TDWI, Claudia Imhoff and SAP: Data Architecture Matters

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.

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