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Day2 Tuesday Morning Sessions
Continuing my coverage of the 30th annual [Data Center Conference]. Here is a recap of the Tuesday morning sessions:
Wells Fargo: Data Center Lessons Learned from the Wachovia Acquisition
This was the next in their "Mastermind Interview" series. The analyst interviewed Scott Dillon, EVP and Head of Technology Infrastructure Services for Wells Fargo bank. Some 13 years ago, Wells Fargo merged with Norwest, and three years ago, Wells Fargo merged again, this time with Wachovia bank. Today, the new merged Wells Fargo manages 1.2 Trillion USD in assets, some 12,000 ATMs, and 9,000 branch offices within two miles of 50 percent of the US population.
On the technical side, Scott's team has to deal with 10,000 IT changes per month, spanning 85 discrete businesses that Wells Fargo is involved in. To help drive the consolidation, they formed a culture group called "One Wells Fargo".
Often, Wells Fargo and Wachovia used different applications for the same function. The consolidation team took the A-or-B-but-not-C approach, which means they would either choose the existing application that Wells Fargo was already using (A), or the one that Wachovia was already using (B), but not look for a replacement (C). They also wanted to avoid re-platforming any apps during the merger. This simplified the process of developing target operating models (TOMs).
Before each application cut-over, the consolidation team did dry-run, dress rehearsals and walkthroughs over the phone to ensure smooth success. They wanted a Wachovia account holder to be able to walk into the bank on one day, and then come back the next day as a Wells Fargo account holder, into the same branch office but now with Wells Fargo signage, with minimal disruption.
Wells Fargo also adopted a test-to-learn approach of choosing small test markets to see how well the transition would work before tackling larger, more complicated markets. For example, they started in Colorado, where Wells Fargo has a huge presence, but Wachovia had a small presence.
This was first and foremost a business merger, not just an IT merger. Each decision to 6-18 months to act on, and the IT team spent the last three years working every weekend to make this a reality.
A Satirical Look at Business and Technology
Comedian Bob Hirschfeld presented a light-hearted look at the IT industry. Bob actually attended sessions on Monday at this conference so his satire was exceptionally hard-hitting. He took jabs at the latest IT job requirements, padding on light poles, IBM Watson, social media's impact on dictators, various industry acronyms, virtualization, the various reasons why printer ink is so expensive, and the evil masterminds behind Powerpoint.
Storing Big Data takes a Village
Two analysts co-presented this session on the 12 dimensions of information management that revolve around the volume, variety and velocity of "Big Data".
In the past, it took a while to gather data, and a while to process the data, so annual, quarterly and monthly reports were common. Today, with high-velocity streams like Twitter, especially during cultural events or natural disasters, data is produced and analyzed quickly. It is important to sort the steady-state from the anomalies.
Myth 1: All data fits nicely into relational databases. The analysts feel the concept of putting everything into one big data base is dead. Some data sets are so complicated that traditional database joins would cause smoke to come out of the sides of the servers. Instead, new technologies have emerged, including NoSQL, Cassandra, Hadoop, Columnar databases, and In-memory databases. XML has helped to bring together disparate data formats.
Companies need to adapt to this new reality of Business Analytics. Here is a poll of the audience on how many are in what stage of adaptation:
Myth 2: Everyone will do Big Data with commodity hardware. Businesses want commmercial offerings that don't fail every day. (For example, instead of using open-source Hadoop, consider IBM's [InfoSphere BigInsights] commercial product based on Hadoop designed for the Enterprise).
Myth 3: Big Data is too big for backup. Certainly, traditional full-plus-incremental approaches fail to scale, but that is not the only option you have. Consider disk replication, snapshots, and integrated disk-and-tape blended solutions that adopt a more progressive backup methodology.
Capacity forecasting can be difficult with Big Data. Scale-out NAS systems, including IBM SONAS and the various me-too competitive offerings, were originally focused on High Performance Computing (HPC) and the Media & Entertainment (M&E) industries, are now ready for prime-time and appropriate for other use cases.
It's like the game of Clue, but instead of Professor Plum with the candlestick in the library, it was Chuck with the Cluster in the Closet. To avoid shadow IT creating huge Hadoop Clusters in your closets, encourage the use of Cloud Computing for "sandbox" projects. IBM, Amazon and others offer hosted MapReduce engines for this purpose.
What type of storage do you plan to use for Big Data? The top five, weighted from a list during a poll of the audience were: (78) traditional disk arrays, (71) Scale-out NAS, (46) pre-configured appliances, (30) Hadoop clusters, and (23) Cloud Storage.
Big Data is about doing things differently. Do your employees understand analytical techniques? Your company may need to start thinking about policies for capturing Big Data, storing it correctly, and analyzing it for insights and patterns needed to stay competitive.
It was good to mix reality with a bit of humor. Some of these conference attendees take themselves too seriously, and it is good to be reminded that IT is just part of the overall business operation.