BLM--Buzzword Lifecycle Management
From Technology Evaluation/William Sheppard
The information technology (IT) industry is alive with buzzwords (BW). The management of BW represents a significant area for improvement for both the BW users (BU) (for example, vendors, analysts, and consultants), BW consumers (BC) (mostly end-users), and BW fellow travelers (BFT) (for example, the media). BW lifecycle management (BLM) is a proven discipline being applied to this crying need within the software industry.

A BW is a word or phrase that enters the collective mind of the marketplace (MP) and that signifies some set of ideas. To become a true BW, it must be universally applied and lose all meaning.

BWs are often supplemented with acronyms. Acronym lifecycle management (ALM) is a closely related science to BLM. The creation of the BW versus the acronym is often a chicken and an egg issue: which comes first is often driven by how clever the acronym is (with the BW designed to fit the clever acronym). If the BW comes first and it gains acceptance, an acronym is usually created with the intent of pointing out to those people not in the know that they are not in the know, to the benefit of those who are in the know. (Why is it that no acronyms exist for acronyms?)

The lifecycle of a BW is described by the well-known IRCED2 cycle:

Invention: BWs are not discovered: they are invented (hence, the process of BW invention [BWI]). They do not occur in nature, only in the minds of a select group of people, the BW creation resources (BCRs), typically located in the marketing department (MD) of the BU. The motivation of the BCR is pure, as it is driven by financial considerations. A good BW will one-up the competition, sell more books, and allow the recycling of old reports with new covers. BUs have put significant BCRs in place to build and maintain a BW leadership position (BLP). The BW inventor (BI) positions the potential BW in the MP, with the MP deciding if it justifies true BW status. The primary determinant of BW acceptance is the MD budget of the BI. The MD budget is also known as the BW leadership capability (BLC).

Thus, we have seen BLM come from the minds of a few highly trained BCRs, and placed into the MP of BWs.

Recognition: If the MP accepts the BW, it has gained BW recognition (BWR2) status. BWR2 is signaled by three nearly simultaneous events. The market place remembers the BW, and competing BUs and BW followers (BF) begin using the BW. BWR2 also sees the first use of the BW from the BFTs. These events impact the meaning of the BW. The MP does not know the original meaning of the BW, so it either pretends to understand, or each member of the MP makes up its own meaning. The competitors use the BW, but assign a meaning that is more advantageous to them in terms of financial reward. The BFT focuses on the BW in a way that creates a “need to know,” which leads to selling more magazines and books. Thus, BLM has reached BWR2 status in the MP of BWs, and has been copied and redefined to meet the needs of the masses. For example, this BFT has focused on BLM in order to gain its own recognition in the MP.

Compliance: BWR2 leads to a mass application of the BW to existing products, reports, ideas, and so on. BFs rush to apply the BW as often as their MD budget allows. Now, every BU seeking attention in the MP is using the BW. It is important to note that BW compliance (BWC) does not mean any change to the underlying products and ideas to which the BW is applied. The same products, reports, and ideas that existed before the BWR still exist without change. They have been relabeled with the BW in an attempt to get the MP to see their existing (old) products, reports, and ideas as “new.”

Erosion: BW erosion (BWE) begins once the BW attains the status of BWC. Full BWC means that it no longer has any meaning. The word has been twisted in so many directions that no meaning exists. The BW begins to lose its ability to produce financial gain in the MP.

Derivations: As BWE is detected, the BW is extended or modified with the addition of one or more words. Typical words used for BW derivations (BWD1) include “based,” “centric,” “compliant,” and so on. Thus, we see some sources claiming that they are BLM-based or BLM-centric. The word that signals the ultimate BWE is the prefix “true.” Alas, the BWE of BLM has been signaled by the claim by some parties that they have true BLM.

Death: With both the passage of time (and more importantly, the entry of new BWs into the BW MP), the BW dies. BW death (BWD2) does not mean it is forgotten. BWD2 means that it is no longer a positive in the MP, but a negative. A BU clinging to a BW that has reached BWD2 is signifying that it is not “with it”.

BCs should place those vendors with a tradition of BLP on their short list. These vendors have proven the ability to continually redefine the needs of the MP in their favor, creating significant value for the BLP.

Vendors—All vendors must decide on their BW strategy. Vendors choosing the BF strategy need to watch those with a BLP and quickly follow their lead. Those seeking a BLP should invest heavily in both BCR and BLC. If they are successful, they will be recognized for their BW superiority (BS).
http://www.technologyevaluation.com/


Using Predictive Analytics within Business Intelligence: A Primer
From Technology Evaluation
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.

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.

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.

Practical applications of all of these analytical models allow organizations to forecast results to predict financial outcomes, hopefully increasing revenues in the process. Within BI, aside from financial outcomes, predictive analytics can be used to develop corporate strategies throughout the organization. What-if analyses can be performed to leverage the capabilities of predictive analytics to build various scenarios, allowing organizations to map out a series of outcomes of strategic and tactical plans. This way, organizations can implement the best strategy based on the scenario creation.

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.

Using predictive analytics helps organizations identify forward-looking trends based on identified data patterns. Predictors and models can be used to discover sales patterns and detect high risk credit card holders. They can also be leveraged and embedded within BI and BPM solutions. Organizations using BI and performance management tools should take advantage of the built-in predictive analytics tools to perform what-if scenarios, create regression models, and build decision trees to benefit from the patterns identified within the data mining tools that are embedded within BI.

Performance management initiatives within an organization can help drive forward-looking business decisions. Whether for the finance department, government compliance, call center performance management, or an organization's sales and related shipping patterns, developing what-if scenarios and using predictive models, the use of these techniques within performance management has changed the face of BI.

Selecting the appropriate predictive analytics tools is not a simple task. The following capabilities must be considered before implementing a predictive analytics tool: algorithm richness, degree of automation, scalability, model portability, web enablement, ease of use, and the capability to access large data sets. The more diversified the business, the more functions and unique models that are required. Model portability is important even within different business units in the same company. The scalability of the solution and its ability to handle expanded functionality should also be verified and based on the growth of a business.
http://www.technologyevaluation.com/


Removing the Bull-Whip Effect by Using Integrated Business Systems and RFID
From CapGemini
Directors of small and medium-sized businesses dragged into discussions about supply and demand are always alarmed when the conversation turns to the "bullwhip effect". Is the whip really necessary to get products delivered or orders placed on time, they ask. Somebody responsible for the supply chain will explain that the term refers to the curve that often appears in a process that should really be a straight line.

"Oscillation often occurs in the supply chain, especially if the manufacturer is in one country and the customer in another, with someone in between required to hold buffer stock," says John Mangan, professor at the recently launched Institute of Logistics at Hull University Business School. "Stock levels become unsynchronised, with someone along the way over or under ordering." This costs time and money and can threaten future business. Two technological developments, however, can get rid of the bullwhip effect: integrated business systems that give everybody in the chain immediate access to what is going on and radio frequency identification, a tagging system that tracks goods along the chain. "They are giving us what we have always dreamed of, end-to-end visibility, and have the potential to revolutionize the supply chain," Mangan adds.

Rexam, the soft drinks canning company, is one of many medium-sized businesses that have benefited from integrated processes. It wanted a system that would link its production facilities with its suppliers and customers, including Coca Cola, Pepsi-Cola and Dr Pepper, and turned to Capgemini, the IT services and business consultancy.

The new system ensures that staff in the factory know as soon as an order is received as will the suppliers of the aluminium to make the cans. "This makes sense from an organizational point of view but also helps to cut costs because Rexam always has only the aluminium it needs," says Jonathan Ebsworth, a vice president of Capgemini. While just in time supply provides cashflow and cost benefits, the systems that enable the supply chain to be more efficient can also help with customer service. Another Capgemini client has built on the savings it had made in the supply chain by adding a function to let customers know when they can expect products to be delivered.

Ebsworth adds: "When a customer places an order for machinery, the system lets the company know what materials it must order, checks what materials are in stock, works out when the machinery will be ready and gives the customer a delivery date.

The company has rolled this out on to a business to business web portal so that customers can go online, place an order and get delivery confirmation within seconds. The system confirms the order, an invoice is prepared, all without any human intervention."

Mangan adds that companies must analyse the benefits of integrated systems carefully "because they tend to be expensive and complex" and be aware of the complications that can arise if they change from one system to another, because of the number of processes that are already embedded in the original system.
http://www.capgemini.com/


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