Sybase IQ

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April 23, 2010

Expanded Options for Sybase IQ on HP Integrity Servers

While Sybase IQ runs well in a variety of platforms, Hewlett Packard has a strong record of innovation in their Integrity server line, providing growth in performance, flexibility and affordability that has attracted many Sybase IQ users.

If you are a user of HP Integrity servers or Sybase IQ and are looking for the latest hardware platforms, I’d like to bring an upcoming event to your attention.

HP and Sybase invite you to attend a webinar on Hewlett-Packard’s latest innovations in the Integrity line, on Tuesday 4/27 at 11:00 EDT. The event will include important new information from HP.

You may register for the event on this registration page. Should you have any questions, feel free to contact Sybase’s Joe Santos for more information.

Have a great afternoon,

Bill Jacobs

Posted by Sybase IQ at 10:07 PM

April 22, 2010

Leading Indicators

I was just looking over some notes on the recent earnings announcement from IBM and something caught my attention. IBM looks to be doing quite well, with 11% growth in their overall software business and an unspecified double-digit growth in their analytics software (Cognos) business. Glad to see a partner company doing well, of course, but what really caught my eye was an observation about the uptick in services revenue. A pick-up in consulting business, it was noted, is typically a leading indicator for IT spend.

Very interesting, and potentially a sign of better times for us all on the horizon.

In pondering IBM’s results, I got to wondering whether there are any leading indicators to be found in Sybase’s Q1 results as just announced by our Chairman, CEO, and President John Chen.

If there are, the first thing we will note is that these indicators could speak to some fairly long-term trends. Sybase has recently completed its third consecutive record year — imagine that, in light of what has been happening with the economy overall during that time — and we have just announced that Q1 of 2010 was another record quarter. That makes 10 such quarters in a row for those who are keeping score at home. (If all this sounds kind of familiar, it should.)

We’ll take as a given that these results show outstanding planning and execution on the part of Sybase. The question is, is there another trend over the past three to four years that such results might reveal? Note that our total revenue in Q1 grew 10% year over year, and specifically, database license revenue this past quarter was up 25% compared to Q1 of 2009. John says that Sybase’s overall offering in the analytics space (including RAP, CEP, and Sybase IQ) is “an engine” of this strong momentum. This would also seem to echo what IBM was saying about the contribution of Cognos to their overall software growth.

Among other trends, the success that Sybase has had over the past few years looks to be an indicator of a major shift towards analytical performance, over and above operational performance, as a core focus of IT infrastructures within business. Good economic times, bad economic times, it doesn’t matter — this shift is happening, and Sybase is at the forefront.

Organizations are increasingly on the lookout for technology that will turn their information into better decisions. Our customers (and others) clearly recognize the technological and market leadership that Sybase continues to demonstrate in analyzing, as well as managing and mobilizing, data. It’s no stretch to predict that these results are also an indicator that Sybase’s leadership, and widespread recognition thereof, will continue to grow.

Posted by Sybase IQ at 4:12 PM

April 21, 2010

Guest Blog: Business Benefits of In-Database Analytics

During a recent blog entry Introduction to Fuzzy Logix and In-database analytics, I wrote about high performance analytics and how they can be pervasively deployed. In this entry, we’ll highlight some of the business benefits of in-database analytics with examples from recent client engagements. All of our examples share a few common themes. Each company benefited from putting analytics into the hands of businesspeople and each did so at multiple levels in their organizations. They also leveraged advanced analytics to find previously hidden patterns of behavior that led to changes in their business practices and all witnessed significant business benefits. Let’s look at a few examples that include forecasting, customer targeting and cost of sale reduction, and provider scoring and fraud for insurance.

First let’s look at forecasting. Usually, forecasting is a process driven by sales people and management or perhaps, pushed down via finance. Either from the field up or from the executive suite down, there is always some tweaking along the way to the final ”guestimate.” While these processes are common and indeed necessary (because they capture the viewpoint and experience of the business), they are often time consuming and based on many layers of subjectivity. It can help a great deal to pair a subjective forecast with a quantitative forecast based on transaction data. Think of it as a ”check and balance” for the process. While the idea is sensible, in practice it can be challenging.

A good example is deploying a forecasting system for one of the largest consumer packaged goods manufacturers in America. They have hundreds of SKU’s, over 500,000 sales locations and 18 months of transaction data; about 2 Billion rows in total. Add a matrix management structure and the requirement to allow each manager to forecast their area of responsibility plus related areas and it became clear that a traditional approach to analytics would not work. If we tried to use their already overworked statisticians, who had to move many set of data and then run individual models, we’d never finish in time to deliver the forecast and we certainly wouldn’t be able to make this part of their weekly operational process.

Our innovative solution was to leverage the power of in-database analytics and deploy the forecast via their existing reporting environment. Now managers simply select from a list of drop down parameters, for example they can choose to forecast all sales of a particular brand (or group of brands) in a state then see the expected changes in demand from worst decline to highest improvement. They can even click on button and have the report sent via email to the person who can address the issues. This allows our client to focus their teams on key areas of future risks and start to address issues before they happen. Now their sales teams work with distributors and customers to address the root causes of loss of future sales and in many cases their actions can reduce or eliminate the lost sales. There are many other benefits, such as decreasing variability in their supply chain and cash flow, but we’ll leave that article for later.

As for customer targeting, it can take many forms. Many of our clients want to understand their most profitable customers and then target that part of their business for growth. Many want to personalize ads for each customer and prompt them for their next likely purchase or reduce customer churn. Let’s use an example that combines reducing churn with stimulating next likely purchase. Based on transaction, customer and demographic data, we can use in-database analytics to understand the patterns of behavior that indicate that a customer has a high propensity to attrite. Once we know who has is likely to leave, we can target them with a number of programs including suggesting their next likely purchase. For one of our clients we did just that and saw a 10% reduction in churn. Additionally, once we developed our model for churn, we built a tool for marketing to understand the churn adjusted cost of sale for each marketing program. While this example was for a company with a large call center, we are also working with a telecom company to combine social networking analysis with the programs mentioned above to ensure that those with a high propensity to churn, who are also influencers of behavior, receive special attention because losing an influencer can have a painful add-on effect as those in their network also attrite.

Finally, let’s look at provider scoring and fraud in healthcare. One of the largest insurers in America wanted to score the quality and efficiency of care given by their providers. The process took 6 weeks and required the IT team to run the legacy programs 25 times. Since the scoring was based on patterns in claims data, the volume was simply too large to process it all in one run with their row-based RDBMS and traditional analytics software. This meant they need to move the data from their EDW to an analysis server for each run. Using in-database models, we were able to reduce the time needed to process the scoring models to less than 2 hours (and one model run). So the while the difference in time was 6 weeks to 2 hours, the effect on risk management was dramatic. Now the company could evaluate the doctors giving care to their customers on a weekly basis (or on demand) and immediately identify situations when the care provided was not up to standard.

We are now working on a project to find hidden fraud and expect to see benefits in both speed of processing and fraud prevention. Our client reviews deviation from the norm in charges for services. They analyze 10 practice areas (cardio, OB/GYN, etc.) on a state-by-state basis. We wanted to know why they didn’t just run all practice areas and combinations of charges and identify the areas with the highest potential risk. We also wanted to know why they were only looking at standard deviation and were not using clustering (segmentation) or market basket analysis to find the hidden deviations in the care given to patients. The reason was that they couldn’t build the models without buying expensive and complex software and also didn’t have the processing power. Another issue was that they wanted to put the analysis tools in the hands of their employees in the special investigative unit (SIU) so they could analyze the data and act quickly. To satisfy their needs, we are building a custom set of models for their end-users. The models will deployed will include types of outlier analysis, market basket analysis and classification models (such as multiclass support vector machines). Soon, investigators will simply run the models and then look at the cases of highest likely fraud. They will also have the flexibility to drill down and see the reports based on geography, practice area or even episodes, treatments and practices. We haven’t fully quantified the value as the project is on-going, but I expect this to easily produce over $1 million in benefits to our client.

Hopefully these examples will spur some thoughts about how in-database analytics can be deployed in your company to drive business benefits. We are finding that our clients who leverage these solutions see a return on investment of 10X or more. In fact, one recent client saw a 32X return on investment in 12 months. That’s good proof that companies who deploy analytics will see dramatic results. In our next installment, we’ll dig deeper into the technical reasons that these benefits are larger with in-database analytics than with traditional solutions.

Posted by Sybase IQ at 3:35 PM