The Business Intelligence Blog

From the Press Release:

WiseWindow, developer of Mass Opinion Business Intelligence, the next generation of web measurement, today announced that company founder and chief technology officer, Rajiv Dulepet, has been named advisor and architect for a new project funded by the National Institute of Health and executed by Caltech. The open-source project will develop a web-based bio-computational tool that allows bio-scientists and bio-computation engineers to “crunch data in the cloud” for large-scale tasks such as processing gene sequence data sets on a large cluster of computers. The new tool allows scientists to save considerable time that’s now spent waiting for computations on their desktops by moving these operations to the cloud, thereby freeing up their computers for other work.

“Working as a lead advisor to Caltech on cloud computing is both a privilege and passion for me,” said Dulepet. “It allows me to exercise skills in Internet data gathering and analysis as well as computational framework development.”

The days have changed forever as far as the user interfaces are concerned. In the past, instead of developing user-friendly systems, the technologists had to look for “system-friendly” users. Because of new “consumer-friendly” interfaces, many more people are using computers not only in their workplaces, but also in their personal lives, day in and day out. Since the interfaces developed for the consumers by Lands’ End, L.L Bean, Costco, WebMD and the like are much friendlier, the same consumers – when they function as employees in their jobs – have come to expect similar ease of use and interactivity.

The businesses are aware of it, and if they are not, they should be. They have to not only provide friendly interfaces, but they also have to provide access to information to the right people, at the right time and at the right price so that the employees can make sound business decisions. That is business intelligence in a nutshell.

From a well thought about article on B-Eye Network by Shaku Atre.

Chris Brady, CIO at 450-employee Dealer Services Corp., a Carmel, Ind.-based financer for car dealerships, is starting to play with predictive analytics as well. In particular, she is looking at a forecasting tool by SPSS Inc. to gather and model data from the data warehouses in order to analyze what is happening at a dealership before deciding whether to approve a loan.

A dealership’s credit worthiness is not as black and white as a consumer’s, Brady explained. “Sometimes what looks like a bump in the road is just that — it’s not a sign that they are going out of business, and if we take the wrong action without the right information we could put them out of business,” she said. “We need to be able to see patterns in our customers’ behavior to predict what may happen next, and take the right course of action based on those patterns.”

From Search CIO article.

Over at The Health Care Blog, Deb Bradley, Vice President, Client Solutions at Verisk Health in Waltham, Massachusetts writes about some examples of the next generation healthcare analytics. Most of us who do analytics engineering as apart of our day jobs would agree, healthcare is on area where analytics should grow vastly. There is a lot of data that can be intelligently massaged to answer some of the most challeging health related questions.

Medical claims, pharmacy claims, lab values, HRAs, genetic markers, biometrics – the abundance of data is having an immediate impact on how analytics shape healthcare. Next generation analytics are bringing attention to health and wellness rather than disease-specific guidelines, and generating novel approaches to value-based medicine and care management.

Traditionally, analytics, such as predictive modeling, have been used to identify individuals for chronic care management and to set rates. New predictive models, however, include financial and clinical algorithms, which allow healthcare organizations to implement advanced ways to identify, manage and measure risk across and within a population.

ARRIS (Nasdaq: ARRS), a global video, data, voice and next-generation advertising technology supplier and OpenTV Corp. (Nasdaq: OPTV), a leading software and technology provider of advanced digital television and advertising solutions, announced that they will present a joint demonstration of their next generation linear television ad platform at the upcoming NCTA Cable Show, April 1-3 in Washington, D.C. The collaboration is designed to make TV advertising more relevant, accountable and dynamic and revolutionizes traditional ad insertion technologies.

From Press Release.

A must read about Semantic Intelligence, the next generation business intelligence. Very similar to the concept of semantic web.

Semantic intelligence provides early identification and analysis of consumer sentiment, purchasing trends, market deals, and competitive information – and uncovers this data not only from within a organization’s network, but also from the most unstructured corners of the Web. You may be thinking that a normal Google search can uncover any Web-based information, but unlike simple keyword search, semantic intelligence uncovers the meaning the words express, in their proper context, no matter the number (singular or plural), gender (masculine or feminine), verb tense (past, present, or future), or mode (indicative or imperative).

For example, say you’re a chef and you’re looking for details on how to make soup with healthier ingredients, so, you keyword search “apple stock.” Try it right now – you’ll get dozens upon dozens of pages about Apple, the company. If you try to narrow the search and type, “apple stock and cook,” you will still get hundreds of erroneous search results about Tim Cook and Apple, the company.

Semantic intelligence incorporates morphological, logical, grammatical, and natural language analysis that translates into higher precision and recall when searching for information. By providing information in the requested context and form, semantic intelligence helps organizations strategize, analyze, and make predictions because you’re getting the correct data – and in these economic times, having the right foresight can save a business.

TDWI has a great article on Data Warehouse Appliances which includes all-in-all solution for enterprises. Neat Read.

In the BI world, the data warehousing appliance extends this metaphor to the enterprise data center with the vision of a high-performance database system that satisfies business intelligence (decision support) requirements and includes the server hardware, network interconnect, database software, and selected load, workload, scheduling, and administration tools needed for quick installation, loading, and ongoing monitoring.

Enterprise data warehousing appliances are popular because they get the job done in many data scenarios. However, in spite of their significant success, data warehousing appliances are not a one-size-fits all proposition, nor, as any vendor will tell you, are they appropriate for every workload profile or data warehousing challenge. A diversity of appliance vendors have emerged, including appliance offerings from the large, established information technology (IT) stalwarts such as HP, IBM, Oracle, and Microsoft. Teradata objects to be called an “appliance,” though it also objects to not being named as an IT stalwart that is relevant to the appliance market.

Best-of-breed innovators continue to contribute to market dynamics. Key differentiators — about which, as a prospective buyer of a data warehousing appliance, you should examine –include the number of successful installed customers in production willing to speak about their experiences (both positive and negative); the details of the technology itself (whether the database is open source and how it is customized, whether the server, disk, and networks are a commodity components and how they can be customized; the breadth and maturity of complementary tools such as inquiry and reporting, ETL, data quality solution); and the price of acquisition and cost of operation. Published results from public benchmarks (such as tpc.org) are also useful for starting a conversation about performance and price, though don’t rely exclusively on the benchmark “winner” since results are frequently updated.

For most of its eight-year history, Cataphora has focused on digital sleuthing. The company hunts for statistical signs of fraud. But in the past few years, Cataphora has been dispatching its data miners into a new market: statistical studies of employee performance.

The trend, though early, is unmistakable, and it extends far beyond Redwood City. Number crunching, a staple for decades in the quantifiable domains of engineering and finance, has spread in recent years into marketing and sales. Companies can now model and optimize operations, and can calculate the return on investment on everything from corporate jets to Super Bowl ads. These successes have led to the next math project: the worker. “You have to bring the same rigor you bring to operations and finance to the analysis of people,” says Rupert Bader, director of workforce planning at Microsoft (MSFT).

Such a mission might have been laughable a decade ago. But as the role of computers in the workplace expands, employees leave digital trails detailing their behavior, their schedule, their interests, and expertise. For executives to calculate the return on investment of each worker, their human resources departments are starting to open their doors to the quants.

From Business Week, an insightful article on how value of each employee is determined by HR using Data Mining/Analytics.

Bill Inmon writes on the virtual data warehouse. Interesting Read.

Why then is the virtual data warehouse such a supremely bad idea? There are actually lots of reasons for the vacuity of virtue manifested by the virtual data warehouse. Some of those reasons are:

A query that has to access a lot of databases simultaneously uses a lot of system resources. In the best of circumstances, query performance is a real problem.

A query that has to access a lot of databases simultaneously requires resources every time it is executed. If the query is run many times at all, the system overhead is very steep.

A query that has to access a lot of databases simultaneously is stopped dead in its tracks when it runs across a database that is down or otherwise unavailable.

A query that has to access a lot of databases simultaneously shuffles a lot of data around the system that otherwise would not need to be moved. The impact on the network can become very burdensome.

A query that has to access a lot of databases simultaneously is limited to the data found in the databases. If there is only a limited amount of historical data in the databases, the query is limited to whatever historical data is found there. For a variety of reasons, many application databases do not have much historical data to begin with.

Sensors everywhere. Infinite storage. Clouds of processors. Our ability to capture, warehouse, and understand massive amounts of data is changing science, medicine, business, and technology. As our collection of facts and figures grows, so will the opportunity to find answers to fundamental questions. Because in the era of big data, more isn’t just more. More is different.

This month’s Wired magazine carries one of the most important growing concerns of the scientific community, the uncontrollable growth of data. This growth of data in many directions is nearly killing theories as everything is becoming more and more data controlled.

There are a series of articles ranging from what data miners are digging today to elaborate algorithms that predict air ticket prices to how we can monitor epidemics hour by hour.

If you are a BI entusiast or not, this month’s Wired cover story will challenge all your predictions about science and technology, even if you have a petabyte of data to support it !! Read it, like, right now !!