Informatica Perspectives

Predicative analytics sifts though current and historical data to predict future events or behaviors. It incorporates statistical and data mining techniques to determine patterns, highlight risks and identify opportunities.

It doesn’t just extrapolate future performance based on past performance, although that is an input. It also identifies the relationships across many factors to lay out not only what is likely to happen, but also why it may occur along with potentially how to alter the outcomes.

Where there’s data warehousing, there’s predictive analytics. It is increasingly in many industries including retail, telecom, pharmaceutical, insurance and financial services. These industries typically leverage predictive analytics to analyze and predict consumer and business behaviors along with economic predictions. [Read more]

The United States and world economies are going through tough times. Many financial services firms have suffered severe losses and many other industries are being impacted. Although business spending on IT has yet to contract overall, it has been reduced in the worst hit sectors to date, and is likely to be challenged overall in the coming months.

Although IT spending is not immune from a downturn, there are many facets of this spending that have become invaluable to business such as data warehousing and business intelligence.

At the heart of DW and BI programs is data integration. Whether it is performance management, predictive analytics, Master Data Management (MDM) or other data-centric initiatives, data integration is the cornerstone of these efforts. [Read more]

To date many companies have fragmented their integration efforts across applications and groups. The classic split is between the group developing data warehouse and business intelligence applications versus the ERP (enterprise resource application) applications. Typically with this fragmentation different integration approaches are taken with ETL being what is used with data warehousing /business intelligence and either EAI or EII used with the ERP applications. In addition to these integration silos, many companies today have, or are launching SOA (service oriented architecture) initiatives generally independent of either the data warehousing/business intelligence or ERP applications.

If achieving consistent numbers across reporting is a goal maybe the investments should be in data integration. When people look at inconsistent reports or analysis that use different BI tools it is easy to understand why they assume the problem is using different tools. Using different BI tools, however, is a symptom rather than the reason for the inconsistent numbers. The symptom is silos using different BI tools but the underlying reason are data silos created using different data integration tools, processes, standards and people. The best practice is to establish an ICC (Integration Competency Center.)

ICCs also need to take into account the BI practice. There has been a good deal of attention paid to BI tools over the years. Many companies have instituted a BI-CC (business intelligence competency center), a concept promoted by Gartner research. [Read more]

There is a lot of worry on Wall Street and Main Street these days. Are we in a mild or severe recession? Is it the next Great Depression? How long will it last? No one knows the answers to these lofty questions, but Forrester Research has been busy recalibrating on the impact the economy is having on IT spending.

First, the good news, IT spending was better in the first half of this year than expected. The bad news, IT spending is being hit adversely now and probably into 2009. According to Forrester Research:

“The economy's affect on IT spending is evident in some specific data points contained in the report: Forty-three percent of firms have already cut their overall IT budgets in 2008 in reaction to the slow down in the global economy, while 24 percent of firms have put discretionary spending on hold. Twenty-eight percent of respondents said the economy has had no impact on their IT budgets.”

“Forrester: Impact Of Economic Downturn On Tech Spending Varies By Region And Sector”, Forrester Research, September 9, 2008

Even under the best circumstances it’s important to maximize the value from your BI/DW projects.  But with these conditions it becomes even more of an imperative.

No one can afford to be sloppy or wasteful in their business intelligence and data integration strategies. Cost cutting and getting by with what you have is the norm.

But mistakes are expensive. Businesses, now more than ever, need to understand who their current and potential customers are as well as how much revenue and profit each product or service line generates. This demands current, consistent, clean and comprehensive data. [Read more]

It’s funny that Forrester’s Boris Evelson recently received a slew of comments from data warehousing and business intelligence pros wondering why they hadn’t included an evaluation of Excel as a business intelligence tool. Boris goes on to explain why Excel doesn’t work as a standalone BI tool.

Here’s my take on the subject:

Like it or not, the most widely used BI tool today is the spreadsheet. If you are a BI or data warehousing manager, you need harbor no illusions that you are gathering and manually entering data into spreadsheet. Furthermore, you need to work with the logic captured in Excel spreadsheets and enable this logic to be used for analysis and decision support.

[Read more]

People have been using the term “business intelligence 2.0” for a few years, but it’s described in different ways. In Business Intelligence 2.0: Simpler, More Accessible, Inevitable Neil Raden says:

"…the current era of BI is coming to an end and will be succeeded by a BI 2.0 era that promises simplicity, universal access, real-time insight, collaboration, operational intelligence, connected services and a level of information abstraction that supports far greater agility and speed of analysis. The motivation for this "version upgrade" for BI is the need to move analytical intelligence into operations and to shrink the gap between analysis and action."

Charles Nichols writes, in BI 2.0: The Next Generation that:

"BI 2.0 is a term that encapsulates several important new concepts about the way that we use and exploit information in businesses, organizations and government. The term is also intrinsically linked with real-time and event-driven BI but is really about the application of these technologies to business processes."

BI 2.0 is not really about a new generation of BI tools to perform analytics but getting more comprehensive, consistent, correct and CURRENT data.

[Read more]

Maintaining product lists is often cited as a great example of Master Data Management (MDM). Many companies that manufacture or sell products need to get a consistent list of products for a variety of business reasons. The business value includes tracking what you sell to customers and also how you manage your supply chain. Product firms create products organically (internally) and through acquisitions. In both cases, each product line has, at least for part of its life, been operated as a separate business. At some point in the product life and sales cycle, business conditions dictate a transition into the company's product portfolio. Although managing product lists is not always a simple task, it is only the tip of the iceberg for companies that design or engineer products. These companies have a need for a more complete PIM (Product Information Management) solution that extends far beyond simple product lists.

If you design or engineer products, then you need to track product designs and configurations that evolve and change over time. This applies to many manufacturing industries from high tech, consumer products, defense, and automobile to even farm machinery. These designs and configurations are likely scattered across many databases and often unstructured data sources. This data is not stored in your classic data warehouse (DW) or integrated through your run-in-of-the-mill ETL tool. You need to think outside your typical DW effort and determine how to get that data integrated into your PIM solution.

I did work for a farm machinery company many years ago. Their product data issues involved configuring combines, harvesters and other machinery that cost six and sometimes seven figures. This data challenge was not a trivial endeavor. These farming machines are highly customizable by the agricultural organizations purchasing them. The vendor needed to track what was available, what was sold and to whom. The customers needed to know what was available for their agricultural needs, have those machines built and then be able to service them for years.

What have these companies done and what should you do if you are just starting to design and implement a PIM solution?

• Get your data warehousing and data integration in place to support the classic product, sales and customer data stored in your various source systems
• Work with your engineering and product design groups to understand what they have in place to manage engineering drawings, specifications, configurations and all the associated versioning
• Get your sales and customer support organizations to define the PIM requirements
• Implement a data governance effort to get a handle on both your structured and unstructured data
• Leverage data integration capabilities that can handle the variety of data supporting PIM

The business benefits are to both the top line (increased sales) and bottom line (managing and reducing costs) when engineering and product design firms implement a PIM solution.

Healthcare is one of the last industries where you hear the term MDM (Master Data Management) mentioned. Most IT industry analysts, software firms and consulting organizations are geared towards your typical company that sells products to people or businesses.

MDM examples are always getting a master list of products or cleansing your way to a consistent list of customers, which is not exactly the mindset of healthcare organizations. But lack of MDM is precisely what is adding untold costs on healthcare organizations (and ultimately on all of us) and inhibiting these organizations from improving the quality of health care services at an affordable cost. [Read more]

The most recent TDWI Boston Chapter meeting focused on how companies should approach and implement Master Data Management (MDM). Although the meeting had a keynote presenter and panelists with strong industry expertise and experience, the key to the discussions were the questions and insights of the audience attending the meeting.

The Boston chapter, with industries representing financial services, insurance, high tech, medical devices, biotech, retail, professional services and consumer products goods companies offers a diversity of perspectives about the challenges and benefits of addressing MDM.

Two key insights kept being reinforced during the discussions:

1. Leverage, Leverage, Leverage

Any company that will benefit from tackling MDM has most likely already been attacking the problem but not in the focused manner that MDM needs to truly be successful. But the biggest mistake that people saw with their peers and early adopters was the belief that MDM was something different than before. [Read more]

The most recent TDWI Boston Chapter meeting focused on MDM (Master Data Management). The keynote presentation titled “MDM: Data Salvation or the Next Round of Silos?” was followed by a panel discussion. The panelists included representatives from IBM, Oracle (Hyperion), SAP (Business Objects) and Kalido.

In keeping with TDWI principles, the panel discussion concentrated on the how’s and why’s of MDM in customer situations and avoided sales pitches. I moderated the panel discussion and was very impressed with the panelists’ knowledge and insights on their customers’ experiences in approaching and implementing MDM.

The panel discussed the most common characteristics that customers who are experiencing success in their MDM efforts have:

“Educated consumers”

There is a men’s clothing store that has an ad that says an educated consumer is our best customer. That is absolutely true in MDM. Companies that have been involved in data warehousing and enterprise data integration understand the complex and conflicting world of trying to get consistent, comprehensive and current master data or conformed dimensions in DW terminology.

Significant business participation

MDM is not a program that can be undertaken with IT alone. In fact, if you do not have business participation in the beginning there is little chance of success. IT has to sell the MDM program to the business and get real resource and time commitments.

Data governance program

Data governance is more important than what products you use to implement MDM. The old saying about garbage in, garbage out (GIGO) is absolutely true in MDM. You can’t cleanse data nor make it consistent unless you can define the data, business rules/transformations and performance measures you wish to use across your enterprise.

Enterprise Data Integration

Companies that have been implementing enterprise data integration efforts can leverage those efforts in their MDM program. They have gained an understanding of the complexities that a formal MDM effort will entail. For these companies MDM is not a new thing but simply taking their EDI efforts to the next level.

MDM is a journey and companies that have been in the trenches trying to address the problems of inconsistent data are well-positioned to take the next step.

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