Hot on the heels of his keynote presentation at our Internet of Insurance event in London, MicroStrategy’s Robert Davis, VP product management EMEA, sat down with Andrew Hobbs to discuss how the rise of the ‘third revolution’ in data analytics is making the technology accessible to all.
Robert Davis leads the international product management team at enterprise analytics and mobility software provider, MicroStrategy. It’s the team’s job to be embedded in the markets that the company serves and intimately understand customers’ analytics needs – while developing MicroStrategy’s products to better serve them.
So how does Davis see the present and future of enterprise analytics – and how the technology is reshaping businesses?
Internet of Business: In your presentation you referred to the ‘third revolution’ in data analytics. Can you explain what you meant by this?
Robert Davis: “The third revolution is the fruition of what analytics was always supposed to be. The first revolution, which happened in around 1990 and was typified by the rise of companies like Business Objects, was the first time users were able to access data sources without having technical knowledge.
“Without having to understand SQL or MDX, they could get reports that were meaningful – and make business decisions. The difficulty was that the round trip in getting business insight from the data was very slow, hence the reports were often out of date and not relevant to the business by the time you got them.
“Fast forward to around 2005, and companies like Tableau and Click. We entered the second revolution, which was really the rise of the analyst. It tackled the challenge of, ‘How can the analyst get quick access to data sources, whether it be corporately blessed data, or desktop or cloud data sources, get rich insights from it, and make business decisions from that?’
“While the time-to-value went down drastically in the second revolution, the problem was the audience was very small. It was only the analysts who wanted to have that deep look at the data.
The third revolution is now bridging the gap between the analyst, the report factory, and the end user, who traditionally isn’t experienced with data.
“So, this is someone who works on the shop floor in a factory or behind a till in a retail environment – people that have not traditionally used data analysis to make their jobs easier and more efficient.
“In order to serve that audience we need to rethink how we look at data analytics and the analytics platform, to make data just part of the pervasive fabric that people have every day in their tools like email and on mobile devices – so they just see the right bit of data at the right time to make the right decision.”
What’s changed to enable this third revolution?
“There are a couple of things. First, we come back to the semantic layer, or the single version of the truth being important again. The second revolution was all about ‘Wild West’ access to data, but ‘trustability’ and the single version of the truth wasn’t important.
“What we’ve done to enable the third revolution is the coming together of the first and second. The first revolution was all about governance and trustability and that single version of the truth. The second revolution was all about access and getting analytics tools and data into the hands of more people. And when you bring those two together on a single platform, you can enable the third revolution. That’s really what’s changed at MicroStrategy.”
How do you see this reshaping businesses in practice?
“I think it just allows people to be better at what they already do. Take insurance as an example. If the pricing models that are changing all the time could be communicated through an enterprise application to the brokers and underwriters almost immediately, those underwriters would be able to make more profit, and sell better products that are better matched to clients more quickly, thus allowing them to do their jobs better. It will be a subtle effect, but what we’ll see is people not even realising they’re using data, but doing their jobs better and more efficiently.
On the whole, we’ve been successful if people find the analytics to be invisible – just a part of their normal work stream. I would like MicroStrategy to be the company that enables this.”
Your keynote presentation touched on the ability of analytics to anticipate challenges and opportunities in business. How does it enlighten employees in this sense?
“Anticipating challenges comes from closing the loop from the analyst to how end users use this data. This is something I call telemetry or a feedback loop. You get insight into the future by seeing how end users use the data.
“If brokers and underwriters aren’t trusting your pricing model or risk analysis, you will see them not using that part of the data in their business decisions. If your enterprise is intelligent enough to see both the use and non-use of the algorithms in the data that you’re putting out there for end-users, you will foresee challenges and opportunities – where you could be doing better with the data and with the business.
“So, I think it’s super important to close that loop and not only give that data to the end users, but also to record and look at how they’re using it.”
And you use that usage data at MicroStrategy to reshape your product as well, and how you cater to your customers’ needs?
“Absolutely, it’s almost like an Internet of Things platform in itself, in that our platform self-reports its health statistics and keeps track of them. In the next few releases, we’re going to be putting in more and more self-healing capabilities into the platform too.”
Your presentation focused on three pillars: ‘pervasive’, ‘responsible’, ‘open’. Why are these principles so important to analytics?
“The ‘pervasive’ pillar is all about getting data to the end users and the practitioners of the business – embedding the analytics in the applications they use every day and not forcing them to go to a separate program to get insight.
You need to have rich APIs, so that you can embed analytics in applications. You also have to have a good mobile strategy, so that people can get access to analytics, whether they’re in front of their computer or out in the field. And you need to have the ability to make it simple to understand and use.
“I find the ‘responsible’ pillar really interesting. We’re at a point in the data industry where it’s maturing rapidly. We’re realising that data can be used for evil, as well as good. We’re now responsible for providing technical platforms that allow risk officers to see how data is being used and flag risky behaviours before they become a problem.
“Finally, the ‘open’ part came up in one of the questions I got in the panel discussion, which was, ‘How do you build a perennial data strategy and platform?’. We’re in one of the fastest-moving technical spaces in the industry. The way that data is stored and analysed is going to change almost monthly. We need to build a data strategy and platform that lasts longer than those technical solutions. That’s really the lifeblood of the semantic layer, the ‘single version of the truth’ approach, and the data dictionary you build with MicroStrategy.”
Do you think the ability to spot those longer-term trends and overarching influences is where the greatest value in analytics lies, rather than in the short-term fluctuations?
“That’s correct. I’m not sure I said it explicitly this morning, but the intelligent enterprise gets more intelligent over time. It’s like a growing organism, and the more time your give the enterprise to become intelligent on the same platform, the more useful it will be.”
You mentioned finance and insurance corporation AIG in your keynote, and that company’s use of a MicroStrategy-enabled mobile application. How has that influenced their business?
“The specific application allows the risk analysis to actually be shown to clients on a dashboard on a mobile device. The interesting thing is it has built better client relations with the underwriters and brokers, because the underwriter can go out in the field and show the client their risk profile.
“AIG can demonstrate in real time how it would affect a client’s risk profile if they were to increase the maintenance frequency of a certain piece of equipment, and, therefore, potentially the pricing. And they do this on the fly, using the algorithms and the risk profiles that their data scientists have created.
It’s interesting how data sometimes has cultural effects as well. It makes the underwriter more trustworthy to the client and it makes the client feel like they are taking part in minimising the cost of their insurance policy.”
What challenges do businesses face when adopting such technology? Is there a cultural shift that needs to happen?
“There is. I talked a bit about it this morning. There is still a deep fear in using data to make decisions in organisations. I still see cases of people saying, ‘I have a hunch that’ or ‘I have a feeling that’, and they don’t want to use data to support it.
“Some of that is because people are still scared of maths. They think if they have to do some data analysis someone in a white jacket is going to come over and explain five equations to them and they’ll be lost.
“This is a huge part of what we’re trying to do with the ‘pervasive’ part of the platform – to just make data like running water. So people think ‘I know what this number means and I know how to act on this number, today.’ As opposed to wading through five charts and six visualisations and trying to convince yourself, or others, what the action should be.”
So, people need to understand that the data is for their benefit and not going against the instincts or conventional models that they have used for years?
“Definitely, we are instinctive beings and I think, often, that instinct is based on real analytics, it’s just that the experience you’ve had for so long makes that a gut feeling or emotion.”
Where do you see analytics technology going in the near future?
“I see a re-entrenchment in governance and the single version of the truth, while keeping open to agile data analysis, and finding new data sources and trends that are relevant to the business.
“And I see work in mixing these things together so that they’re transparent to the organisation – so they know when they’re making decisions on data sources that have been corporately blessed and when you are out in the sandbox playing and trying to find new things that are important.
It’s not enough to just enable the analyst anymore, you have to enable the entire workforce with trusted data.
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