How Predictive Analytics Are Used within BI, and How They Drive an Organization's BPM

Data mining, predictive analytics, and statistical engines are examples of tools that have been embedded in BI software packages to leverage the benefits of performance management. If BI is backward looking, and data mining identifies the here and now, predictive analytics and their use within performance management is the looking glass into the future. This forward-looking view helps organizations drive their decision making. BI is known for its consolidation of data from disparate business units, and for its analysis capabilities based on that consolidated data. Performance management goes one step further by leveraging the BI framework (such as the data warehousing structure and extract, transform, and load [ETL] capabilities) to monitor performance, identify trends, and allow decision makers the ability to set appropriate metrics and monitor results on an ongoing basis.

With predictive analytics embedded within the above processes, the metrics set and business rules identified by organizations can be used to identify the predictors that need to be evaluated. These predictors can then be used to shift towards a forward-looking approach in decision making by using the strengths from the areas identified above. Scorecards are one example of a performance management tool that can leverage predictive analytics. The identification of sales performance by region, product type, and demographics can be used to define what new products should be introduced into the market, and where. In general, scorecards can graphically reflect the selected sales information and create what-if scenarios based on the data identified to verify the right combinations of new product distribution.

What-if scenarios can be used within the different visualization tools to create business models that anticipate what might happen within an organization based on changes in defined variables. What-if analysis gives organizations the tools to identify how profits will be affected based on changes in inflation and pricing patterns as well as the impact of increasing the number of employees throughout the organization. Online analytical processing (OLAP) cubes can be created to identify dimensional data, and patterns within changing dimensions can be compared over time to contrast scenarios using a cube structure to automatically view the outcome of the what-if scenarios.

Components of Predictive Analytics

Data mining can be defined as an analytical tool set that searches for data patterns automatically and identifies specific patterns within large datasets across disparate organizational systems. Data mining, text mining, and Web mining are types of pattern identification. Organizations can use these forms of pattern recognition to identify customers' buying patterns or the relationship between a person's financial records and their credit risk. Predictive analytics moves one step further and applies these patterns to make forward-looking predictions. Instead of just identifying a potential credit risk, an organization can identify the lifetime value of a customer by developing predictive decision models and applying these models to the identified patterns. These types of pattern identification and forward-looking model structures can equally be applied to BI and performance management solutions within an organization.

Predictive analytics is used to determine the probable future outcome of an event, or the likelihood of a situation occurring. It is the branch of data mining concerned with the prediction of future probabilities and trends. Predictive analytics is used to analyze automatically large amounts of data with different variables, including clustering, decision trees, market basket analysis, regression modeling, neural nets, genetic algorithms, text mining, hypothesis testing, decision analytics, and so on.

The core element of predictive analytics is the predictor, a variable that can be measured for an individual or entity to predict future behavior. These predictors are based on models that are created to use the analytical capabilities within the generated predictive models. Descriptive models classify relationships by identifying customers or prospective customers, and placing them in groups based on identified criteria. Decision models consider business and economic drivers and constraints that surpass the general functionality of a predictive model. In a sense, statistical analysis helps to drive this process as well. The predictors are the factors that help identify the outcomes of the actual model. For example, a financial institution may want to identify the factors that make a valuable lifetime customer.

Multiple predictors can be combined into a predictive model, which, when subjected to analysis, can be used to forecast future probabilities with an acceptable level of reliability. In predictive modeling, data is collected, a statistical model is formulated, predictions are made, and the model is validated (or revised) as additional data becomes available. One of the main differences between data mining and predictive analytics is that data mining can be a fully automated process, whereas predictive analytics requires an analyst to identify the predictors and apply them to the defined models.

A decision tree is a variable within predictive analytics that allows the user to visualize the mapping of observations about an item and compare it to conclusions about the item's target value. Basically, decision trees are built by creating a hierarchy of predictor attributes. The highest level represents the outcome, and each sub-level identifies another factor in that conclusion. This can be compared to if-else statements, which identify a result based on whether certain factors meet specified criteria. For example, in order to assess potential bad debt based on credit history, salary, demographics, and so on, a financial institution may wish to identify multiple scenarios, each of which is likely to meet bad debt customer criteria, and use combinations of those scenarios to identify which customers are most likely to become bad debt accounts.

Regression analysis is another component of predictive analytics that allows users to model relationships between three or more variables in order to predict the value of one variable in comparison to the values of the others. It can be used to identify buying patterns based on multiple demographic qualifiers such as age and gender which can be beneficial to identify where to sell specific products. Within BI, this is beneficial when used with scorecards that focus on geography and sales.

Overview of Analytics and Their General Business Application

Analytical tools enable greater transparency within an organization, and can identify and analyze past and present trends, as well as discover the hidden nature of data. However, past and present trend analysis and identification alone are not enough to gain competitive advantage. Organizations need to identify future patterns, trends, and customer behavior to better understand and anticipate their markets.

Traditional analytical tools claim to have a 360-degree view of the organization, but they actually only analyze historical data, which may be stale, incomplete, or corrupted. Traditional analytics can help gain insight based on past decision making, which can be beneficial; however, predictive analytics allows organizations to take a forward-looking approach to the same types of analytical capabilities.

Credit card providers offer a first-rate example of the application of analytics (specifically, predictive analytics) in their identification of credit card risk, customer retention, and loyalty programs. Credit card companies attempt to retain their existing customers through loyalty programs, and need to take into account the factors that cause customers to choose other credit card providers. The challenge is predicting customer loss. In this case, a model which uses three predictors can be used to help predict customer loyalty: frequency of use, personal financial situations, and lower annual percentage rate (APR) offered by competitors. The combination of these predictors can be used to create a predictive model. The predictive model can then be applied and customers can be put into categories based on the resulting data. Any changes in user classification will flag the customer. That customer will then be targeted for the loyalty program. Financial institutions, on the other hand, use predictive analytics to identify the lifetime value of their customers. Whether this translates into increased benefits, lower interest rates, or other benefits for the customer, classifying and applying patterns to different customer segmentations allows the financial institutions to best benefit from (and provide benefit to) their customers.

Embracing Complexity: A Speedy Business Performance Management Solution

Applix provides a complete performance management software solution for finance and operations, without compromising its strong customer focus. The company has over 2,200 unique customers, and is growing at a steady rate. Aside from being top-rated in vendor satisfaction by BPM Partners Pulse Survey, Applix was ranked by The OLAP Survey as a leader in overall business benefits achieved by customers, and was ranked in the top three for customer loyalty.

Headquartered in Westborough, Massachusetts (US), Applix is a provider of business intelligence (BI) and business performance management (BPM) solutions. Incorporated in 1983, Applix first targeted software applications for the UNIX market. To capitalize on the emerging market, thirteen years later it acquired Sinper Corporation, an online analytical processing (OLAP) software developer. In 1998, Applix released its first TM1 product, followed by five subsequent releases, which aimed at enhancing its capabilities for performance management, Microsoft Excel integration, 64-bit platform support, and complex analysis. The Applix focus is to drive operational performance management by investing heavily in its TM1 product line. Operational BI (OBI) and performance management software provide the necessary shift from the traditional backward-looking view of BI, towards a forward-looking approach. OBI allows performance management functionality to be embedded in overall business operations. This takes the shift away from purely using BPM tools for financials, and creates the ability to leverage strengths provided by performance management software for use across the organization and to create alignment with an organization's business process flow.

Product Overview

Applix TM1 gives customers the ability to solve difficult business decisions and perform what-if analyses in a user-friendly environment, making it an above-average performance management solution. TM1 incorporates dashboards, workflow, and OLAP cubes in a fully integrated Excel and Web-enabled environment. Users are able to transfer their skill set seamlessly, due to the familiar interfaces. The product has an integration layer which connects easily to open database connectivity (ODBC), object linking and embedding database for OLAP (ODBO), SAP, and legacy data sources to capture the appropriate data, and has a powerful in-memory data management server engine to help accelerate query return times, thus improving performance. Additionally, TM1 dashboards, Web sheets, and OLAP cubes aid in planning, budgeting, and forecasting activities. TM1 enables complex data modeling and rules creation. Also, data can be updated in real time and reflected in Excel Web sheets, dashboards, and cubes, which can all be posted on the Web. TM1 uses its Web portal as a gateway for users across the organization, in order to allow them to exchange work items by viewing the same sets of data, and to manage decisions based on those data views. This allows TM1 users to collaborate on multiple tasks across the organization. Customizable dashboards and cubes help users analyze and answer defined business questions. This helps to drive potential opportunities (and to avoid risk), by identifying data patterns, collaborating with multiple task stakeholders, and creating business scenarios.
TM1 has the ability to perform powerful what-if analyses against large data sets, faster than many competitors. Applix TM1's complex analytical queries are dealt with in memory through the use of a 64-bit processing platform and a caching architecture, as opposed to having calculations stored within a server (disk space). The development of 64-bit processing represents a significant trend in BI, for accommodation of large amounts of data. The advent of 64-bit processing also allows data to be updated effectively in real time. Vendors such as Information Builders and MicroStrategy also have this capability, and Hyperion is working on developing a platform compatible with Microsoft. Compared with most performance management vendors, however, Applix has taken the lead in providing a platform that provides users with the ability to create complex queries quickly, and to reflect those results on a Web portal in real time. Additionally, calculations are performed in memory and not within a server environment, which contributes to the quick response times.

Applix TM1's integration with Excel is above average, and permits users to use spreadsheets as the basis for creating Web sheets and dashboards, and to post their data to the Web. TM1 has integrated the use of Excel into its BI platform. Multiple users across an organization or across multiple geographic locations are able to access published data sheets, to edit the sheets, and to have the data automatically written to the server and updated on the Web. Cubes empower users to analyze data by drilling through multidimensional data views, and by identifying trends as well as data sources. With TM1 cubes, an organization can post OLAP cubes to the Web in different forms, such as graphs or as data integrated with Excel.

TM1's analytical capabilities focus on providing users with user-friendly access to cubes and reports. As opposed to developing complex and robust cubes that are only used by one or two super-users, Applix TM1 allows users to create compact cubes to hone in on an organization's business questions and on developing user-friendly intuitive cubes that help answer essential questions, and that are accessible to anyone in the organization. This is done by limiting a cube's scope to between five and ten main dimensions, and by authorizing users to choose what dimensions are available to drill through. These cubes provide users with the functionality to edit, change, and transfer data to Excel; to post changes to the Web; and to view the updates in real time. Although cubes are usually designed with a limited number of dimensions for focusing on actual business questions, it is possible to reflect up to 256 dimensions, which makes it a robust analytical tool. Users can also identify what dimensions they want to view and drill through, as opposed to the dimensions that are reflected in the background but not viewed online.

TM1 Rules helps users define the cube rules, allowing for complex and repetitive calculations within each OLAP dimension. Users create cubes through wizards, giving business users primary control of their analytics (as opposed to relying on the information technology [IT] department). Cubes and the associated data are then connected via defined rules, and the cubes can be customized for maximum efficiency by using rules from multiple applications.
TM1 Planning Manager manages an organization's workflow processes to grant key decision makers administration and access privileges. Each assigned task can be edited to create collaboration between employees, departments, and projects. Users can be assigned tasks, be given access to change report data, and submit those changes to the appropriate decision maker for approval. The user tasks are then submitted as work items for approval based on a task list accessed by the appropriate decision maker. Approvals and rejections with comments are submitted and stored within the task process, and users can view the logs and resubmit any additional edits or changes.

TM1 Planning Template features a planning module that supports the ability to create budgets and identify top-down goals and bottom-up plans, with pre-built worksheets. The template structure can be modified to suit organizational needs, and it is possible to load information into the modified model.

TM1 Consolidations provides users with the ability to view financial and operational data in a centralized structure, from any number of organizational units and general ledgers (GLs). Several different views of data are possible, which gives users the ability to perform variance analyses of budget-to-actual data. Key features include full support for recurring and reversing journal entries, the ability to create customized journal entry reports, and built-in controls for the journal entry process (including automatic generation of inter-company elimination journals), as well as the ability to post journal entries over the Web in Internet Explorer.

The TM1 Financial Reporting module lets users set up reporting structures and create queries for their financial reporting requirements. Reports are built once, and automatically maintained within the structure of TM1. Reports are updated automatically, and financial data from multiple GL systems can be consolidated to represent one view of GL and general financial data in real time.

Product Challenges and Opportunities

Applix needs to develop scorecarding capabilities and the ability to set metrics within its application, in order to develop a successful long-term strategy and to stay competitive within the market. Most other performance management vendors offer these features within their main product offerings, including leading BI vendors such as Cognos, Hyperion, and Business Objects. Not having scorecarding capabilities is a major disadvantage for organizations that want to track and to structure their goals and performance requirements, and to measure them over time using one integrated software suite. To address this issue, Applix has become highly integrated with Microsoft's new scorecarding functionality and current integration abilities with Excel, to pave the way for continued integration with Microsoft BPM tools. Applix hopes that customers will choose to integrate the two, as opposed to choosing a solution that already has scorecarding capabilities. Additionally, Microsoft is working on enhancements to their BI tools, including enhanced dashboarding capabilities. Thus, the Applix goal of aligning itself closely with Microsoft could be overshadowed by prospects considering Microsoft for a full performance management suite which is intuitively integrated and competitively priced, and which could eclipse the benefits of implementing TM1.

The ability to define key performance indicators (KPIs) is also an important component of performance management. Currently Applix does not have built-in functionality defining structured key performance indicators that are aligned to an organization's corporate strategy. TM1 is planning to provide this feature with a future release; however, other solutions that already have this feature provide users with the opportunity to set strategic goals, align those goals to the business unit at each level, and measure performance to drive decisions. This means that organizations that want to drive decision-making intuitively based on their KPIs, may have to put more processes into place to achieve the same result as organizations implementing a solution with built-in scorecarding and KPI functionality.

Technology's Role in Strategic Human Resources

Most chief executive officers (CEOs) are challenging their human resources (HR) departments to make more strategic contributions to the organization. With HR traditionally viewed as a cost center, it is often difficult to know precisely what that means. CEOs, who are focused on growth, earnings, and shareholder returns, want HR to support corporate business objectives and to have the necessary data to support business decisions. These roles are necessarily integrated with HR's responsibility to ensure that there are qualified and satisfied workers when and where they are needed. The way to fulfill these roles is through process excellence, integrated HR systems, and accurate and actionable data from all HR departments. When these elements come together, HR can have a tremendous and meaningful impact on the bottom line.

It sounds like a lot to ask, but these demands are achievable today. And the HR department doesn't have to go it alone. There are technologies and service providers that can help move HR from the administrative rut, free up manpower for strategic tasks, and employ business intelligence capability to align HR with desired business outcomes.

The Role of Outsourcing

Human resources outsourcers play a critical role. Companies often choose to work with outsourcers to gain access to the latest technologies without having to make the associated capital investment. At most enterprises where HR functions have been outsourced, the initial tier of value is well-established. Processes are standardized and employee interactions are professionalized. Transactions are faster, more user-friendly, and less costly. As employee programs continually become more complex and difficult to administer, outsourcing consistently delivers high levels of service.

But it's that next critical tier where advanced HR outsourcing technologies are delivering strategic leverage by gathering and combining fragmented data from discrete vertical HR systems. When data from various departments is integrated into a reliable, consistent source of centralized information, HR can make better-informed and more strategic business decisions daily. The impacts of HR programs and practices can be assessed, and critical insights into the workforce revealed.

Sophisticated analytics can measure how HR systems and programs affect employee behavior and influence customer behavior (for example), ultimately impacting financial results and corporate growth. Companies are beginning to see that reducing HR administrative costs is only the tip of the iceberg. A new priority is to employ the technologies that provide data and analysis, in order to realize the savings that lie in HR.

For example, your time and attendance program tracks worker hours and absences, and is the entry process for generating payroll. A separate program handles short-term and long-term disability payments. Both of these systems are important. But viewed separately, they reinforce HR's traditional administrative role. An outsourcing solution that combines information from both systems and employs business intelligence functionality delivers a human asset management program that tracks absenteeism, peak work periods, and turnover. Now your data shows impacts on labor costs, overtime, and the amount of money spent on temps and employee replacement. This business intelligence can be used to closely align the workforce with long-term labor needs, manage absence and labor utilization, and thereby reduce operating costs.

Training, staffing, and recruiting programs can be linked in beneficial ways, too. There are lots of technology tools that enable prospective employees to submit r�sum�s online. But does your HR department use that information beyond the recruiting process? By integrating prospective employee data and skill sets against the company's development plan and training programs, qualified individuals can be "pipelined" into the organization over time, and existing staff can be educated. This ensures more strategic hiring decisions from the outside, and better use of existing personnel.

Succession planning is another key area where HR outsourcing can provide strategic value. For example, if a company has a 10 percent turnover rate, and it typically takes 30 days to fill a job, what does that mean for its staffing at any given point in time? It means the company is nearly one percent understaffed at all times. In an organization of 50,000 employees, that's 400 workers not meeting deadlines or producing, which negatively impacts customer satisfaction.

In that same scenario, add in the ramp-up time required for new hires to fill the open slots, and the "downtime" could be as much as sixty days per opening. Factor in absenteeism, short- and long-term disability, sabbaticals, maternity and paternity leave, job sharing, and other benefits, and the staffing levels are likely to be much lower than imagined. Using business intelligence technologies and analytics allows HR departments to better see and manage what is really happening with staffing levels, and predictive measurements can help plan more accurately for the normal ebbs and flows of business.

Selecting the Right Outsourcing Provider

As important as deciding to outsource HR functions, however, is selecting the right partner. Partnering with an HR provider is a critical business decision, and should be considered with the same due diligence as a merger or joint venture. Companies should be culturally compatible and share a common vision.

An outsourcing partner's service framework and delivery model should be engineered to meet your requirements, and there should be a clear definition of the scope of services and defined service levels. The objectives of outsourcing should be translated into service-level agreements so performance can be measured against stated expectations. Most large enterprises will want a full-service provider rather than one that handles just one element (such as payroll). References should be checked, and the provider should demonstrate capabilities in full-spectrum HR outsourcing (and have the financial backing to be around for the long term).

Remember, working with an outsourcer is not about giving up control. Rather, it is about finding the best ways to deliver quality service, impact organization economics, and provide the data that aligns the HR department with business outcomes.
In today's economic climate, all CEOs have a growth agenda that requires a solid and committed workforce—in other words, they need to have the right people in the right place at the right time. The true value of the human resource team will be measured in how well it aligns with this growth agenda. Effectively integrating HR business intelligence technologies is foundational to HR's metamorphosis from administrative cost center to strategic contributor to corporate growth.

Examples of Strategic HR

Here are some quick takes on how companies can strategically leverage HR for measurable gains. The impact areas and results in the list below are far from complete, and are provided here only as samples:

* Staffing levels: Aligning time tracking with disability and leave information fosters greater understanding of staffing needs.
* New hires: Melding r�sum� data with future business needs "pipelines" qualified individuals for impending job openings.
* Succession planning: Assessing skill sets of existing employees and overlaying it with upcoming job openings promotes hiring from within.
* Benefits cost: Integrating HR survey data with corporate goals can help predict changing corporate contribution rates that would result in more job turnover.
* Hiring assessments: Extracting data from various HR functions allows you to determine if increased hiring is due to growth and skill upgrades, or to unwanted turnover.

Essential Considerations When Selecting an Outsourcing Provider

Beyond general considerations with respect to the utility of outsourcing providers, there are specific questions which enable companies to determine the compatibility of a prospective provider:

* Do the provider's systems have the capabilities to meet specific technology and business requirements? Note that inadequacy with respect to this question can of course come on two counts: either the provider's systems are too "generic" to meet these specific requirements, or else (in the case where they do in fact address the particular requirements) they simply underperform.
* Does the outsourcer have a clear understanding of needed capabilities?
* Will the operation be transparent, both financially and managerially?
* Do the outsourcer and your company share a common vision?
* Does the outsourcer have a partnering mindset?
* Is the outsourcer's culture compatible with your corporate culture?
* Will the outsourcer be proactive in engaging your company to resolve problems?
* Are the scope of services and performance levels clearly defined in a service level agreement?
* Can the provider enable your company to deliver business performance impact?

Sybase and MicroStrategy Team on Vertical Market Portal Applications

"EMERYVILLE, Calif., Nov. 1 /PRNewswire/ -- Sybase�, Inc. (Nasdaq: SYBS) today announced a comprehensive, multi-year licensing, technology and service agreement with MicroStrategy Incorporated (Nasdaq: MSTR). The alliance offers customers MicroStrategy's Intelligent E-Business� software coupled with customer relationship management (CRM) and business performance management (BPM) applications. Under the terms of the partnership, Sybase will embed and re-market MicroStrategy Intelligent E-Business� Platform for the Industry Warehouse Studios� (IWS) offerings; thereby leveraging MicroStrategy's core analysis, personalization, and broadcast technology within Sybase's complement of analytical CRM and BPM applications". As stated by Eric Miles, senior vice president and general manager of Sybase's Business Intelligence Division, "As Sybase expands its growth in the business intelligence, CRM and BPM markets, it is critical to form strategic partnerships with companies that share our vision".

Each of the Sybase Industry Warehouse Studios consists of five analytical customer relationship management applications and one industry-specific module. The suite of six applications, designed for sophisticated, business performance management and customer relationship management include marketing campaign analysis, customer profile analysis, sales analysis, loyalty analysis, customer care analysis, and business performance management (the vertical component which delivers operational scorecards and analytical reports).

MicroStrategy powered versions of the Sybase Industry Warehouse Studios for Property & Casualty and Life Insurance, Telecommunications, Healthcare, Retail Banking, Credit Card Companies, and Capital Markets will be available during the first quarter of 2000 on UNIX and NT platforms. Industry Warehouse Studio applications start at $100,000 (US).
According to Sybase "The Company is leveraging core enterprise product strengths to capitalize on the emerging enterprise portal market to provide powerful new solutions that deliver on the promise of e-Business." Due to their decreasing hold on the overall database market, Sybase is attempting to focus vertically in an attempt to improve their profitability. (Sybase saw share value decrease 44% in 1998, revenues have decreased for the last two years, and they have suffered four years of negative earnings per share). MicroStrategy has been very successful in the portal arena, and their stock has appreciated over 300% in the last three months alone (at the time of this writing, the stock was selling at $94 per share). In addition to this agreement, MicroStrategy has also announced alliances with Unisys and NCR. If Sybase is successful in leveraging this marketing relationship, it should help restore some market confidence in the firm.

Business Objects Objects Again

Software developer Cognos Inc. said on May 9 that it will fight vigorously a patent infringement lawsuit filed by arch-rival Business Objects SA, a suit that it says is without merit.

Ottawa-based Cognos, one of the largest vendors of business intelligence software used to access and analyze corporate data, said the patent is "invalid" and "unenforceable". "I don't consider this to be a huge concern," said Brandon Osten, analyst at Sprott Securities. "Most of these lawsuits don't go too far...patents are very difficult to defend in technology."

France-based Business Objects filed suit on Friday in the northern district court in California. Cognos, which has retained law firms in both Canada and the U.S. for representation, said it believes its market leadership is a factor in the litigation. "Unfortunately, some technology companies use litigation in an attempt to achieve in the courtroom what they may find difficult in the marketplace," said Cognos chief executive Ron Zambonini in a statement. "This lawsuit is without merit and seeks to deflect attention from the fact that Business Objects has been unable to dislodge Cognos from its leadership."

The patent refers to the process in which data is retrieved from a database in answer to a query or question, said Cognos vice-president and general counsel John Jussup. Business Objects filed a similar patent action against California competitor Brio Technology Inc., which was settled in September, he added. "The speculation was rife in the market that we would be next on their list and since that time we've been preparing," Jussup said. "The fundament of our defense is that we were there before...it's essentially the been there, done that defense."
Brio Technology was forced to acknowledge the validity of Business Objects patent (U.S. Patent 5,555,403) and settle with Business Objects (for more information see Business Objects Outguns Brio Technology in Patent Dispute, September 13, 1999). Brio's 10Q statement included the following: "On September 9, 1999 Brio and Business Objects, S.A. executed a memorandum of understanding settling Business Objects' pending patent litigation against Brio involving patent number 5,555,403 for $10.0 million. Settlement costs of $9.1 million, which represent the net present value of the 10 quarterly payments, are included in non-recurring operating expenses for the nine months ended December 31, 1999. The remaining $900,000 represents interest and will be recognized over the payment term using the effective interest rate method."

Cognos will likely be forced to recognize the patent also, and will have to take a charge against earnings in order to pay the fines. Business Objects can argue that it holds the patents on the technology and Cognos has infringed on them. In addition, management at Cognos will undoubtedly be distracted for a time in dealing with the legal issues