The world around us is inevitably becoming more pertinent. Even big companies like Google are striving to provide users with the most relevant information in order to avoid the messy alternative that comes with large volumes of information. Finding and retrieving relevant information is becoming increasingly difficult in the age we live in, as information is being created at exponential rates.
I remember when its convenience first made Google so famous. Google easily dominated the market by providing people with the most relevant information according to their needs and in a matter of minutes. Previously, the alternative was searching for information in huge archives such as a library.
Likewise, business intelligence has become a valuable asset for organizations as it also provides information and insight from stored data. Such capabilities are essential because highly relevant insights are needed for companies to make better strategic decisions to run their business.
Today BI platforms are no longer limited by the technology assets, like physical memory. Nowadays the possibilities are endless. From this, however, arose a new problem. Companies are struggling to separate and identify which insights are key to their business, due to the high volumes of information stored by companies. BI industry experts are saying this is the major problem, and we need to fix it.
Let me give you an example, once again involving Google (sorry Mr. Google for referring too much to you today, but you are the brightest case). The company’s success really began to take off when it perfected search engine advertising. Google was able to provide relevant ads to users conducting searches, and even better targeting for advertisers looking to reach a specific audience. Again, this is similar to BI, which measured the essential data for the company and provided day-to-day insight into what was going on.
The model worked for both Google and the BI world until the systems became overloaded with information. For Google, too many websites appeared and more users demanded quick access to more information. For BI, users thought, “OK, so we understand what BI tells us and we are keeping track of it, but what if we ask it something different?” And, that’s how the overload started.
BI software vendors adopted a concept of answering the unknown questions, which produced amazing results, but at the same time, swamped the system with endless amounts of poor quality data.
Recently, Google announced that it’s working to increase the intelligence of their relevancy engine by providing people with the ability to get fresh results.
“If I search for ‘Olympics,’ I probably want information about next summer’s London Games, not the 1900 Summer Olympics,” Google fellow Amit Singhal wrote for the company’s blog. “Google Search uses a freshness algorithm, designed to give you the most up-to-date results, so even when I just type ‘olympics’ without specifying 2012, I still find what I’m looking for.” (Official Google Blog)
This is only the latest innovation from Google. It has been working in this direction for a while now, introducing many things that anticipate contextual discovery based on the user’s behavior and online habits (e.g. past searches for airplane tickets, contextual map searches, and so on).
Business Intelligence today is likewise striving for such capabilities. Everyday users have to deal with more and more information that flows around them, and we need to analyze their behavior, anticipate what they are looking for a direct them there.
Making data relevant today is more important than ever before. Imagine how great would be a BI system that automatically tells you where to look at and why instead of spending hours or even days on trying to find and filter the right data.
Contextual discovery (or Relevancy) is everywhere around us and many tech companies already trying to bring it to the Enterprise. I truly believe it will become the real next big thing for Business Intelligence in the years to come. Good thing Panorama has launched this and been practicing this for years.
