Sybase IQ

July 16, 2010

Sybase IQ - Making Data Easy to Consume

There are many approaches to decision making –evidence based, algorithmic, intuitive, dictatorial, and consensual – and the course of action should be tailored to fit the particular characteristics of the situation.  However, most business leaders operating in today’s highly competitive environment would agree that making critical decisions without supporting data is folly.  The challenge is getting quick and easy access to substantive analysis in order to make timely decisions.

This blog is a follow on to Phil’s “Just Like TV” post below.  Mike Vizard writes in his blog, ”The Rebirth of Business Intelligence” (http://www.itbusinessedge.com/cm/blogs/vizard/the-rebirth-of-business-intelligence/?cs=42070, that users want easy access to BI from the application environment they are working in.  They don’t want to leave that environment to access a separate BI tool.  Increasingly, application developers are embedding BI directly into the application, such as spreadsheets.  ”Ease of use” is critical to adoption of BI.  Claudia Imhoff talks about ”ease of consumption” in her blog entitled ”Easy to Use? Nah.  We Need BI that is Easy To Consume” (http://www.b-eye-network.com/blogs/imhoff/archives/2009/02/easy_to_use_-_n.php).  Her point is that consuming data is more than ”just an isolated viewing of data points or reports”, or the manipulation of pretty pictures.  BI is about easy consumption of data that adds to ”complete and full comprehension of a situation, event or activity”.

The thrust of BI is accessibility to good intelligence derived from real data.  Sybase IQ 15 includes support for a variety of programming languages and interfaces that you can use to build and deploy database applications – it offers support for .NET, OLD DB and ADO development environments, Perl scripting, Python and PHP, along with the traditional ODBC and JDBC interfaces.  With Sybase IQ, you can integrate access to data analytics within multiple application environments – offering ease of use and ease of BI consumption for good decision making.

Posted by Sybase IQ at 6:01 PM

July 15, 2010

Just Like TV

Over at ITBusinessEdge, Mike Vizard presents some interesting analysis of why more widespread use of BI is not occurring within organizations, and what he thinks will ultimately drive higher rates of adoption:

There are plenty of reasons, ranging from cost of application licenses to reliance on spreadsheets. But the biggest issue might be that using a BI application seems unnatural — the data first has to be imported, then users have to leave the application environment in which they are working in to access BI.

According to JasperSoft CEO Brian Gentile, application developers are rapidly becoming more aware of this issue and increasingly embedding BI directly into their applications. Open source makes it easier and less expensive to do so without having to develop that functionality themselves.  Gentile says BI capabilities are well on their way to becoming a standard component of other applications.

This doesn’t necessarily mean the end of stand-alone BI applications. But the number of people accessing BI software within another application environment will dwarf the number using stand-alone BI software, who tend to be business analysts.

There is no question that enterprise application vendors have added and (will continue to add) BI capability to their environments, from simple reporting and dashboards in some instances to fairly sophisticated analytical capability in others. And Mike may well prove prophetic in his assertion that there will one day be more people accessing BI capability via these applications than via dedicated BI software.

Interestingly, that doesn’t mean that there will be fewer people accessing dedicated BI and analytics software than exist today. Let’s analogize for a moment and liken analytics capability to video content. Like analytics, video content can be accessed in a number of ways. You can use a TV, which is a dedicated environment analogous to BI / analytics system, or you can access video as an auxiliary function of some other device — laptop, smart phone, SUV — which is similar to accessing analytics capability within an enterprise application. Although more and more people are accessing more and more video content via these other methods, the number of TVs and the number of people watching them has hardly gone down. If anything, it has increased significantly.

Likewise, the number of users of analytics systems is increasing rapidly. Some of these users may, indeed, find it “unnatural” to have to leave the application in order to do analyses on their data. On the other hand, most of them were already leaving that environment to access those incomplete, inconsistent, and time-consuming spreadsheets. The reality is that it isn’t just the number of analytics users that’s growing, it’s the level of sophistication that all BI and analytics users require. As these users grow more sophisticated in their requirements, many will make analytical demands that enterprise systems will have a very difficult time fulfilling.

Why? Well, first there’s a problem of scope / feature creep. Development teams supporting enterprise applications find it challenging enough just keeping up with the demands placed on the core business processes they support. They are happy to provide reporting and analysis of data related to these processes (up to a point), but in light of the demand for increasingly broad and deep analytics capability, left unchecked they might ultimately find themselves supporting analytics / BI systems which also happen to have some operational features!

Moreover, as analytical needs grow more sophsiticated, the importance of having the correct underlying infrastructure becomes increasingly clear. One day we may well have a data management technology that can be simultaneously configured to provide optimal results for both analysis and transactions, but we aren’t there yet. Today, putting both functions against a single data source requires making sacrifices either to transactional performance, analytical performance, or both. With demands for both analytical and transactional performance growing rapidly, users are not likely to embrace those sacrifices.

So, yes, BI and analytical functions will continue to be embedded within enterprise applications, and the number of people using these capabilities will grow rapidly. At the same time, dedicated BI and analytical systems will  be more widely accessed than ever before, supporting increasingly robust and sophisticated capabilities. One does not come at the expense of the other, any more than millions of Youtube views cut into sales of widescreen LCD HD TVs. They both just keep growing together.

Posted by Sybase IQ at 3:45 PM

July 9, 2010

Summer School

I almost gave this entry the title “Back to School,” but as a kid I always hated it when, seemingly right around the Fourth of July, the stores all started up their back-to-school promotions.

“Too Soon!” I would invariably lament. “Can’t this wait a few weeks?”

Much more timely is the recent introduction of The Analytics Performance Series, a Sybase-sponsored certification program offered via Beye UNIVERSITY. This four-part program provides an overview of analytics performance from both a business and technical perspective. Organizations increasingly rely on their analytics infrastructures to power rapid and complex decisions, to track performance in real time, and to monitor risks and opportunities. With so much riding on the analytics infrastructure, analytics performance becomes critical.

To deliver the courses, we put together a Dream Team of BI and Analytics experts to step participants through a better understanding of the crucial role that analytics performance plays in today’s business. Here’s a quick description of the four courses that make up the certification series:

Part One: The Warning Signs Claudia Imhoff lays out the Seven Early Warning Signs that your analytics infrastructure is not ready for current or future business challenges.

Part Two: High Performance Analytics Architectures David Loshin explores typical architectural paradigms for high performance analytics databases, looking at the types of reporting and analytics usage scenarios and then considering architectural suitability of the different approaches to those usage scenarios.

Part Three: Performance Advances In Analytics Seth Grimes discusses new technologies and capabilities that businesses are using to take their analytics to the next level: in-database analytics, text-analytics, and web-enabled analytics.

Part 4: Four-Phase Approach to Analytics Claudia Imhoff discusses the attributes that a company that utilizes analytics effectively has. She also steps through the four-phase approach to analytics maturity.

The Analytics Performance Series is aimed at everyone involved in managing analytics environments, as well as the business users of these environments. Anyone who is responsible for, or who relies upon, analytics performance will gain a better understanding of what is at stake and how to move forward.

And surely that’s worth taking a summer class (or four.)

Posted by Sybase IQ at 10:21 PM

June 21, 2010

Using Sybase IQ 15.2

In my previous entry, I listed out the major new features in Sybase IQ 15.2. This time, we’ll examine some scenarios to get a better understanding of how these new features address business problems for Sybase IQ users.

Text Search and Analysis

An insurance company implements a Sybase IQ-based  system to retain emails and other electronic documents in anticipation of future litigation and investigations. When a legal hold is placed on the company’s electronic information, or when an internal investigation is necessary, all the archived information can be searched and delivered within that system. The ability to quickly access vast amounts of electronic information that was previously inaccessible due to its unstructured format accelerates the legal process, aids investigative analysis, and improves the odds of mounting a successful case.

Using Sybase IQ 15.2 with Text Search and Analysis, this company can search for and retrieve relevant documents from a large set of continuously archived historical information. With  the ability to perform complex searches involving scoring, proximity and multiple terms, finding relevant information is now far easier than manual processes, plus simpler and more efficient than implementing a separate system just for text search.

Query Federation

A distributor maintains a Sybase IQ decision support system to aid in allocating stock to retailers and issuing procurement orders to manufacturers. Information regarding inventory levels at retailers would be helpful to the distributor, but some retailers’ inventory information is proprietary and they will not allow the distributor to maintain a copy. The distributor, however, is allowed to access the current inventory information for the SKUs it supplies. Query Federation is used to access the retailers’ database, leading to a more efficient procurement process.

Using Sybase IQ 15.2 with Query Federation, this distributor can access information in its original location, without moving it or copying it into the data warehouse.

Web-Enabled Analytics

Business analysts and statisticians at a marketing services firm are developing a customer segmentation model for a client who wants to target the most profitable segments. The analysts require fast time-to-result and access to data in Sybase IQ while validating their model. Python is their language of choice for fast prototyping. The analysts are able to quickly iterate in order to develop a satisfactory model that is tested against large data sets in Sybase IQ.

The model can now be ported to C/C++ for analyzing live customers in production use. Using Sybase IQ 15.2 with Web-Enabled Analytics, this marketing services firm reduced the time needed to meet its clients’ requests. Support for Python means that business-focused analysts can quickly develop and test their prototype models, and that the entire environment can be moved to the web should such a delivery model be required.

Real-Time Loading

An online shopping site employs a complex predictive model based on the customer’s profile combined with deep demographic data to drive upsell offers both at the time of purchase and during follow-up communications. The upsell offers come at various stages in the order  and fulfillment process. They are based on the customer’s profile and similar purchases made by other shoppers — with significant weighting on current and most recent purchases. Acceptance of an upsell offer is treated as an exception within the overall order fulfillment process.  Whenever possible, new purchases are added to the current customer shipment before it is sent out. Shipping staff rely on real-time reporting of these exceptions in order to support just-in-time shipping updates. In order to ensure the highest levels of upsell, improve shipping efficiencies, and maintain customer satisfaction,  the shopping site requires both carefully culled and  transformed data to power their predictive model as well as instant access to data for the shipping exception reports.

 Sybase IQ 15.2 offers multiple, flexible ways of bringing disparate data into the analytics environment in real time. Sybase Replication Server 15.5 supports immediate data access from Sybase ASE, while Sybase ETL 4.9 can support both batch and continuous data load from data sources such as Oracle and DB2. With this capability, the online shopping site can ensure the best upsell results without slowing their shipping schedule.

Posted by Sybase IQ at 9:59 PM

June 11, 2010

Smarter Answers Now with Sybase IQ 15.2

The BI and analytics environments that support business decision-makers require greater capability, reach, and versatility than ever before. Sybase IQ turns your information into better decisions, enabling you to gain the answers and insights that will drive your business forward.

The competitive playing field is changing dramatically. Top-performing companies are replacing gut-feel with data-driven decision and analysis. Three expansive forces are re-shaping the environment in which companies innovate and compete: the growing amount of data created and captured, the proliferation of mobile devices, and the resulting increase in action points made possible in part through ubiquitous mobility and cloud computing. As companies seek answers to which markets to pursue, which customers to serve, how to maximize profits, or where to optimize efficiencies, they will push existing systems far past their limitations. Today’s BI and analytics systems must be able to keep pace with massive volumes of data, adapt easily to evolving requirements, and serve a growing number of users, often via the Web or mobile devices.

As the market’s original and most widely used column-based analytics server, Sybase IQ powers the most demanding BI and analytics environments. Sybase IQ sets the standard for performance, flexible scalability, security, manageability, and affordability. Sybase IQ’s scalable, grid-based columnar architecture and patented data compression technology have made it the solution of choice for more than 1800 customers (and more than 3200 installations) worldwide. Recent additions to Sybase IQ’s robust set of capabilities include in-database analytics, data partitioning for information lifecycle management, and flexible assignment of read and write nodes.

Now, with version 15.2, Sybase IQ continues to extend the capability, reach, and versatility of analytics environments:

• Text Search and Analysis
• Query Federation
• Web-Enabled Analytics
• Real-Time Loading

Text Search and Analysis

Businesses can no longer afford to allow key insights to remain locked in e-mail, file systems, and other unstructured formats. To open up these assets, Sybase IQ provides:

•      The ability to search for words and phrases within text data

•      The ability to perform Boolean and proximity searches

•      The ability to score the frequency with which a term occurs within a document.

Don’t miss our webcast, The Critical Role of Text Analytics in the BI Ecosystem.

Query Federation

Results-driven businesses demand new ways to overcome organizational and structural limits to accessing the information they need. To meet this challenge, Sybase IQ provides the ability to federate queries across both Sybase IQ and external data sources: This gives organizations:

• A greater reach of analysis and understanding across the organization
• Query capability expanded into highly secure environments with no compromise to security
• Real-time access to previously unavailable information, including from datasets which cannot be moved or copied

Web-Enabled Analytics

A key component of meeting ever-changing business demands is an analytics infrastructure that is responsive to the evolution of how, when, and why businesses use analytics as well as the prevailing technologies used to meet these demands. For example, businesses need an analytics environment that is Web 2.0 app-friendly, including support for the major Web development tools. Sybase IQ provides support for the most common and preferred programming languages for developing Web applications:

• Python
• PERL
• PHP
• ADO.net
• OLE-DB

Real-Time Loading

Business decision-makers demand access to mission-critical information delivered both when and how they need it. Sybase IQ 15.2 offers multiple, flexible ways of bringing data into the analytics environment.

Sybase Replication Server 15.5 supports immediate data access (from Sybase ASE), while Sybase ETL 4.9 can support both batch and continuous data load.

Smarter Answers Now = Better Business Decisions

No matter the volume or type of information, the speed at which it’s needed, the detail to which it is examined, or the number of decision-makers trying to access it, Sybase IQ delivers precise, actionable answers.  For enterprises turning to mission-critical, high-performance and cost-effective analytics solutions as a better way to innovate and win, Sybase IQ turns your information into better decisions.

Posted by Sybase IQ at 5:00 PM

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

March 29, 2010

Guest Blog: Introduction to Fuzzy Logix and In-database Analytics

Hello, my name is Mike Upchurch and I’m the COO of Fuzzy Logix. Our company was founded based on 12 years of research and development related to creating analytics and optimizing their performance. When we started, our goals were to develop a library of analytical models that had very high performance and which could be deployed pervasively.

To meet our performance goals, our team of engineers wrote the models in C/C++, designed them to take advantage of parallelism and to leverage other performance enhancing techniques. Once we had a highly tuned set of models, we found another challenge. We noticed that the performance of analytics was hamstrung due to the time required to move large datasets. As we researched the solution, our CEO, Partha Sen, had a revelation. His question to our engineers was ”Why move the data to the analytics, if we can move the analytics to the data?” And thus begun our quest to convert our large library of data mining and predictive models so that they would run beside the data source; in-database. Effectively, we perform the analytics on the data as it leaves the database and we only move the results to the presentation layer. In terms of speed improvement, we see a 10X to 100X (or more) gain in performance. In fact, the larger the data, the greater the speed advantage. Since the amount of data being created and stored is growing daily, this is one of the best ways for companies to meet the challenges of deriving insight from their ever expanding databases without seeing a corresponding (and linear) growth in processing time.

Once we attained our speed goal, we set out to find a way to make analytics pervasive. There were a number of reasons based on years of observation and experience. For example, we noticed that most analysis was being performed by a small group of people with specialized skills. Not only did they need to understand quantitative models, but they had to learn special languages and complex analytics platforms. We also noticed that statisticians and quantitative teams spent a lot of time running and re-running models on behalf of their business colleagues. Most of the people requesting the analysis could understand and leverage the results of the models, but didn’t have the skills or access to the expensive tools required to run them. This time our question was ”How could we create models that could be run by any device, presentation layer or development tool and push the models out into all areas of an organization?” And the answer was to use the most common data access language ever invented; SQL. To use our solutions, you simply write SQL statements and pass parameters.

The result of combining performance and pervasiveness was that we saw an exponential increase how companies were leveraging analytics. Statisticians no longer had to own all modeling tasks, but could instead build models and give access to end-users via standard reporting tools such as MicroStrategy and Excel. Now users could run their data through the models by simply selecting items from a drop down list or off a menu. Because almost any tool can call SQL, we were able to embedded analytics into business processes and operations. That meant that large numbers of people, in many different areas, could make decisions based on the output of analytic models. The quantitative teams saw other benefits. Not only did they have more time to work on custom models, but they also gained a library of building blocks that could be combined to create higher order models. Since they were able to run the models so effectively on entire datasets, we saw a great leap in their efficiency.

Since we now we had a set of analytical solutions that delivered incredible performance and were easy to deploy, the next question was ”So how do we leverage these advantages to create value?” And the answer will be covered in our next entry; where I’ll highlight some examples of how companies have found success using in-database analytics and Sybase IQ.

Posted by Sybase IQ at 6:37 AM

March 22, 2010

Sybase IQ In-database Analytics: Unlocking Value in Big Data with Partner Solutions

I had recently co-authored an article on Predictive Analytics in Sandhill.com where buzzwords like Data Mining, Forecasting, Predictive Modeling, and Decision Optimization were generously sprinkled. Surely, we had no intention of sounding glib unless these words were significant in what they represent in practical terms. Just to understand how critical these advanced analytical concepts have become in today’s world, you just have to appreciate the context in which they are applied.

In a series of articles on information management in The Economist the focus was on uncovering value from Big Data. Big Data being the context for business intelligence. In one of these articles, The Economist points out that according to a 2008 study by International Data Corp (IDC), a market-research firm, around 1,200 exabytes of digital data would have been generated that year. The critical issue, however, is not just the mangement of such vast amounts of data, but analyzing them using the buzzword concepts above that will lead to a myriad of effective business decisions, or at least corroboration of such decisions, on the basis of computer algorithms rather than hunches. And these new business insights don’t just come from analysis of ”dead data”: stored information about past transactions but also through analysis of real-time information flows. The application areas are both deep and broad – from analytics in capital markets to mining customer data in telecomunication, e-commerce and healthcare. The common roadblock, however, continues to be inefficiencies in sifting through big data in these scenarios. Speed, Accuracy, and Data Volume continue to confound traditional database and analytical tools. Enter Sybase IQ In-database Analytics.

Back in summer of 2009, we introduced Sybase IQ v15.1 with advanced In-database Analytics capabilities that was built ground up to specifically show the business world a new and efficient way of dealing with analysis of Big Data. The key elements of Sybase IQ In-database capabilities rests on the simple (yes, simplicity does offer elegance) fact that if we push complex analytical computations closer to data – good things happen. We have not only embraced this concept ourselves but also invited partners to plug-in their sophisticated algorithms into the high performance Sybase IQ engine. One such partner is Fuzzy Logix – who have ported their extensive array of efficient statistical and predictive analytics algorithms library under the package name DBLytix on Sybase IQ using the In-database Analytics API. In a three part series blog following my write up today, Fuzzy Logix COO, Michael Upchurch, will outline his views on the world of In-database predictive analytics from a real world application angle. On to Mike ….

Posted by Sybase IQ at 3:07 AM