BeyeNETWORK Blogs BeyeNETWORK Blogs. Copyright BeyeNETWORK 2005 - 2012 http://www.b-eye-network.com/rss/content.php 150 31 BeyeNETWORK Blogs http://www.b-eye-network.com/images/logo_b-eye_rss.gif http://www.b-eye-network.com/rss/content.php Yes, you are a philosopher too... Every profession has its own philosophy. There's political philosophy, medical philosophy, legal philosophy, religious philosophy, scientific philosophy and so forth. There is also a branch called philosophy of technology, but during a brief moment of popularity, it was dominated by cyberpunks and futurists. Somehow philosophy is not very popular in the IT industry. In this new series of articles I will argue that this is odd, at the least. First of all, as I point out in the first article, IT professionals and philosophers have a lot in common. Well, except that IT professional are paid better perhaps. Also, I think it makes sense to know a little bit about philosophy for practical purposes in our daily affairs. In the second article I will show why IT philosophy is important right now, and will make it actionable by explaining what it takes to be an IT philosopher. Wouldn't we all want to be one? frank Twitter: @FrankBuytendijk

]]> http://www.b-eye-network.com/blogs/buytendijk/archives/2012/05/yes_you_are_a_p.php Mon, 21 May 2012 13:50:01 MST http://www.b-eye-network.com/blogs/buytendijk/archives/2012/05/yes_you_are_a_p.php Exploring Big Data - Taxonomies In the blur of Big Data, there is a element of suspense and mystery that prevents one from adopting to the same, what information is available and where to find integration points for linking the same to your enterprise. While there are several technologies available to address the volumetric's problem, there is one way to address the complexity and ambiguity side of Big Dat, using Taxonomies to create a Data Discovery exercise.

Taxonomies have long been used as catalog or index creation mechanisms in the world of metadata driven approach to data management and more so in the Web driven architecture where you need linked context behind the scenes. The very same taxonomy family can simply be used to create what we call word clouds or tags from content that is within Big Data. these tags can be used to create powerful linkages that will form a lineage and a graph.

What about Data Quality? that is the biggest advantage of using Taxonomies. When you have spelling errors and language issues, due to the intrinsic nature of taxonomies, you can land to a margin of error equation and often arrive at a close match.

Will this work on all types of big data, from my experiments and learning's it has worked with almost all types of data that can be deciphered by human minds. My next article in this channel will be focusing on this subject.

What can you do with the output from such a discovery? the obvious answer is that you can create a data road-map with linkages to all data across the enterprise. This is a foundational first step in a bigdata journey.


]]> http://www.b-eye-network.com/blogs/krishnan/archives/2012/05/exploring_big_d.php Thu, 17 May 2012 12:51:20 MST http://www.b-eye-network.com/blogs/krishnan/archives/2012/05/exploring_big_d.php More on MicroStrategy, Tableau, and QlikTech My blog last week, "QlikTech Goes Enterprise" created quite a stir from all quarters, much to my surprise. I presented an even-handed portrait of QlikTech, and I stand by everything I wrote. However, I'd like to elaborate on some issues that came under scrutiny from readers. First, I never claimed QlikView (QlikTech's product) is an enterprise BI product. Today, QlikView is an extremely successful departmental BI tool. The problem with all good departmental BI products is that customers push them upstream into enterprise deployments. And that's exactly what's happening with QlikView, for better or worse. The same thing happened with today's vanguard of enterprise BI players--namely, MicroStrategy, SAP BusinessObjects, and IBM Cognos--all of which started out as desktop BI products in the 1990s. The fact that a small, but increasing number of QlikView customers is purchasing and deploying thousands or, in some cases, tens of thousands of seats doesn't mean that QlikView is a bonafide enterprise BI product. At least yet. QlikView customers and partners are currently doing somersaults to work around product limitations that I mentioned last week. I have no doubt that QlikTech will address these deficiencies in the near future. So enterprise BI players need to stay alert lest QlikView ambush them from behind. The bigger question is whether QlikView will lose some of its appeal--or more specifically, it's ease of use, performance, agility, and affordability--in making the transition from a departmental to enterprise BI product. The BI Triumvirate Many people last week commented on my notion of a BI triumvirate consisting of MicroStrategy for reporting, QlikView for interactive dashboarding, and Tableau for visual discovery. First, I view these capabilities as distinct and separate categories of BI, each of which addresses different information requirements and groups of users. (In truth, there are two additional BI categories--OLAP cubes and data mining--but these are smaller niches.) Second, the vendors I referenced are examples only. I could have substituted any number of vendors in that list. For example, Oracle BI Enterprise Edition is an enterprise dashboard tool and IBM Cognos recently released a visual discovery tool, called Insight. However, I chose the three I did because I view them as leaders in their respective categories. But just because I reference a vendor in one category doesn't exclude it from other categories. For example, MicroStrategy also provides exceptionally good dashboards, and last year, it unveiled a Tableau-like product called Visual Insight. So, if you are a MicroStrategy customer, your triumvirate could easily be: Microstrategy Report Services for reporting, MicroStrategy Report Services for dashboarding, and MicroStrategy Visual Insight for visual discovery. (And to boot, you can also use MicroStrategy OLAP Services for OLAP cubes and MicroStrategy Data Mining Services for data mining.) Obviously, one of the benefits of going with a BI platform vendor like MicroStrategy is that you get all the BI functionality you need in a single, integrated environment. Interactive Dashboards The real question is whether MicroStrategy and other comparable products are best of breed in each category. In terms of dashboards, MicroStrategy and QlikView both offer significant value but in different ways. MicroStrategy dashboards are pixel-perfect reports that run against a cached cube or data warehouse and are viewed through either a HTML/AJAX, Flash, or native mobile interface, while QlikView dashboards run against a server-based in-memory database and viewed via a Web/AJAX or desktop interface. Filters in QlikView expose relationships (or lack thereof) among all elements displayed on a dashboard screen, while filters in MicroStrategy constrain views of data to support drill down and drill across navigation. Obviously, these are different interfaces and architectures powered by different database structures. Broadly generalizing, QlikView dashboards are more horizontally interactive (via its associative model of data), while MicroStrategy dashboards are more vertically interactive (via its dimensional data structure.) The best product is in the eyes of the beholder. Visual Discovery In terms of visual discovery, MicroStrategy Visual Insight is a first-generation product that currently lacks many of the features in Tableau. For instance, today MicroStrategy Visual Insight only accesses one data source at a time and displays one visualization per page. Customers also need to purchase and implement the entire MicroStrategy stack (version 9.2) to use Visual Insight. Thus, it's not a downloadable product like Tableau, which you can install and start using within minutes. To compensate, MicroStrategy now offers a free cloud-based version of Visual Insight, called Cloud Personal, that lets users upload and manipulate Excel spreadsheets without having to install any software. Touche! MicroStrategy plans to release a new version of Visual Insight later this year that will move the 1.0 product closer to the current version of Tableau. Of course, Tableau isn't sitting still, either. It's working on a new version slated for a fall delivery and continues to raise the bar for what's possible in a visual discovery environment. Dashboard Development Environments Although Tableau is a market-leading visual discovery tool, it can do other things as well. I've run into many customers that use Tableau as a development environment for building departmental dashboards. As such, Tableau often butts heads with QlikView for these types of accounts. In the past year, Tableau has added many features, including an in-memory database, server-side data storage, and data blending of multiple sources that transform it from just a very good desktop analyst tool to a departmental dashboard development environment that competes with QlikView. Summary Clearly, vendors watch each other carefully and mirror each other's moves. If one succeeds in the marketplace, then others quickly adopt similar functionality to staunch real or potential losses in market- and mindshare. As a result, BI innovations spread quickly across vendors and products. The key is to understand whether new functionality is more a marketing makeover than a bonafide product extension. At some point, all customers face an "all-in-one" or "best-of-breed" decision. Enterprise BI customers have to decide whether to go with an upstart that offers market-leading innovations or wait for their BI vendor to catch up. Conversely, departmental BI customers need to decide whether to jump ship for an integrated BI platform or wait for their pet BI vendor to embrace enterprise-scale computing. This is when it pays to know your vendor. If you have confidence in its direction and ability to execute, then it might be wise to stay put. Otherwise, it's probably time shake the dice and look at alternatives.

]]> http://www.b-eye-network.com/blogs/eckerson/archives/2012/05/more_on_microst.php Thu, 17 May 2012 11:07:47 MST http://www.b-eye-network.com/blogs/eckerson/archives/2012/05/more_on_microst.php The changing state of data integration - looking at Informatica 9.5 One of the great things about the business intelligence industry is the fact that the market is in a constant state of change with the goal of helping organizations use technology to take advantage of the vast amounts of information available for analysis. The way in which data is handled has changed from traditional ETL jobs and data warehouse storage that required batch processes to deliver results, towards more real-time and ad-hoc interactive capabilities. As we've seen, the market is now able to handle large and complex data sets from a wider variety of sources that include social media and unstructured sources. The addition of cloud also makes it easier to to develop alternate frameworks external to the firewall.

All of these changes, as well as the countless other trends within the marketplace beg the question - what about data integration? After all, it is fine to load data and use ETL to transform data, but what about the complications of gaining valuable insights out of data in a constantly shifting world that demands up to date information that can be acted upon immediately? How can data be securely and successfully placed in the cloud? How will unstructured data sources be supported as social networking becomes a more prominent way of identifying customer sentiment and business opportunities? These questions are a subset of what organizations are asking themselves as they try to adopt technologies that will help them stay ahead of their competitors.

Although many vendors are meeting these newer challenges head on, since Informatica 9.5 was released earlier this week, it makes sense to take a closer look at how they are handling the changing data landscape. Informatica has always provided a wide range of data integration solutions. Their 9.5 release actually ties in the aspects mentioned above - such as cloud, big data, social media, etc.- to provide organizations with all of the pieces they require to develop a data infrastructure that addresses all of the key aspects of BI related trends. For instance, Informatica 9.5 provides:
    • Embeddable cloud service that sends data where it is required through the use of a hybrid IT platform, leaving control for information in the hands of the organization
    • Support for big data through broader data virtualization and replication, as well as partition to use resources more effectively
    • The use of Natural Language Processing to analyze social data from sources that are becoming more relevant to organizations, such as LinkedIn, Facebook, social networks, etc.
    • The ability to stream data and support real-time events through CEP (Complex Event Processing) to provide alerts and notifications to be able to act upon important issues that arise
    • Interoperability with Hadoop to support big data processing
All of these areas are becoming increasingly important to organizations. What interests me most aside from the ability to access a large stack of offerings that cover the key aspects required for a strong data integration platform, is the additional focus on data stewardship and ensuring that the full data integration process is governed effectively to ensure that data quality or other issues that arise can be dealt with in an effective manner by data owners.

Most organizations want to focus on dashboards and getting the insights they require. In some cases, this occurs at the expense of strong data management. The reality, though, is that to gain an understanding of what is happening within the realm of Facebook or on Twitter, or to access relevant information through Hadoop, a strong set of data integration processes are required. Consequently, Informatica seems to be focusing their efforts on the areas that will most matter to businesses to maintain competitive edge within the marketplace today.





]]> http://www.b-eye-network.com/blogs/wise/archives/2012/05/the_changing_state_of_data_integration_-_looking_a.php Thu, 17 May 2012 09:25:59 MST http://www.b-eye-network.com/blogs/wise/archives/2012/05/the_changing_state_of_data_integration_-_looking_a.php Next query: NoSQL and Business Intelligence
just-say-nosql.png
Business intelligence (BI) has long been associated with relational databases and the SQL language. From the earliest days of data warehousing, the qualities of the relational model have been highly valued in the quest for data consistency and quality. In addition, it was assumed that business users are comfortable with tables of information. This has been proven true, especially by spreadsheets, much to IT's chagrin. Tables are also the lingua franca of BI tools and simple Select / Where queries are familiar to many users. But, whatever the rationale, the association of BI and SQL is deeply embedded in the minds of most practitioners. So, the question arises--what about NoSQL; how does this relate to BI? Can it be of use in data warehousing?

Good questions. But first, you need to know what flavor of NoSQL you're speaking about. For brevity, I'll focus only on one of the five or so varieties: document-oriented data stores. (If you are interested in the others, the bigger picture--and a trip to Rome--I propose my two-day seminar there on 11-12 June!) As I discovered about a year ago in a fascinating conversation with Max Schireson, president of 10gen / MongoDB, in this context a document is neither about e-mail contents nor Word documents; it refers to a particular data structure where records consist of an arbitrary set of fields, each identified by a name and value pair, structured in JSON (JavaScript Object Notation) or similar language. For more details, refer to my white paper. So, let me release you from your suspense now. Can this be of use in BI? The short answer is yes. But to fully grasp the extent, I'd like to introduce you to two MongoDB customers and how they are easing into BI using NoSQL.

I spoke to David Chancogne, CTO of Traackr, a web business measuring the influence of people who blog, tweet and otherwise contribute to the impression the general public forms of brands, products and more on the web. The goal is to assist marketers and advertizing agencies track and target such influencers more effectively. Traackr has built a MongoDB database of the contents of blogs, tweets, etc. and gives its customers reports and analyses of the top influencers in their areas of interest. Is this BI? In its broadest sense, yes. The scope is very specific and the queries pre-defined, but this is still BI at its most basic. Did Chancogne think of it as BI? Actually not, it's simply his business to provide analytics to his customers. Probing a little deeper, I discovered that Traackr is continually trying to optimize its algorithm to rate influence. They do this by extracting data from their database and playing with it in--wait for it---Excel! More BI, but like many a start-up business before them, the choice of Excel was more through familiarity and ease-of-use. Generic BI tools that run against a JSON data store, such as Pentaho's NoSQL solution, Nucleon Software's BI Studio, are beginning to appear that allow generic querying on the data without extracting it to Excel.

A conversation with Julian Browne led to further interesting insights. Browne is the architect of Priority Moments (a location-aware customer loyalty program that offers discounts at affiliated retailers) at O2, the second-largest provider of mobile/cell phone services in the UK, with more than 20 million customers. MongoDB was chosen as the platform for this service largely to deal with the complexity and variability of their product catalog. The challenge is that there exists a bewildering variety of product sets that can be offered to different customers, and changes constantly at the whim of marketing. The absence of a predefined schema, a key characteristic of document-oriented data stores, was a compelling argument for the technology choice. But, what of BI? Customer loyalty programs are prime BI territory, of course, and in this case tracking of uptake of offers is vital. As with Traackr, initial BI was provided through hand-crafted Java programming, although there is growing interest in using the emerging BI tools. Of more interest, however, is the experimental use of a specific feature of the database that allows a query to be left open and as records arrive in the database, they automatically appear in the result, which can be routed to a live HTML5 graph(1) giving real-time feedback to monitor program activity.

How would we summarize the situation regarding BI for document-oriented NoSQL databases? What we see is a fairly recent database technology with its query facilities being used for basic, predefined BI. As might be expected, more generic tooling for building queries is appearing. The type of BI supported is focused, application-specific querying and reporting--the type associated with data marts in traditional BI. This is exactly as we saw in the emergence of BI against relational databases. Note that some of the querying is being performed against the live operational sources. Again, we see the similarity with early reporting approaches with similar concerns about performance impacts on operations. MongoDB addresses this through the creation of eventually consistent replicas. Nonetheless, the demand for real-time BI continues to grow and certain classes of operational analytics will need such real-time or near real-time access.

Where NoSQL does not play a role in BI is also important. Enterprise data warehouses (EDW), with their focus on creating consistent, integrated, historical stores of core business information are set to remain squarely in the relational database world. But, where operational needs drive the choice of a NoSQL document-oriented data store, it is clear that BI can flourish in this environment too. See my latest white paper, "Business Intelligence--NoSQL... No Problem", for further details.


(1) For background on this approach, see hummingbird and data-driven documents.

]]> http://www.b-eye-network.com/blogs/devlin/archives/2012/05/next_query_nosq.php Thu, 17 May 2012 03:37:43 MST http://www.b-eye-network.com/blogs/devlin/archives/2012/05/next_query_nosq.php Please check out the Text Analytics Summit, Boston, June 12-13
The next Text Analytics Summit is coming up in four weeks. The June 12-13 conference will be the 8th annual Boston summit, the 8th Boston summit I've been privileged to chair. Will you join us?

The summit series was the first business-focused conference dedicated to BI on text, to techniques that turn text into data in the service of diverse applications. It remains the best, a testimony to outstanding speakers, great networking opportunities, and the unparalleled importance text plays in the Social, Big Data era.

As chair, I can extend to you a special $300 registration discount, via the code SG12. Use it and hear speakers on customer experience, marketing, e-discovery, financial services, and social-media analytics -- from organizations that include American Express, eBay, Fidelity Investments, Maritz Research, Monster.com, NASA, and Walt Disney. Visit www.textanalyticsnews.com for information, and follow the Registration link to register now.

Whether you're a veteran user or just getting started with text analytics, I hope you'll join us next month in Boston!


]]> http://www.b-eye-network.com/blogs/grimes/archives/2012/05/please_check_ou.php Wed, 16 May 2012 08:29:36 MST http://www.b-eye-network.com/blogs/grimes/archives/2012/05/please_check_ou.php Real World Data Governance Webinar: Data Governance, Big Data and The Cloud

Webinar - Thursday May 17, 2012 - 2pm EST


DATA GOVERNANCE, BIG DATA and THE CLOUD

Robert S. Seiner - KIK Consulting & TDAN.com

Register Today!

What is the impact of Big Data and The Cloud on Data Governance?

Or should it be ...

What is the impact of Data Governance on Big Data and The Cloud?

Since these aspects of data management are becoming discovery topics in the least
and hot topics at best ...

What should organizations do to prepare for governing Big Data?

What should organizations do to prepare for governing data in the Cloud?

Will traditional data governance suffice or do we need a different type of data governance?

People in the real-world are looking for answers to these questions.

Join me (Bob Seiner) and DATAVERSITY for the fifth in a series of monthly webinars titled
"Real-World Data Governance"

where I will share information directed at helping organizations answer these questions.

Through this webinar, Bob will provide a considerations for determining the
future of governing Big Data and data in the Cloud in your organization.

Join us for further adventures in "Real-World Data Governance".

Date: May 17, 2012

Time: 2 PM Eastern / 11 AM Pacific

Price: Free to all attendees.

Register Today!





]]> http://www.b-eye-network.com/blogs/seiner/archives/2012/05/webinar_data_go.php Mon, 14 May 2012 14:32:54 MST http://www.b-eye-network.com/blogs/seiner/archives/2012/05/webinar_data_go.php What influences acceptance of decision support technologies? by Dan Power
Editor, DSSResources.com


Acceptance is an important issue when implementing decision support capabilities especially those that are expensive, novel and/or innovative. Developers and managers want users to have a favorable reaction to a new capability. Sadly acceptance is not always the response. Expensive software is sometimes hostily received, little used or "worked around." Acceptance of change can be grudging and even coerced or at the other extreme enthusiastically received and welcomed. Developers should seek cooperation and approval rather than compliance and acquiescence. How can positive acceptance be achieved? In a worst case, how can grudging acceptance be achieved?
Continue reading at http://dssresources.com/faq/index.php?action=artikel&id=242

from DSS News


]]> http://www.b-eye-network.com/blogs/power/archives/2012/05/what_influences.php Sun, 13 May 2012 17:50:31 MST http://www.b-eye-network.com/blogs/power/archives/2012/05/what_influences.php Start Small: Single Version of the Definition

"Every meta-effort is for effectiveness improvement," I think. I think that this think is big enough.

If I ask Frank Buytendijk how I should start small with this big one, he would surely say: "Have a guess how the great philosophers would do." He wrote a little story about Confucius at the beginning of his article "The Myth of the One Version of the Truth":

"When Confucius was asked what he would do first if he were in power, he responded: 'Cleanse the definitions of terms we use!'
According to Confucius, nothing is so destructive for peace, justice and prosperity as confusing names and definitions."

I am not in power. But I am starting to blog at a powerful platform, the BeyeNETWORK.com! Thus, I should "cleanse the definitions of terms we use" with you as my start, hopefully small enough. Later, perhaps much later, I will consider the problem of "Single Version of the Interpretation," a bigger one, not only from the governance perspective, but also from an engineering point of view. As mentioned at the beginning, everything here is about effectiveness improvement, the biggest one in my opinion.

As the first step, I would like to invite you to have a look at the definition of Data Warehouse in an article series on the BeyeNETWORK. In the first article of the series, "Is Yours Really a Data Warehouse?", I will review the definitional situation in the field. Then in the second one, "Is Inmon's Data Warehouse Definition Still Accurate?", I will have a close look at Inmon's definition. In the third one, a revised definition is supposed with detailed explanations. The closing one of the series proposes a classification of data warehouses.



]]> http://www.b-eye-network.com/blogs/jiang/archives/2012/05/start_small_sin.php Thu, 10 May 2012 05:07:55 MST http://www.b-eye-network.com/blogs/jiang/archives/2012/05/start_small_sin.php Socrates Reloaded: In Search of Wisdom We have enough intelligence, what we need is more wisdom. Perhaps a discussion on wisdom feels a bit esoteric for this forum, but I think it makes sense to understand a little bit more about wisdom, for a number of reasons: * It is a humbling experience to experience that with all our analytical skills, we can't really define a term such as wisdom. Obviously there are limits to our business skills... * Wouldn't many of us want to be seen as wise within our profession? That people turn to us for advice? * But most important, with all the intelligence that systems are offering, we need guidance on how to use that. The goal of data is to provide information, information is the fuel for intelligence and knowledge, and wisdom provides guardrails for intelligence. In the first article in this series, I introduce the topic of wisdom, and point out how hard it is to even define it. This first articles also miserably fails doing so, but there is a point to that. In the second article, I will explain why we failed in our analysis, we simply used the wrong toolkit. Business analytical skills don't solve all problems. Lastly, in the third article, I will show the business relevance of this whole discussion. More wisdom, anyone?! frank (@FrankBuytendijk)

]]> http://www.b-eye-network.com/blogs/buytendijk/archives/2012/05/socrates_reload.php Sun, 6 May 2012 10:40:00 MST http://www.b-eye-network.com/blogs/buytendijk/archives/2012/05/socrates_reload.php QlikView Goes Enterprise I was fortunate to attend QlikTech's annual Partner conference in Miami Beach in April, and I discovered a few things about the fast-growing in-memory visualization vendor. Historically, QlikTech has sold QlikView to departmental business leaders and then used a "land and expand" strategy to spread its reach within an organization and grow revenues. This strategy catapulted the company to a successful 2010 initial public offering and 40+% annual growth. With $320 million in annual revenues, QlikTech now is determined to eclipse the $1 billion revenue mark. To do this, it's pushing hard and fast into the enterprise BI market, which has been the province of industry BI heavy weights, such as SAP, Oracle, IBM, and MicroStrategy. Enterprise Deployments. The good news is that QlikTech's customers are leading the way into enterprise territory. QlikTech is increasingly signing six- and seven-figure deals to support tens of thousands of users. As a result, QlikTech is working hard to transform what started out as a desktop and departmental tool into a bonafide enterprise platform. The company took its first enterprise steps in QlikView 10 when it moved security and administration from individual applications to a shared server environment. QlikView 11, released last fall, makes incremental improvements to performance, administration, team development, metadata management, clustering, and security capabilities. But QlikTech still has much work left to do. In particular, QlikView needs more granular clustering, a bonafide semantic layer, graphical data design and mapping tools, native change control and version management, an improved administrative console, and rationalized global licensing. Courting IT. As part of its push into the enterprise, QLikTech is trying to make nice with information technology (IT) departments, which tend to view QlikView as an invasive species that threatens to undermine information consistency and their control over corporate data. The real truth is that QlikView is an IT professional's best friend if it sources data from the data warehouse. Thanks to its intuitive visual interface, QlikView can help liberate data locked in the data warehouse and offload development from besieged IT staffers. There are 1,500 QlikView partner companies that have the technical expertise and project management skills to implement QlikView and can serve as extensions to the IT department. It would behoove IT departments to embrace QlikView and its partners if they want to stay a step ahead of the QlikView tsunami. Here are a few other insights I picked up from the Partners event: Mobility. Last year, QlikTech did an about-face with its mobile strategy, converting from native applications for iOS to generic Web-based mobile applications. A Web-based approach to mobile applications aligns better with QlikView's in-memory architecture which requires keeping large volumes of data in memory. Since memory is limited on mobile devices, native applications effectively turn QlikView into static dashboard viewer, which cheapens its value. However, many QlikView users are upset with the new Web-based applications because they lack native iOS features that aren't yet baked into HTML5 and Web-based mobile applications can't be used in disconnected mode. It will be interesting to see what fixes QlikTech makes, if any, to its mobile applications. QlikView versus Tableau. The two darlings of the BI space these days are QlikView and Tableau, which many people lump together as visual analysis tools. In reality, these two tools are quite different, serving different users and purposes. In fact, the tools are complementary, making a nice one-two combination in any BI toolkit. QlikView is an application development platform that requires an IT team (or QlikView partners) to set up, build, and maintain the applications. Companies use QlikView to build small, purpose-built, interactive dashboards for casual users. Architecturally, the tool creates in-memory data marts to ensure fast performance. Dashboards query these in-memory data sets rather than source data directly. IT administrators generally update the data marts in batch at night, although the tool supports incremental updates in near real-time as well. Tableau, on the other hand, is a visual exploration tool designed for power users. It's primarily a desktop tool that users can download, install, and start visualizing data in minutes. Tableau recently added an in-memory cache to improve performance when querying large databases, but in-memory processing is an adjunct to its architecture, not the core piece, unlike QlikView. Although Tableau can be used to build departmental dashboards, it is a better exploration tool than an application development platform. Basically, companies should purchase QlikView to drive their interactive dashboards, Tableau to support visual exploration, and a standard BI tool, like MicroStrategy or IBM Cognos, to handle scheduled reporting. That's a nice, modern day BI tool triumvirate. This standardization strategy also lower total cost of ownership and puts pressure on standard BI tool vendors to lower prices. Future. QlikTech announced nice collaboration and comparative analysis features in version 11, and I expect more enhancements in these areas going forward. I also expect QlikTech to fill in some holes in its product lineup. These include things like predictive analysis, support for unstructured data, location intelligence, graphical ETL tools, a visual semantic layer, better support for near-real time data delivery, and printing. Software partners provide some of these capabilities today, but QlikTech will need to acquire or build such capabilities if it's going to assume the mantle of a true enterprise BI vendor.

]]> http://www.b-eye-network.com/blogs/eckerson/archives/2012/05/qliktech_goes_e.php Thu, 3 May 2012 09:30:47 MST http://www.b-eye-network.com/blogs/eckerson/archives/2012/05/qliktech_goes_e.php Death by a thousand analytics
knives.jpg
Donald Farmer, now of Qliktech, offered to the Boulder BI Brain Trust (BBBT) last week that what we in "BI" do is better described as decision support rather than business intelligence. The comment was greeted by a flurry of Tweets and Grunts of agreement. It's an observation I've also made, and for similar reasons. In essence, BI tools support decision making; to attribute intelligence--business or otherwise--to software seems somewhat presumptuous. And yet, there is a further problem with the term business intelligence. It implies a level of rationality in decision making that is beyond the reality most of us encounter. This implication is carried even further as various analysts and vendors begin to talk about business analytics as if it will be the ultimate solution to all business decision-making needs.

There are facts, we are told. And if we have all the facts and we apply comprehensive analytics, we will discover the past, understand the present and predict the future. We are told this is the scientific method; the truth is in the numbers. Is this a valid way of interpreting the way the world works? I would argue that it is so far from reality that we are in danger of creating a fantasy world worthy of Tolkien.

History, it is said, is named thus because it is "his story". History, they say, is written by the victors. The implications are far reaching. Yes, there are indeed facts, but it's the stories we weave around the facts that are what really matter. To quote Liz Greene(1): "Mehmet the Conqueror invaded Constantinople in 1453. That is an historical fact. But depending on which history book we read, Mehmet was either a redeemer or a cruel tyrant, a warrior for the True Faith or a vile heretic." In terms of the story we tell ourselves about this incident and its value as guide for the future, which part of the quote is more relevant - the historical fact or its interpretation? And if you haven't yet looked at the endnote for the source of this quote, do so now. And be brutally honest with yourself. Do your beliefs about astrology affect the weight you attach to the quote? And when I tell you that Dr. Liz Greene is also a fully trained and qualified Jungian psychoanalyst, how does that cause you to re-evaluate your judgement.

Our current obsession with analytics is dangerous. It's based a number of simplifications, misconceptions and downright errors. It is a simplification that business is an entirely rational, fact-driven process. It is a misconception that given sufficient data you can predict the future. It is a downright error to assume that in the future, business can be entirely (or even largely) driven by business analytics.

Does that mean we should abandon analytics? Of course not. There are facts to gather that have so far remained undetected. These facts can influence our interpretations. If they are indeed relevant to the story at hand. And if we allow them to do so. And if our business users can avoid statistical errors such as confusing correspondence with causality. There are many examples already of significant benefits to be gained for businesses who adopt analytics.

The questions of relevance and abuse of statistics are ones of good analytic practice and education of users. I have no doubt that, as we move beyond the hype phase, these issues will be addressed. The issue of interpretation is much more difficult to tackle. Because it is at the heart of how we imagine our decision makers behave. Our focus on intelligence--rational and logical--obscures two other keys aspects of decision making: intent and intuition. Intent we ignore and intuition we dismiss. All decision making includes the intent of the decision maker. That intention drives everything from what data is gathered, through how it is evaluated, all the way to the final choice of action. How many decisions are post-justified by careful data selection and evaluation? If a decision maker is motivated by personal gain (and they do exist, you know), won't analytics be enlisted to support that goal? And regarding intuition, it is evident that not all decision contexts are wholly driven by measurable or predictable metrics. Low prices may be important, but so too are ambience, history, ethos and personal relationships when customers choose where to shop. Data measures for the latter are hard to define and capture. The intuition of an experienced manager is needed in such circumstances. Target's decision to focus marketing of maternity products to women in the early stages of pregnancy was based on sound analytics according to a story in the New York Times, but the reaction of prospective customers was intuitively obvious.

The bottom line is that we focus exclusively on big data and analytics at our peril. We need to move beyond traditional concepts of business intelligence and decision support. I see our goal as supporting full-spectrum business insight.

(1) Greene, L., "Apollo's Chariot - The Meaning of the Astrological Sun", CPA Press, (2001)



]]> http://www.b-eye-network.com/blogs/devlin/archives/2012/05/death_by_a_thou.php Wed, 2 May 2012 06:56:42 MST http://www.b-eye-network.com/blogs/devlin/archives/2012/05/death_by_a_thou.php Can computerized decision support reduce information asymmetry? by Dan Power
Editor, DSSResources.com


In multiparty decision situations, one party often has better information than another. These situations commonly involve a purchase/sales transaction or a principal agent situation where a person acts on behalf of another and the principal attempts to monitor and control the agent. Information symmetry means all parties in a decision situation have the same information. Information asymmetry involves decisions in transactions where one party has more or better information. Providing complete information symmetry or what has been called "transparency" is probably impossible, but the goal is often to strive for transparency and information symmetry. Supposedly with the same information, the decisions of the parties will be better.

Continue reading at http://dssresources.com/faq/index.php?action=artikel&id=237


]]> http://www.b-eye-network.com/blogs/power/archives/2012/05/can_computerize.php Wed, 2 May 2012 05:42:48 MST http://www.b-eye-network.com/blogs/power/archives/2012/05/can_computerize.php Blood in the Water or Just Fishing Where the Fish Are?

The story goes that Willy Sutton robbed banks because "that's where the money is." While this attribution appears to be an urban legend, it's no myth that Oracle has a lion's share of databases - both transactional and analytic.

IBM started an advanced land grab for Oracle customer conversions by bringing a high compatibility of PL/SQL into the DB2 database.

Now, Teradata has invested resources in facilitating the migration away from Oracle. With the Teradata Migration Accelerator (TMA). structure and SQL (PL/SQL) code can be converted to Teradata structures and code. This is a different philosophy from IBM, which requires few code changes for the move, but also doesn't immediately optimize that code for DB2.

While data definition language (DDL) has only minor changes from DBMS to DBMS, such as putting quotes around keywords, Teradata's key activity and opportunity in the migration is to change Oracle cursors to Teradata set-based SQL.

"Rule sets" - for how to do conversions - can be applied selectivity across the structure and code in the migration. TMA supports selective data movement, if desired, with WHERE clauses for the data. TMA also supports multiple users doing a coordinated migration effort.

TMA also works for DB2 migrations.

While it will not do the trick on its own, having these tools, which convinces a shop that the move could be more pain-free than originally thought, will support DBMS migrations.



]]> http://www.b-eye-network.com/blogs/mcknight/archives/2012/04/blood_in_the_wa.php Mon, 30 Apr 2012 10:15:41 MST http://www.b-eye-network.com/blogs/mcknight/archives/2012/04/blood_in_the_wa.php The increasing importance of collaboration

Most BI offerings have some level of collaborative feature sets. What this really means in many cases, is that users can email dashboards or share links, and in some cases add annotations or notes. Essentially, when it comes to capabilities that let people collaborate the way they naturally would by the water cooler or in a meeting, much is lacking in the world of BI. Whether this is because of the limitations that exist due to vendor infrastructure or a lack of imagination, most solutions are far from providing organizations with the ability to interact with their BI solutions in a way that enables broad collaboration.

Despite this lack of innovation in the area of collaboration, there are a few vendors that seem to be getting it right. The ones I think of first are Yellowfin and Lyzasoft - they both build their platform in a way that takes advantage of social media concepts and expectations. While at QlikView Qonnections, I also noticed that their view of collaboration extends to support the way in which business users interact with technology. Aside from being able to share screens and work simultaneously, there is a strong focus on the user experience and how people interact with technology and each other.

In an age where Facebook and Twitter mark the way people interact, annotations within a solution just doesn't cut it. Sending a link to a colleague is no longer enough. Now people need to be able to share their screen, work together, and create real-time conversations to help identify what is happening and how to deal with it. Until BI can do this effectively, solutions will continue to lack in overall effectively.

Obviously, until more recently, this was not the purpose of business intelligence at all. The ability to consolidate information across disparate business sources, and make sure that targets were being met provided a large part of the value add. Now, with market trends such as mobility, social media analysis, and collaboration focusing around better and immediate access to information in an easier to consume way, it makes sense that the importance of collaboration is increasing.

Overall, the fact remains that the majority of the BI market is missing the point of collaboration and what it needs to be. Where QlikView provides the ability to work simultaneously on dashboards and Lyzasoft lets people create real-time conversations around analytics, the same cannot be said for many other vendors in the marketplace. And even though many of these solution providers are working towards providing this functionality, unless these tools are provided in an easy to use and intuitive interface, their value proposition will continue to be low at best.





]]> http://www.b-eye-network.com/blogs/wise/archives/2012/04/the_increasing_importance_of_collaboration.php Sun, 29 Apr 2012 11:31:37 MST http://www.b-eye-network.com/blogs/wise/archives/2012/04/the_increasing_importance_of_collaboration.php