Qlik CEO Lars Björk Discusses Big Data and Artificial “Stupidity”
Big data has been a hot topic for awhile now, putting companies such as Qlik in an in-demand market—that also gets noisier and noisier by the hour.
Startups and larger software developers trip over each other trying be the service that sorts out mountains of digital information. Microsoft has teams of researchers here constantly creating new ways to work with big data; local startups such as Datadog, with its acquisition of Mortar Data, offers analytics and a platform for creating big data apps. Big data analytics is also a driver of jobs in the city’s fintech industry.
Needless to say the flurry of activity makes it hard to stand out for any developer in this space. Qlik (NASDAQ:QLIK) is trying to do so by focusing on making big data simple and easy to understand; its Qlik Sense software provides a visual way to decipher data analytics says CEO Lars Björk.
Founded in Lund, Sweden, Qlik moved its headquarters about ten years ago to Radnor, PA, and has additional offices in cities including New York, Boston, Dallas, San Francisco, and Raleigh, NC, as well as Europe, Latin America, and Asia. Qlik is a $600 million a year company and Björk said it will be “a little above that this year.” While not as big as say Oracle or SAP, Björk says Qlik has been in the analytics industry from its start. He came through New York last Friday and I sat down with him to discuss ways big data is turned into useful content, and what he called artificial “stupidity.”
Xconomy: What is happening in big data, and why does Qlik have offices in New York?
Lars Björk: We’re in a time when data is exploding around us. More and more people are coming to a realization that if we can turn that into insightful information, not just for a few experts but for the broad employee and user base in an organization, there is tremendous benefit.
The outset for this company was to do something that was based on ease of use. Make it very simple for the user to get going and make changes to the applications. This consumerization of enterprise software today is common; if it doesn’t work on a smartphone, if it isn’t super intuitive, it’s simply not going to be used at all.
New York is our beachhead for one of our biggest verticals, financial services, because of the concentration of financial institutions. Take the 25 largest banks in the world, in some way or fashion they are all customers of ours. They are literally drowning in data.
XC: What can you do with big data that they aren’t doing on their own?
LB: For a financial institution in private wealth management, in their compliance and trading, we’re providing deep analytics for their salesforce on their clients. So when a client calls them, they’re not just looking at a spreadsheet. You can look at correlations between investments, you can look at it over time, and how a portfolio would have done if they had invested a different way.
We’ve support the New York City Marathon with big data. You can go in and look at all the runners, previous times, and age groups. The organization is using it to organize the race.
Our strength is that we want to give you the whole data set. The initial thing that spurred your interest, or the question that drove you to look into the data is not where you’re going to end up. You’re going to ask more questions depending on what the initial findings show you, the question you couldn’t formulate in your head before you started looking into the data set.
XC: Are your target users senior executives who want to understand big data to plan strategy, or employees with specific tasks?
LB: The sweet spot has moved from being a more senior executive or analysts’ tool, from the CEO to the janitor. While executives might use it, entire salesforces, everyone in finance, or working in a supply chain uses it. If you go to a hospital—the physicians, nurses, administration people—what do they have in common? Multiple data sources, and they are trying to figure out a way to optimize their workflow. That’s the common denominator.
XC: Do you only work with big data from large enterprises?
LB: Big data is extremely interesting, but it’s a bit of a hype word. There can be equal benefit in small data. The point isn’t so much the size of the data set, or the diversity, or velocity of the data. You can get enormous benefit by highlighting stuff in a smaller data. We are agnostic there. We might not engage in the same way with a very, very small use case, but we have a partner community that works with smaller clients.
XC: How is the use of big data changing, and where can analytics be seen these days?
LB: You have millennials coming into the workforce with the expectation that big data analytics software works like anything else on their smartphones. Another trend is collaboration, analyzing data in a workgroup. Big data analytics is not standalone tool anymore, it gets embedded in other environments. If you go to NCAA baseball, their statistics on their website is our software.
This is not a typical IT type of deployment where only a few people have the skills to deal with it. There’s another trend with packaged data as a service. What if you could be approached through software with a link to start acquire curated, normalized data that is readily available instead of spending time searching for it? It could be benchmark data from an industry, socioeconomic and macroeconomic data.
XC: What are you doing to stand out as startups and larger companies also get into the big data analytics game?
LB: Focus on our view of where this industry’s going. We might pick up some of those smaller companies; we have picked up some of them that fit into ours. I wouldn’t rule out competition from the big guys, but I don’t fear them either. They don’t tend to be the innovative ones. Innovation comes to them by opening their purses and buying something else. They are more like supermarkets, doing everything in so many industries.
All we do, and have been doing for more than 20 years, is analytics.
XC: What technology changes do you see coming to big data?
LB: Artificial intelligence and machine learning are clearly going to make inroads. It might be artificial “intelligence” at the outset—someone else said it is actually artificial “stupidity.” What you’re trying to do is automate and take over for mundane, repetitive decisions. That isn’t really intelligence.
Machine learning, Internet of things, the ability to collect information from sensors is going to explode. We can debate how much is hype and how much has real value. You can expect it to happen in health tech and the health industry. Think about an industry that needs to go, in a short period of time, from being reactive to proactive. It’s early days, but it’s moving quickly.