Breakthroughs in Analytics, Part 1

Oh customer, so mysterious … what have you done … what will you do … and what decisions should a business make to keep you or get rid of you?

Seeking answers to these key questions, the commercial world has turned in a big way to analytics — the science of logical analysis. Analytics involves the extensive use of data, statistical and quantitative analysis and explanatory and predictive modeling. Key fields within analytics include enterprise decision management and marketing analysis. Analytical activities are expanding fast in government agencies, not-for-profit organizations and business enterprises — business analytics.

In the process, business analytics — with fecund subsets like CRM analytics, financial analytics, real-time data analytics and Web analytics — has become big business. A huge market exists for business analytics development tools and packaged intelligence applications that dig through data looking for patterns and possible trends. The industry topped US$20 billion in 2007, according to IDC estimates.

IDC’s definition of business analytics software comprises performance management applications and data warehouse platform software — specifically, “solutions used to access, transform, store, analyze, model, deliver and track information to enable fact-based decision-making.” The median return on investment for companies that implement a data analytics program is 112 percent, according to one IDC study.

The mega-serious money surrounding the business analytics play has attracted to the scene familiar giant enterprise software muscle like IBM, Microsoft, Oracle and SAP. But it has also helped spur the growth of a plethora of smaller, more tightly focused players like SPSS.

DNA of Intelligence

SPSS, which provides predictive modeling, statistical analysis and data mining services for customers in nearly every industry, is a 40-year-old pioneer in the idea of using statistics to turn raw data into information essential to decision-making.

From the beginning, the company’s mantra has been “data and how you analyze it is the driving force behind sound decision-making — the DNA of intelligence.” The company’s original statistical software system was called “SPSS,” which stood for the “Statistical Package for the Social Sciences.”

SPSS customers provide some typical examples of the challenges companies seek to address through the use of analytics:

  • Analyze data gathered in clinical populations in a study of the causes and treatment of general anxiety disorder (GAD) and determine which characteristics correlate.
  • Analyze a bank’s data warehouse of 2.5 million customers, each with more than 400 attributes, to identify potential leads and intelligently market to them based on individual preferences and histories.
  • Make use of predictive analytics technology in an enterprise feedback management program to enable a large cable network operator to better understand its customers in terms of their characteristics, behaviors and attitudes in order to improve retention rates.

“Predictive analytics is no longer an IT spend, but a business investment,” asserted Colin Shearer, SPSS’s senior VP of market strategy. “In today’s uncertain economic environment, organizations are using predictive analytics to find, grow and retain their customers more effectively.”

Moreover, hybrid “fact-plus-fancy-based” decision-making has emerged with the increasing importance of analyzing social media. Consumer blogging, social networks, community boards and wikis are changing how firms measure marketing effectiveness, according to Shearer.

“Analyzing the online ‘Voice of the Customer’ and coupling that with demographic and transactional history, permits more accurate results, better predictive modeling and customer understanding,” Shearer told TechNewsWorld.

As a sign of the times, vendors are starting to aim products directly at a younger generation immersed in Google and YouTube and comfortable with Web 2.0 technologies.

Tableau Software, a privately held company based in Seattle, grew out of a Stanford University research project designed to help people better see and understand the information in databases. Tableau markets its data visualization tool to tech-savvy end users as a “light and fast” analytics application in a market dominated by complicated and expensive business intelligence (BI) tools.

“Ten years ago, if you were using top-of-the-line analytics functionality, you were either a statistician, a programmer or someone highly trained on an expensive, specialized application,” said Elissa Fink, Tableau’s VP of marketing.

“Top-of-the-line analytics required what I would call ‘triple-specialized specialists,'” Fink told TechNewsWorld.

Being able to use those complex applications meant possessing deeply specialized expertise (such as statistical training), specialized programming capabilities (usually to extract, transform and load the data into another form) and then specialized application knowledge.

“Those tools still exist and are as frustrating as ever,” Fink said. “But with today’s applications, almost anyone can get started with asking questions and interacting with their data.”

Custom Fit

Companies in fields as diverse as insurance, banking, financial services, healthcare, high technology, retail, real estate, human resources, consumer packaged goods, market research, telecommunications, utilities and manufacturing are using advanced technologies like predictive modeling, data management and economic forecasting to collect, analyze, develop and deliver information.

In addition to helping executives make better decisions, the goals of analytics use in business include helping management optimize operational processes, evaluate and manage risk, and protect people, property and financial assets.

The sheer scope of corporate expectations regarding business analytics has created a need for specific solutions to specific problems and issues within organizations, which has led to permutations in the basic analytics model.

Financial analytics, for example, can be used to examine a company’s performance through financial, internal and learning and growth analysis. Financial analytics integrate critical internal and external data from across an enterprise’s value chain, seeking to transform it into timely, actionable information to ultimately improve business performance. These activities could be around building fraud detection models, developing an equity research model by using various fundamental analyses and cash flow analyses, or focusing on what should happen to stock or corporate bond prices by examining why they move.

Customers First

But the customer is the heart of any business. CRM (customer relationship management) analytic processes invoke all the programming that analyzes data about a company’s customers and presents the results so that better and quicker business decisions can be made across the enterprise.

Like SPSS, Attensity also provides a “Voice of the Customer” platform built on its text analytics software technology for transforming unstructured customer feedback into actionable “First Person Intelligence,” the backbone of the company’s flagship offering to the enterprise. Attensity has launched an online on-demand version of the Voice of the Customer software that enables companies to analyze customer feedback through customizable dashboards. The software identifies facts, opinions, requests, trends and trouble areas from unstructured feedback found in surveys, service and call center notes, e-mails, Web forums, blogs and other forms of customer contact, the company said. The idea behind Attensity’s text analytics engine is to help companies react more quickly to customer problems and to discover product and service offering opportunities.

In lieu of partial offerings around CRM, implementing a centralized “decisioning” authority that drives all customer interactions can allow companies to “virtualize'” their business, said Rob Walker, VP of decisioning solutions for Chordiant.

“Instead of offering an analytics solution that just analyzes data, what we call ‘decisioning’ starts with analytics and then recommends next steps or next-best actions for agents and company representatives,” Walker told TechNewsWorld. “It’s a sort of artificial intelligence for the customer experience.”

A Competitive Edge

Business analytics enables employees and their managers to out-think and out-execute the competition, according to Jeanne Harris, director of research at the Accenture Institute for High Performance Business.

“Analytics fundamentally change the way people behave and make decisions,” Harris noted. “Analytics are the extensive use of corporate and external data, statistical and quantitative analysis, and explanatory and predictive models to drive decisions and actions. Business intelligence and performance management technologies empower people to develop new insights and outmaneuver the competition.”

Analytics is not just useful for large-scale strategic decisions, and not just for “quants,” either, said Tim Wormus, analytics evangelist for TIBCO’s Spotfire division.

“Applying analytics to day-to-day, operational decisions is just as important, if not more so, and it’s something that anyone interested in getting fact-based answers to their questions can do,” he told TechNewsWorld.

One game-changing development in analytics has been the arrival of interactive visualization of large datasets to the analytics scene, according to Wormus.

“Much of the value of analytics is the discovery of patterns in data that otherwise seemed to be not much more than noise,” Wormus stated. “The human eye is one of the best machines for pattern identification ever developed to separate signal from noise.”

Real-Time Analytics

Real-time analytics, also known as “real-time data integration” and “real-time intelligence,” consists of dynamic analysis and reporting based on data ideally entered into a system less than one minute before the actual time of use. The adjective “real-time” refers to a level of computer responsiveness that a user senses as immediate or nearly immediate or that enables a computer to keep up with some external process (for example, to present visualizations of brokered buy-sell telecom broadband connectivity service quotes and transactions as activity constantly changes).

Real-time analytics has benefited from the force that has dominated change in all of computing –speed. Faster computers, faster networks, faster storage.

“One thing that this speed allows us to do in analytics is to solve problems that were previously intractable,” said Vince Wiggins, StataCorp VP of scientific development.

“By that I don’t just mean that we can solve bigger problems, but that we can solve fundamentally different problems,” Wiggins told TechNewsWorld. “One example is modeling the effect that policies and other factors have on outcomes of interest. Historically, we have ignored entire classes of models because they did not have mathematically closed forms. That is to say there was no way to write down explicitly the probability of observing what we indeed observed. Computational speed lets us estimate and evaluate such intractable models using either approximations or repeated Monte Carlo-type simulations that cover the space of interest.”

Breakthroughs in Analytics, Part 2

Breakthroughs in Analytics, Part 3

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