What Options Does the Solution Architect Have

The simplistic approach of dropping all special characters (e.g., by treating "Müller" as "Muller") will lead to: (a) passing of a transaction that is meant to be blocked; or (b) blocking of a genuine transaction. In the first case, the company risks non-compliance with regulations relatin to terrorism and money laundering. By introducing a "false positive," the second case leads to the risks of customer dissatisfaction and higher repair costs when the error is realized later and has to be manually fixed. In other words, a solution architect cannot afford to take a simplistic approach.

On the other hand, by incorporating extended character sets into all applications, the solution architect can help the company avoid these risks altogether since there won't be any mismatch between the various applications in the SEPA system landscape in the event that fund transfer instructions contain special characters.

But if this change turns out to be too far-reaching to implement immediately, the solution architect could, in the short term, tweak all applications to use generally-accepted English equivalents for non-English accents. For example, "Mueller" for "Müller" in the above example is derived by substituting the umlaut symbol with an "e" as per standard convention instead of ignoring the umlaut symbol.

In the medium term, the solution architect could evangelize the adoption of latest standards and technology stacks that support extended character sets and concurrent multilingual support.

Multilingual Support in Custom-developed Applications

Apart from usability and productivity, compliance considerations will increasingly raise the bar on the level of multilingual support demanded from software applications. This trend has already been noticed during implementations of the Single Euro Payments Area (SEPA) regulation among banks and corporations operating in different countries in Europe.
At one such pan-European SEPA implementation, the system landscape was comprised of a payments product processor, a trans-European liquidity manager, a Society for Worldwide Interbank Financial Telecommunication (SWIFT) gateway, and a sanctions-screening product, which was meant to block cross-border fund transfers suspected of having links to terrorism, money laundering, and other nefarious activities.

Since SEPA intrinsically involves cross-border fund transfers, all transactions had to be screened by the sanctions screening application, which would approve a transaction or block it depending upon the outcome of verifications being implemented against various hot lists. Having been built with a fairly sophisticated level of multilingual support this application supported the extended character set of national languages of all the European countries complying with SEPA. Therefore, it was able to handle German umlauts and other accent symbols in various European languages. For example, it could treat the German name "Müller" differently from "Muller", which is derived simplistically and incorrectly by ignoring the umlaut symbol.

Now, since the existing legacy payment product processor did not support an extended character set, there was a strong potential for mismatch between the various systems included in the system landscape whenever a term contained umlauts or other special characters (`, ´, ˚, ˜, etc.) commonly found in non-English European languages.

Design and Technology for the Software Product Manager

While different technologies are available to implement the next level of multilingual support in software applications, we examine one design approach based on .NET's technology elements.

Satellite assemblies can be used to provide multilingual support for menus since they contain localized resources. Microsoft Developer Network defines a satellite assembly as follows:

A .Net framework assembly containing resources specific to a given language. Using satellite assemblies, [the developer] can place the resources for different languages in different assemblies, and the correct assembly is loaded into memory when the user opts to use that specific language.

Using satellite assemblies, designers can place resources for different languages in different assemblies. Depending upon the language selected by the user, the corresponding assembly will get automatically loaded into memory. The application will need to incorporate individual satellite assemblies for each specific culture (the combination of a particular language with a particular country). For example, the culture "fr-FR" refers to the French language as used in France, whereas "fr-CA" refers to the French language used in Canada. These satellite assemblies can be placed in a specific location and loaded by the parent framework based on the culture setting of the software.

Support for multilingual messages can be provided by storing the culture-specific user messages in separate culture-specific message files (one file per culture). Message files appropriate to the specific culture setting of the software will be used to retrieve and display messages to the user.

By leveraging the locales facility, developers can build support for different date formats and decimal separators. Microsoft Developer Network defines a locale as follows:

A collection of rules and data specific to a language and a geographic area. Locales include information on sorting rules, date and time formatting, numeric and monetary conventions, and character classification.

While the user-selected default locale would be applied by default to all information presented to the user, the user can also select any other locale from a list of pre-defined locales to view the same information in another format.

Multilingual support for special characters in queries can be incorporated by maintaining separate culture-specific mapping files (one file for each culture). A mapping file maps a character in the chosen culture to the special accent characters in the other cultures supported by the software. The application would scan the mapping file corresponding to the language setting (culture) of the software and construct additional search phrases for all other mapped cultures.

By consciously designing the application so that it enables the user to select the language culture at the time of installation, one can prevent a "forced install" in the local language culture derived from the PC's regional settings. By designing the application to install and load all—and not just the user-selected culture files, designers bestow it with the capability of being re-started in another culture.

By leveraging the facility available to set the user interface (UI) culture property in report controls, reports generated in the native locale can be toggled into another locale. As a result, the application will be able to support recasting of data, reports and charts generated in one language into another.

In this manner, product managers and designers can combine sophisticated features available in the .NET technology stack with an innovative design approach in order to deliver the next level of multilingual support in a software product, with the overall goal of improving usability and boosting productivity.

Taking Multilingual Support to the Next Level

With the increasing globalization of the twenty-first century, co-workers who are spread across many countries in multinational corporations need to work cohesively as one unit. The traditional level of multilingual support no longer suffices. Multinational corporations need the next level of multilingual functionality from their software applications to boost productivity and, in some cases, even meet compliance with local regulations.

Product managers in product companies and solution architects of custom-developed applications can enhance the marketability of their software applications by creating a roadmap for incorporating the sophisticated multilingual requirements that such multinational corporations need.

Multilingual Support in Software Products

Let's take the case of an analysis and research application for European asset management companies (AMCs) spread across France, Germany, Italy, Spain, the United Kingdom (UK), and other European countries.

It would be typical for a branch of one such AMC in one country to have employees belonging to another (e.g., the Paris office would have a few German employees). As a result, not only would the product be expected to support multiple languages (e.g., French and German), but it would need the capability to permit an employee to choose the language (e.g., German) instead of installing the French-language version by default.

In many AMCs, investment recommendations for funds traded in one country would need the approval of the chief investment officer (CIO), located at the headquarters in another country, before they could be presented to customers. This means that after logging into the application in the German language, the asset manager in the AMC's Frankfurt office might need to switch over to the English language later while reviewing the data with his UK-based CIO prior to obtaining his or her approval. Once the approval is obtained, it might be necessary to recast all data, reports, and charts into the French language before presenting them to a prospective customer in Paris as part of an investment recommendation. Therefore, the application should not restrict users to the language chosen at the time of logging in, but let them toggle between multiple languages "on the fly."

Apart from the multilingual screens, menus, and help features, the application will have to cater to data entry, querying, and reporting needs in the different supported languages. This means support for various accents such as the German umlaut, French grave, acute, cedilla, circumflex, and the Spanish tilde. In addition, sensitivity to different date formats such as 15 June 2006 in the UK versus 15. Juni 2006 in Germany; and decimal separators such as a comma in the UK versus a period in Continental Europe (e.g., 1,000 versus 1.000) are respectively prevalent in different European languages.

Now, when a London-based asset manager queries for all French funds containing a certain letter in their name, he or she would expect the application to return funds containing not just that letter by itself but its various applicable accents like acute (e.g., "ć"), grave (e.g., à), cedilla (e.g., ç), and circumflex (e.g., ĉ). This means that the application will need to support multilingual query literals, characters, and messages.

Choosing a Vertical- or Horizontal-based Solution

Organizations should identify whether more value will be provided by a vertical solution that is built specifically for the organization's industry or department, or by a horizontal solution that can grow with the organization. For example, does the organization need a generic reporting, querying, and analysis tool that will extend across the organization, or does the organization need to develop a process and compliancy that will adhere to the US Sarbanes-Oxley Act (SOX) or Health Insurance Portability and Accountability Act (HIPAA) standards? The answer to this question will help the organization define which type of solution will best meet its needs.

In addition, anticipated use of BI in the future may help determine whether a horizontal or a vertical solution will best meet the organization's needs. Organizations that must adhere to compliance standards should take advantage of vertical-based solutions, because vendors have developed solutions that meet specific compliance requirements. Horizontal solutions need a large degree of customization to bring them up to par, leading to extra time and money spent on developing the solutions.

Organizations in key vertical industries should strongly consider vertical-based solutions that will meet their needs, out of the box. Vertical-based solutions are likely to meet the general requirements of a specific industry or department, but since horizontal BI solutions do not base themselves on specified data models, they may be more versatile to the changing demands of the organization. Therefore, if an organization anticipates rapid BI growth across the organization, having the ability to develop solutions based on individual needs may be more beneficial. This relates to identifying the business problem and anticipating the future needs of the organization.

Rolling Out Training Initiatives

Deciding when to roll out training contributes to project success. Training initiatives should begin right before or during the implementation phase. However, in many organizations, training is rolled out months before actual implementation, creating hype among the employees about the new system and what they will be able to do with it. By the time implementation actually occurs—sometimes months later—the initial excitement and buy-in has subsided, and more importantly, users have forgotten their newfound skills. To build momentum again, training needs to be repeated—wasting time and money.

Buy-in related to change is never easily achieved within organizations. Users become attached to their current processes, whether or not those processes are productive. Buy-in does not occur immediately upon showing users the inherent value of BI because it means the entire way they do business will change. Creating a training program—and delivering that training in a timely fashion—helps users apply their newfound skills immediately, thus helping to increase user buy-in.

Understanding Delivery of Data

The BI solution's ability to collect the right information for reporting and analysis is essential if it is to deliver value to organizations. Although identifying the data required is time-consuming, it is the backbone of BI. Additionally, determining how data will be delivered, what the appropriate data cleansing activities should be, and whether the data is to be delivered in batch or in real time, should all be defined in advance. If data is not cleansed or delivered when needed, then the front-end BI tools will not provide the proper value to the organization. BI solutions impart value through the analysis of data, so it is essential that data arrives when required, in the proper format, and at the right time.

In addition to extract, transform, and load (ETL) tools, data quality and data cleansing need to be inherent aspects of the delivery of BI within the organization. In reality, short of an organization-wide master data management (MDM) initiative, the responsibility of providing accurate data will fall on the shoulders of the business units implementing BI.

Some organizations are misguided and think that their BI solution will provide the tools to fix their data problems. BI solutions can provide ongoing data quality processes, but these are not innate to software offerings. Some vendors' BI tools include enhanced data quality and integration features, and other vendors assume this responsibility should fall to the organization. Organizations should implement data management structures to minimize frustrations that result from data issues.

Determining Expectations of Use

Once BI is implemented within an organization, its usage usually grows beyond initial expectations. For example, an organization may assume that its BI solution will be used by 10 to 20 users, when in reality over 400 users query data on a monthly basis. Because the initial design of the platform will have been based on a low number of potential users, the system may not be able to sustain such a high number of queries, and will most likely "crash" (fail), causing users to lose faith in the new system and potentially revert to their pre-BI environment for stability. In addition to lacking confidence in the new system, the organization may see the challenge of getting an unstable system up and running as not worth the effort, delays, and time required.

With unrealistic expectations, frustration may cause the organization to rethink its use of BI. Generally, once BI adoption occurs within one part of the organization and other departments or business units see its benefits, adoption begins to spread throughout the entire organization. For a BI solution to meet these increasing needs, organizations should anticipate the use of BI before implementation of a solution.

Another consideration is the type of BI tool use. For example, if a sales manager needs to increase sales and therefore wants to analyze trends, product distribution, and sales performance, creating a set of static reports will not be helpful. A data visualization tool to manage these items and to develop a plan based on trend analysis will more likely produce the appropriate results.

Five Steps to Business Intelligence Project Success

Successful business intelligence (BI) projects encompass more than implementation of a solution on time and within budget. True success should be measured by how the BI solution improves the organization's overall performance through increased efficiency in reporting, planning, financial functions, and performance measurements. This will help ensure organizations' BI projects fall into the estimated 30 percent success rate.

Much has been written about measuring return on investment (ROI) for BI, and the general conclusion is that gaining tangible insight into the initial benefits is not easy. Identifying long-term benefits becomes more practical as planning and analysis, compliancy, and forward-looking approaches become more mainstream within organizations. To gain insight into how to implement a BI solution successfully, organizations should benchmark the success of other organizations—including their implementations and use of BI—against their own current initiatives. It is equally important that organizations learn from other organizations' failures—and avoid repeating them.

This article identifies and explores five steps organizations should take to avoid the common pitfalls encountered by many businesses when implementing a BI solution. These steps also provide an overview of items that need to be considered before implementing BI within an organization or business unit.

Identifying the Business Problem

Identifying the BI business problem is the first step to ensuring a successful project. Once an organization knows what is broken, not only can it start to find ways to fix the problem, but it can also identify the proper resources, create user buy-in, and prioritize how to tackle the project. To produce an ROI, a BI solution needs to address specific business problems. Otherwise, implementing an ad hoc query tool, an online analytical process (OLAP) cube, or a dashboard will not result in lasting benefits.

Unfortunately, it is common for BI solutions to be pushed onto a business unit in order to meet an IT objective rather than an organizational need. Sometimes organizations get caught up with general initiatives and lose sight of the actual benefits BI provides in terms of performance management, collaboration, workflow, process improvement, etc.

To attain buy-in, the user community should be a part of the problem identification process. An implementation decision that comes from management still requires input from users as to what their requirements are, and this information can make the difference between the implementation of a tool that works as a value proposition and an implementation that may be seen as useless.

Vendor Positioning

The following represents a general listing of vendors within the CDI industry and their product functionality. This list is in no way comprehensive, and organizations should use this as a general guide rather than a potential vendor short list.

* Siperian Hub is a complete, integrated software platform for customer-centric master data management that creates real-time unified views of customers, organizations, and products, from disparate data silos. This allows organizations to create a unified customer view and framework. Siperian's solution is broken down into three separate modules. Siperian Master Reference Manager (MRM) is used to consolidate multiple customer profiles in order to identify customers uniquely across all channels of the organization. Some key features of MRM are template-driven data models, rules-based modules that are configurable, and built-in audit and historical lineage functionality. Siperian Hierarchy Manager (HM) creates a unified view of the multiple relationships that exist among customers and other entities across all applications to provide organizations with a consistent, complete view of the customer. Some key features include the ability to configure and to manage data relationship consolidation, rules and metadata maintenance for relationship unification across all organizational sources, and exception-handling capabilities. The third module, Siperian Activity Manager (AM), allows organizations to create relevant customer views that drive business actions based on the transactional data captured in the hub and distributed via analytical and operational activities.

* Oracle's Siebel Customer Data Integration is comprised of three solutions: Universal Application Network, Data Quality, and Universal Customer Master. Oracle's Siebel Data Quality identifies duplicate customer records and provides pre-built integration to third-party data cleansing tools. Oracle's matching server functionality allows the organization to search, match, and identify duplicate customer records based on key customer attributes such as name and address. The data quality connector provides real-time and batch request capabilities that connect to an external data cleansing engine to eliminate duplicate customer records. Additional features of Oracle Siebel include pre-built integration functionality, and fuzzy searching for identifying variations in spelling and word sequence.

* IBM, with its acquisition of DWL on August 31, 2005, provides a real-time transactional CDI solution. WebSphere Customer Center provides multiple interfaces to front- and back-office systems to access and manage a complete customer master record. It focuses on customer-data transaction management that is operational in nature, and its customer hub provides approximately 500 out-of-the-box services. IBM's CDI solution is implemented within a service-oriented architecture (SOA) and its business services, to help manage and maintain customer data. The CDI solution is a part of the IBM Master Data Management (MDM) suite of products, and integrates with IBM Information Integration and Entity Analytics products.

* Initiate Systems provides CDI solutions to organizations to help control customer interactions related to sales, service, and customer relations. The Initiate Identity Hub software system focuses on CDI, enterprise master person index (EMPI), and entity resolution. It also leverages customer data in real time and enables organizations to find any data set, account, or transaction, based on person, household, or organization. The Initiate Identity Hub software also links duplicate and fragmented records within and across disparate data sources. Initiate Systems has several additional software components to complement the hub, and provides a complete CDI solution. Initiate's Data Federation compiles data maintained outside of the Initiate Identity Hub software, allowing organizations to overcome security restrictions that prevent a complete customer view. Initiate Synchronization functions as the central management point for all customer data, in order to keep data accurate. Initiate Data Profiling Report Pack helps monitor and improve data quality so that organizations can identify and understand the impact of data on business operations within the organization. Initiate Enterprise Integrator provides distributed access to the searching and linking capabilities offered by Initiate Identity Hub. With the integrator, the capabilities of legacy and operational applications can be extended to provide data integration services at the point of service, enabling customization and deployment at any stage of the enterprise that requires accurate customer identification.

* DataFlux's CDI solution works by aggregating information from disparate applications, databases, and customer touch points into one centralized data source using an SOA. Using a combination of data quality and identity management technology, the DataFlux CDI Solution creates a master data reference file to consolidate information, and then feed those records and update information within a database or application as needed. Data problems are inspected and analyzed before being migrated to the CDI repository. Also, the results of the analysis are used to build targeted data quality routines to correct, standardize, and validate customer data. Some main features of the DataFlux CDI Solution include data profiling, access to multiple data sources, connection and access to multiple data sources to allow for easy and timely analysis, and matching and de-duplication functionality.

CDI and Data Integration

CDI represents a consolidated view of customer data. Aside from MDM, which looks at the whole organization, data integration generally focuses on specific initiatives, and is the type of software used to perform data transfers, consolidations, etc. Thus, when an organization is looking to implement a CDI initiative, its focus should involve identifying the data integration vendors that specialize in CRM or that focus specifically on validating and consolidating customer data.

Data integration is defined as the act of bringing together or moving data from one or multiple locations to a centralized or replicated data store. The development of a data warehouse and the consolidation of information across the organization is an example of how data integration is applied in organizations. Sub-sets of data from disparate locations within the organization are loaded into the centralized structure of a data warehouse or dedicated database. This centralized structure creates a specified view of data to measure an organization's performance, to generate reports, to provide analytics, and so forth.

Not all data integration is equal when it comes to CDI. Different forms of data integration are used within different industries and for diverse initiatives. For example, when implementing a business intelligence (BI) solution, data mapping, data cleansing, and hourly data loads are likely the most important factors to consider. Also, different vendors within the data integration space may specialize in sub-categories such as data quality, and may partner with larger industry- or solution-specific vendors to have their solutions embedded within larger software packages. This gives organizations the ability to mix and match solutions based on their needs.

Customer Data Integration: A Primer

Implementing a customer data management system can be the difference between success and failure in terms of leveraging an organization's customer relationship management (CRM) system. Since customers drive profitability, organizations need a way to provide their employees with a single view of the customer and to provide that customer with above-average customer service. Unfortunately, this is not always the case. Disparate applications such as billing and call center systems do not always feed into one another, and even when they do, lack of data cleansing and management can cause employees to see only a portion of a customer's history, interactions, or profiles. A widely used example is that of an organization sending multiple marketing brochures to one customer because of inaccuracies and lack of customer data synchronization. A more alarming example is having more than one customer record for a specific customer, with the collections department calling that customer to collect on an account that is actually current.

Why do CRM initiatives fail? Because implementing a system to manage customers does not guarantee that CRM applications will work successfully within the organization. The old adage—"garbage in, garbage out,"—definitely applies to the realm of CRM. If organizations do not have clean, reliable, centralized data, their customer view will not be complete or accurate, and their business goals will not be achieved. Consequently, customer data integration (CDI) has become an essential component of an organization's management of data, along with any CRM initiative.

This article will provide an overview of CDI within CRM, and see how it differentiates itself from the general data integration industry. Additionally, the components of CDI will be explored, to identify the important areas that should be considered when implementing master data management (MDM) for CRM within the organization. Finally, key vendors in the industry and their key product features will be identified.

Defining Customer Data Integration

Within CRM, CDI is the management and consolidation of customer information from across the organization. This includes, but is not limited to, information stored in call centers, sales and marketing departments, and accounts receivables and payables. CDI ensures that each department requiring customer contact has access to timely data, to provide employees with a complete view of customer profiles or histories. This creates a standardized view of each customer and promotes positive customer interactions.

Most enterprise organizations have built or acquired their computer applications over an extended period of time, creating a series of complex systems that work independently or that interoperate with one another. Even if these systems have high interoperability, many times the business rules and data structures of each application and business unit have not been taken into account, as they were developed independently of one another. This means that data may be captured in different ways. For example, customer address information and name may be recorded in different formats within different business units. When data is pulled from one system to another, this particular customer information may not be synchronized.

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.

Using Predictive Analytics within Business Intelligence: A Primer

Predictive analytics has helped drive business intelligence (BI) towards business performance management (BPM). Traditionally, predictive analytics and models have been used to identify patterns in consumer oriented businesses, such as identifying potential credit risk when issuing credit cards, or analyzing the buying habits of retail consumers. The BI industry has shifted from identifying and comparing data patterns over time (based on batch processing of monthly or weekly data) to providing performance management solutions with right-time data loads in order to allow accurate decision making in real time. Thus, the emergence of predictive analytics within BI has become an extension of general performance management functionality. For organizations to compete in the market place, taking a forward-looking approach is essential. BI can provide the framework for organizations focused on driving their business based on predictive models and other aspects of performance management.

We'll define predictive analytics and identify its different applications inside and outside BI. We'll also look at the components of predictive analytics and its evolution from data mining, and at how they interrelate. Finally, we'll examine the use of predictive analytics and how they can be leveraged to drive performance management.

Customer Relationship Management: Putting Customers at the Center of the Business

Customer Relationship Management (CRM) has sustained success through its ability to help companies sell but, only focuses on a portion of the customer relationship, not taking into account pervasive business processes that can affect customers. Yet nearly all facets of the organization are driven and affected by the customer relationship. Incorporating all organizational procedures across the enterprise will serve to advance these relationships and make companies more profitable.

CRM systems can be very effective solutions for managing sales cycles. However, using current CRM point solutions will not build and manage an entire customer experience, merely the components of the sales and marketing cycles. There is promise, however. While traditional CRM systems have lacked the ability to encompass the full realm of business processes, new technologies are emerging that empower businesses to realize the full potential of customer relationships.

This paper addresses how CRM technology has evolved, the current challenges in merging existing enterprise-wide processes, and the necessary requirements for making CRM an integral success throughout the entire organization, leading to better customer communication and retention.

Customer Relationship Management can only be a success if the solution is integrated throughout the entire organization, leading to better customer communication and retention.
The Evolution of CRM

Over the past two decades, CRM solutions have evolved from contact databases that assisted salespeople in tracking prospects, to complex real-time customer relationship management environments that enabled better responsiveness to customer needs.

Early CRM systems followed standard protocols based on company size, product type, and buyer. The early 1990s saw technology advances come to fruition with the addition of lead generation and customer service. However, these systems were isolated in functionality and did not incorporate a broad-based business model.

Later, CRM systems encompassed customer-facing front-office functions, such as marketing, sales and customer service for a more integrated approach to serving customers. While companies implemented technology that improved sales and service components of customer transactions, customers and salespeople alike were left in the dark about much of the back office interactions that affected them.
Current Challenges

CRM point solutions achieve what they state: a focus on selling to customers. However, by concentrating exclusively on pre-sales, marketing programs and customer support instead of building long term relationships, companies are not realizing the full return on investment with existing CRM technologies.

The problem lies in capturing the entire customer experience as it relates to the enterprise and its integrated components or business processes. Managing customers involves more than storing, updating and managing customer information. It requires both internal and external knowledge sources to have the inherent data necessary to continuously cultivate the customer relationship.

Standard CRM systems typically consist of three core areas: sales force automation for managing prospects from initial lead through sale close; marketing automation for managing campaigns and tracking success metrics; and customer support, including FAQs and problem resolution. But true customer relationship management goes far beyond sales, marketing and support management.

The issue at hand is that these three core areas run fairly autonomously from other parts of the corporation, such as billing, employee and customer workflow, document management and projects. The interrelationship between financial, asset management, project management, documentation, and workflow processes all affect the customer experience, and should be associated so they are accessible by employees and customers alike.

While the enterprise may have a centralized source of interaction, this data is not available to customers, who typically interact with a company through fragmented contact points, with disparate data storages. Typically, CRM systems were developed without considering all of the elements that it takes to put the customer at the center of the overall business. Thus, "built-out" CRM solutions have not truly improved the ability to manage the customer lifecycle.
Functionality Requirements

Building upon and improving CRM involves recognizing and linking all business processes, including workflow, documents, employee and client communications, departments and data storage, to better monitor and look after the customer relationship. CRM functionality should, by default, be integrated with the entire business operations rather than focused and remote functionality. A truly customer-centric solution not only ties customer relationships with enterprise business functions, but can address other functions related to CRM, such as human resources and financial management.

For example, if a sales manager wanted to see which employees worked with which customers, he could see not only the relationship but also any workflow that occurred between the two parties.

Service Metrics

According to a recent Aberdeen Research Study3 of services executives, best-in-class service organizations are nearly two and a half times more likely than laggard firms to analyze service data daily or near time. Additionally, Aberdeen found that nearly 80% of service executives either have in place or plan within the next twelve months to implement aBusiness Intelligence (BI) and analytics solution for their service operation. These executives are using better data analysis to balance customer, competitive, and cost pressures. Increasing customer demand for faster and more efficient service performance and rising costs are challenging service executives to make faster and more accurate decisions.

According to a recent Aberdeen Research Study3 of services executives, best-in-class service organizations are nearly two and a half times more likely than laggard firms to analyze service data daily or near time. Additionally, Aberdeen found that nearly 80% of service executives either have in place or plan within the next twelve months to implement a Business Intelligence (BI) and analytics solution for their service operation. These executives are using better data analysis to balance customer, competitive, and cost pressures. Increasing customer demand for faster and more efficient service performance and rising costs are challenging service executives to make faster and more accurate decisions.

Exhibit 2. Service Business Metrics

Customer Support	Profitability	Growth
Response Time Resolution Time Call Center Performance Order-Fill Ratio On-Time Delivery Training Quality Service Engineer Competency First Time Fix Rate Warranty Compliance Equipment Utilization (OEE) MTBF	Service Engineer Utilization Productivity Sales Discounting List Price Historical CAGR Gross Margin Contract Profitability Concession Expenditures Warranty Expenditures Inventory Turns Accounts Receivable Logistics Costs Parts Scrap & Obsolescence	Service Engineer Utilization Productivity Sales Discounting List Price Historical CAGR Gross Margin Contract Profitability Concession Expenditures Warranty Expenditures Inventory Turns Accounts Receivable Logistics Costs Parts Scrap & Obsolescence

Metrics for organizations focused on service growth are centered on gaining a deeper understanding of their customers business and on services innovation. As a result, New Service Introduction metrics are important and might include the number of new service offerings, new offering market adoption rates, and time-to-market cycles. Service portfolio mix is also important and might include metrics that indicate actual and variance to target portfolio mix ratios blend of product, managed, professional, and multi-vendor offerings.

The metrics shown in Exhibit 2 are of course not exhaustive, and it should be recognized that organizations may in fact span various phases for different product lines, customers, service offerings, regions, etc. Generally speaking, however, the examples should provide directional guidance and ideas of the type of metrics and factors that are relevant within each stage. It is the responsibility of the services leadership team to select and prioritize these metrics for their situation.

Services Account Management & Reporting

Leading manufacturing and capital equipment service organizations are now playing offense and engaging with their customers in a truly interactive fashion. Customer dashboards, key performance indicators (KPIs), and reports have long been available to internal account teams. Now, however, leading edge organizations are making these same tools and data available to their suppliers and customers through business portals with aggregated information from multiple enterprise and transactional data systems. Both internal service, and customer personnel, can now see the same view of service performance and activity. This is really the next frontier, which can generate not only internal efficiency but can also create opportunities for real customer stickiness and business growth.

As indicated by Jonathan Byrnes, a prominent customer service thought leader and author, service innovation and growth requires proactive account management and intimate customer understanding.

Today, leading companies are redefining customer service . Phrases like building a relationship with the customer, and understanding the customer better than the customer understands herself underscore this new objective.
Jonathan Byrnes
HBSWK, Jul7, 2003, Out-of-the-Box Customer Service

Exhibit 1 portrays the respective characteristics relating to account management and reporting across the various stages. Generally speaking, as organizations move from customer support to the growth stage their account management reporting tends to:

* Include customer profitability & productivity metrics in addition to classic internal measures
* Have more cross-functional account ownership and input
* Leverage and aggregate data that is housed in multiple departments and sources
* Provide more sharing and easier access to data for strategic suppliers and customers
* Require a much deeper understanding and segmentation of the customer base to drive priorities and assure resources are optimized for strategic customers

Customer Support	Profitability	Growth
Break-Fix Delivery Metrics Internal Metrics Typically No Account Owner Limited X-Department Data No Data Aggregation Internal Data Access Only Limited Customer Segmentation	Account Profitability Metrics Service Partner Metrics Account Owner Additional Data Sources Functional Data Aggregation Supplier Data Access Large Account Focus	Customer Total Cost Metrics Customer Productivity Metrics Cross-Functional Team Multiple Data Systems X-Function Data Aggregation Customer Data Access Customer Experience Map

Insights to Accelerate Services Growth: Account Management, Service Metrics, and Customer Dashboards

Much has been written about customer satisfaction, account management practices, and measurement systems for services businesses. Some of the approaches take a simple, monolithic approach and propose a standard model for management of all service businesses. We suggest a different approach and recommend that a service business should be managed and measured based on the maturity of the service business and the specific requirements of its customers.

To help operationalize this approach, we provide a framework for understanding how a services organization and its customer engagement should be measured. This framework is based on the premise that these organizations often progress through three distinct stages Customer Centric, Profit Centric and Growth Centric as they evolve. We specifically outline various information and reporting approaches to support strategic account management of services businesses at each stage of their evolution, we provide examples of what service metrics are most relevant, and then discuss the effective intersection of account practices and metrics by means of a customer dashboard tool used by many leading firms.
Service Business Framework

Service organizations have fundamental business objectives and service specific goals that guide their operating norms, business behavior, capability development and resulting key performance indicators (KPIs). This is driven by multiple factors, including service organization tenure, competitive influence, business nature, customer maturity & requirements, specific corporate strategy, product enterprise mandates and synergies.

Service entities can be logically categorized into three distinct stages: Customer Support Centric, Profit Centric, and Growth Centric. There is a recognition that objectives are not static and most organizations will migrate over time into subsequent stages as they and their customers mature. That said, it should not be implied that growth centric service organizations are better than those focused on pure customer support. It is, however, important for an organization to understand where they are to help guide their information and customer engagement approach.

At a summary level, service companies in each respective stage typically exhibit some or all of the following behaviors and focus:

* Customer Support Centric:

Primarily focused on providing baseline product related break-fix and warranty service to customers who purchased their equipment. Service is largely viewed as an entitlement by the customer. These business units are typically measured as a cost center. Initiatives and priorities are centered on delivery process improvements, service quality & responsiveness, and assuring baseline customer satisfaction.
* Profit Centric:

Primarily focused on driving service business gross margin and profitability. Initiatives are centered on automating manual processes and becoming more efficient in all service back-office functions (services delivery, services supply-chain, services order management, call center & technical support, etc). Some voice of the customer input is incorporated along with the introduction of some front-office process improvements (pricing practices, competitive positioning, product sales education of services, expanded service contract offerings, etc). The end customer is usually more open to alternate service offerings beyond traditional product break-fix work.
* Growth Centric:

Primarily focused on expanding the service business and contributing to proportionate enterprise-wide growth. These companies recognize that customer service innovation is a way to differentiate themselves from the pack. An emphasis is placed on introducing new offerings both enhanced product-services and new value-added managed and professional services. Comprehensively understanding customer processes and total cost-of-ownership are fundamental. Front-office processes services marketing, services sales and pricing gain more internal mindshare. Executive leadership appreciates the ability of the services team to create product pull-through opportunities and generate long-term customer retention. Customers more often view and treat their service providers as strategic partners versus commodity suppliers.

We believe account management and service metrics should be top of mind for service executives since they both are foundational to execution and success. Regardless of what stage service company you are, leadership requires data-driven insight to guide their strategic decisions spend time to get the metrics right, and then meticulously measure them. The mapping of services account management reporting requirements, service metrics, and customer dashboard usage within the prescribed Services Framework is discussed in the following sections.

Product Strengths

Based on the wide use of Microsoft Office products within organizations, Microsoft has a natural advantage due to its far reach. Organizations which have not implemented BI solutions and that cannot afford the large price tags associated with BI vendor solutions can use Microsoft's general BI solution, as it encompasses a back-end solution to complement its front end analytics. This means that Microsoft, although coming late to the BI game, may actually push a broader use of BI tools throughout organizations, making it easier for other vendors to penetrate the mid-market and to expand their client base.

Also, the ease of use (due to MS Office integration) provides users with a big bonus in terms of integrating BSM into the current Microsoft product suite. Users can leverage their current products to define the required metrics to help measure performance. An example of linking BI with the broader Microsoft offering is through the use of Microsoft's Web portal, SharePoint. In SharePoint, documents can be linked across the organization; enterprise search and Web forms can be used; document management can be leveraged; and the various functionalities can be accessed within the same portal and within the BI structure as well.

In terms of functionality, accounting for SCDs is an important factor when implementing a BI solution across an organization. SCDs are an evolutionary process because business needs change over time. This means that the data identified and captured, as well as the relationships identified between data elements, likely will change over time. Therefore, having a built-in wizard that helps account for business model changes that can be related to metrics set is an important (and often overlooked) feature that is not always provided in a user-friendly manner—or at all—by other vendors.
Microsoft's late arrival to the market may have consequences, as many other BI and performance management vendors already integrate with Microsoft. Within the enterprise market, many BI tools have already been implemented across the organization, thereby making the adoption of yet another Microsoft product unlikely. Although Microsoft may choose to focus on BI and its complete performance management offering, the functionality provided by BSM does not outweigh that provided by other vendors. Microsoft's BSM functionality is fairly standard in terms of its competition. Additionally, even though Microsoft is focusing on using its partnerships to increase its user base, the BI suite offered has not been differentiated enough to provide users a reason to implement it on top of an existing BI or performance management software suite.

Business Intelligence Software Components

Microsoft's BI offering is built on an inclusive platform designed to encompass an entire business intelligence suite, while being fully integrated with its other product offerings, including Microsoft Dynamics (enterprise resource planning [ERP]) (for more information on Microsoft Dynamics see Microsoft's Dynamic New Approach to Professional Services Automation). Its BI product line includes online analytical processing (OLAP), extract, transform, and load (ETL), and reporting services, as well as providing front-end scorecarding functionality that integrates with SharePoint and Excel, making it an integrated approach to BI.

Business Scorecard Manager (BSM) 2005 sets and monitors key performance indicators (KPIs), allows teams to analyze issues based on metrics set, and allows users to drill down to data stored in the SQL Server to identify the structured and unstructured source data, in order to view more detailed information. BSM offers collaboration among users, personalized notification of status changes within a scorecard, task assignment to individuals based on KPI, and distribution of scorecards via email.

SharePoint Server 2007 provides a centralized Web portal for Microsoft's BI offerings. Information can be distributed across the organization to enable all employees to collaborate, edit, post, distribute, and reuse documents and information. Other applications using SharePoint can be leveraged as well, aligning BI with other business initiatives.

SQL Server 2005 RDBMS uses its SQL Server BI Development Studio as an integrated development environment (IDE) for the development of its BI applications. The BI Development Studio is a centralized tool to leverage multiple technologies such as OLAP, relational databases, reporting, Web pages, and Microsoft coding languages. It includes the following components:

* SQL Server Integration Services (SSIS) provides ETL capabilities. In addition, data can be transformed without the use of staging tables, thereby reducing data latency. Both structured and unstructured data can be extracted and converted between various types of data such as numeric and string (and so on), and data audits can be performed. Also, to account for slowly changing dimensions (SCDs), SSIS has a built-in wizard.

* Analysis Services provides data mining and OLAP capabilities to enable users to identify and set KPIs, set custom aggregations and semi-additive measures, and create decision trees and perform regression analyses to identify forward-looking business needs. Unified Dimensional Model (UDM) uses one platform to address the needs of multiple dimensional models and relational reporting, creating a centralized structure improving the performance of summary type queries.

* Reporting Services provides advanced authoring tools to allow ad hoc and user report creation through the use of its Reporting Services Report Builder. Report Builder allows users to create reports from an existing business model, build new reports, modify existing reports, and build reports based on OLAP or relational database tables. Aside from the flexibility of its interactive reporting environment, Reporting Services leverages multiple data sources and provides the ability to embed its Web services architecture, to allow users the ability to collaborate on initiatives for Web-enabled reporting tools.

Additionally, MS Excel 2007 is the newest version of Excel that works with the BI platform identified above. It leverages performance management functionality within its structure by using SQL Server 2005 to embed heat graphs, KPIs, and so forth, allowing trends to be identified more easily. Excel spreadsheets can also be published to the SharePoint server to allow collaboration among various employees across the organization as well as interaction with real-time data that can be published to the server.

Microsoft Takes A Shot at the Business Intelligence Market

Although business intelligence (BI) touts itself as being able to provide analytics to the general user population within organizations, in most instances only a small portion of decision makers actually use BI tools and applications. Generally, applications are implemented in silos across an organization. In some cases, this means implementing various applications and vendor solutions based on the needs identified within individual departments. For example, an organization may implement a Cognos solution to manage its sales force performance, and a Hyperion solution because of its strong financial consolidation functionality within the finance department. As BI has changed to accommodate real-time data analysis, and to provide forward-looking business planning and strategies, the need to bring a single set of analytical tools to every decision maker across the organization has grown.

Enter Microsoft, with its release of Business Scorecard Manager (BSM) in November 2005. Microsoft's goal by adding BI to its platform is to bring BI to the small-to-medium (SMB) market, making it accessible to all decision makers across the organization. Additionally, Microsoft's recent acquisition of ProClarity and its recent announcement of next year's full release of PerformancePoint 2007, an integrated performance management suite, indicate that its focus now includes the BI market. Microsoft's low cost high-volume strategy, and its goal to make its BI and performance management suite as popular as e-mail, will help drive demand for BI in the broader market, and push towards the performance management concept of using software to help drive an organization's business goals.