2010 Was the Year for the Wakeup Call for Information Management That Is Driving Dynamic

Title:

2010 was the year for the wakeup call for Information Management that is driving dynamic analytics. 2011 is the year of execution of Information Management and predictive Business Analytics that will drive the time to value for business and customers.

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Version Date: January 2011

Systems Group

Chuck Gray Senior Architect

2010 was the year for the wakeup call for most companies in reviewing their definition of analytics, and the need for expanding to Information management to validate and improve the quality of Business dynamics as customer demand for change is occurring.

The economy shows signs of recovering. Companies which weren’t using BI to work smarter are no longer with us. BI budgets are once again expanding. The challenge is to continue to spend wisely, but also, to keep up with insatiable user demand. Central BI can’t handle it all. Bringing BI to the masses also means balancing what to handle centrally and enterprise-side, and when to let users do their own thing. It’s not always a matter of departmental BI being a throw-away or stand-alone application. Sometimes, it’s a matter of intelligently separating responsibilities.

The next generation company will need to focus on Business Performance enabled by business analytics and will improve the odds of business success "CIOs will have to concentrate on improving top-line visibility and bottom-line operational transparency through the use of business intelligence and analytics" Source: Gartner - Meeting the Challenge: The 2009 CIO Agenda January 2009. This statement won't change any time soon. In combination with core Business Intelligence features, business performance solutions provide tools and templates to help businesses with scorecards, metrics, key performance indicators (KPIs), what-if modeling and budgeting. This is part of a complete Information Management Framework (IMF).

A huge opportunity for businesses to support this is to provide a better understanding of customer data, for selling and servicing as well as for managing risk. Most businesses are really not there yet. Businesses are swimming in data and have made some initial steps towards qualifying the opportunity and initiating the work. In terms of using data and facts towards decision-making, businesses in general are pretty far off the curve.

First, in the online realm, businesses can provide a better seamless experience and to be effective in attracting and acquiring customers. Second, data can help business market to those customers in an effective way.

Many businesses have underestimated the value of selling online, both new and existing customers. Those actually are two separate issues: the strategy for attracting and selling to new customers is different from selling to existing customers on a secured site.

Other areas where businesses can better use data are in operations. An example of this could be when it comes to extending credit, particularly if you get into small business or larger commercial space, Most businesses have not automated the processes of gathering the information about the opportunity or capabilities to interpret the information. These business enhancements could bring a potential for better relationships, decision-making, managing and servicing new and existing customers.

The ability to turn around decisions quickly is a huge win for a business. When the business has that not so rare need, you want to be able to respond quickly. The businesses want to be able to provide that line of credit in a logical way that doesn't expose the business to more risk. This allows you to use analytics almost as an early warning system around where risk may raise, either on an individual customer or on a portfolio basis.

Conventional wisdom suggests that organizations need to evolve from Descriptive to Predictive then prescriptive Analytics, but I encourage the concurrent use of math, business and technology decision sciences across what is called the DIPP Index:

D - Descriptive Analytics -- What happened in the business (using reports and dashboards)?

I - Inquisitive Analytics -- Why something happened in the business (using analysis)?

P - Predictive Analytics -- What will happen in the business (using predictive modeling)?

P - Prescriptive Analytics -- the So What, Now What?

As companies adopt analytics and information management as the new science of winning, the future of analytics will not just be based on applied math, business and technology, as it is today. In the future, decision sciences will make use of Math + Business + Technology + Behavioral Economics.

So following this very brief overview, have you thought about how information management enabled business analytics can make a difference fore your company. It is important to understand we have thought about it and developed a very rich set of tools, methods, and skills to make it happen in 2011 and beyond. Would it be of business value to you to go to one location for all your information and analytical needs? This would include one call support of total sourcing and ownership of issues. Success is based on time to value, and comprehensive service resolution.

2010 was the year of innovation for IBM! 2011 is where you can take it to your customer. You can benefit by bringing this game changing technology to your customer's infrastructure to make it smarter, faster and more resilient. This is generally at less cost, with optimization to more efficiently run the workloads that are important to your business.

IBM Smarter Systems and Information Management Frameworks can transform the way you work to help you become more agile and competitive, turn information into insight, deliver better services faster, enable collaboration and mitigate risk. Take a look at how this technology can benefit you.

IBM has in place, regardless of methodology, the processes and tools to create predictive models that incorporate the following steps. This is ingrained in the IBM Information Management Framework:

1. Project Definition: Define the business objectives and desired outcomes for the project and translate them into predictive analytic objectives and tasks.

2. Exploration: Analyze source data to determine the most appropriate data and model building approach, and scope the effort.

3. Data Preparation: Select, extract, and transform data upon which to create models.

4. Model Building: Create, test, and validate models, and evaluate whether they will meet project metrics and goals.

5. Deployment: Apply model results to business decisions or processes. This ranges from sharing insights with business users to embedding models into applications to automate decisions and business processes.

6. Model Management: Manage models to improve performance (i.e., accuracy), control access, promote reuse, standardize toolsets, and minimize redundant activities.

Most experts say the data preparation phase of creating predictive models is the most time-consuming part of the process, and industry survey data agrees. On average, preparing the data occupies 25% of total project time. However, model creation, testing, and validation (23%) and data exploration (18%) are not far behind in the amount of project time they consume. This suggests that data preparation is no longer the obstacle it once was. However, if you combine data exploration and data preparation, then data-oriented tasks occupy 43% of the time spent creating analytic models, reinforcing the notion that data preparation consumes the lion's share of an analytic modeler's time.

The mission our team (IBM) has is to help our clients avoid the normal entry into business intelligence.

Fire ready AIM (FRA) approaches is common in today's descriptive analytics deployments costing companies in real dollars and reduced value return business decisions. IBM's Information Management Framework and Business Analytics can help ready your goals, aim to the future and fire on an effective plan.

Today every company does some form of intelligence. IDC did a survey and 98% of the respondents said they use spreadsheets from some use to exclusive use to do business analytics. This method allows for a very simple model of populating rows and columns with questionable results and decisions.

In closing our competitors are working to develop their tool selection, but IBM has the complete set today. So taking a quote and applying it to our competitors -- "If the only tool you have is a hammer, you tend to see every problem as a nail", Abraham Maslow (1908- 1970)

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