It’s Time To Drink Our Own DataOps Champagne

“Turning on your DataOps Advantage.” That was the headline for Brian Householder’s recent blog which tackles what he calls the DataOps Dilemma.

Brian’s post is one voice in a fast-developing DataOps buzz that’s sweeping the industry and Hitachi Vantara. And, unless you’ve been living under a rock, or in this case, a huge pile of data, you’ve heard why – DataOps is the key to unleashing data’s ultimate potential by automating the processes that help the right data get to the right place at the right time.

That’s not a simple undertaking when you consider that data is becoming denser as well as more distributed and siloed than ever. Just consider the fact that today, roughly 2.5% of all data is actually analyzed (IDC). It doesn’t have to be that way. In fact, with DataOps, the possibilities are incredible.

I was recently talking with our CIO, Renée Lahti, whose IT team is building a data lake that will be home to vast amounts of data with potential value throughout our own business, from product development and sales to human resources, IT and of course, marketing.

The key word in that last sentence is “potential,” because without the right approach, that’s all we have – potential. As you’re likely aware, Hitachi is very committed to using our own technology and Renée is helping to lead the charge (check out her recent blog on that topic here). So naturally, our conversation quickly shifted to turning this lake of data into an ocean of actionable insights using our own DataOps methodology.

When you marry a data lake with DataOps, the possibilities are literally endless, so, as with any major initiative, the first hurdle is resisting the urge to boil the ocean and, instead, focus on a specific task with very defined goals. So, who is getting to experience their DataOps Advantage first here at Hitachi Vantara? Marketing, of course!

Right now, we are using what we call our Digital Value Enablement Lifecycle approach to examine ways we can apply DataOps within the marketing function to help our sales colleagues. Here are some areas that we are currently looking into:

• Marketing Mix: Here, DataOps could be applied to connect marketing interactions (e.g., downloads, conferences, emails) with Pentaho sales. By identifying the combination and sequence of marketing interactions that result in a shorter sales cycle, we can apply that same combination and sequence to future sales opportunities. The big win here, of course, would be to help teams close deals faster than ever.
• Targeted Selling: We could create detailed profiles of accounts and their sales histories and then generate a profile to create hyper-targeted campaigns precisely suited to each customer. Our team can then test these targeted campaigns for effectiveness versus traditional, less-specific campaigns.
• Conversion Rate Analysis: Through our DataOps methodology, we can dissect sales of Pentaho and/or midrange storage offerings and specifically document the time it took for each sale to move between stages (e.g., MQL-SAL-SQL-Close1). Then we can identify associations, if any, between the speed of sales stage and booking rate.
• Salesperson Superusers: Have you ever wondered how, or if, your sales team is taking advantage of your marketing campaigns? Here we can identify those we call our “salespeople marketing superusers” (e.g., campaigns, conferences, lead take-up) and note their effectiveness versus the effectiveness of others who are doing less with marketing and marketing leads. This will not only help us prove our value to the sales team, but it could also provide key feedback on how we can improve things.
• Cross-Sell and Upsell: As marketers, we’re always looking to proactively gain a better understanding of customer issues and potential opportunities. By examining our Hi-Track data, we can create cross-sell or upsell offers that come to the customer at precisely the right time. That’s the type of experience our customers crave!
• Training and EBCs: How effective are your Executive Briefing Center or training sessions with customers? With DataOps, we’ll be able to find out by researching and understanding whether customers who take advantage of these resources buy more than those who do not. With that insight we can find out which sessions are more effective and which are not, and then implement changes based on those results.

In the upcoming weeks we will gather as a team to home in on which use case to tackle first. I’m personally looking forward to working with Renée and her team to continue our DataOps journey. And, as a marketer, I’m excited to further infuse data throughout our marketing function.

This DataOps journey is just beginning. Stay tuned for more updates!