Human work starts where analytics stops

It has been a while since the hype, and now it’s time for some reality check.

Everyone has been riding on the data wave and analytics has been the biggest buzz word for almost a decade now.

Let us talk about the impact, success and ROI now.
◾How many companies do you think are really changing the way they have been , by adopting analytics?
◾How many businesses are data driven? In real sense?
◾How many enterprises are running at scale with big data and Machine learning algorithms driving decision making?
◾How many data scientist are proven to be different than a BI developer ?
◾How many dollars are being saved because every decision you make in business is driven by some data sense?
◾How many companies have developed a culture of analytics and data driven thinking, elevating their position in the competitive game?

While Gartner recently emphasized on the fact how BI and analytics is shifting to a decentralized, non-IT centric and self –service approach, I think there is still a huge gap in what “Actual Value” from analytics is vs. the “perceived value”

Here are some of the factors I think need to be pondered over to bridge the gap, and which is more of a human change than vouching for machines to achieve this.

Positioning Analytics in right sense, and creating champions for driving the change

It is extremely important to position analytics in alignment with the objectives of the organization. The organizations need to be firm on the “need for data driven thinking”. Analytics is not a “good-to-have” function, it is must-to-have. A dollar saved in a dollar earned. An opportunity unexplored is 100% incremental. Especially, if you are starting or have recently started with on-boarding an analytics team or a consulting company, you need to do a PR for the “Expected Value” by having an analytics team in place. There needs to be people in the company who take accountability for helping analytics position itself as “in demand” and “Risk mitigator”

Embedding analytics in the strategy, than keeping analytics as a separate strategy

Most of the companies keep analytics as a separate strategy, the goal is to build an analytics infrastructure, team and so to say practice. That’s a traditional IT approach. It is only partially about systems, it is much more about education and integration. Analytics will not work on it’s own. There has to be someone who should act on what data and algorithms find out, and at the same speed. There need to accountability for every single insight to be worked upon. If not, There are always new problems to solve, and the problems too get old and redundant.

Democratization of data as “capability” vs “availability”

There has been great talks about how analytics is moving to be self-service where business owners are taking data into their own hands and marketing technology is enabling marketers to be data-savvy. This whole propaganda is about availability. I bet, no one is looking at this as capability enchantment. Is the self-service realization making marketers smarter and efficient? Or it is just that they do not have to depend on IT to get some data? Think

Creating room for “change in business” ideas vs “Business as usual” ideas

Innovation has always been the escape word. If you do not have a strategy , just tell them you are working on innovation. Now, innovation is extremely debatable in its definition. When talked about results from investment on innovation, an easy answer is – Innovation is not only about creating something new, it is also about improving the existing stuff. Well, I think this needs to stricter than it is now. You have to have a dedicated team with dedicated time working to change things and innovate using analytics and data. Process improvement with data is a low value fruit, the real value is in disruption and in pivoting strategies with data

Introducing constraints for Optimization, than perceiving improvement as Optimization

I have rarely met anyone who takes “Optimization” in the right spirit. Usually, people perceive improvement as optimization. Optimization is about pushing the boundaries. It is about putting constraints on your targets and over achieve. If you want to Optimize your digital channel, for example, you need to introduce constraints around cost, exposure and spend around the same and then push for maximizing the output. Analytics is a great tool to drive Optimization and that’s where it is different from traditional BI.

So, if you think you are already doing great with data, it is time to introspect a bit.

Source: datasciencecentral.com

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