Tag Archives: BI

Self Service BI

29 May

Good article on Self Serve BI. The term has been around a while, but never seems to get old.

The different aspects of BI

5 Dec


I like the recognition that approaches need to be integrated in order to create useful insights. Valuable insights come from balancing the needs and capabilities of  business strategy, business analysis, business intelligence and advanced analytics.


The addition of analytical functions to databases

14 Nov

The trend has been for database vendors to integrate analytical functions into their products; thereby moving the analytics closer to the data (versus moving the data to the analytics). Interesting comments in the article below on Curt Monash’s excellent blog.

What was interesting to me, was not the central premise of the story that Curt does not  “think [Teradata’s] library of pre-built analytic packages has been a big success”, but rather the BI vendors that are reportedly planning to integrate to those libraries: Tableau, TIBCO Spotfire, and Alteryx. This is interesting as these are the rapid risers in the space, who have risen to prominence on the basis of data visualization and ease of use – not on the basis of their statistical analytics or big data prowess.

Tableau and Spotfire specifically focused on ease of use and visualization as an extension of Excel spreadsheets. They have more recently started to market themselves as being able to deal with “big data” (i.e. being Hadoop buzzword compliant). With the integration to a Teradata stack and presumably integrating front end functionality into some of these back end capabilities, one might expect to see some interesting features. TIBCO actually acquired an analytics company. Are they finally going to integrate the lot on top of a database? I have said it before, and I will say it again, TIBCO has the ESB (Enterprise Service Bus), the visualization tool in Spotfire and the analytical product (Insightful); hooking these all together on a Teradata stack would make a lot of sense – especially since Teradata and TIBCO are both well established in the financial sector. To be fair to TIBCO, they seem to be moving in this direction, but it has been some time since I used the product).

Alteryx is interesting to me in that they have gone after SAS in a big way. I read their white paper and downloaded the free product. They keep harping on the fact that they are simpler to use than SAS, and the white paper is fierce in its criticism of SAS. I gave their tool a quick run through, and came away with two thoughts: 1) the interface while it does not require coding/script as SAS does, cannot really be called simple; and 2) they are not trying to do the same things as SAS. SAS occupies a different space in the BI world than these tools have traditionally occupied. However,…

Do these tools begin to move into the SAS space by integrating onto foundational data capabilities? The reason SAS is less easy to use than the products of these rapidly growing players is that the rapidly growing players have not tackled the really tough analytics problems in the big data space. The moment they start to tackle big data mining problems requiring complex and recursive analytics, will they start to look more like SAS? If you think I am picking on SAS, swap out SAS for the IBM Cognos, SPSS, Netezza, Streams, Big Insights stack, and see how easy that is! Not to mention the price tag that comes with it.

What is certain is that these “new” players in the Statistical and BI spaces will do whatever they can to make advanced capabilities available to a broader audience than traditionally has been the case with SAS or SPSS (IBM). This will have the effect of making analytically enhanced insights more broadly available within organizations – that has to be a good thing.

Article Link and copy below

October 10, 2013

Libraries in Teradata Aster

I recently wrote (emphasis added):

My clients at Teradata Aster probably see things differently, but I don’t think their library of pre-built analytic packages has been a big success. The same goes for other analytic platform vendors who have done similar (generally lesser) things. I believe that this is because such limited libraries don’t do enough of what users want.

The bolded part has been, shall we say, confirmed. As Randy Lea tells it, Teradata Aster sales qualification includes the determination that at least one SQL-MR operator — be relevant to the use case. (“Operator” seems to be the word now, rather than “function”.) Randy agreed that some users prefer hand-coding, but believes a large majority would like to push work to data analysts/business analysts who might have strong SQL skills, but be less adept at general mathematical programming.

This phrasing will all be less accurate after the release of Aster 6, which extends Aster’s capabilities beyond the trinity of SQL, the SQL-MR library, and Aster-supported hand-coding.

Randy also said:

  • A typical Teradata Aster production customer uses 8-12 of the prebuilt functions (but now they seem to be called operators).
  • nPath is used in almost every Aster account. (And by now nPath has morphed into a family of about 5 different things.)
  • The Aster collaborative filtering operator is used in almost every account.
  • Ditto a/the text operator.
  • Several business intelligence vendors are partnering for direct access to selected Teradata Aster operators — mentioned were Tableau, TIBCO Spotfire, and Alteryx.
  • I don’t know whether this is on the strength of a specific operator or not, but Aster is used to help with predictive parts failure applications in multiple industries.

And Randy seemed to agree when I put words in his mouth to the effect that the prebuilt operators save users months of development time.

Meanwhile, Teradata Aster has started a whole new library for relationship analytics.

Data Visualisation » Martin’s Insights

23 Apr

This is a good article on data visualization. The author indicates in his considerations section that “real data can be very difficult to work with at times and so it must never be mistaken that data visualisation is easy to do purely because it is more graphical.” This is a good point. In fact in some respects determining what the right visualization is can be harder than simply working with the data directly – however, much harder to communicate key insights to a diverse audience.

What rarely gets enough attention is that in order to create interesting visualizations, the underlying data needs to be structured and enhanced to feed the visualizations appropriately. The recent Boston bombing where one of the bombers slipped through the system due to a name misspelling recalled a project years ago where we enhanced the underlying data to identify “similarities” between entities (People, cars, addresses, etc.) For each of the entities, the notion of similarity was defined differently; for addresses it was geographic distance; for names it was semantic distance; for cars, it was matching on a number of different variables; and for text narratives in documents we used the same approach that the plagiarism tools use. In this particular project a name misspelling, and the ability to tune the software to resolve names based on our willingness to accept false positives, allowed us to identify linkages that identified  networks. Once the link was established we went back and validated the link. In the above example, the amount of metadata generated to create a relatively simple link chart was significant – the bulk of the work. In terms of data generated, it is not unusual for data created to dwarf the original data set – this is especially true if there are text exploitation and other unstructured data mining approaches used.

So … Next time the sales guy shows you the nifty data visualization tool, ask about the data set used, and how massaged it needed to be.


This should come as no suprise… Using Excel for complex analysis on an ongoing basis is asking for trouble!

22 Apr

This report on how using Excel has caused some major miscalculations should come as no surprise… Excel exists because it is pervasive, easy to use and can be applied to a range of decision making activities. However, have you ever had the experience of trying to create a repeatable, defensible and transparent report using excel WITHOUT having to make sure you had done it correctly? The attached article talks about a number of mistakes. I have had a number of discussions over the years with companies that are struggling with whether or not to implement a BI system, and if so to what extent should it provide structure and guidance to the process of using Excel?

The easy implementation of BI is to implement a tool such as Tableau that in essence takes spreadsheets and allows you to pivot the data and visualize more easily that one could in excel. I realize that Tableau does more than that now, but that is how it started and most people appear to use it that way still. This gives you great looking dashboards, and allows you to roll around in the data to bubble up insights. However, it does nothing to address the quality of the report and the issues raised by the article.

At the other end of the spectrum are enterprise level tools that do a great job of locking down the source data, and tracking everything that happens at the desktop to make the final report.These tools are focused on creating the SAME report with exactly the same inputs and calculations as all previous times. To the extent changes are made, they are tracked, and capabilities exist to flag and route changes for review and approval. The downside of course is that they often limit what the user can do with the data.

Somewhere in the middle is the happy spot. To the extent tools are not able to support the requirements for transparency, traceability, and defensibility, these requirements must be addressed through policy, process and standards.  Most of the enterprise tools are configurable to create a well defined set of gates between which analysts and report creators can have complete flexibility.

In the cases mentioned in this article, the technology exists to create the safeguards required. However, the user communities were able to resist change, and management – for whatever reason – did not make the decision to invest in underlying data management, BI and analytical capabilities. In a data driven world, it is only a matter of time before that comes back to bite you.

TIBCO – Buys another company

1 Apr

TIBCO buys another company in the analytics space. I have always thought that with Spotfire, the Enterprise Service Bus business, and the acquisition of Insightful some years ago, TIBCO had the makings of a company that was putting together the Big Data analytical stack. With this purchase, the have added a geo capability. Will they ever get all these pieces integrated to create a solutions package – like SAS’s Fraud Framework? Not sure why they have not done that to date. It may just be that it is too hard to sell complete solutions, and it is easier to get in the door with a point solution? Anyway – I like Spotfire, and anything they do to build out the back end is good stuff. Price point still seems a little high for a point solution, but they seem to be making it work for them, so who am I to argue… interesting to see how this plays out.

See also here – as they post in the MDM Magic Quadrant as well.


Microsoft Powerpivot

17 Feb

Microsoft Powerpivot

I have always thought that Tableau was initially just a cool way to create Excel pivot tables and populate the results in a graphic – something you can do in Excel, but was  a lot easier in Tableau. Is Powerpivot the  MS answer to these tools that have leveraged excel, but have not used the excel visualization capabilities or been willing/able to write the VB code to get Excel to do what you want it to do?

I do not have Office 2013, but look forward to playing with this when I do.

Gartner BI & Analytics Magic Quadrant is out…

10 Feb

The report can be obtained here. Along with some industry analysis hear

Well the top right quadrant is becoming a crowded place.

Gartner BI Quadrant 2013

I have not had time to really go over this and compare it to last year’s but the trends and challenges that we have been seeing are reflected in this report; some interesting points:

  1. All of the Enterprise level systems are reported to be hard to implement. This is not surprise – what always surprises me is that companies blame this on one company or another – they are all like that! It has to be one of the decision criterion when selecting one of the comprehensive tool sets.
  2. My sense is that IBM is coming along – and is in the running for the uber BI / Analytics company. However, the write up indicates that growth through acquisition is still happening. This has traditionally led to confusion in the product line and difficulty in implementation. This is especially the case when you implement in a big data or streaming environment.
  3. Tibco and Tableau continue to go head to head. I see Spotfire on top from a product perspective with its use of “R”, the purchase of Insightful and building on its traditional enterprise service bus business. HOWEVER, Gartner calls out the cost model as something that holds Spotfire back. This is especially true when compared to Tableau. My sense is that if TIBCO is selling an integrated solution, then they can embed the cost of the BI capabilities in the total purchase and this is how they are gaining traction. Regardless – Spotfire is a great product and TIBCO is set to do great things, but their price point sets them up against SAS and IBM, while their flagship component sets them up against Tableau at a lower price point. My own experience is that this knocks them out of the early stage activity, and hence they are often not “built in” to the later stage activity.
  4. SAS Continues to dominate where analytics and  Big Data are involved. However, interesting to note that Gartner calls out that they are having a hard time communicating business benefit. This is critical when you are selling a enterprise product at a premium price. Unlike IBM who can draw on components that span the enterprise, SAS has to build the enterprise value proposition on the analytics stack only – this is not a problem unique to SAS – building the value proposition for enterprise level analytics is tough.
  5. Tableau is the darling of the crowd and moves into the Gartner Leader’s Quadrant for the first time. The company has come out with a number of “Big Data” type features. They have connectors to Hadoop, and the article refers to in-memory and columnar databases. While these things are important, and the lack of them was holding the company back from entering certain markets, it is a bit at odds with their largest customer segment, and their traditional positioning approach. Addressing larger and a more integrated approach takes them more directly into the competitive sphere of the big guys (SAP, IBM and SAS), and also into the sphere of TIBCO Spotfire.
  6. It would be interesting to run the Gartner analysis along different use cases (Fraud, Risk Management, Consumer Market Analysis, etc.) In certain circles one hears much of companies like Palantir that has a sexy interface and might do well against Spotfire and Tableau, but is not included here.  Detica is another company that may do well. SAS would probably come out on top in certain areas especially with the new Visual Analytics component. There are probably other companies that have comprehensive BI solutions for particular markets – If anyone has information on these types of solutions, I would be interested in a comment.

More to follow – and there is probably much more to say as things continue to evolve at a pace!


It is about what you do with the data!!

7 Feb

Hadoop and Big Data – it is about what you do with the data!!

Some good videos from TechTarget and Wayne Eckerson – for a data guy he talks a lot about analytics.

The next thing – how visualizations interact with Big Data?

29 Jan

If big data is to be really useful for the masses, it needs to be distilled down to something that can be intuitively understood on mobile devices

Very positive outlook on the BI space – Cloud will bring it to the masses; visualization will make it understandable.

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