Learning Qlik Sense??: The Official Guide Second Edition - Second Edition
Christopher Ilacqua QlikTech International AB Henric Cronström James Richardson
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Packt
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 individual 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.

What is data discovery?

So we've already mentioned the new style of BI that Qlik pioneered, data discovery, a few times. In this section, we'll look at that in more detail.

Over the years, there have been many names of the different business intelligence methods and tools, such as:

Executive Information Systems (EIS)

Management Information Systems (MIS)

Online Analytical Processing (OLAP)

Decision Support Systems (DSS)

Management reporting

Ad hoc query and reporting

Do we really need an additional label for something that in principle is the same thing? The answer is yes.

There is a fundamental difference between older technologies and data discovery, and it is in the approach. Most of the preceding tools are oriented towards technology, but data discovery is not. Instead, data discovery is oriented towards people—towards the users who need the information in their daily work.

Most of the preceding tools were developed for a small, select number of decision-makers, but again, data discovery is not. Data discovery is for everyone.

Decisions are made at all levels in a company. Obviously, managers are decision-makers, but we sometimes forget that machine operators and receptionists are also decision-makers, albeit at a more local level. They also need information to make better decisions.

We, at Qlik, believe that information can change the world and that every user can contribute to this transformation. Everyone should easily be able to view data, navigate in data, and analyze data. Everyone should be able to experience that "a-ha" moment of discovery.

Data discovery is not just business intelligence. It is user-centric, dynamic, and empowering. And it is fun!

The empowered user

Since its founding in 1994, Qlik has believed that a business could improve its processes and product quality by empowering employees and encouraging them to engage in lifelong learning. And Qlik meant all employees—we saw everyone as a decision-maker, not just managers.

Although some things have changed since then, much remains the same. Users are still often in a situation where they are unable to analyze their data—data that they have the right to see, or should have the right to see, in order to do a good job. Rigid systems, technical limitations, and poor user interfaces are usually the culprits.

However, people's expectations of software have changed dramatically during the last decade. Applications from Google and Apple invite users to interact with simple, friendly interfaces. Search bars, Like buttons, and touchscreens have transformed the way people explore, consume, and share information. Today, people want the same ease of use from their business tools as they get from their consumer tools at home.

The current trends such as the consumerization of software, performance improvements of hardware, usability improvements of software, mobile devices, social networks, and so on just accelerate this change. All these trends are reshaping user behavior. Yesterday, a user was a passive end user, but the user of tomorrow will be both able and demanding. They will demand tools that are fast, flexible, and dynamic. They will demand tools that they can use themselves. The empowered user is here to stay.

Interaction with data

The classic picture of business intelligence is that the user has one or several questions, and that the data holds the answers. So the problem boils down to creating a tool where the user can enter their questions, and the tool can return the answers.

However, this picture is incorrect. The truth is that the user does not always know the question initially. Or rather, if the user knows the question, they often already know the answer. So, the first thing the tool should do is help the user find the questions.

Finding the questions is a process that involves exploring the data. It involves testing what you suspect but don't know for sure. It also involves discovering new facts. Further, it involves playing with data, turning it around, scrutinizing the facts, and formulating a possible question. You use your gut feeling as a source of ideas and use the data to refine the ideas into knowledge; or to discard the ideas, if facts show that the ideas are wrong. You need to be able to play with the data, to turn facts around and look at them from different angles before you can say that you understand the data, and you need to understand the data before you can talk smartly about it.

When you have found a relevant question, you also need to be able to conduct an analysis to get a well-founded answer to the question.

Finally, the process involves presenting the answer to the question to other people as a basis for a decision or an action. The tool must support the entire process of going from ignorance to insight.

Hence, one major difference between data discovery and the more old-fashioned tools is that data discovery software supports the entire process—the process of coming from a blank mind, not knowing what you are looking for, all the way to attaining knowledge and taking action.

This is what data discovery is all about—helping you to prepare before you speak, act, or make a decision. It is the process of going from the darkness to the light, from the unknown to the known, from ignorance to insight. It is the process of going all the way from a blank mind to a substantiated argument.

Traditional business intelligence architecture

It is quite clear that users representing the business want the ability to ask and answer questions on their own so that they can make better decisions, but traditional business intelligence solutions aren't well suited for user demands. Instead, it is common that the systems are created in a report-centric manner, where governance and system demands set the goals, rather than user demands. The solutions often have preconﬁgured dashboards, ﬁxed drill-down paths, predeﬁned queries, predefined views, and very little flexibility.

With traditional BI, the creation of the business intelligence solution often belongs to the IT organization, which has to do the following: create data models, establish a semantic layer, build reports and dashboards, and protect and control the data. Often, the creation of business intelligence solutions is not driven by user demands. The following figure depicts the traditional BI architecture:

When analyzing data, you might want to set filters so that you can make selections, but with traditional tools, you often need to start at the top of predefined hierarchies. So, instead of selecting a customer directly, you may need to answer this question, "Which market does this customer belong to?", then the country the customer belongs to, and only then can you specify the customer.

Further, in the drill-down hierarchy, you are often limited to the choice of one or all. For example, you can look at either a single customer or all of them. The possibility of choosing two or three specific customers doesn't exist, unless this has been specifically predefined by the data model developer.

Numbers are often precalculated to ensure short response times, but this has a drawback that if the developer hasn't anticipated a specific calculation, the tool will not be able to show it.

Further, the architecture of the tool is often made in three layers—referred as the stack. The first layer is the Extract, Transform, Load (ETL) layer, or the data load layer. The second is the Data Store / Engine layer, and the third is the User Interface (UI) layer. The three layers are different pieces of software, sometimes delivered by different software vendors.

These three layers also demand different skill sets. Often, the ETL expert knows little or nothing about the UI software, and the UI expert knows little or nothing about the ETL.

This architecture also leads to problems. When an application is built, the feedback comes from users trying to use the application. It could be that KPIs are incorrectly calculated or that dimensions or measures are missing. It could also mean that the user realizes that the initial requirements were incorrect or insufficient. The feedback could imply changes in the UI, or in the data model, or even in the ETL component.

This type of feedback is normal—it happens with all business intelligence tools. It only means that the development of applications is a process where you need to be agile and prepared. The expectation that you should be able to define an application completely and correctly prior to a prototype or an intermediate version is just unrealistic.

This is where the architecture leads to problems. In order for a project to be successful, you need to be able to implement change requests and new user demands with short notice, and this is extremely difficult since three different pieces of software and three different groups of people are involved. The distance between the user and the ETL component is just too great for efficient communication. Hence, the traditional architecture leads to a broken process.

The Qlik® way

Qlik has tried to solve all the drawbacks discussed in the preceding section by doing things differently.

First of all, you click and view. You don't need to formulate your question or tell the system more specifically what you want to look at. You just click, and by that, you say, "Tell me more about that…". Then you look at the calculation, KPI, or the field that might be interesting.

Color coding

The color coding defines the answer. Some things are associated with what you clicked on, and they remain white. Others that are not associated become gray. The color coding is for simplicity. The user quickly gets an overview and understands how things work.

Showing the excluded reveals the unexpected, creates insight, and creates new questions. Hence, the gray color is an important part of making the Qlik experience an associative one—a data dialog and an information interaction—rather than just a database query. Showing you that something is excluded when you didn't expect it means answering questions you didn't ask. This surprise creates new knowledge in a way that only a true data discovery platform can.

Freedom of data navigation

Via the associative experience, a user has total freedom to navigate through data and make any combination of selections. Any number of values can be selected. No drill-down paths need to be predefined. This allows the user to follow their own train of thought instead of someone else's. Start anywhere and just follow your intuition.

This total freedom when exploring data is really the core attribute of data discovery.

Calculation on demand

Further, no numbers need to be precalculated. Via the QIX engine, QlikView and Qlik Sense calculate everything on demand, usually in a fraction of a second. The short response time allows the user to have a conversation with the data, where one answer leads to the next question, which in turn leads to next, and so on. Only this way can you interact with data so that you learn from it.

The developer does not need to anticipate all questions that the user will pose. All they need to do is to create a logical, coherent data model, and Qlik Sense will be able to answer the question correctly:

The stack (ETL-Data Store / Engine-UI) is replaced by a single integrated environment. This makes it possible to develop applications in close cooperation with the users, and it can often be done by the users themselves. Feedback is implemented instantaneously and the changes can be evaluated just seconds later. This shortens the development cycle and ensures that the application meets the user demands much sooner than it would otherwise.

This stepwise implementation is crucial for the success of a business intelligence project. It is also the core of modern agile methodologies that are used in all types of software development.

The development of business intelligence applications must be done as close to the user as possible to enable user feedback and short development cycles. It does not necessarily imply self-service capability, although it is good if this capability exists.

With the introduction of Qlik Sense, the groundbreaking work continues by enabling a new class of users who are highly mobile and require greater self-service capabilities. In Qlik Sense, the self-service capability has become a core feature. Users can define new graphs and visualizations that the app developer didn't think of. This functionality empowers the users even further.

With Qlik Sense, it has also become easier to share your findings and communicate them. This is something that is necessary in all environments where human interaction is important, which is pretty much everywhere.