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The Return of Domain Analytics

I was cleaning out my home office this past weekend, and found a portable hard drive containing thousands of files that went back nearly 20 years. Just so you know – for the first decade of the 2000s, I was an industry analyst, first at AMR Research and then at Gartner, starting in late 2009.

Reading through some of the documents, I was quickly reminded that the COVID-19 pandemic is not the first shock to the system we’ve experienced.  

The turmoil after Sept 11, 2001 was palpable in some of the research I authored, especially since I focused on the Financial Services industry at that point in time.  Fast forward 7+ years, and the financial meltdown of 2008/2009 turned our world upside down yet again, but in a different way, as the economics of the world changed in a matter of weeks…and stayed in limbo for years. Like many of us at that time, I put my head down, tried to focus on what I could actually impact, and even made some predictions of where I thought the analytics market would head in 2009 and beyond.   

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In context analytics accelerates yet again

That was one of my predictions for 2009—especially as organizations were coming to terms with cost containment and doing more with less. I wrote:

“In 2009, buyers will hone in on products that specifically answer thorny business questions, not just provide a host of capabilities. To be honest, (we) predicted this for 2008 as well, but it didn’t work out that way. We’re even more convinced of it this year….. Regardless, software vendors and service providers will need to present their products in the context of business, not just IT”.

Fast forward to 2020. This “prediction” has still not fully come to pass.  Since 2009, much of the attention has been on enabling business user self-service, delivering products and services that were easy to understand, simple to use, and put analytic power into the hands of ordinary business people.  Yet, not much was done to enable analytics for common domains, such as Finance, HR, Sales & Marketing, and Supply Chain. Sure, dashboards and reports are delivered with these applications, but they can be challenging to expand and difficult to mix with related information from other parts of the organization.

This week, Industry visionary Donald Farmer participated in a broad ranging conversation with Bruno Aziza as part of the Oracle Analytics Summit. It’s well worth your time to listen to his comments as part of the Roadmap session originally presented on 9 June.  One area he talked about was Domain Analytics.

Domain Analytics vs. Analytic Applications

How do you get business people to consume and use analytics in their daily routine?  You deliver information they need in the context of their role and responsibility.   

  • recruiting director needs to know applicant flow, pipeline of candidates, time to hire, cost to hire, and hiring success.
  • supply chain executive needs to know about customer demand, supply constraints, cost implications, supplier risk, etc. 

These examples often go beyond one type of data—they include content from varied sources, and are contextualized for the role. You can try to predict everything a person would need, but there’s a good chance that people will go “off road” very quickly as they want to explore nooks and crannies of their business operations.   That’s why analytic applications sometimes fall short. 

But, domain analytics takes a more open stance to contextual analysis. Donald put it best in his session: 

“You must empower people to do analytics work in their domain and cross domains so they can understand the view from their standpoint and understand their domain in context.” He added, “Analytics without actions is a wasted effort.” 

Empower, Not Constrain

To empower business leaders, you must make sure the right data is easily available to support domain and cross-domain analysis.  That’s where the heavy lifting comes in—sourcing, preparing and modeling content to reflect the business use of data to make decisions and take action. In my analyst days, I would advise buyers to evaluate pre-packaged data models more closely than the dashboards and metrics that sat on top of them. I trust you’d agree that the data pipeline is where a lot of the magic happens, and significantly shortens the time to value for any domain analytics.

In-context analysis has been an active conversation for well over 10 years. But it’s more than just connecting to different data sources, it’s making sure that data is contextualized for the people/roles who need it to drive insight and take action to close the loop.  And to learn how you can benefit from Oracle Analytics, visit Oracle.com/analytics, and follow us on Twitter @OracleAnalytics.

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