We’re on a journey. We are moving from managing data, to managing analytics, to managing insight. In terms of the traditional approach to managing data and delivering business intelligence, we’re going to see a shift in the way people approach the challenges of this journey.
That shift will transpire in the necessary skills to get the job done and in the direction of prescriptive and predictive outcomes. Analytics has emerged as essential to all of it. Because, at the end of the day, our goal is to enable decision-making, cost optimization/efficiencies of operation, and incremental revenue growth through data usage.
Shifting to a use-case model for data, delivering on faster time to insight, and governing data from harvest to delivery are the keys to achieving these goals.
Fit-for purpose data
What we see evolving is a purpose-driven world of data assets that are deployed, built, and configured for a specific use case or purpose the business has. So, what we have to do is:
- Look at each one of those use cases
- Understand the characteristics and dynamics of the selected use case
- Look at the portfolio of options to deliver data and insight through a technology platter that has lots of options to configure and deliver that information.
That’s a really big shift in the way we think about these things now.
Speed to insight
In the traditional data warehousing context, data integration required data architecture work, the creation of a semantic model to represent the integration of data, a lot of work to do the ETL, and to prepare that data and make it available. But all of that takes time. Today’s businesses need faster time to insight.
The new technologies that are evolving out there in the marketplace, the Hadoop platform being a perfect case in point, are creating the opportunity to stage data into a Hadoop environment and to have that data be immediately accessible for the purposes that the data can fuel.
So, as we look at the architectural implications of driving the future of analytics and reporting through the use case lens, we need to ask:
- What problems are we trying to solve?
- What characteristics or constraints are around the time to value of solving that problem?
- What data assets can be created or technologies can be applied, like data virtualization, as an example, that will allow us to access data in place but not have to replicate it?
If you take the time for preparation out of the equation, we have a greater smorgasbord of options for delivering value.
Because speed matters
According to the annual IBM Institute for Business Value study, the demand for data-driven insights will continue to accelerate.
Companies at the forefront of the shift from volume to velocity use analytics pervasively throughout their organization and have the technology and agility to act on insights quickly.
To become a competitive, speed-driven organization, your business must excel throughout the analytics lifecycle:
- Acquire: Harvest data quickly by exploring evolving big data technologies.
- Analyze: Identify the most impactful insights.
- Act: Implement the insights iteratively and strategically.
Harvest-to-delivery data governance
Exhaustive data and the opportunities to analyze that data just continue to increase. Ideally, you’re going to want your business to have the complementary ability to understand and strategically position technology and approaches to harvesting, integrating, and providing analytic insight over that data, and then be able to deliver it through omnichannel capabilities. That’s really where the industry and business are moving.
Increasingly mobile, increasingly digital
The world is increasingly becoming digitized. Our behaviors are more often happening on the road in a mobile context. We’re shopping, booking reservations, researching, investing, and conducting banking transactions on mobile devices in real time on the go.
All of those activities leave behind a very significant digital footprint with an associated geospatial dimension.
So, in terms of your business’ infrastructure, it’s no longer a question of “Do we need a CDO?” It’s now a question of, “Should we add analytics to the CDO title?” That is, Chief Data and Analytics Officer, or the Chief Digital, Data, and Analytics Officer. Because that’s the direction we’re headed, and it’s also the right direction.
And, once we get there, perhaps it should be Chief Analytics, Data, and Digital Officer, because really, the insights and value come out of the analytics and that’s where we’ve got to be.
• Article adapted from an interview with Dave Vellante of theCUBE, SiliconANGLE’s media team at MITCDOIQ Symposium 2015.
• The MIT Chief Data Officer Information Quality Symposium is jointly hosted by the MIT Information Quality Program at SSRC, the MIT Sloan School of Management, and the International Conference on Information Quality.
• SiliconANGLE promotes the highest quality and fastest news, analysis, information coverage on innovation, entrepreneurship, the tech athletes, and latest inventions.