One of the biggest and frustrating barriers to successful analytics projects is lack of agility.
Analytics systems must be built for constant change. The whole point of analytics is “actionable insight.” The information accessed is supposed to result in changes to business process, and this in turn should trigger new opportunities and new analysis. Business intelligence is like a bicycle — if it’s not moving, it’s
falling over.
And riding a bicycle takes a certain amount of agility. Analytics systems have been complex and slow while business change has been accelerating. In order to provide the core figures required to run the business, IT organizations felt that they had to protect the data warehouse from new or unverified sources.
It isn’t that IT doesn’t know how to do analytics “right” — they just couldn’t act fast enough to satisfy their business customers. The result was regular crashes each time the business went around a tight corner and the analytics bicycle couldn’t turn in time.
The good news is that analytic technology has made huge leaps forward. The biggest benefit of new in-memory systems isn’t speed, it’s simplicity. The data is stored just once, for both transactions and analytics (Gartner calls this Hybrid Transaction/Analytical Processing or “HTAP”). And there’s no longer any need for “materialized views” and “aggregate tables” to ensure acceptable response times. This has eliminated most of the overhead and time required to prepare reliable data to answer business questions.
In addition, systems like Hadoop have enabled the storage and access of “schemaless” information that doesn’t require the rigid, predefined structures associated with today’s data warehouses.
Now that the technology has changed, analytic processes and organizations also have to adapt, and we’re starting to see this process in leading edge organizations. Analytics competency centers, aware that they no longer have a veto over departmental IT investments, are taking steps to ensure that “self-service BI” doesn’t turn into “self-centered BI.”
At the last SAP TechEd Conference, I had the opportunity to host a session talking about Agile Analytics with my guests r.konijnenburg/contentr.konijnenburg/content, SAP Mentor and Principal BI Consultant for CGI and Andy Steer, UK Group CTO for itelligence.
We had a great discussion covering topics such as:
You can view the discussion above or on YouTube
What do you think about Agile Analytics? What did we miss in our discussion? Comment below!
[This post first appeared on the Analytics Blog]
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