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Archive for the ‘Business Intelligence’ Category



Posted on October 26th, 2008 by Steve Wooledge

In a down economy, marketing and advertising are some of the first budgets to get cut. However, recessions are also a great time to gain market share from your competitors. If you take a pragmatic, data-driven approach to your marketing, you can be sure you’re getting the most ROI from every penny spent. It is not a coincidence that in the last recession, Google and Advertising.com came out stronger since they provided channels that were driven by performance metrics.

That’s why I’m excited that Aster Data Systems will be at Net.Finance East in New York City next week. Given the backdrop of the global credit crisis, we will learn first-hand the implications of the events in the financial landscape.  I am sure the marketing executives are thinking of ways to take advantage of a change in the financial landscape, whether it’s multi-variate testing, more granular customer segmentation, or simply lowering the data infrastructure costs associated with your data warehouse or Web analytics.

Look us up if you’re at Net.Finance East - we’d love to learn from you and vice-versa.

Posted on October 6th, 2008 by Steve Wooledge

Aster announced the general availability of our nCluster 3.0 database, complete with new feature sets. We’re thrilled with the adoption we saw before GA of the product, and it’s always a pleasure to speak directly with someone who is using nCluster to enable their frontline decision-making.

Lenin Gali, Director of BI, ShareThis

ShareThis logo

Lenin Gali, director of business intelligence for the online sharing platform ShareThis, is one such friend. He recently sat down with us to discuss how Internet and social networking companies can successfully grow their business by rapidly analyzing and acting on their massive data.

You can read the full details of our conversation on the Aster Website.

Posted on October 6th, 2008 by Mayank Bawa

It is really remarkable how many companies today view data analytics as the cornerstone of their businesses.

acerno logoaCerno is an advertising network that uses powerful analytics to predict which advertisements to deliver to which person at what time. Their analytics are performed on completely anonymous consumer shopping data of 140M users obtained from an association of 450+ product manufacturers and multi-channel retailers. There is a strong appetite at aCerno to perform analytics that they have not done before because each 1% uplift in the click-through rates is a significant revenue stream for them and their customers.

Aggregate KnowledgeAggregate Knowledge powers a discovery network (The Pique Discovery™ Network) that delivers recommendations of products and content based on what was previously purchased and viewed by an individual using the collective behavior of the crowds that had behaved similarly in the past. Again, each 1% increase of engagement is a significant revenue stream for them and their customers.

ShareThis logoShareThis provides a sharing network via a widget that makes it simple for people to share things they find online with their friends. In a short period of time since their launch, ShareThis has reached over 150M unique monthly users. The amazing insight is that ShareThis knows which content users actually engage with, and want to tell their friends about! And in its sheer genius, ShareThis gives away its service to publishers and consumers free; relying on delivering targeted advertising for its revenue: by delivering relevant ad messages while knowing the characteristics of that thing being shared. Again, the better their analytics, the better their revenue.

Which brings me to my point: data analytics is a direct contributor of revenue gains in these companies.

Traditionally, we think of data warehousing as a back-office task. The data warehouse can be loaded in separate load windows; loads can run late (the net effect is that business users will get their reports late); loads, backups, and scale-up can take data warehouses offline –which is OK since these tasks can be done on non-business hours (nights/weekends).

But these companies rely on data analytics for their revenue.

·    A separate exclusive load window implies that their service is not leveraging analytics during that window;
·    A late-running load implies that the service is getting stale data;
·    An offline warehouse implies that the service is missing fresh trends

Any such planned or unplanned outage results in lower revenues.

On the flip side, a faster load/query provides the service a competitive edge – a chance to do more with their data than anyone else in the market. A nimbler data model, a faster scale-out, or a more agile ETL process helps them implement their “Aha!” insights faster and gain revenue from a reduced time-to-market advantage.

These companies have moved data warehousing from the back-office to the frontlines of business: a competitive weapon to increase their revenues or to reduce their risks.

In response, the requirements of a data warehouse that supports these frontline applications go up a few notches: the warehouse has to be available for querying and loading 365×24x7; the warehouse has to be fast and nimble; the warehouse has to allow “Aha!” queries to be phrased.

We call these use cases “frontline data warehousing“. And today we released a new version of Aster nCluster that rises up those few notches to meet the demands of the frontline applications.

Posted on August 19th, 2008 by Tasso Argyros

I am curious if anyone out there is attending the TDWI World Conference in San Diego this week? If so and you would like to meet up with me, please do drop me a line or comment below as I will be in attendance. I’m of course very excited to be making the trip to sunny San Diego and hope to catch a glimpse of Ron Burgundy and the channel 4 news team! :-)

But of course it’s not all fun and games, as I’ll participate in one of TDWI’s famous Tool Talk evening sessions discussing data warehouse appliances. This should make for some great dialogue between me and other database appliance players, especially given the recent attention our industry has seen. I think Aster has a really different approach to analyzing big data and look forward to discussing exactly why.

For those interested in the talk, here are the details..come on by and let’s chat!
What:TDWI Tool Talk Session on data warehouse appliances
When: Wednesday, August 20, 2008 @ 6:00p.m.
Where: Manchester Grand Hyatt, San Diego, CA

Posted on August 5th, 2008 by Mayank Bawa

Today we are pleased to welcome Pentaho as a partner to Aster Data Systems. What this means is that our customers can now use Pentaho open-source BI products for reporting and analysis on top of Aster nCluster.

We have been working with Pentaho for some time on testing the integration between their BI products and our analytic database. We’ve been impressed with Pentaho’s technical team and the capabilities of the product they’ve built together with the open source community. Pentaho recently announced a new iPhone application which is darn cool!

I guess, by induction, Aster results can be seen on the iPhone too. :-)

Posted on July 25th, 2008 by Tasso Argyros

Stuart announced yesterday that Microsoft has agreed to acquire DATAllegro. It is pretty clear Stuart and his team have worked hard for this day: it is heartening to see that hard work gets rewarded sooner or later. Congratulations, DATAllegro!

Microsoft is clearly acquiring DATAllegro for its technology. Indeed, Stuart says that DATAllegro will start porting away from Ingres to SQL Server once the acquisition completes. Microsoft’s plan is to provide a separate offering from its traditional SQL Server Clustering.

In effect, this event provides a second admission from a traditional database vendor that OLTP databases are not up to the task for large-scale analytics. The first admission was in 1990s when Sybase (ironically, originator of SQL Server code base) offered Sybase IQ as a separate product from its OLTP offering.

The market already knew this fact: the key point here is that Microsoft is waking up to the realization.

A corollary is that it must have been really difficult for Microsoft SQL Server division to scale SQL Server for larger scale deployments. Clearly, Microsoft is an engineering shop and the effort of integrating alien technology into their SQL Server code-base must have been carefully evaluated for a build-vs-buy decision. The buy decision is a tacit admission that it is incredibly hard to scale their SQL Server offering with its roots in traditional OLTP database.

We can expect Oracle, IBM, and HP to have similar problems in scaling their 1980s code-base for the needs of data-scale and query-workloads of today’s data warehousing systems. Will the market wait for Oracle, IBM, and HP’s efforts to scale to come to fruition? Or will Oracle, IBM, and HP soon acquire companies to improve their own scalability?

It is interesting to note that DATAllegro will be moving to an all-Microsoft platform. The acquisition could also be read as a defensive move by Microsoft. All of the large-scale data warehouse offerings today are based on Unix variants (Unix/Linux/Solaris), thus leading to the uncomfortable situation at some all-Microsoft shops who chose to run Unix-based data warehouse offerings because SQL Server would not scale. Microsoft needed an offering that could preserve their enterprise-wide customers on Microsoft platforms.

Finally, there is a difference in philosophy between Microsoft’s and DATAllegro’s product offerings. Microsoft SQLServer has sought to cater to the lower end of the BI spectrum; DATAllegro has actively courted the higher end. Correspondingly, DATAllegro uses powerful servers, fast storage, and expensive interconnect to deliver a solution. Microsoft SQL Server has sought to deliver a solution at a much lower cost. We can only wait and watch: will the algorithms of one philosophy work well in the infrastructure of the other?

At Aster Data Systems, we believe that the market dynamics will not change as a result of this acquisition: companies will want the best solutions to derive the most value from data. In the last decade, Internet changed the world and old-market behemoths could not translate their might into the new market. In this decade, Data will produce a similar disruption.

Posted on May 20th, 2008 by Mayank Bawa

I’ve remarked in an earlier post that the usage of data is changing and new applications are on the horizon. Over the past few years, we’ve observed or invented quite a few interesting design patterns for business processes that use data.

There are no books or tutorials for these new applications, and they are certainly not being taught in the classrooms of today. So I figured I’d share some of these design patterns on our blog.

Let me start with a design pattern that we internally call “The Automated Feedback Loop”. I didn’t invent it but I’ve seen it being applied successfully at search engines during my research days at Stanford University. I certainly think there is a lot of power that remains to be leveraged from this design principle in other verticals and applications.

Consider a search engine. Users ask keyword queries. The search engine ranks documents that match the queries and provides 10 results to the user. The user clicks one of these results, perhaps comes back and clicks another result, and then does not come back.

How do search engines improve themselves? One key way is by recording the number of times users clicked or ignored a result page. They also record the speed with which a user returned from that page to continue his exploration. The quicker the user returned, the less relevant the page was for user’s query. The relevancy of a page now becomes a factor in the ranking function itself for future queries.

The Automated Feedback LoopSo here is an interesting feedback loop. We offered options (search results) to the user, and the user provided us feedback (came back or not) on how good one option was compared to the others. We then used this knowledge to adapt and improve future options. The more the user engages, the more everyone wins!

This same pattern could hold true in a lot of consumer-facing applications that provide consumers with options.

Advertising networks, direct marketing companies, and social networking sites are taking consumer feedback into account. However, this feedback loop in most companies today is manual and not automated. Usually the optimization (adapting to user response) is done by domain experts who read historical reports from their warehouses, build an intuition of user needs and then apply their intuition to build a model that runs everything from marketing campaigns to supply chain processes.

Such a manual feedback loop has two significant drawbacks:

1. The process is expensive: it takes a lot of time, trial and error for humans to become experts, and as a result the experts are hard to find and worth their weight in gold.

2. The process is ineffective: humans can only think about handful of parameters and they optimize for the most popular products or processes (e.g., “Top 5 products or Top 10 destinations”). Everything outside this area of comfort is left under-optimized.

Such a narrow focus on optimization is severely limiting. The incorporation of Top 10 trends into future behavior is akin to a search engine saying that it will optimize for only the top 10 searches of the quarter. I am sure Google would definitely be a less valuable company then, and the world a less engaging place.

I strongly believe that there are rich dividends to be reaped if we can automate the feedback process in more consumer-facing areas. What about hotel selection, airline travel, and e-mail marketing campaigns? E-tailers, news (content providers), insurance, banks and media sites are all offering the consumer a choice for his time and money. Why not instill an automated feedback loop in all consumer-facing processes to improve consumer experience? The world will be a better place for both the consumer and the provider!

Category Archives

Relevant Blogs

  • Converting data exhaust into data valueOctober 20th, 2008
  • Why MapReduce matters to SQL data warehousingOctober 20th, 2008
  • The new paradigm of in-database cloud analytics, and Google’s role as catalystOctober 20th, 2008
  • Thoughts on category creation and information access platformsOctober 20th, 2008
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