Q1: Do narratives generated by natural language generation in BI defeat or counter the purpose of visualizations?
A1: This is an excellent observation. To a certain extent, it does. But it also complements it, because you also need context. I've used maps as an analogy in the past. When we were small, and travelled with our parents in a car - they needed a thick map book, which you had to browse to find the right page. As GPS technologies evolved, you now had the right context on a screen on your dashboard. And in some cases, it's enough to have a voice, and an arrow pointing to what you need to do next. However - we still want to have our screens that we want to zoom in an out of, because it provides much better context to the data point of where we are at. The only difference is that the visualizations have become more contextualized and accurate. I think with voice and narrations, it will be the same thing. You might be able to have a conversation with your data, which gets you to you right answer. But then you might want to ask how that answer relates to a broader context, by saying "show me how that relates" or (doesn't relate for that matter), which puts the answer into a contextualized, associative visualization. What I think will change is that the finding will drive further visualizations, rather than visualizations trying to render a finding.
Q2: Where is Qlik on conversational analytics? When will we see this materialize (in the industry)?
A2: Qlik was one of the first vendors in the analytics space to build a bot, and the good news is that we've just put them on Qlik Branch, for anyone to leverage.
Q3: Even today we see a gap in visualization vs. analytics. How do you see this understanding gap widen or reduce in 2018? And how will tools like Qlik contribute to this?
A3: Great question. To me they overlap. Analytics can be without visualization, and visualization can be without analytics, but great tools have both. I think this is where most visualization vendors fall down. Qlik is a data discovery tool in which you can do analysis in the application, which is displayed through dashboards, visualizations, and increasingly text/voice. The analysis is in the discovery and the workflow. Hence, the easier the tools are to work with the more analysis you can do, because the workflow becomes easier in ingesting, combining, analyzing and communicating with data. Of equal importance are the analytical apps where you can also do discovery/analysis, merely as a consumer. But because of the associative model, the discovery, interaction and analysis is still strong. Or, a finding can be brought into the workflow of where you are, which is still analytical if it's highly contextualized, because it meets the user where they are, in their "moment" of decision.
Q4: How will the Qlik product develop to meet the changing trends in BI?
A4: Qlik is addressing those trends head on.
- Qlik has a unique data literacy program, not only for Qlik users, but for everyone. Many of the courses are free in Qlik Continuous Classroom.
- Qlik is actively pursuing a hybrid multi-cloud offering, which you will see launch over the course of 2018
- Microservices will enable Qlik to gradually address edge computing better
- Big Data Indexing is a big bet that Qlik is making.
- Qlik has a big initiative under way for catalogues and big data.
- APIs are extremely important to Qlik as we go to market with a platform story.
- Beyond research lab activity, there isn't a lot in the area of blockchain yet, apart from some Qlik partner's building blockchain connectors
- Conversational analytics is a key innovation initiative for Qlik. Qlik was one of the first vendors in the analytics space to build a bot, and the good news is that we've just put them on Qlik Branch, for anyone to leverage
- Qlik is working a lot on redefining reporting. Watch this space.
- Immersive analytics is also a key development area. Watch this space, especially when it comes to collaboration.
Q5: What kind of machine learning capabilities are offered by Qlik? Can they be easily applied on very large datasets?
A5: You'll see more and more machine learning capabilities offered by Qlik in our engine and insights board. Our technology is augmented as we innovate UI paradigms, and experiences that bring the human and machine together, that use:
- Human with what the human is good at: awareness, perception, and decision making. Our unique UI and data visualizations enables this.
- Machine with what the machine is good: running advanced algorithms on large scale data. Qlik Cognitive Engine enabling this.
Our vision is not only about “the machine augmenting the user”, but also “the user augmenting the machine”: Qlik’s Cognitive Engine learns from the user’s interaction with the data, and it is contextual. This is how it generates specific, relevant insights for a given user’s analysis intent.
Our Big Data Indexing and the Cognitive Engine will ultimately work together in the future to generate associative insights for the users with Big Data.
Our unique interactive, selection based UI will enable this; to learn and know more about user’s intent.
Our technology is Associative: Associative Indexing addresses the need to create enterprise-wide schemas that support the full integration of all data assets.
Our technology is Intelligent: Qlik cognitive engine leverages advanced algorithms and machine learning to provide insights to broader range of users.