Chapter 1. Qlik Sense? and Data Discovery

In this chapter, we'll start getting to grips with what Qlik Sense offers by getting a better understanding of Qlik's background and how Qlik Sense was developed. In addition, we will examine the discovery-based approach to business intelligence that Qlik invented.

We will cover the following topics:

• Qlik's history in business intelligence and the evolution of data discovery
• The QlikView.Next project
• The Qlik philosophy and approach to data discovery
• The importance of the empowered user
• How a user really interacts with data
• The difference between traditional BI and data discovery

Continuing disruption

In the world of technology, there's a lot of talk about creating new products that disrupt existing markets, but very few organizations can say they've done it for real. Qlik is one of them.

In 2007, the business intelligence (BI) software market changed forever. Oracle bought Hyperion, SAP bought Business Objects, and IBM bought Cognos. The conventional wisdom was that BI would effectively cease to exist as a standalone market, subsumed into larger stacks of technology.

However, this wasn't the case. In fact, by 2007, a revolution was already well underway. The BI world was being fundamentally disrupted, challenged by the new approach pioneered by Qlik (then called QlikTech). The disruptive technology Qlik developed was called QlikView. To differentiate QlikView from the established BI products, Qlik began to call the new disruptive approach Business Discovery, later adopting data discovery as this term gained industry-wide adoption.

Surprisingly though, when it was launched in 1994, what became QlikView was not consciously targeted at the BI software market. Rather, its initial task was to help its customer understand which of a number of inpidual parts and manufacturing materials were used across the range of the complex machines it manufactured, and which parts were not associated with particular items (a critical point we'll explore later in this chapter and revisit throughout this book). The goal was to visualize the logical relations between the parts, materials, machines, and products. This origin led to an approach completely different from BI at the time, one in which all the associated data points are linked automatically, enabling discoveries to be made through free exploration of data.

As it became more widely used and deployed, it was evident that what QlikView was being used for was a new type of BI. QlikView's speed, usability, and relevance challenged the standard approach that was dominated by IT-deployed data reporting products, which are slow performing, hard to use, and built around models that struggle to keep up with the pace of modern business needs.

QlikView's intuitive visual user interface, patented associative data handling—running entirely in memory—and its capability to draw data together from disparate sources changed the landscape. Discovery-led BI is about giving people the power to interact with and explore data in a much more valuable way than the older, reporting-led BI incumbency could. This is massively compelling to people who need to quickly ask and answer questions based on data in order to learn and make decisions, and proved very compelling to people jaded with the way things had been done before. QlikView became very successful, dominating the market it pioneered.

So what did Qlik do then? Sit back and relax, proud of its disruptive chops, safe in the knowledge that it had recast an established market in its image? No. Far from it. Instead, Qlik took the decision to transform the BI market again with a new product.