The New Face of Big Data and Analytics

The conversation around big data is tipping away from talking about the technology to the desired end results, says Shawn Rogers, the senior director of analytic strategy at TIBCO Software.

Rogers was in Singapore for a conference earlier this month, and he sat down with CDOTrends to share his thoughts about the state of big data and analytics in the enterprise.

Look beyond the technology

“If we were here four years ago, we would be talking about topics like Hadoop. Now we are moving on to the ‘why’ of the return on investment, as well as topics such as how do you operationalize big data and analytics and make them a true part of your corporate culture?” said Rogers.

“A lot of companies have run out and got distracted by the bright shiny toy of technology. And people think that technology would fix everything. If you go for technology only, you could find yourself in a bad place. It’s a balance of people, technology and culture.”

Rogers shared an anecdote from when he used to teach classes on big data. Only a handful was there because they have a critical or compelling project that big data can solve; the predominant majority were there because were tasked to learn more about the topic by CEOs or bosses.

Interestingly, the fact that the Asia Pacific isn’t necessarily at the cutting edge of technology isn’t a negative when it comes to big data: “[The APJ region] leads with culture. Technology is important but the most important thing is making sure there is an innovation culture to make use of the technology. About how this region attack innovation and how things are done here.”

The mistakes they make

Are there common mistakes that Rogers sees organizations making with their big data initiatives? Rogers noted that some organizations don’t spend enough time managing and preparing data so that it can be used for analytics. Just piping data in from various sources is inadequate and can lead to significant time wastage because the data isn’t ready.

“Companies will get blinded by developments such as AI and analytics and want to jump straight in, but they don’t have data inline and unified, and connected the way it needs to be. And they start on a project and figure out quickly – we can’t go any quicker because the data is not ready.”

“Overuse” of data is another mistake that some organizations make.

“I think it is ok to suggest that you can buy a shirt with your pants. Or even if the site was to highlight some items that your friends purchased. However, you must be careful not to cross the line between innovation and ‘eeky’. Sometimes you can innovate so far that you step out of transparency, out of permission, and out of context,” said Rogers.

The transparent and approved use of data is easy to understand. But what of context? Rogers explained: “Context is important. If you are my bank, I have a contextual relationship with you. But if the bank website suddenly suggests a pair of pants, you will freak out.”

Moving boldly into the future

The other thing is to operationalize your analytics and data, you have to consider the center of excellence or analytics center of excellence. This is generally under a CDO or CAO. And then they get executive level stakeholders and budget. They become repeatable, governable and standardize.

As with everything else, and then you get good at it. We’re at that point where everyone is getting serious about analytics and data. Operationalize – make it part of your core foundation approach to everything.

Organizations looking to develop their data competency should ideally “hire the top of the pyramid and then build it down” suggests Rogers. An alternative is to bring existing business analysts and data scientists under a cohesive structure, with perhaps a chief data scientist put in charge.

“Whether it is in healthcare or other industries, everyone is being disrupted. Being data-driven and applying analytics is now key imperatives in many businesses, who see it as a way to stay ahead of their peers in their respective industries.”