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TDWI Upside - Where Data Means Business

Everybody's Talking: The Vegas Buzz

Jill offers advice to three people she met at random at TDWI Las Vegas.

Rain turns Las Vegas into another town. Tourists abandon the slick sidewalks of the Las Vegas strip, jumping into the waiting taxis snaking around hotels. Concession stands close and strangers huddle together in doorways, awaiting a break in the clouds. Business people attending conferences renounce their poolside lunches, instead grabbing sandwiches and returning to their workshops.

For Further Reading:

Why Blockchain Will Never Kill the Database

How to Cut Data Preparation Time for Visualization Tools

Reimagining the Analytics CoE

I met Jay, Sunil, and Jennifer at various workshop breaks. Each of them was attending TDWI Las Vegas last month to learn more about a specific topic. I asked them to fill me in.

The Buzz about Blockchain

Jay, a data architect for a university, was happy to escape the dreary midwestern winter back home. This was his third time at a TDWI event. The desert rain had been a disappointment, but, he explained, "it's forced me to really focus on what I'm here to learn."

What did he come to learn?

"I want to understand how our data warehouse might co-exist with blockchain," Jay said, biting into one of the conference's complementary muffins. "We think we could use blockchain technology to identify our students, track them across applications, admissions, courses, and work study. If we can do that, we could also recommend ways to enhance their experiences inside and outside our academic environment, increasing their chances of success. This could also give us ideas about how we can expand our reach."

It sounded like Jay had done his research, but had one very important concern. "I'm not sure I get how blockchain would be applied to data analysis and recommend actions."

Blockchain is most often used to automate modernized business processes. It might be useful, I suggested, for verifying student application and enrollment more quickly and securely across the university's various campuses. Once the transactions are processed, the resulting history -- including student course history, grades, degrees and certifications, and personal information -- can be available for further analysis.

Yet the analytics itself might be independent of the blockchain, with certain user types authorized to see only certain attributes. As we talked, it dawned on Jay that perhaps blockchain and analytics could co-exist on a common platform, each capability processing canonical data according to what it was optimized to do. I asked him to check back with me once he and his team land on an approach.

Data Doldrums

I reconnected with Sunil in the coffee line during the afternoon break. He'd been attending Donald Farmer's workshop, "10 Principles of Modern Data Analytics." Farmer had been showing some interesting and complicated data visualizations. Deadpan, Sunil described himself and "impressed but depressed." This made me laugh.

"We should be at a point where we could defend our own complex data visualizations," said Sunil, a project manager at a mid-market bank, "but our executives are tired of having the data conversation. We've scaled back our data-specific investments and are re-coupling all our data capabilities with business applications."

"What about data discovery?" I asked. We talked about how the data resulting from heterogeneous business applications could be integrated and enriched to transcend each business silo, potentially revealing new insights. Although this isn't a revolutionary concept, few companies have formalized discovery as a routine activity, and Sunil seemed crestfallen.

"It's unfortunate," Sunil lamented. On the one hand, they won't invest in data appropriately. On the other hand, they insist they want to be a 'data-driven bank.'"

I asked Sunil what he thought the future looked like at his company.

"Oh, they'll probably hire a consultant to come in and give them a road map to becoming a data-driven bank," he said cynically, "but that road map will shortchange the actual skills and technology investments we need to continue to make in data."

Sunil considered the rice crispy treats on the snack table, rolled his eyes, and smiled ruefully. "We'll see," he said.

I hope they do.

The COE is Dead -- Long Live the COE

Jennifer is the manager of an analytics team focusing on customer experience for a large insurance company. She was also "kinda bummed" about the rain and lack of tram service from her hotel.

She'd promised her boss that she'd return to work the following week with some ideas for the firm's Analytics Center of Excellence.

Did they have an analytics COE? I asked her.

"Yes and no," said Jennifer, resigned at having to explain her COE yet again. "We've had a team we call a 'COE' for five years now, but no one honestly thinks it's really a COE." It turned out that she'd just inherited the COE and was trying to figure out what to do with it.

Listening to Jennifer, it seemed to me there was nothing about her COE that was COE-ish. It was a group of people who knew where important data was kept -- the company's customer data was "very federated," she acknowledged. These people were experts at finding, wrangling, and reporting on data. Everyone, from agents to actuaries, relied on them.

I suggested that Jennifer leave the team alone until she could define a taxonomy for the types of analytics happening in the so-called COE. Then she could start to carve off smaller teams of experts. Perhaps simple reports could be shed to the lines of business, who would be provided with appropriate training and tools according to their skill sets. More advanced analytics capabilities and even some nascent artificial intelligence proofs of concept could become the purview of a new data science team. There might be other, smaller teams of experts for specific data or analytics categories.

"If I take that tact, they'll think I'm empire-building," Jennifer said, thinking aloud.

"Then go build your empire!" I told her. "If you can pull this off, you'll deserve one."

I had to leave to attend a vendor demo down the hall, and Jennifer decided to join me. She might learn something new, she said.

Besides, who wanted to go out in all that rain?

About the Author

Jill Dyché has advised clients and executive teams on their analytics and data programs for as long as she can remember. Longer, in fact. She’s the author of four books on the business value of technology and regularly talks to teams about what keeps them up at night. Ambivalent about analytics? Maddened by management? Constricted by your culture? Check out Jill’s Q&A column, Q&A with Jill Dyché, here.

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