Category Archives: big data

Google and Salesforce announce major business intelligence acquisitions

Less than a week apart, Google announced the acquisition of Looker and Salesforce announced the acquisition of Tableau. Both large companies have begun to realize they’ve never really addressed the need for analytics. They’re great at saving and organizing data, but neither has a suite worth anything to provide easier to use dashboards and analytics that are key component of business intelligence.

I have two brief articles on forbes.com discussing the announcements.

TDWI Webinar Review: IoT’s Impact on Data Warehousing: Defining IoT in Terms of Its Data Requirements

Two TDWI webinars in one week? Both sponsored by SAP? Today’s was on IoT impacting data warehousing, and I was curious about how an organization that began focused on data warehousing would cover this. It ended up being a very basic introduction to IoT for data warehousing. That’s not bad. In fact. it’s good. While I often want deeper dives than presenters give, there’s certainly a place for helping people focused on one arena, in this case it’s data warehousing, get an idea of how another area, IoT, could begin to impact their world.

The problem I had was how Philip Russom, Senior Research Director for Data Management, TDWI, did that. I felt he missed out on covering some key points. The best part is that, unlike Tuesday’s machine learning webinar, SAP’s Rob Waywell, Director Hana Project Management, did a better job of bringing in case studies and discussing things more focused on the TDWI audience.

Quick soap box: Too many companies don’t understand product marketing so they under utilize their product marketers (full disclosure: I was one). I strongly feel that companies leveraging product marketing rather than product management in presentations will be more able to address business concerns rather than being focused on the products. Now, back to our regular programming…

One of the most interesting takeaways from the webinar was a poll on what level of involvement the audience has with IoT. Fifty percent of the responders said they’re not collecting IoT data and have no plans to do so. Enterprise data warehouses (EDW) are focused on high level, aggregated data. While the EDW community has been moving to blend more real time data, it tends to be other departments who are early into the IoT world. I’m not surprised by the results, nor am I worried. The expansion of IoT will bring it in to overlap EDW’s soon enough, and I’d suggest that that half of the audience is aware things will be changing and they have the foresight to begin to pay attention to it.

IoT Basics for EDW Professionals

Mr. Russom’s basic presentation was good, and folks who have only heard about IoT would do well to listen to it. However, they should be aware of a few issues.

Philip said that “the tendency is to push analytics out to the devices.” Not wholly true, and the reason is critical. A massive amount of data is being generated by what are called “edge devices.” Those are the cars, refrigerators, manufacturing robots and other devices that stream information to the core servers. IoT data is expected to far exceed the web and social media data often referred to as big data. That means that an efficient use of the internet means that edge analytics are needed to aggregate some information to minimize traffic flow.

Take, for instance, product data. As Rob Waywell mentioned, many devices create lots of standard data for which there is no problems. The system really only cares about exceptions. Therefore, an edge device might use analytics to aggregate statistics about the standard occurrences while immediately passing exceptions on to be handled in real-time.

There is also the information needed for routing. Servers in the core systems need to understand the data and its importance. The EDW is part of a full data infrastructure. the ODS (or data lake as folks are now calling it) can be the direct target of most data, while exceptions could be immediately routed to other systems. Whether it’s the EDW, ODS, or other system, most of the analysis will continue in core systems, but edge analytics are needed.

SAP Case Studies

Rob Waywell, as mentioned above, had the most important point of the presentation when he mentioned that IoT traffic is primarily about the exceptions. He had a couple of quick case studies to talk about that, and his first was great because it both showed IoT and it wasn’t about cars – the most used example. The problem is that he didn’t tie it well into the message of EDWS.

The case was about industrial worker safety in the area of gas detection and response. He showed the different types of devices that could be involved, mentioned the multiple types of alert, and described different response paths.

He then mentioned, with what I felt wasn’t enough emphasis (refer to my soap box paragraph above), the real power that a company such as SAP brings to the dance that many tinier companies can’t. In an almost throwaway comment, Mr. Waywell mentioned that SAP Hana, after managing the hazardous materials release instance, can then communicate to other SAP systems to create the official regulatory reports.

Think about that. While it doesn’t directly impact the EDW, that’s a core part of integrated business systems. That is a perfect example of how the world of IoT is going to do more than manage the basics of devices but also be used to handle the full process for with MIS is designed.

Classifications of IoT

I’ll finish up with a focus that came up in a question during Q&A. Philip Russom had mentioned an initial classification of IoT between industrial and consumer applications. That misses a whole lot of areas, including supply chain, logistics, R&D feedback, service monitoring and more. To lump all of that into “manufacturing” is to do them a disservice. The manufacturing term should be limited to the actual manufacturing process.

Rob Staywell then went a different direction. He seemed to imply the purpose of IoT was solely to handle event-driven, real-time, actions. Coming from a product manager for Hana, that’s either an understandable mistake or he didn’t clearly present his view.

There is a difference between IoT data to be operationalized and that to be analyzed. He might have just been focusing on the operational aspects, those that need to create immediate actions, without minimizing the analytical portion, but it wasn’t clear.

Summary

This was a webinar that is good for those in the data warehousing and core MIS functions who want to get a quick introduction to what IoT is and what might be coming down the pike that could impact their work. For anyone who already has a good idea of what’s coming and wants more specifics, this isn’t needed.

Cloudera Now, a mini-conference on data, analytics and machine learning, is a good overview

Cloudera held a pretty impressive web event this morning. It was a mini-conference, with keynotes, some breakout tracks and even a small vendor area. The event was called Cloudera Now, and the link is the registration one. I’ll update it if they change once it’s VOD.

The primary purpose was to present Cloudera as the company for data support in the rapidly growing field of Machine Learning (ML). Given the state of the industry, I’ll say it was a success.

As someone who has an MS focused on artificial intelligence (ancient times…) and has kept up with it, there were holes, but the presentations I watched did set the picture for people who are now hearing about it as a growing topic.

The cleanest overview was a keynote presentation by Tom Davenport, Professor of IT and Management, Babson College. That’s worth registering for those who want to get a well presented overview.

Right after that, he and Amy O’Conner, Big Data Evangelist at Cloudera, had a small session that was interesting. On the amusing side, I like how people are finally beginning to admit that, as Amy mentioned, that the data scientist might not be defined as just one person. I’ll make a fun “I told you so” comment by pointing to an article I published more than three years ago: The Myth of the Data Scientist.

After the keynotes, there were three session of presentations, each with three seminars from which to choose. The three I attended were just ok, as they all dove too quickly into the product pitches. Given the higher level context of the whole event, I would have liked them all to spent more time discussing the market and concepts far longer, and then had much briefer pitch towards the end of the presentations. In addition, they seem too addicted to the word “legacy,” without some of them really knowing what that meant or even, in one case, getting it right.

However, those were minor problems given what Cloudera attempted. For those business people interested in hearing about the growing intersection between data, analytics, and machine learning, go to Cloudera’s site and take some time to check out Cloudera Now.

P-values and what they mean for business intelligence and data scientists

I’d been thinking of writing a column on p-values, since the claim that data “scientists” can provide valuable predictive analytics is a regular feature of the business intelligence (BI) industry. However, my heavy statistics are years in my past. Luckily, there’s a great Vox article on p-values and how some scientists are openly stating that P<.05 isn’t stringent enough.

It’s a great introduction. Check it out.

DBTA Webinar: Too many cooks, yet again

Sadly, DBTA is becoming known for taking interesting companies, putting them in a blender and having each lose their message. A recent webinar included Cask, Attunity and HPE Security – all in a one hour time slot – again shows the problem. It was a mess.

Cask is a young Hadoop company with an interesting opportunity (Disclosure: As I’m discussing marketing, I need to mention I recently interviewed for a position at Cask). The company is working to put wrappers around Hadoop code to make it easier for IT to use the data platform. One of their products is Cask Hydrator, to help populate the database. That begins to move the message of Hadoop out of the early adopter phase and into a business message, but the presentation was still far to technical.

Attunity then presented and a key point was that they make data ingest easy. If that sounds like a similar message to Cask’s, you’re right. Why the two were together on the webinar when much of what they said sounded like competition wasn’t clear. On the good side, Attunity did a far better job at presenting a business message, both in how the presenter talked about the products and in which case studies were used.

HPE Security made another appearance, tacked onto the end of a presentation. Data security is critical, and HP has put together a very good message on it, but it didn’t vaguely fit the tone and arena of the previous presenters.

When Companies Should Share a Stage

The smaller companies seem to have a problem. It’s simple: Their involvement in webinars might be driven by marketing, but it’s being controlled by bean counters. Each of the three companies had something good to say, and each should have taken the time to say it in a stand-alone webinar. However, sharing costs was made to be the primary issue and so the mess ensued.

When should firms share the spotlight? That should happen when the item missing from the top of my presentation is there. The missing piece is having a joint story to tell. None of the case studies mentioned the companies working in partnership. None. When multiple vendors work to provide a complete solution to a client, even if the vendors might sometime compete, there’s a strong case for multiple companies in a webinar.

This webinar was not that. It was companies not feeling strongly enough about themselves for the other executives to overrule the COO’s or CFO’s and push a solid webinar about themselves.

All of these companies are worth looking at within the big data arena, just not in such a forced together setting. Stand on your own or show a joint project.

Webinar Review: Oracle Big Data Cloud, Understanding Business

People at technology startups love to call the industry giants dinosaurs. The analogy fails for a number of reasons. The funniest is that the dinosaurs existed for many millions of years. As the large companies exist now, are the startups are saying the big companies will only disappear if we’re hit by a meteor? Companies became large by filling a need. While many might not be as nimble, their experience, especially in enterprise software, means they often see the needs of the business community while the small companies are focused too much on their “cool” technology.

This week’s Oracle webinar, hosted by the DBTA, was a good example of that. The speakers were Rich Clayton, VP Business Analytics Product Group, and Omri Traub, VP Software Development, and the subject was, no surprise, Oracle Big Data Cloud Service (OBDC. Yeah, I know. Too close to ODBC…). Before we get into the details, people need to be aware that Oracle is fully committed to the cloud, as pointed out in a recent advertorial in Forbes. Oracle is clearly competing with Amazon for enterprise cloud business. Big data is only one part of that.

Rich Clayton began the presentation by pointing towards Thomas Edison’s laboratory as an example of using the ideas from many people to not only invent things but also to figure out how to market those inventions. He brought that directly into the evolution of corporate data labs. The biggest problem, Rich stated, is that that labs are usually only populated by very technical people while they require a broader array of talents. That requirement is one of the data labs principles he defined and one I’ve also described as the missing component of many corporate data labs.DBTA Webinar - Oracle - Principles of the Data Lab

A related problem is that most products are so complex and silo’d that very technical people are needed. At this stage in business intelligence and big data, that’s the horse that needs to be addressed before the broad access cart can move.

Omri Traub then took over for the demonstration portion of the presentation. Unfortunately, he unintentionally proved the point about technical folks missing business needs by the setup he used for the demonstration. The demo was built around an enormous amount of information on New York City taxi information. While manipulating a billion record data set is cool and powerful, he never presented a business message. He pointed to the large volume of data, talked about other data sources he combined, and then played with the data to show correlations.

The problem? Omri, claimed we were gaining insight. Correlations aren’t insight. Understanding how those correlations might impact your business and ideas how to adapt business to meet what you find is insight. Nothing in the demonstration pointed towards insight.

Fortunately, Rich Clayton earlier had given a couple of case studies showing business insight gained by OBDC early customers. It would have been much better if Mr. Traub had focused on one of those cases or something similar.

The best point of the demonstration was when Omri showed how, in the middle of playing with some relationships, he easily incorporated some analysis created by a different person. As mentioned above, collaboration is critical and it looks like Oracle hasn’t limited that to just a marketing message but has worked to make sure that Oracle’s product helps the team. As many companies claim to do that and it was only an overview, your mileage might vary. Make sure when you talk to them to follow through and see whether the collaboration (not to mention the entire product…) meets your needs.

The final section was the Q&A. I’m a marketing person, so I have to be honest and state that it sounded like canned questions they wanted to address, as there was way too much about the full Oracle ecosystem brought into discussion at this point compared to what I’d expect from customers. Still, there was one important point.

A question was asked about what advanced analytics might be added. Mr. Taub had the perfect response. After quickly mentioning that, yes, Oracle was always looking at advanced analytics and how to add them, he made a much more important point. Collobaration is key and OBDC is designed to get business people involved. All analytics need to be added in a usable manner, in a way that is understandable and can be leveraged by more people than just the technical resources.

That is the critical viewpoint that a large, enterprise focused company can bring to BI, the cloud and big data. That’s why it’s foolish to write off the large companies, the ones with expertise in not just technology, but in business and business relationships. They might not move as fast, but they can move to the right places with the right products and the right business messages.