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.

Big Data Analytics – The Journey from Transactions to Interactions

Big Data Defined

Enterprise systems have long been designed around capturing, managing and analyzing business transactions e.g. marketing, sales, support activities etc. However, lately with the evolution of automation and Web 2.0 technologies like blogs, status updates, tweets etc. there has been an explosive growth in the arena of machine and consumer generated data. Defined as “Big Data”, this data is characterized by attributes like volume, variety, velocity and complexity and essentially represents machine and consumer interactions. 

Case for Big Data Analysis

Machine and consumer interaction data is forward looking in nature. This data available from sensors, web logs, chats, status updates, tweets etc. is a leading indicator of system and consumer behavior. Therefore this data is the best indicator of consumer’s decision process, intent, sentiments and system performance. Transactions on the other hand are lagging indicators of system or consumer behavior. By definition leading indicators are more speculative and less reliable compared to lagging indicators; however, to predict the future with any confidence a combination of both leading and lagging indicators is required. That’s where the value of big data analysis comes in, by combining system and consumer interactions and transactions, organizations can better predict the consumer decision process, intent sentiments and future system performance leading to revenue growth, lower costs, better profitability and better designed systems.

So, which business areas will benefit via big data analysis? Think of areas where decision-making under uncertainty is required. Areas like new product introduction, risk assessment, fraud detection, advertising and promotional campaigns, demand forecasting, inventory management and capital investments will particularly benefit by having a better read on the future.

Figure 1: Combination of big data and transactional data delivers better insights and business results

Big Data Analytics Lifecycle

The big data analytics lifecycle includes steps like acquire, organize and analyze. Big data or consumer interaction data is characterized by attributes like volume, velocity and variety and common sources of such data include web logs, status updates and tweets etc. The analytics process starts with data acquisition. The structure and content of big data can’t be known upfront and is subject to change in-flight so the data acquisition systems have to be designed for flexibility and variability; no predefined data structures, dynamic structures are a norm. The organization step entails moving the data in well defined structures so relationships can be established and the data across sources can be combined to get a complete picture. Finally the analysis step completes the lifecycle by providing rich business insights for revenue growth, lower costs and better profitability. Flexibility being the norm, the analysis systems should be discovery-oriented and explorative as opposed to prescriptive.

Getting Started

Oracle offers the broadest and most integrated portfolio of products to help you acquire and organize these diverse data sources and analyzes them alongside your existing data to find new insights and capitalize on hidden relationships. Learn how Oracle helps you acquire, organize, and analyze your big data by clicking here.

Figure 2: Oracle’s engineered system solution for big data analytics