more and more BI providers can add features that allow users to simply search across billions of rows of data to gain instant insights. 

• Enable Ad-hoc Reporting: The next natural step in the Big Data & Analytics evolution is enabling ad-hoc reporting for the broader organizational audience. Yet, this turns out to be more difficult than anticipated. Less technical personnel are faced with a blizzard of arcane data names and a mountain of hard-to-understand tables. They are forced to be dependent on the technical report writers for any new reporting. Gartner reports that IT takes an average of six months before a single report is generated on any new data source. The nimble nature of the Big Data & Analytics opportunity is lost for them.

Drawing a BI strategy doesn’t mean just choosing which mega vendor your enterprise will work with. Gartner estimates that no more than 20% of business users actually use BI proactively. It indicates that BI is not being widely used to manage performance. IT must look for means to align BI initiatives with business objectives, keeping in mind the increasing speed of technology change.

How Search Insights Solves the Last Mile through Search Layer

While a quickly-evolving Big Data revolution ripples across multiple industries, enterprises are still using archaic business intelligence. They are forced to rely solely on scarce technical resources to accomplish anything with Big Data & Analytics. Big Data adoption will remain limited, if the last-mile challenges continue to obstruct users in accessing data. A focused shift back to the BI end user via search will be a promising step forward.

What enterprises really need is an easy-to-use, simple interface that gives non-technical users self-service reporting ability and actionable insights. A layer that has a Facebook or Google like consumer grade experience can bridge the last mile. Non-technical users should no longer have a difficult time deriving meaning through data structures or programming languages. To derive useful search insights, a search layer should have the following characteristics: 

• A search layer should understand the complexities of the data structure.
• It should translate technical jargon into recognizable business names, and organize them in a format that seems logical to the front-line business person.
• It should enable non-technical users to create queries and build reports based on terminology that is familiar and meaningful.
• It should enable natural language search.
• It should have a more real-time, interactive, and iterative user interface for data exploration and analysis.

Let’s look at some use cases of the search layer.

• The sales team can easily access everyday sales data by asking ad hoc questions.
• The Accounting and Finance group can use the search layer to review the general ledger.
• The Operations team can benefit from a version that focuses on branches.
• The Marketing team may need search insights that focus on both campaign management and geo-demographic analysis.

Once in place, the beauty of the search layer is readily apparent. In most cases, typical reporting and analytics tools can easily access the underlying data. But, the people costs associated with everyday report generation, professional services, and configurations required for running data reports will easily outweigh the licensing costs of any past generation business intelligence solutions.

Creating a search layer is a virtual necessity for any enterprise to push the power of Big Data & Analytics out to the wider organization. It is better described as a technique for bringing data closer to the user in a meaningful way. With a user-focused BI technology, enterprises can overcome the last mile challenges and discover search insights for important decision making.