Tag Archives: business intelligence

Qlik Sense at the BBBT: Setting Up for the Future

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Qlik was at the BBBT last week to talk about Qlik Sense. The presenters were Josh Good, Director of Product Marketing, and Donald Farmer, VP of Innovation and Design. It was a good presentation and Qlik Sense seems like the start of a good product, but let me start by discussing a tangent.

A startup’s voice: A marketing tangent

Startups usually have a single voice, the founder, CTO or somebody who is the single and sole owner of the vision. Sometimes it’s somebody who is put forward as the visionary, correctly or incorrectly. It takes a level of maturity in a company to clarify a core message to the level where it’s replicable by a wider variety of people and for the original spokespeople to let go. While the modern BI industry is still fairly young and every analyst group talks about the untapped market, Qlik is one of the biggest players in our nascent business.

Donald Farmer is a great presenter, a smart man and has been, until recently, the sole Qlik voice I hear in every presentation. While I don’t always agree with him, he’s a pleasure to hear. Yet I continually thought “why him, always?” There might be somebody else briefly doing a demo, but he was THE voice of Qlik.

It’s not only because of my product marketing experience that I was pleased to hear from Josh. He wasn’t the demo dolly, but let the presentation with Donald chiming in. They worked well together. It’s clear that both of the startup issues I mentioned are being addressed by a maturing Qlik marketing organization who are now using multiple voices well.

Qlik Sense

I’ve blogged about other companies recently, talking about the focus on UI. Thankfully, it’s spreading. Companies who focused, in the early days, on the business analysts are realizing that they need to better address the business knowledge worker. Qlik Sense has a nice, clean interface. It’s nowhere near the overcrowded confusion of most products from a few years back. For those who want to see it, the client software is freely downloadable to you can try it out.

The one issue I have is, again, the same one I’ve mentioned with many other vendors: ETL. Josh was another person who started the demo by importing a spreadsheet. Yes, I know there’s a lot of data in them and all products need to access spreadsheets, but it’s one way of avoiding the ETL issue. Other than very basic, departmental data, more complex decision making always involves other sources. It’s the heterogeneity of data that is today’s big issue. However, that’s a weak spot hidden by just about everyone.

What was nice was the software’s intelligence in building an initial data relationship diagram base on field name relationships. It’s a start and if they keep at it the feature can grow to something that can more easily show the business user the links between different pieces of information.

A number of vendors have recently begun to have their software look at data and propose initial visualizations based on data type. It’s an easy way for users to get going. Qlik Sense doesn’t do that and the response was marketing fluff, but the display to choose types is better than most. Rather than drop down to select charts, it displays the types with mini-images. That will do for now.

Mobile done well

One fantastic part of the demo was in how well they’ve integrated mobile into the system. They were going to show it anyway, but before Josh could get to it there was a problem with his PC. He quickly pulled up his iPad and, using the same account, continued on his way with the same information that was well formatted to the new display. A key point to that is that Qlik isn’t just using mobile devices for display, he was working to create visualizations on the device.

That other data…

I’ve already mentioned heterogeneity. A number of younger companies, focused on the Cloud, have created clear links to Salesforce and other cloud data sources to easily let SMB and departments access those data sources. Qlik does not have that capability, furthermore access to major ERP and CRM systems. That will still take strong interactions with IT to create links and access for the users.

That matters to me, for one example, because of the repeated demo examples from the sales arena. Yes, sales managers remain heavy users of spreadsheets, but SFA systems have made strong inroads and the ability to combine those sources quickly for sales management is critical.

Data Governance: Thinking ahead

One area where Qlik seems to excel is in thinking about the issues of data governance. Even in this early version of Qlik Sense they’ve included some powerful ways of controlling access, both from administration and a business user standpoints. I’ve seen other vendors talk about it and only some of them willing to show if questioned. Josh and Donald brought it up as part of their basic presentation and showed a nice interface.

Just as with the growth of PCs giving individuals power while hurting data governance, BI needs to get ahold of those issue and help the end user and IT work together to manage corporate data to follow business and legislative polices. Qlik’s focus on that is an important differentiator.

Summary

Qlik Sense is a new product. It has very good visualization, which should be expected from Qlik, and has moved forward to an improved UI for ease of use. While they still have issues of concern with data access, their data governance implementation seems to be ahead of the curve and is well thought out. It’s an early generation product, so it doesn’t bother me that it has some holes. The critical thing is to look at the products in the perspective of your timeframe of needs and see if it’s right for you.

Just as importantly, from my marketing perspective, is the maturation of the marketing message and team. I’m hearing multiple voices speaking the same message. On the product and corporate fronts, Qlik is moving ahead in a good direction.

SiSense at the BBBT: High Performance BI at Low Cost?

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The latest presentation at the BBBT was by Amit Bendov, CEO, Sisense. First marketing warning: If you’re going to their web site, be prepared. Maybe it’s only for some weird Halloween thing, but the yellow and black background of the web site is the one of the ugliest thing I’ve seen for a professional company. However, let’s look under the covers, because it gets better.

The company was founded in 2004 and Amit says the first sales were in 2010. There’s a good reason for that delay. They are yet another young company who talks about being a full stack BI provider, being more than a visualization tool but also supposedly providing ETL, data storage and the full flow for your information supply chain from source systems to display. That technology took a while to develop.

Technology: Better integration of memory and Disk

The heart of their system is a patent pending technology that tightly integrates cpu cache, RAM and disk to better leverage all storage methods for higher performance. The opportunities that theory provides are enough that they’ve received $50 million (USD) in venture funding, $30 million in their latest round, earlier this year.

As they are a startup, it’s no surprise that the case studies given were for SMB or departments within enterprises. That’s the normal pattern, where a smaller group takes advantage of flexibility to try new products to solve focused problems. As their customer list includes companies such as Ebay, Wix, ESPN and Merck, companies with lots of data, those early entrants increase the potential if Sisense continues to perform.

Another key technology component is their columnar database. They created a proprietary one to be able to support their management technology. That’s completely understandable as their database isn’t purely on disk or memory, but in a combined mix that needs special database management.

The final key to their technology is that they worked to ensure the software runs on commodity chips from the X86 heritage. That means it runs on normal, affordable, off the shelf servers, not on high priced appliances.Sisense hardware price comparison

The combination of the speed and affordability of the technology is justification for the rounds of funding they’ve received.

Really full stack?

One fuzziness that I’ve mentioned with other full stack vendors is the ETL side of the process. The growth of Cloud companies such as Salesforce, and the accessibility of their APIs, means that you can get a lot of information out of systems aimed at SMB. However, true enterprise ETL means accessing a very wide variety of systems with much less easy or open APIs. When Mr. Bendov talked about multiple systems, it seems, from presentation and demo, that he’s talking about multiple instances of simple databases or open APIs, and not a breadth of source types. There wasn’t a lot of choice in the connection section of his application.

That’s not a problem for companies at Sisense’s state of maturity, as long as there’s a business plan to expand to more enterprise sources. They need to focus on proving the technology in the short term and having more heterogeneous access in their tool bag for the future.

Another issue is the question of what, exactly, their database is. Amit Bendov made a brief comment about not needed data warehouse, but as I and others quickly brought up, there are two problems with that statement. First, they would seem to be a data warehouse. They’re extracting information from source systems, transforming that information even if not into the old star-schema structures, and providing the aggregate information for analysis. Isn’t that a high level description of a warehouse? Second, as they’re young and focused on SMB or departments, as with other companies who serve visualization, they might need to look at customer demands and get access to corporate data warehouses as another source.

The old definition of a federated data warehouse seems to be evolving into today’s environment where sometimes an EDW is a source, other times a result and sometimes it’s made up of multiple accessible components such as Sisense and other databases. Younger companies who disparage EDWs need to be careful if they wish to address the enterprise market. The EDW is evolving, not dying off.

User interface and more

One of my first trips to Israel was, in part, when my boss and I had to bring a couple of UI specialists to show Mercury Interactive’s programmers why it might be nice to rethink application interfaces. It’s wonderful what twenty years have wrought. Amit Bendov says that Sisense has one UI specialist for every two programmers, and the user interface shows that. While I mentioned that they need broader ETL access, the simplicity of getting to sources is clear. While you still will need a business analyst to understand some column names, it’s a very easy to use interface.

The same is true in the visualization portions of their application. While it’s still a simpler tool, it has all the basics and is very clear to understand and use.

Paving the way for their spread into enterprise, the Sisense team also supports single-sign on, basic data access control, both in global administration and in the user interface, and other things that will be needed to convince a larger corporation to spread the technology.

Summary

Sisense looks like a startup in a great position. Their technology is well thought out and seems to be performing very well in the early stages. Affordable, fast, business intelligence is something nobody will turn down.

The challenge is two-fold:

  • Do they have the technology plans to help them address larger enterprise issues?
  • Do they have the mindset to understand the importance not only in marketing, but in changing the marketing to a more business focus?

This is the same refrain you’ve heard from me before and which you’ll hear again. This is the Chasm challenge. Their technology has a great start, but their web site and presentation show they aren’t yet thinking bigger and we’ll have to see what the future holds both for the technology and the messaging.

Business intelligence is a very visible market and one growing quickly. While small companies need to focus on the early adopters, they must very rapidly learn how to address the enterprise, both in products and marketing.

High performance BI at a reasonable cost is a great sell, but Sisense isn’t yet read for full enterprise. Sisense has a great start but life is fluid.

TDWI / Actuate Webinar on Visualization: Not much there

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Maybe it’s because of the TDWI conference now going on in San Diego, but this morning’s webinar on “Making Data Beautiful for Business Users” seemed a bit of an afterthought. The presenters were Dave Stodder, TDWI Director of Research, and Allen Bonde, VP Product Marketing and Innovation, Actuate. There were a few interesting moments, but not a lot of even basic content.

Dave Stodder began with a whole bunch of quotes from other people. I admit, it’s a quick way to put together a presentation, but then you should paraphrase and explain why the quotes matter rather than just reading them verbatim – we, the audience, are already doing that.

However, then he got to the three main goals of improving visualization in BI:

  • Improving self-service
  • Shortening the path to insight
  • Advancing business agility

To be honest, those are accurate but also valid for every other point in reporting throughout history. Businesses always want to enable decision makers to help make more accurate and timely decisions through better information.

What followed was one of the keys to TDWI success: An interesting slide based on one of their surveys.TDWI Visualization ROI Focus slide

Improved operational efficiency was a clear number one. The problem is that the data is most likely from IT respondents rather than from business users. I asked the question about that but it wasn’t answered. I predict that if you asked business users you’d find the second two items, faster response and identify new opportunities, would be at the top.

One important point Dave Stodder made was about alert fatigue. It’s tempting to have visualizations and other tactics that alert anytime things change, but too many alerts mean people stop paying attention. It reminded me of my days as a sales engineer, back in the days of pagers. Another SE and I had to sit down one of the sales people and explain that if he appended 911 to every page then nothing was important.

The only part purely focused on visualizations were two slides. One was just a collection of a few visualization types and the other was another TDWI survey about which visualization types are currently being implemented. There wasn’t a discussion of the appropriateness of the ones being used the most, any reason to better focus on some being ignored, or any discussion about how many are provided by packaged BI tools versus are home grown by the supposedly valuable data scientists.

Allen Bonde then took over and didn’t focus on visualization. He gave a rather generic Actuate sales pitch, mentioning platforms built for scale, the importance of an open community and didn’t show any visuals on visualization.

It wasn’t that the presentation was terrible, it’s only that it was far too generic. What was said about visualizations could be said about just about any reporting and there wasn’t really any direct focus on visualization. It’s one thing to quote Tufte, it’s another to have a discussion about current tools and what’s coming. That later was missed.

Maybe after the conference we’ll see another webinar with clearer focus.

SQL v Hadoop: The Wrong Conversation

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“No SQL!”

“Hadoop doesn’t require you to work in SQL!”

The claims are everywhere, but do they mean anything? To ruin the suspense: No.

There seems to be a big misunderstanding or a big lack of communications in the realm of big data. I keep hearing company after company compare Hadoop to SQL, claiming the former is somehow better than the later. Sadly, that’s comparing apples to screwdrivers.

Hadoop is a database technology. It’s based on MPP architecture for the Cloud. Hadoop compares to flat files, relational databases and other methods for storing information in structures.

SQL is an query language. It’s similar to an API in that it’s just a way to communicate with the data source. Long ago, in the dawn of time, SQL was tightly tied to DB2 and the relational environment that spawned the syntax. However, along came the 1980s, Unix servers and PCs, and the need to access lots of different data sources and an unwillingness to have to have very separate query languages for each data source.

Along came ODBC to the rescue. It standardized core query syntax using the SQL paradigm and allowed, under the covers, the ODBC developer to use an API to translate almost standard queries into the language of each data source. It extended SQL to access new things.

In the meantime, as RDBMS technologies began to try to find ways around the basic limitations of relational databases, the companies added extra features such as stored procedures that extended SQL even further from the origins of basic definition and query of relational structures.

So now we have a mass of coders who have only worked with large, primarily Web oriented databases using non-RDBMS technology. No surprise, they had to code their own interfaces and queries, getting into the details of the newer systems. At the same time, they probably brushed through and overview of RDBMS and SQL in school and then never used it again.

That meant a misunderstanding of the difference between database and query. Therefore, the message of No SQL will retard their progress in integrating their solutions with the existing IT data infrastructure.

There’s a large need for people who can work with Hadoop and other younger data sources. There’s also a vast pool of people who know SQL. Yes, there will always be a need for Hadoop gurus just as there is for every technology, but the folks wanting to get information out of data sources don’t need to know the data sources, they need to get the information – and they know SQL.

A number of vendors have figured that out and are now offering SQL as a means to access Hadoop. It’s a natural fit, an extension of what the people pushing Hadoop are hoping to achieve. Hadoop and other distributed, non-row based architectures are there to expand knowledge. They’re great ways to better understand the vast body of data coming in from many new sources. However, until you can get that data to the business knowledge worker, it’s not information. SQL is the clearest way to quickly bridge that gap.

The people who realize that it’s not an either/or decision, who understand that Hadoop and SQL not only can but should work together are the people who will drive their companies forward by quickly addressing real business needs.

SQL v Hadoop is the wrong conversation. SQL and Hadoop is the right one.

Webinar: IBM, Actuate and Cirro describe faster analytics

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Today a webinar was hosted by Database Trend and Applications. While there are important things to talk about, I’ll start with the amusing point of the inverse relationship between company size and presenter title found in every webinar, but wonderfully on display here. The three presenters were:

  • Mark Theissen, CEO, Cirro
  • Peter Hoopes, VP/GM, BIRT Analytics Division, Actuate
  • Amit Patel, Program Director, Data Warehouse Solutions Marketing, IBM

The topic was “Accelerating your Analytics for Faster Insights.” That is a lot to cover in less than an hour, made more brief by a tag team of three people from different companies. I must say I was pleasantly surprised with how well they integrated their messages.

Mark Theissen was up first. There were a lot of fancy names for what Cirro does, but think ETL as it’s much easier. Mark’s point is that no single repository can handle all enterprise data even if that made sense. Cirro’s goal is to provide on-demand distributed analytics, using federation to link multiple data sources in order to help businesses analyze more complete information. It’s a strong point people have forgotten in the last few years during the typical “the latest craze will solve everything” focus on Hadoop and minimizing the role of getting to multiple sources.

Peter Hoopes then followed to talk about doing the analytics. One phrase he used should be discussed in more detail: “speed wins.” So many people are focused on the admittedly important area of immediate retail feedback on the web and with mobile devices. There, yes, speed can win. However, not always. Sometimes though helps too. That’s one reason why complex analysis for high level business strategy and planning is different that putting an ad on a phone as you walk by a store. There are clear reasons for speed, even in analytics, but it should not be the only focus in a BI decision.

IBM’s Amit Patel then came on to discuss the meat of the matter: DB2 Blu. This is IBM’s foray into in-memory, columnar databases. It’s a critical ad to the product line. There are advantages to in-memory that have created a need for all major players to have an offering, and IBM does the “me too!” well; but how does IBM differentiate itself?

As someone who understands the need for integration of transaction and analytic systems and agrees both need to co-exist, I was intrigued by what Amit had to say. Transactions going into normal DB2 environment while being shadowed into columnar BLU environment to speed analytics. Think about it: Transactions can still be managed with the row-oriented technologies best suited for them while the information is, in parallel, moved to the analytics database that happens to be in memory. It seems to be a good way to begin to blend the technologies and let each do what works best.

For a slightly techhie comment, I did like what Mr. Patel was saying about IBM’s management of memory and CPU. After all, while IBM is one of the largest software vendors in the world, too many folks forget their hardware background. One quick mention in a sentence about “hardware vendors such as Intel and IBM…” was a great touch to add a message that can help IBM differentiate its knowledge of MPP from that of pure software companies. As a marketing guy, I smiled big time at the smooth way that was brought up.

Summary

The three presenters did a good job in pointing out that the heterogeneous nature of enterprise data isn’t going away, rather it’s expanding. Each company, in its own way, put forward how it helps address that complexity. Still, it takes three companies.

As the BI market continues to mature, the companies who manage to combine the enterprise information supply chain components most smoothly will succeed. Right now, there’s a message being presented by three players. Other competitors also partner for ETL, data storage and analytics. It sounds interesting, but the market’s still young. Look for more robust messages from single vendors to evolve.

IDC says business analytics an 89.6 billion (USD) market by 2018

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For those who might have missed it, IDC published a press release last week pointing to strong growth in business analytics. That’s going to keep a lot of companies busy, good or not, but results will begin to weed out a number of them. A lot of companies, established and startup, are making a lot of promises to meet that demand but not all will do the right things.

I think we’ll see a shake-out begin to happen in the next 2-3 years, even with the demand.

HP Vertica at the BBBT: Technology v Solution

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The latest BBBT presentation was from HP Vertica’s Will Cairns and Steve Sarsfield. I know it’s hard to miss HP’s presence in any market, but for those few of you who may have done so HP acquired Vertica in early 2011. Vertica is a columnar database focused on large data sources for analytics. Will and Steve were a good tag team, switching back and forth as need be; so unlike other presentation reviews I will rarely be noting who said what.

The smallest installation they mentioned runs on HP Vertica is 1.5 terabytes up to very large ones such as at Facebook, their largest customer. Without a doubt, HP plays at the larger end of the analytics market. They have a strong and powerful database and it seems HP’s hardware experience and Vertica’s database knowledge seems to have been integrated far better than other HP acquisitions in the previous decade.

The problem I often come back to discuss, whether talking about a startup or a company such as HP, is the issue of technical problems versus business solutions.

Will Cairns did say one thing that should be paid attention to by many who talk about unstructured data. His very accurate point is that “unstructured data doesn’t stay unstructured long.” We talk about conversations as unstructured, but to get information from those, we must part the syntax of sentences, look for key words and meaning, and extract semantics with meaning. Those items can then be similarly structured in order to compare, analyze and draw conclusions.

However, the weak spot in his eyes is his title. He constantly referred to “supporting data scientists” rather than supporting data science. As the programmers who know statistics create more and more packages that can analyze data, it’s the analytical capabilities being provided to business people that matters, not the people who call themselves data scientists who also just exist to serve the end business use.

One interesting techie note about their MPP database is that there isn’t an automatic lead node. While there’s no independent analysis for intelligence allocation of notes other than, it seems, basic load balancing, the idea that you can automatically define a lead node based on balancing, not before, does imply a good ability to manage distributed resources.

One thing I’ve asked a few folks who push columnar databases came up again in this presentation. They were talking about something called projections, which seemed to be ways to index the data for faster access. However, they claimed it’s not indexing but gave no clear explanation.

I then asked the question that always intrigues me. It’s clear that columnar databases have a great strength in analytics across records because indexes aren’t needed for columns, but it’s clear that both row and column based analyses have value, so getting a clearer picture how any database supports both would seem to be important. I pointed out that indexes in row-based databases exist to allow faster search of columns. The question is: What techniques are used to speed up row based searches in columnar databases if no indexes exist. They didn’t have an answer.

One slide that created a great conversation was one of the types of analytics and their definitions. Claudia Imhoff and others questioned the difference between predictive, prescriptive and pre-emptive analytics. While better clarity is definitely needed, the attempt is a great conversation starter for the industry.

HP Vertica - Hindsight to Foresight slide

Summary

HP Vertica seems to be a database that should be evaluated for large data volume analytics. However, they seem to have a focus on the technology not on why companies want the technology. There was no real discussion of results, or of partnerships with BI vendors to provide end user value. I expect that successful sales won’t be purely HP. They are focused purely on IT and programmers who are building very complex algorithms. They’ll need either a channel or ISV partner to round out the picture to an enterprise who needs to see the full business value chain.

It seems to be a very strong product, but only part of the solution.

TDWI, Claudia Imhoff and SAP: Data Architecture Matters

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In a busy week for TDWI webinars, today’s presentation by Claudia Imhoff, Intelligent Solutions, and Lother Henkes, SAP, was about how the continuing discussion of the place in the data world for the data warehouse.

While many younger techies think the latest technology is a panacea and many older techies are far too skeptical for too long, the reality is that while the data warehouse is going nowhere, it has to integrate with the newer technologies to continue improving the information being provided to business knowledge workers.

One of Claudia’s early slides talked about data sources. While most people are focused on both the standard packaged software and the rush of non-structured data from the Web, call centers, etc, Claudia makes clear the item that companies are just beginning to realize and address: Sensor data is just as important as the rest and also driving data volumes. Business information continues to come from further afield and a wider variety of sources and all must be integrated.

Much of her talk, she mentioned, has come out of a couple of years of work between herself and Colin White, in formalizing the changing data architecture environment. Data warehouses are still the place for production reports and analytics, where data provenance and clarity are absolutely necessary while the techniques used on early stage data such as in streaming, Hadoop analytics, etc, are more exploratory and investigative. The duo posit that the combination of data integration, data management (including EDWs), data analysis and decision management are the “glue in the middle,” those things that bind sources, deployment and distribution technologies, and reporting and analytics options into a real system that provides value.

The picture they put together is good and Claudia Imhoff’s presentation should be looked at for a better understanding of where we are; but I wouldn’t be me if I didn’t have a couple of issues.

The first is a that she is a bit too enamored of mobile technology. It’s here and must be addressed, but statements such as “nobody has a desktop, everything is mobile” must be corrected. A JD Power survey last year showed that only 20% of tablets are used for work. On the other side, Forrester Research has pointed out a strong majority of business people are now using two devices for their information.

The issue for business intelligence is not that people are switching from desktops (including laptops in docking stations) but that smart providers of information need to build UIs that address the needs of large monitors, tablets and smartphones, addressing each device’s uniqueness while ensuring a similarity of user experience.

The second issue is a new term thrown out during the presentation. It’s “data refinery” and, as Claudia mentioned in her presentation, it’s the same thing others are calling a data swamp, data lake or numerous other terms. There’s an easy term everyone has used for years: Operational Data Store (ODS). I’m a marketing guy and I understand the urge for everyone to try to coin a term that will catch on, but it’s not needed in this case.

While it’s a separate topic (yeah, another concept for a column!), I’ll briefly point out my objections here. Even back in the late 1990s, during my brief sojourn at Informatica, we were talking about how the ODS can be used for more than only a place to use in order to quickly extract information from operational system so as not to stress them by doing transformations directly from such systems. They’ve always been a place to take an initial look at data before beginning transformations into star schemas and the like. The ODS hasn’t changed. What’s changed is the underlying technologies that support larger data stores and the higher level analytics that let us better analyze what’s in the ODS.

That brings us to one main point Claudia Imhoff made during her wrap-up, the section on business considerations. She points out that people really need to understand the importance of each data source and the data within it. Just because we can extract everything doesn’t mean we need to save everything. Her example was with customer sampling. Yes, you can get all the customer data, but only that which you need to narrow cast. For higher level decision making, those who understand confidence levels know that sampling can get to very high levels of certainty so sampling can still speed decision making and save costs. Disk space might be less expensive in the Cloud, but it’s not free. We’re in the job of helping businesses improve themselves, so we need to look at the bigger picture.

Her presentation was clearly strategic: We need to rethink, not reinvent, data modeling. Traditional techniques aren’t going away and neither are many of the new ones. Data management people need to understand how they combine.

No surprise, that was a great transition to Lother Henkes’ presentation. His key point is that SAP BW now can run on SAP HANA. It’s important even if all the capital letters look like shouting. HANA is SAP’s in memory, columnar database that’s their entry into the Cloud market to manage the high volumes of modern data. It’s a move to bridge the gap between the ODS and relational database arenas with one underlying infrastructure.

In such a brief webinar, it’s hard to see more than the theory, but it’s a clear move by SAP to do what Claudia Imhoff suggested, to take a fresh look at data models in order to understand how to better support the full range of data now being incorporated into business decision making.

TDWI and IBM on Predictive Analytics: A Tale of Two Focii

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Usually I’m more impressed with the TDWI half of a sponsored webinar than by the corporate presentation. Today, that wasn’t the case. The subject was supposed to be about predictive analytics, but the usually clear and focused Fern Halper, TDWI Research Director for Advanced Analytics, wasn’t at her best.

Let’s start with her definition of predictive analytics: “A statistical or data mining solution consisting of algorithms and techniques that can be used on both structured and unstructured data to determine outcomes.” Data mining uses statistical analysis so I’m not quite sure why that needs to be mentioned. However, the bigger problem is at the other end of the definition. Predictive analysis can’t determine outcomes but it can suggest likely outcomes. The word “determine” is much to forceful to honestly describe prediction.

Ms. Halper’s presentation also, disappointingly compared to her usual focus, was primarily off topic. It dealt with the basics of current business intelligence. There was useful information, such as her referring to Dave Stodder’s numbers showing that only 31% of surveyed folks say their businesses have BI accessible to more than half their employees. The industry is growing, but slowly.

Then, when first turning to predictive analytics, Fern showed results of a survey question about who would be building predictive analytics. As she also mentioned it was a survey of people already doing it, there’s no surprise that business analysts and statisticians, the people doing it now, were the folks they felt would continue to do it. However, as the BI vendors including better analytics and other UI tools, it’s clear that predictive analytics will slowly move into the hands of the business knowledge worker just as other types of reporting have.

The key point of interest in her section of the presentation was the same I’ve been hearing from more and more vendors in recent months: The final admission that, yes, there are two different categories of folks using BI. There are the technical folks creating the links to sources, complex algorithms and reports and such, and there are the consumers, the business people who might build simple reports and tweak others but whose primary goal is to be able to make better business decisions.

This is where we turn to David Clement, Product Marketing Manager, BI & Predictive Analytics, IBM, the second presenter.

One of the first things out of the gate was that IBM doesn’t talk about predictive analytics but about forward looking business intelligence. While the first thought might be that we really don’t need yet another term, another way to build a new acronym, the phrase has some interesting meaning. It’s no surprise that a new industry where most companies are run by techies focused on technology, the analytics are the focus. However, why do analytics? This isn’t new. Companies don’t look at historic data for purely nostalgic reasons. Managers have always tried to make predictions based on history in order to better future performance. IBM’s turn of phrase puts the emphasis on forward looking, not how that forward look is aided.

The middle of his presentation was the typical dog and pony show with canned videos to show SPSS and IBM Cognos working together to provide forecasting. As with most demos, I didn’t really care.

What was interesting was the case study they discussed, apparel designer Elie Tahari. It’s a case study that should be studied by any retail company looking at predictive analytics as a 30% reduction of logistics costs is an eye catcher. What wasn’t clear is if that amount was from a starting point of zero BI or just adding predictive analytics on top of existing information.

What is clear is that IBM, a dinosaur in the eyes of most people in Silicon Valley and Boston, understands that businesses want BI and predictive analytics not because it’s cool or complex or anything else they often discuss – it’s to solve real business problems. That’s the message and IBM gets it. Folks tend to forget just how many years dinosaurs roamed the earth. While the younger BI companies are moving faster in technology, getting the ears of business people and building a solution that’s useful to them matters.

Summary

Fern Halper did a nice review of the basics about BI, but I think the TDWI view of predictive analytics is too much industry group think. It’s still aligned with technology as the focus, not the needs of business. IBM is pushing a message that matters to business, showing that it’s the business results that drive technology.

Businesses have been doing predictive analysis for a long time, as long as there’s been business. The advent of predictive analytics is just a continuance of the march of software to increase access to business information and improve the ability for business management to make timely and accurate decisions in the market place. The sooner the BI industry realize this and start focusing less on just how cool data scientists are and more on how cool it is for business to improve performance, the faster adoption of the technology will pick up.

DataHero at the BBBT: A Startup Getting It Right

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First, on a tangent not directly focused on the product: Thank you Chris Neumann, CEO or DataHero. After hearing presenters from multiple companies consistently use the wrong words over the last few months, you used both premise and premises in the appropriate places. Thanks!

As you might gather, Wednesday’s presentation at the BBBT was by DataHero. A fairly young company, less than three years old, DataHero is focused on “Delivering a self-service Cloud BI solution that enables enterprise and SMB users to analyze and visualize their SAAS-based data without IT.”

Self-service BI is what almost all the players, both new and mature companies, are trying to provide these days. This just means they’re another player in attempting to help business knowledge workers to connect to data, analyze it and gather useful and actionable information without heavy intervention by business analysts and IT.

Cloud is also where everyone’s moving since it has so many advantages to all areas of software. DataHero, as a small company, isn’t just in the Cloud. They’ve smartly decided to begin by focusing on public Cloud applications with accessible API’s.

While that initially simplifies things, the necessity to handle complexity still exists in that world. Mike Ferguson, another BBT member analyst, pointed out that many of his clients have multiple, customized Salesforce.com instances and that’s bringing the upgrade issues seen in on-premises systems into the Cloud world. Chris acknowledges that and understands the need to grow to handle the issue, but knows that at the current size of DataHero there’s enough of a market for an initially more focused solution.

A strategic issue comes up with the basic nature of the Cloud. Mr. Neumann mentioned Cloud being opposed to centralized data, but that’s not quite so. Depending on how Cloud systems are set up, they can help or hinder centralization of data. However, right now he is accurate in that most of the growth of Cloud is departmental in nature. It’s also further blurring the always fuzzy line between enterprise and SMB markets by providing applications that both groups can leverage.

Another area that shows thought in their growth strategy is entry into new market. Chris is clear that they dip their toes into an arena, check reactions, and if positive then try to partner with as many companies in the space as possible to maintain neutrality. That means they don’t get locked into the first vendor the first client wants to work with, regardless of market control, leaving flexibility for customers. Their partner page, though young, clearly shows that strategy in effect. That’s a good move and I wish more vendors would think that way.

Another key growth issue is data cleansing. Right now, DataHero does none, expecting that the source system provides that capability. However, as clients use more and more source systems, there’s a cleansing need to clarify data clashes from different systems. That’s something the team at DataHero says they’re aware of while, again, that’s future growth (no time frames, as per legal sanity…).

The demo was very interesting. The other founder, Jeff Zabel, has a strong history in designing interfaces for software in vehicles, meaning usability really matters. That can be seen with a very clear and simple interface. It is easy to use. However, as pointed out by many other companies, 80% of business data has a location component and many DataHero vendors are far ahead of them in the area of geospatial information. That’s a key area they’ll have to improve.

Summary

DataHero is a young company with a young product. The key is that they aren’t just looking at their cool product and customizing solely on first sales. They have thought through a clear growth strategy. The BI tool is clearly fully fledged for the market segment they’ve chosen for initial release and they have thought through their growth strategy in far more detail than I’ve seen in other vendors who have presented at the BBBT.

If they execute their vision, and I see no reason why they wouldn’t, the folks at DataHero have a bright future.