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We have launched the new Sybase IQ Marketplace – it’s a place for buyers and sellers from all over the world to discover the latest Data Warehousing, Business Intelligence and Analytics solutions integrated with SAP Sybase IQ. Also learn about the Sybase IQ Express Edition which is available for educators, DBAs and developers as a free download from the site to test drive SAP Sybase IQ and all its features.

Learn about our partner solutions that can help you to stay competitive when you need to deliver innovation and increase value to your customers. To do this, you need a partner that can help you create a new enhanced offering to expand your portfolio of products. By creating new analytics applications, or adding analytics capabilities to an existing application, you gain an efficient way to maintain a leading edge.

The Sybase IQ Marketplace allows us to showcase our best in breed partners and create a community in which to allow them to expand the market reach of their software solutions. So, please visit the new Sybase IQ Marketplace at and check out what new innovations are available! Check out Anayltics Solutions, Data Warehouse Infrastructure and Business Intelligence Tools partners and check back periodically as we add more partners.

Why Partner with Sybase IQ?
For over 15 years Sybase has pioneered column-based databases for analytics with Sybase IQ. Today Sybase IQ is an enterprise-class Big Data Analytics platform. Sybase IQ delivers the performance partners require, in a form factor that can be optimized for deployment with their analytics solution. Sybase IQ can scale to solve the toughest analytic problems while slashing maintenance costs for you and your customer. It’s that simple.

The Sybase partner program is open to ISV and OEM Application Vendors. Click here to read more

“What if” analysis? Sybase IQ 15.4 leads the way

A common thread amongst data and analytics driven organizations is to push the boundaries of discovery, outcome probabilities, and forecasts. In their quest to understand and act on every opportunity related to innovation, revenue, and savings, these visionary firms tend to rely on answers to a string of “What if” questions. And rightfully so, a McKinsey study has found that data and analytics driven firms on the average improve their top line and bottom line by almost 5%. That’s significant.

So, “What if” – a fundamental question, yet very profound in implications is actually quite difficult to answer in practice. So, what approaches do these visionary organizations use to get their answers? Many industry pundits seem to think the answer lies in “Big Data Analytics”. And they appear to be right – organizations that get their “What if” analysis right, rely on a well thought out “Big Data Analytics” strategy that captures and delivers meaning from an ever increasing volume, variety, and velocity of data that flows directly through the enterprise. In their book “Competing On Analytics”, Davenport & Harris outline several firms who have adopted a culture of analytics from large data sets.

In today’s competitive climate, it is not sufficient to understand only the past history and present status. “What if” questions are an effort to look into the future – the risks and opportunities that must be tackled moving forward. The direction an organization is headed is impacted by a large number of variables and unknowns. Understanding which variables are significant, altering the values of the variables, and plugging them into a valid model provides some potential insight. And with foresight and planning, it may be possible to alter course and be more in control of outcomes.

Let us take a deeper look at what is involved in getting to “What if” in these visionary organizations. First, they are constantly seeking out pieces of data that needs to be tagged, filtered, and analyzed to help them with “What if” analysis. To do this, they rely on Big Data originating from emails, machines (e.g. RFIDs, web logs), transactions, customer call notes, social networks, and syndicated data to name but a few. It is the job of the contemporary superheroes – the “Data Scientists” – to discover the needles in the digital haystack to find out what really matters.

Second, these “needles” or valuable data points must be put in context of the overall business issue on which the “What if” analysis is targeted, for example, stress tests for banks to simulate various edge case economic scenarios that test their solvency. It is the task of the business analysts to build mathematical models around the business issue with these valuable data points.

Third, the models need to be made consumable to the end user who will end up asking the “What if” questions under various contexts. The BI developers build these context sensitive, user friendly reports and dashboards for the end users.

Finally, the end to end “data supply chain” and workflow between these constituencies must be managed and provisioned efficiently for success. This increasingly sophisticated and mission critical orchestration is usually performed by the System and Database Administrators. So, as you can see, getting to “What if” is not trivial but at the same time it does not have to be complicated either.

While visionary organizations tend to take the giant steps in these uncharted waters, they do so with plenty of risks that can prolong or sometimes, even derail these initiatives. The key impediments to “What if” nirvana tends to be lack of a cohesive end to end plan, compartmentalized initiatives, and discrete and incompatible technology tools. Meanwhile, surveys in the recent past have found that the Big Data Analytics (and its end point in “What if” analysis), to steal a term from the youtube world, is going “viral”. So something has to change significantly for “What if” and Big Data Analysis to go main stream – something that will simplify adoption.

At Sybase, we have seen the value of data and its implications through the technology lens for more than two decades now – initially with transaction processing, followed by embedded processing, and today with analytics. Looking at the “What if” issue today, especially in the context of Big Data Analysis, we can appreciate the tools and technology challenge. We have concluded that the answer lies in providing a mature platform for Big Data Analysis that will get answers to “What if” questions quickly, consistently, and accurately.

On November 1st, 2011, we announced our latest solution targeted at “What if” and Big Data Analysis: Sybase IQ v15.4 with PlexQ® technology. The figure below provides an overview of how this new architecture comprehensively handles the multi-phase enterprise wide “What if” analysis described earlier, while efficiently dealing with the data supply chain, collaboration with privacy among user communities, and service level commitments.


I will end by saying that it is an exciting time to be asking “What if” questions. But challenges stand in the way of main stream adoption. However, at the same time, it is an exciting time to be a vendor with a vision and foresight to revolutionize the way “What if” and Big Data Analysis is done. “What if” we were to provide a lot more insights into our vision as well as practical applications in subsequent posts? This post is, after all, just the opening act.

Big Data, Big Opportunity

Would you believe that Fortune 1000 companies could gain $2.01 billion each year in employee productivity if they merely increased usability of their data by 10 percent? As revealed in a research study by the University of Texas at Austin, we found that many leading enterprises are not realizing the true value and opportunities of intelligently mining the gluttony of information – a.k.a “big data”.

Check out the below infographic, showing significant increases to employee productivity and profitable gains in sales that could be achieved by just a 10 percent improvement to data usability and accessibility. A surge in sales could even translate to the much needed creation of jobs our economy currently needs or development and investment projects that will fuel growth in vertical markets.

Who could say no to turning the “big data” challenge into a big opportunity?

Big-Data-Big Opp-infographic

Partners in Crime – Data Mining with Sybase IQ

What if a computer could process a visual image, extract key characteristics from it, and then use that data to find comparable images with a high degree of accuracy?  That’s exactly what state of the art image processing algorithms can do.  One of Sybase’s OEM partners is using IQ as a platform for its facial recognition technology.  Sybase IQ stores the image data, and executes the image processing functions inside the database through its UDF (User Defined Function) interface.  This technology partnership has produced a data mining solution with application in intelligence gathering and law enforcement activities.

So, how does all this work?  Every visual image can be represented by numeric data, called “Image DNAs”.  Each image has hundreds or thousands of DNAs, depending on its complexity.  It is possible to extract these DNAs, and compare them to those of other images, detecting the same kinds of similarities as the human eye and brain.

At TechWave 2011 in September in Las Vegas, Sybase showcased this technology in a remarkable demonstration.  First, an IQ database is loaded with a set of training images having particular characteristics deemed “important” – in the demo, multiple individuals, some wearing glasses, gathered at particular public places.  Then a batch of new images is compared against the set of training images to filter out those which have similar characteristics.  This much smaller set of images can now be analyzed further.

A filtered and processed image will display with people’s faces outlined with colored boxes.  You can select a particular face in the image, click on a “Search” button, and within a few seconds all images in the database that include THAT PARTICULAR INDIVIDUAL will display.   I am sure you can imagine the multitude of applications for this kind of technology – tracking down the bad guys, gathering circumstantial evidence, and of course, ferreting out all those embarrassing photos of yourself that your friends posted on Facebook.

Sybase IQ has the ability to store large volumes of blob data and retrieve it quickly.  In addition, Sybase IQ’s in-database analytics feature, and architecture that scales with query complexity and multiple concurrent users, makes it an ideal foundation for data mining applications of this kind.  Sybase IQ continues to evolve its capabilities and extend its breadth of partnerships to offer increasingly sophisticated solutions to complex problems.

You can learn more about Sybase IQ here:

Have fun, and be careful out there!

Fluid Scalability with Sybase IQ 15.3

Just in time for those dog days of summer – a cool Dreamsicle to refresh and energize your organization – Sybase has just announced the release of Sybase IQ 15.3 PlexQ™. 

This analytics grid offers so many treats for architects of an intelligent and agile enterprise:

  • A fluid “shared everything” massively parallel processing (MPP) architecture – this allows database queries to utilize the processing power of multiple compute nodes, running on commodity hardware, and sharing a common data store.
  • “Logical servers” to dynamically provision subsets of compute nodes and disk partitions to create “virtual data marts” in a single integrated environment.
  • In-database analytics to run complex analytical models directly within the database, eliminating the need for separation of queries and  data mining logic.
  • Web enabled analytics – web services, Ruby-on-Rails support and a wide variety of database drivers for different programming environments – to empower application developers to build smart applications.
  • Analysis of unstructured data – smart indexing and intelligent search of large character and binary objects.
  • Real-time loading with Sybase Replication Server so that your data warehouse is current with “hot” data.

Sybase IQ PlexQ™ is generating a lot of buzz in the media, including this ComputerWorld article:

This new release is coming out during the same time period as the exciting introduction of SAP’s high performance in-memory computing engine – HANA.  Is this a challenge?   Not at all.  As a near-line storage option, or as an alternative, Sybase IQ complements HANA nicely.  When you need to store a lot of data on traditional disk-based storage, scalable in multiple dimensions, accessed and analyzed quickly without the extreme low latency requirements that HANA is designed for, IQ is a high value option.

Read more about Sybase IQ’s unique architecture and outstanding feature set on our product page:

Together – Sybase and SAP offer a business analytics solution with something for everyone.  So, partake and enjoy!

Text Analytics – Slaying the Unstructured Data Dragon

The origin of the word “dragon” has been traced to a Greek word meaning “sharp-sighted one”.  The dragon is purported to have unusually acute vision, and in legends is known as a guardian of temples, paradises and hidden treasures.

Today’s dragon in the world of data is the massive amount of unstructured text that originates from an extensive array of sources: web pages, email, news, blogs, social media sites, surveys and every kind of document imaginable.  Unstructured data, like a dragon, is a big, scary, fire breathing beast – overwhelming to face, and seemingly impossible to vanquish.  Yet like a dragon, it is the guardian of an enticing treasure trove of information.

It is said that 80% of enterprise-relevant information originates in unstructured form.  Your customers, your competitors, and the public at large engage in an endless stream of conversation that contains nuggets of valuable information.   Timely and pertinent data present opportunities for your business and help you manage risk.  But how do you mine that vast ocean of unstructured data for the gems of particular value to you?

Text analytics is the growing area of data processing that gives you a weapon in your battle with unstructured data.  It involves multiple steps to make sense of the chatter and help you acquire business insight.  Here is a depiction of the process from Forrester Research:


Collecting and preparing the data are the first steps – cleansing and tokenizing the data into parts of speech.  Analytics then attempts to pull meaning from the sequences of words and phrases.  During this stage, concepts, categorization, and opinions are derived.  Analytics makes use of a repository of enriched data to query and run statistical functions on.  Finally, Reporting and delivery offer up the results for people to digest further – for insight, decision making and action planning.

The human brain is extremely adept at understanding language – it easily grasps sentence structure, word meaning and context.  It extracts concepts, draws relationships with the external world, and detects intent and emotion.  Text analytics as exercised by machines is not nearly as sophisticated as what our human brains can do, but computers are superior at processing large volumes of data quickly.  With strong algorithms, an extensive knowledge base, and some human involvement to drive and refine the search, they can be very effective at locating and analyzing the unstructured data that matters to you.

Sybase IQ 15 incorporates text analytics capabilities with its handling of large objects, specialized indexing for locating and scoring terms and phrases, and an integration layer for plugging in language processing libraries.  Sybase IQ is an analytics platform that offers you serious artillery in your battle against the unstructured data dragon.

To read more about Sybase IQ text analytics, check out:

Spicing Up Analytics with Data Variety

They say that variety is the spice of life.

Unless you’re in IT. Then it’s a source of indigestion.

BUT like Big Data, Data Variety is a huge opportunity to find patterns across all the information within an enterprise. Many work processes depend on documents, emails, images, proprietary formats, and even multimedia. To limit your analytics only to information stored in your relational databases is like trying to follow a map with 80% of it is missing.

Consider healthcare insurance fraud. It can be very hard to detect using just data in the claims form, especially if the fraudster has a bit of a brain. For instance, this enterprising fellow made a business selling fake doctor’s notes, knowing that many insurance companies don’t use data mining tools to examine them. Apparently, you can even choose what ailment you have. Healthcare insurance providers aren’t the only companies analyzing unstructured data. Marketing and HR departments are mining more and more information from the Web.

So, smart enterprises that want to predict the future with analytics know they need to add the spice of Data Variety. Yes, IT has the problem of storing all the varieties of data together for effective analysis. But great solutions exist within databases like Sybase IQ to ingest and analyze unstructured text and media. Ultimately, business has an opportunity to discover deeper insights using all the data within the enterprise, both structured and unstructured.

Data Variety is the spice of analytics. The great thing is there are ways for IT to overcome its indigestion.

Is Big Data Really Small Data?

There are two ways to read the question.

Is Big Data really a problem with managing Small Data? In other words, when we talk about managing Big Data are we not really talking about managing data at a more granular level? Adding more dimension and detail to the data we choose to record?

Now someone is going to rightfully point out that all this additional detail and granularity leads to more data. True. The problem with calling it Big Data is that people tend to think about volume – more data to store. And people tend to think about speed – more data to process within a given timeframe. All true. But all that frames the conversation about Big Data as if it were a problem.

In reality Big Data is an opportunity that enterprises deliberately choose. Enterprises choose to manage data at a more granular level instead of in summary form. Why? Because it opens up opportunities to look for patterns embedded within.

Yes, IT has the problem of managing more data and must find a platform that can cost effectively store it all. But business has an opportunity to discover more insights from many small chunks of data. So Big Data is just as much about mining Small Data as it is about managing the volume and speed of data. And IT needs to ensure that it provides the business with a platform that can effectively mine the many small chunks of data. It’s a huge opportunity for the business, even if it’s a huge headache for IT.

Which leads to the question’s alternate interpretation.

Should we label the Big Data challenge as Small Data instead? Probably not. Calling it Small Data creates an internal marketing problem. What CIO will spend millions on a small data problem?

Big Data may be Small Data. BUT if you’re looking for IT budget then it’s all just Big Data.

Threads of Change in Analytics

Exciting times in analytics. The industry is changing. Rapidly. While the industry has been around for decades, I think its true integration into core business processes is still in its early phases. It’s going to change the technologies companies use and the roles business analysts play. It reminds me of the now famous quote from Google’s chief economist, Hal Varian, in McKinsey Quarterly, “I keep saying the sexy job in the next ten years will be statisticians.” Curious that he used the word ‘sexy’. Does this make them statistician supermodels? Either way the point is that it’s exciting to be working in the analytics field today.

In many ways the threads of change are already here. Big data. A growing number of data types and sources. The drive to faster decision making. More business users asking more complex questions. The evolution of platforms to address these new demands. And they have the promise to transform analytics within the enterprise.

I will never be considered a statistician super model. I lack a PhD in mathematics. (The bald spot isn’t helping my cause either.) But then for analytics to be truly transformative, won’t business users and decision makers also need to be comfortable wearing statistical models and computational algorithms? Even if they don’t have the flair to design them?

We’ll be exploring the threads of change over the coming months, and Sybase’s unique approach to weaving them for transformative change. Stay tuned.

When Customers are Collateral Damage

Last week Oracle announced they are dropping software support for Itanium, effectively killing HP servers as a platform for Oracle databases. Apparently half of HP Superdome servers run Oracle. Ouch.

Enterprises looking to upgrade to the latest version of Oracle will face a dilemma. Purchase shiny new Sun hardware at the same time or migrate to a different database. Both are expensive propositions.

Collateral damage. That’s what customers are when vendors look to grow market share by hurting the very customers they want to win over. There’s irony here.

Some people will probably point to this as a reason to consolidate to one vendor with a complete stack. Personally I think there’s wisdom in picking vendors who don’t have this software/hardware conflict of interest. Besides, having the freedom to choose best-of-breed hardware and software will put savings in your company’s pocket, and probably net you a promotion at the same time.

Challenge is there aren’t too many best of breed data warehousing and analytics vendors that fit the profile. (See latest Gartner MQ for Enterprise Data Warehouse DBMSs and Forrester Wave Enterprise Data Warehouse Platforms). Sybase and SAP being exceptions, of course!

And you won’t be collateral damage either.