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Big Data: The Next Inflection Point in Business Analytics

Doron Aspitz, Chief Executive Officer, Verix

Big Data is a big name, all too often baffling and misused, yet we cannot ignore the fact that in recent years, business users are flooded with available data – both structured and unstructured, from non-traditional sources, more varied and complex than ever before. This data incorporates a wealth of valuable business information, some obvious and some hidden in various inter-dependencies. Traditional analytical tools, from Data Mining, to Data Visualization, slice and dice this data to provide all sorts of reports. However, as fast and graphically appealing as these reports have become, we see a growing chasm between the needs of business users and the value delivered by these tools. As a result of this ever-widening gap, only a fraction of potential business intelligence users in the enterprise actually utilize BI solutions. Whether regarded as too complex to use, or too vain to trust, the majority of would-be consumers for valuable business information opt to stay away and leave it to “the experts”, the analysts. This dependency on analysts creates a taxing bottleneck that raises a key question in today’s business intelligence market: how can we connect more business users to the information cycle in a cost effective manner? In other words, how can we allow business users to benefit from all the valuable data and make it more accessible to the masses?

So what are missing in today’s analytical tools?

Traditional BI tools provide abroad, high level view of the business. The better ones allow all sorts of drilling down to create an impressive set of reports, presented graphically with colorful charts, tables, and maps. Alas, what these tools are missing is focus. Many users cannot see the forest for the trees and definitely cannot tell which issues are of highest priority - relevant for every user in the organization.

A new wave of narrowly focused tools is trying to address this excess of irrelevant information. These tools provide a restricted view, focusing on a single area of interest. Concentrating on a specific field, insights generated by these tools are more actionable, though can lack context and perspective, as they view only a narrow sliver of the business.

Before they can actually serve business decision makers in any useful manner, both approaches require professional analysts to compile the information into operational insights. With traditional BI tools, analysts have to provide the focus. With the narrowly focused tools, analysts need to provide the context. On their own, both approaches create too vague a picture to rely upon. This dependency on analysts creates that troubling bottleneck mentioned above and leads to notoriously low utilization of business intelligence.

The solution lies in looking differently at the way business data is being analyzed. Instead of starting with all available data and slicing and dicing through it to provide a wide variety of views - by timeline, geography, sales, etc., a better method begins with understanding common business processes and addressing typical questions that arise in managing these processes. From the business process’ point of view, relevant information is being gathered and presented in an operational manner – focused and relevant, answering specific business questions and providing all needed context to understand the situation and immediately act upon it.

The novelty of this approach is the amalgamation of business logic with all available data, to automatically narrow it down on a case-by-case basis and discover insights that are relevant for each specific process and every person involved in that process.

“As a result of this ever-widening gap, only a fraction of potential business intelligence users in the enterprise actually utilize BI solutions”

The next inflection point in business analytics brings vendors with deep domain expertise and knowledge of a market and its data sources, who can also provide process-oriented analytical applications. These are applications that, on one hand, “see” every bit of data that might affect their process, and, on the other hand, “show” business users a narrow view that focuses them on relevant insights for their job and their role in the organization.

The beauty of this approach is the independence it gives business users, by allowing them to self-serve their basic analytical needs. Analysts love this method, as it frees them to focus on complex, unstructured, and innovative tasks, which they rarely had time for when occupied with the mundane, repetitive work of serving all business users. A win-win situation is created that significantly boosts performance across the organization.

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