Friday May 18, 2012

The Art of the Possible with Business Analytics

It has been established beyond doubt that data and its analysis can have a huge impact on an organization’s top line and bottom line. Business Analytics helps organizations deliver better business performance in two ways – by optimizing business processes and by helping to innovate. Optimization helps organizations be efficient and effective by taking inefficiencies out of the business processes and focusing on the high impact opportunities. Innovation on the other hand helps organizations by uncovering new customer segments, new product categories, new markets, new business models etc.

The styles of analyzing data are many fold from answering questions like “what is going on?” to “why are the things the way they are?” to “what will happen if I do X or Y?” to “what does the future look like?” Broadly speaking the styles of analytics can be classified into three categories:

·         Exploratory Analysis: The objective of exploratory or investigative analysis is exploration and analysis of complex and varied data – whether structured or unstructured for information discovery.  This style of analysis is particularly useful when the questions aren’t well formed or the value and shape of the data isn’t well understood.

·         Descriptive Analytics: The objective of this style of analysis is to answer historical or current questions like what is going on. why are the things the way they are?. This is the most common style of analysis and here the questions as well as the value and shape of data are well understood.

·         Predictive Analysis: Predictive analysis aims at painting a picture of the future with some reasonable certainty.

So, what’s art of possible with business analytics? It’s the application of the above three styles of analytics to a business scenario for better insights, decisions and results. Let’s try and explain this with an example. Consider this scenario:

You are a Financial Services firm e.g. a large bank and are trying to improve profitability. You read Larry Seldon’s book titled “Angel Customers and Demon Customers” and agree with the findings that 20% of your top customers bring in 80% of the profits and would like to manage you business as a portfolio of customers as opposed to portfolio of products. So, how do you do that? The answer is business analytics.

You can start by using descriptive analytics techniques like operational reports, ad-hoc query, dashboards etc. on data collected from different sources like sales, customer service etc. to determine the profitability of each customer. You can then use predictive analysis techniques like data mining, statistical analysis to further enrich your customer data into profitability segments like high, medium, low and loss making customers. Finally, you can choose different customer service channels like personal banker, phone or ATM to cost effectively serve you customers e.g. a high profitability customer can be served by a personal banker free of charge but if the loss making customer wants a personal banker there will be a charge. Once you have implemented such programs you can use exploratory analysis to gauge the sentiment across social media channels like Facebook and Twitter to see if the programs are working as desired. Better yet you may come up with new innovative business models like mobile banking or online only banking to improve profitability.

That’s the art of possible powered by business analytics. Stay tuned, I intend to publish more examples from different industries to show the art of possible with business analytics.

Wednesday May 02, 2012

Analytics And Agility – Why It Is So Important Today

No question that many IT professionals are feeling the pressure to rapidly deliver analytic solutions that respond to the continuous demands of the business user. Primary research shows that analytics in the context of Big Data continues to be top of mind for executive teams at global enterprises. Executives clearly understand the value of Big Data and analytics and many are very vocal proponents of the value it can bring to bear on the business. What executive is not interested in understanding sales trends, KPI’s, social sentiment about the business and critical metrics, even predictive views of the business?

All these requirements and many more have created a tremendous backlog of analytic application requests. This backlog grows significantly as IT professionals are successful in delivering highly tuned internal analytic solutions that quickly deliver value. Everyone begins to say “I want one of those”!

We are no longer operating in the past when these analytic projects took years to complete and where a “boil the ocean” approach was the norm. Today, we see a changing landscape where Agility is what matters most when it comes to delivering rapid returns on key Big Data and analytic investments. In fact, just about every IT professional is pursuing the Agile model for development where product development efforts of the past are streamlined using methodologies that lean towards delivering high value features that are delivered in short bursts of time. This is clearly the method that is emerging when it comes to rapidly delivering analytic solutions to the end user in short bursts of time where value is immediately evident.

Agility in analytics doesn’t require that the product be a desktop business intelligence solution delivered in silos. In fact, Agile analytics requires that IT and the business users work collaboratively and quickly to ensure strong IT governance while also providing powerful analytic solutions to the end user rather than having the user take on the whole effort.

New Information Discovery solutions such as Oracle Endeca Information Discovery clearly have embraced the notion of delivering high value agile analytic applications in short bursts of time. Some including situational analytic apps.  We have seen this play out frequently where POC’s (proof of concepts) quickly go into production in a very short period of time because of the compelling value that was delivered in the POC.

Agility also means developing in short product iterations, this enables one to deliver an analytic application, get immediate feedback from end-users and rapidly iterate to deliver new or expanded requirements that deliver further value. We are talking weeks not many months. Some projects have gone from 8 week iterations down to 2 week iterations. Because Agile analytic solutions are typically delivered through a browser in a private or public cloud, versus a silo desktop only tool, the new enhancements are readily available for test and validation by the end user. This capability combined with agility also gives the analytic application developer a chance to quickly show that they are meeting the key requirements of the end user.

With increased agility and business user success, we will eventually turn the corner and agility will be the new norm in analytics.

New Oracle Endeca Information Discovery YouTube Channel

The Oracle Endeca Information Discovery Product Management team has been busy building a new YouTube Channel to showcase the capabilities of the Endeca Information Discovery product. The team has started to release a new screencast series for "Getting Started With Endeca Information Discovery. This series will help showcase the strong capabilities of the product. It will also give you a sense of what the business user experience is like and also show you how innovative this solution is for building highly interactive, search driven analytics applications on a variety of data including structured, multi-structured and unstructured data, especially on Big Data. 

We encourage you to check it out at http://www.youtube.com/user/OracleEID/


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