What I Believe (About Data)

Man jumping over city

I’m a new analyst here at Blue Hill Research, and this my first official post. (The pressure!) To start, a little about myself: I’m an American, but I live in Canada. I love technology, but I studied liberal arts. I met a president, worked in an Icelandic fish factory, ball-boyed for John McEnroe, and published a novel. (One of those isn’t true. Read on to find out which.) I’ve been a journalist, a management consultant, and most recently, a marketer. I view the world through a business lens, and I am always looking for ways data can influence decision.

I’m Toph Whitmore, and THIS IS WHAT I BELIEVE. (For full effect, read that last part aloud in a pretentiously deep voice.) Well, it’s what I believe right now, at least until the next paradigm shift.

The data prep/integration space is crowded.

Last week I surveyed the data prep/integration vendor landscape. I stopped counting at 85. Sure, my list includes some vendors that only dabble in the technology, or straddle feature segments. (“Now with curation and machine-learning!”) But my count doesn’t include the roughly four gazillion new VC-backed startups aiming to reduce some data-manipulation performance metric by 10%.

Don’t get me wrong. I love this space. But there are a lot of players in it (directly and indirectly). We’re entering a “realistic” phase of data-prep technologies, and vendors that can’t produce attractive-enough margins will start feeling exit pressure from backers. It’s an easy prediction, but expect industry consolidation, technology extensions, and pivots galore in the next two years.

Mediocre self-service data solutions move the bottleneck. Great ones move the needle.
Self-service BI technologies promise to empower line-of-business data consumers with flexible, convenient access to data. In effect, they aim to replace reliance on busy data analysts and accelerate the data-to-insight workflow. When that ideal works, it’s awesome. When it falls short, it’s often because UX/UIs are designed for data analysts (and not say, us marketers). Sure, training can alleviate that, but if your marketing team has to hire data analysts for data access, you’re doing it wrong.

For examples of how to do self-service right, keep an eye on Trifacta, Talend, Paxata, Alteryx, and Sisense.

A data technology solution is only as good as users’ ability to consume the data it produces.
Salesforce? Big fan. But I’ve seen its power overwhelm, particularly in mid-size enterprises. Don’t blame Salesforce. Blame an “over-eager” implementation that leads to an explosion of reports and dashboards, but doesn’t take stakeholders’ ability to gain comprehensive insight from that volume of data.

As someone seeking wisdom from data, any time I spend data-munging is time I could have spent acting upon that supposed insight. Data scales. Bandwidth doesn’t. Just because you can produce a thousand cool custom dashboards doesn’t mean you should. Pick the right ones, automate every metric you can, and return your attention to value-added work. (Or Pokemon Go.)

Isolated, single-silo enterprise data deployments are doomed to fail.
Data crosses silos. Data technologies must too. I once implemented some sweet data-management technology in a client marketing group. We got the system up and running, and anticipated massive insight. But while we built rigor into the marketing systems, sales technology development stagnated. Marketers produced cool funnel data, but progress broke down at the handoff to a decrepit CRM system. We built a cool solution, but we naively did it in a vacuum without buy-in from sales IT.

There’s gold in them thar numbers.
The most interesting application of enterprise data management to me right now? Commercialization. Yep, your properly-managed data provides valuable insight. But imagine making money off it! If you’re a technology vendor, customers using your technology to produce revenue-generating data products is an attractive thing: If your customers actually make money using your technology, they’ll get pretty darn loyal pretty darn fast, and that commitment can be a powerful competitive barrier to entry.

A good example: Industrial marketing intelligence company EDA uses Sisense to aggregate, and then resell insight-rich industry data. How cool is that? (Read all about it here.)

Dataviz is cool. But if it doesn’t drive a business outcome, spare me.
I like pretty pictures as much as the next person. Edward Tufte’s a personal hero. I continue to be awed by what people far more artistic than me can accomplish with data-visualization technologies. But too often that over-produced but lovely illustration leaves me asking, “Now that I know this, what do I do?” Artistic data visualizations have their place—perhaps hanging on a wall—but if I can’t glean actionable insight, thanks, but it’s time to reassess the graphic design budget.

One-trick data-prep ponies’ days are numbered.
Technology solutions that do one thing really well are just neat. But as enterprises look across vertical silos and up and over horizontal functions to derive value from their data, the allure of a “we-can-do-both-BI-and-data-prep” solution becomes more compelling to enterprise adopters. The alternative involves multiple technologies, the potential of complex systems integration, and probably consultants. (Shudder.)

Data technologies deliver value along the entire data value chain from database to insight. But the market is maturing, particularly when it comes to technology adoption. Whether older-school data-tech vendors (ETL-only OLAPers, I’m looking your way) extend their offerings toward the data source or towards BI, staying put won’t be a wise long-term option for them.

The modern enterprise gets it.
For the last five years, enterprise data-prep/integration marketers have pitched the bells and whistles of their sweet data-manipulation technologies. And the message has been received. So, marketing, time for a well-deserved break!

Just kidding. Credit marketing, credit evangelism, credit technical innovation, but data value in the enterprise is recognized. But that has brought new influencers to the technology purchase decision process. Data consumers must derive business value from data initiatives. Technology vendors who’d prefer to stay relevant must build out the business use case for value delivery. (Marketers: time for a “Micky the Marketer” persona.)

What’s next? Making money.
The future of Big Data isn’t about technology. It’s about delivering tangible, measurable business value. There will always be a market for technologies that offer incremental improvements. But please, no technology for technology’s sake anymore. Time to move from data evolution to data revolution.

(By the way, my novel remains unpublished.)

About Toph Whitmore

Toph Whitmore is a Blue Hill Research principal analyst covering the Big Data, analytics, marketing automation, and business operations technology spaces. His research interests include technology adoption criteria, data-driven decision-making in the enterprise, customer-journey analytics, and enterprise data-integration models. Before joining Blue Hill Research, Toph spent four years providing management consulting services to Microsoft, delivering strategic project management leadership. More recently, he served as a marketing executive with cloud infrastructure and Big Data software technology firms. A former journalist, Toph's writing has appeared in GigaOM, DevOps Angle, and The Huffington Post, among other media. Toph resides in North Vancouver, British Columbia, Canada, where he is active in the local tech startup community as an angel investor and corporate advisor.
Posted on August 11, 2016 by Toph Whitmore

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