Power BI vs. Tableau vs. Qlik: Which BI Tool is Best?

“Just give me the data.” When Kenway Consulting engages in a Business Intelligence (BI) project, many of them begin with that simple phrase— “Just give me the data.” Organizations want their data from various source systems in the hands of their power users. Doing so allows them to leverage the industry expertise and analytical mindsets for which they hired these resources. To maximize our value during a BI project, we believe in getting our clients the data that addresses their highest impact business questions early in the data discovery phase and then iteratively developing it in an in-memory data visualization tool.

We use in-memory analytics and data visualization tools because they allow:

Data extraction from disparate data sources without undergoing a full, expensive extract-transform-load (ETL) project and/or data warehouse development project
Users to manipulate the data to create custom fields, groupings, and flags without impacting source systems
Charts, graphs, and filters to be modified on the fly so that we can develop side-by-side with our users, providing immediate feedback as they conceptualize what they need

However, just as no two clients’ needs are the same, we have learned that we cannot simply pick one tool to address every engagement. In an effort to best serve our clients, Kenway recently undertook a hands-on research project to vet Power BI vs Tableau vs Qlik.

Power BI vs. Tableau vs. Qlik: Our Research

Here is how it worked. We built a report in Qlik Sense, and used it to provide a benchmark against two major competitors: Microsoft Power BI and Tableau so we could compare Power BI vs Tableau vs Qlik. We reviewed the products on their ability to fulfill a few of the common use cases we have seen with our clients:

Data Extraction
Summary Dashboard Development
Advanced in-memory analytics

Before we begin our intergalactic adventure in data, here is some background on the exercise:

We used 9 Excel spreadsheets as our “data extracts” (if you have worked with the Adventure Works database, these will look very familiar)
We limited our consultants to 15 hours of development; this is the amount of time that it took us to build the Qlik Sense benchmark with a multiplier to factor in our previous experience with the tool
The specific tools that we used were:
- Qlik Sense Desktop
- Microsoft Power BI Desktop
- Tableau Public

So let’s compare Power BI vs Tableau vs Qlik!

Data Extraction

Directly importing our data files using all three tools was quite easy. They all had user-friendly data loading wizards that allow you to quickly find files on your hard drive, make some minor manipulations, and incorporate them into your application.

The most striking difference was the number of data sources available via the versions we used. Power BI Desktop led the way in this category—out of the box, it allows users to utilize the wizard to extract from various file structures, databases, online services, and other applications. Qlik Sense also allows for a large spectrum of data sources to be incorporated; however, it requires a bit more technical savvy and/or searching to do so. Tableau Public limits users to local files, OData connections, and the Azure Marketplace DataMarket. However, if you choose to upgrade to the Professional Version, you get access to the same breath of sources as above and out-of-the-box connectivity as Power BI.

Outside of using the data loading wizards, Qlik Sense and Power BI provided much more robust scripting languages than Tableau. Qlik Sense’s data load editing language resembles SQL, a language familiar to many people with database experience. Power BI utilizes a language called Power Query. It is similar to F#, an object-oriented coding language. Tableau’s data loader allows users to make minor transformations for a loaded dataset (adding calculated values, grouping values, defining joins between tables, etc.); however, its lack of a coding language limits the number of tasks you can accomplish. For most use cases, the data will have to be prepared at the source level (e.g. modifying the files, creating views and/or tables in the desired model, etc.).

Once the data was loaded into the applications, Qlik Sense is able to differentiate itself from the other two products by the final data model it is able to utilize. Qlik’s associative data model allows Qlik Sense to string together connections between each table with every other table in the data model. This allows users to develop unique analyses across seemingly disparate data tables. While Tableau and Power BI are also able to bring in multiple data sets and data sources into their models, as users add on varying layers of complexity to the data model, they must also be more cognizant of the impacts on the data model.

For more information around each application’s connectivity, scripting, data load times, data compression abilities, and data modeling strengths and weaknesses, please see our full Data Wars Whitepaper.

Data Loading Breakdown

Executive Dashboard

Not surprisingly, all three of the tools were able to address our baseline reporting case—the Executive Dashboard.

As you can see, each tool was able to make a polished, user-friendly dashboard. Users are able to make line charts, scatter plots, and bar charts easily and can enhance them by adding filters. Furthermore, each of them supports a community of custom developed add-ons. The one we used here is by our friends at Tableau Augmented Analytics, formerly Narrative Science, (denoted by their logo). They have developed an add-on for Qlik Sense, Power BI, and Tableau that creates text summaries of your visualizations.

When it comes to Power BI vs Tableau vs Qlik from a default visualization standpoint, Tableau and Power BI came with more visualization types than Qlik Sense. While utilizing Qlik’s marketplace and customizing its standard visualizations allows Qlik Sense to make up some ground, this could be overly burdensome for less technical audiences.

Ultimately, we give a slight edge to Tableau in the visualization creation and organization space—the application’s interface has users create objects in separate tabs and then consolidate them into a single dashboard using a drag and drop design.

Tableau and Power BI also have an advantage when it comes to data manipulation on the visualization layer. They provide the user with wizards on the visualization layer to group fields, create hierarchies within fields, apply rules to fields, and create auto-filters for fields. The uses for these can range from making calendar fields (month, quarter, year, etc.) to developing drill down logic.

If users are embarking upon data discovery exercises, Qlik Sense’ white-green-gray filter functionality differentiates it from the other two. The white-green-gray color pattern defines whether a field is included in the current set, directly chosen for the current set, or excluded from the current set, respectively. This is useful in highlighting items like missed opportunities.

For further details around how the tools recognize field types (dates, locations, etc.), allow for heat map creation, enable users to build custom fields, and facilitate data discovers, please read our Data Wars Whitepaper.

Executive Dashboard Development

Customer Segmentation

With the basic use-cases covered, we wanted to see which tool handled some of our more complex business needs. The first that we looked into was customer segmentation. Many of our clients look to group their customers based on dynamic, automatically updated business rules. As this dataset was sales data, we decided to try and group them using the following:

Whether someone was a New Customer (i.e. their first purchase was less than or equal to 60 days ago)
How recently they purchased (grouped by 0 to 30 days, 30 to 60 days, 60 to 90 days, 90+ days)
Whether someone was a repeat customer
Whether someone was VIP Club Eligible (i.e. their average order was more than $1,0000)

Impressively, all of the tools were able to accomplish this segmentation. We used Qlik Sense’s and Power BI’s aforementioned scripting languages to develop these into the data model. For Tableau, we were able to string together multiple custom fields in the visualization layer to develop the needed segmentations.

Flows

Another key transformation in which our clients have found value is flows. This is used in customer service routing, order fulfillment, customer purchase pattern analysis, and other examples. Because of the ability to create custom scripts in Qlik Sense, we are able to recreate the logic for these. While we were unable to accomplish this with Power BI, we believe it could have been re-created with more time. Tableau would require the data to be prepared outside of the tool, likely in the source system.

For more information around how customer segmentations and flows were incorporated into the tools, please see our full Data Wars Whitepaper.

Summary

Here’s what we learned comparing Power BI vs Tableau vs Qlik:

Qlik Sense provides the most value in regards to data manipulations and data modeling. Its SQL-esque script language will be familiar to many resources that are familiar with databases. The associative data model allows for unique analyses to be showcased to business users, potentially highlighting aspects of their organization they had not yet considered. The areas where we found that Qlik Sense fell short was in usability for non-technical users. It displayed a steeper learning curve than the other two products and most tasks required technical configuration.
Power BI, despite being the newest of the products, showcased an impressive mix of technical functionality and non-technical user friendliness. The Power Query language allows for users to find a multitude of ways to address a problem, which, based on preference, can be seen as a positive or a negative. Microsoft has baked in a large standard list of visualizations as well as some nice additions to the front-end layer that make this an excellent tool for a non-technical user to learn and build upon that learning. The areas where Power BI struggled was in compressing the data and fully utilizing a normalized data model.
While Tableau struggles in the more technical aspects of data manipulation and data modeling, it was the easiest to learn for our experimenter. For environments that rely heavily on IT to present the data but want a tool with which non-technical users can run with little technical background, Tableau is an excellent choice. After the initial pass through the analysis, users have to be wary about what additional drill-downs and complexity will impact the data model to ensure they report accurate data.

Enjoyed our journey, we hope you did—we certainly learned a lot and got to geek out a little. Stay tuned for more information as these tools evolve and shift and new tools are added to the in-memory analytics ecosystem. Maybe we'll compare Power BI vs Tableau vs Qlik again. Or perhaps a new data visualization tool with in-memory analytics will arise.

Want to learn more about Kenway’s experience with Business Intelligence and data visualization tools? Drop us a line at [email protected].

Lack of Insight = Increased Frustration

When work becomes frustrating, it can often become all consuming. We’ve all been there. Whether it’s a tight deadline, a difficult client, or a complex deliverable that isn’t going right, the frustration can take over and often lead to decreased productivity. A few years ago, that was the situation in my household.

My husband is in medical sales. Like most jobs, he is dependent on data to do his job effectively. At the time, his company did all their sales reporting in Excel. I could often hear him swearing at his computer, because the massive Excel document he needed to access would not open. When it did open, it was extremely slow to navigate, the data was static and filled with errors, and it provided him with very little insight into what was happening in his territory and where he had opportunity for growth. This lack of insight was causing him great frustration.

For those of you familiar with Kenway’s “Why” of “To Help and Be Helped,” you know that we are wired to jump in and solve problems. This scenario was no different. One night after dinner, I sat down with my husband, connected his massive Excel spreadsheet to my personal favorite data analytics platform, i.e. Qlik Sense, and built him some dashboards. Suddenly, he had the insight he was craving, and his frustration quickly turned to excitement.

He shared the prototype with his colleagues, his bosses, and his bosses’ boss. Eventually, my husband’s company asked Kenway to help them build a true reporting solution. Kenway used its iterative approach to Information Insight to help their Sales Organization do the following:

Define Requirements

Kenway spent time with the Sales Organization helping them understand what data was available to them, identify what types of information would provide them insight, and document a set of clear business requirements.

Data Cleansing

The Sales Organization received sales tracing from a variety of distributors of medical supplies. There was little to no consistency in how the distributors formatted the names and addresses of customers, making it nearly impossible to identify a “true customer” and their associated sales. Kenway helped develop a master data management solution to clean the data and create a mapping of “true customers” for the Sales Organization.

Business Intelligence

Using the defined business requirements and the newly cleansed data, Kenway built sales dashboards in an interactive business intelligence application using Qlik Sense to show a holistic view of sales, trends and opportunities.

Predictive Analytics

Kenway leveraged an external data set to define sales potential based on number of surgeries at hospitals across the country. This data was used to predict how much of each product a hospital would buy, if the hospital was buying solely from this Sales Organization. That data was married up with actual sales to help set a strategy and identify opportunities for growth.

After successfully using Qlik Sense for the past year, the Sales Organization was told that their entire company was being forced to migrate to Power BI. Not surprisingly, this concept of change made them uneasy. The level of insight the Qlik Sense application provided was extremely valuable and had led to increased sales. Would Power BI provide the same?

As discussed in our “Data Wars” blog post, there are plenty of business intelligence tools from which to choose, and they are not one size fits all. The key to success is understanding the business requirements and ensuring the data is well formed.

Because Kenway and this Sales Organization had taken the appropriate amount of time to document the business requirements and ensure the data was well formed, it was a fairly easy exercise to determine whether a migration from Qlik Sense to Power BI would meet all requirements. With proper change management, the team was able to smoothly migrate to Power BI and continue to get valuable insight from their new business intelligence tool.

Do you need help unleashing the power of your sales data, or determining which application is best to do it? Kenway can use its Information Insight expertise to help. Reach out to us at [email protected] to learn more.

Don’t Be Afraid of Your Data … ATTACK It!!

It’s no secret that the sheer size and availability of data, types of data and ways to access data have evolved considerably over the past several decades. Now, this evolution seems to be growing more rapidly than ever. Your company is deeply engrained in this transformation – data is everywhere!! In addition, everywhere you turn, there's a free online course or seminar discussing the endless jargon, data mining and visualization techniques at a contrived, theoretical level. You have to ask yourself, does anyone know where the rubber meets the road to actually harness this stuff to improve an actual business?

This can be very daunting and cause you to shy away from utilizing all the data at your fingertips to help analyze, investigate and ultimately solve many business problems that your company may be facing. At the heart of it, you may find yourself going down the path of fearing your data. Once the data continues to pile up, you could eventually find yourself extremely data rich but enormously information poor.

At Kenway, we have an entire capability devoted to data, aptly named “Information Insight”. We have a detailed methodology devoted to it:

Kenway uses this methodology to create meaningful, descriptive metrics and information that are used to bridge the gap between mysterious raw data and crucial business problems. End results may be the reduction of expenses, increases in revenue or even increased transparency into new insights that lead to the identification of other opportunities that otherwise would have remained hidden.

How did Kenway create this methodology? How do we continue to evolve and enhance this methodology? The answer lies at the core of Kenway and why we exist. Our culture is built on the maxim: “To Help and Be Helped”. Our employees stay true to this adage by being entrepreneurial in nature, highly motivated and enthusiastic in climbing the learning curve as it applies to mastering and applying new skills and techniques. We lead by example by practicing what we learn day in and day out.

Today, I am in my third year at Kenway. Year one, I decided to master the business intelligence tool Qlikview, and I immediately helped a long-standing client merge multiple data sources to glean new insights that were previously hidden. Year two, I tackled learning a multi-paradigm programming language, C#, and used this to take advantage of a 3^rd party vendor’s API (a set of routines, protocols, and tools for building software and applications) to extract data to form key performance indicators. Over the course of year three and heading into year four, I am developing and enhancing my knowledge in HTML, JavaScript and CSS. These skills will allow me to aid clients in such areas as building out SharePoint sites or pulling data and creating metrics that are viewed via portals and websites to deliver these newly found data insights to business users in ways that make it easier to make decisions.

Rolling back up to the company level, what this means is that at our core, we are always looking to evolve and grow, just as I did these past few years with my own skill set. As data and the tools used to work with it evolve, we evolve as well by continuing to learn and to implement our newfound knowledge. At Kenway, we never fear the data. In essence, we are eager to engage the data and attack it at every angle, so our clients gain the most valuable insights and benefits. If you’re feeling data rich and information poor or would just like to exchange insights on other data methodologies or tools, let us know at [email protected].