Norbert Kulski | Business Intelligence | 24 March 2021
“Storytelling” is a catchy slogan. Stories are used in various areas of life, including data analysis. What is data storytelling in Business Intelligence?
Norbert Kulski: As with any slogan, there are many definitions. To put it simply, data storytelling is the art of communicating information in such a way that the recipient is not overwhelmed with the amount of data. Data storytelling means creating reports in such a way that the person using them perfectly understands what questions are being answered. Our role, as report creators, is to engage, grab users’ attention, and make the user interested in both the message itself and the conclusions resulting from it. Telling a story with data means presenting information in such a way that the recipient feels engaged, and doesn’t only feel that he is looking at raw tables which are not telling him anything.
Do companies know what story they want to tell with Business Intelligence tools?
On one hand, there are companies that know what data they have and which systems they come from. On the other hand – some companies lack this knowledge and have not used Business Intelligence systems before or do not know the capabilities of the tools they have. This is why, when implementing Business Intelligence solutions, we talk to everyone individually.
How to start a conversation about the implementation of Microsoft Power BI or another Business Intelligence tool?
I start with a question about the area that the company wants to explore and what it wants to present in the report. Thanks to the experience gathered in many areas, as Business Intelligence specialists, we can also show the effects which have been achieved in other companies. To do so, we present the capabilities of the tool or report templates. At companies that have no idea what they can achieve and which are at the data analysis stage, the opportunity to learn about the capabilities of the tool is very valuable because it directs the conversation. This is when the client says: “This looks good, we would like to have this kind of report!”
What happens next?
We start to think how to download the necessary data and from which systems. In enterprises that know what data they have and what they want to see in the reports, it may be simpler and, in particular, it is a matter of making a report attractive and easy to use.
What characterizes an attractive report?
Today, reporting tools have a number of functionalities that allow you to design a report in such a way as not only to keep the recipient interested, but even to increase his level of interest. These are, for example, mechanisms of moving from general to specific, conditional formatting, i.e. highlighting with color elements that meet certain rules. This way, using only the color, we inform the recipient: “This is OK” or on the contrary – “This is bad”. Business Intelligence tools make it possible to lay on trend lines or forecasts. There are prompting options and even automatic data analysis based on Machine Learning algorithms.
How does Artificial Intelligence and Machine Learning facilitate working with reports?
AI and ML are mechanisms that really facilitate working with reports! The Microsoft Power BI tool tries to understand which numbers or observations may be of interest to the recipient, on its own, with the use of algorithms. For example, the Quick Insights functionality makes it possible – this is an automated data set analysis. In the background, Power BI analyses our data set to present interesting conclusions, e.g. outliers. After publishing the data collected in the report, the question of whether we want to see Quick Insights appears on the powerbi.com website. For example, in the case of sales, QI can show us the best- and worst-selling products. Sometimes, thanks to Quick Insights, you can come across very interesting findings.
Despite an increased level of facilitation, many users avoid reports. What are the “deadly sins” in the visualizations?
First of all, information overload and conveying the message in an inconsistent way. If we don’t have a consistent idea of how to present information, but only fill the page with a number of unrelated visualizations, the user will lose track because they simply won’t know which data to look at. They will not be able to draw conclusions about what is right and what is wrong. Another common mistake: the person who creates the report wants to present the user with the entire data set without aggregating the data. Experts in a given field may benefit greatly when they have access to the entire data collection. But for others, however, you shouldn’t start by presenting everything at once, but instead use a top-down approach.
During the pandemic, there were many reports on COVID using aggressive colors.
The use of colors allows you to elicit emotions in the scope of data presentation. We highlight undesirable numbers in red, which is a clear signal to users that this is not the expected value. Our brain intuitively perceives colors this way. If someone builds a COVID report using aggressive colors, they hope to build emotions: fear or the belief that it is bad. Using colors also includes a series of practices, but you need to be consistent here. For example, if you use blue in the sales report for the sales value, you should use that color on all pages.
A Business Intelligence specialist should also be a UX Designer, or even a bit of a psychologist then?
Business Intelligence combines knowledge from different fields, including knowledge about how the human brain works. Therefore, Business Intelligence is a wide set of techniques, algorithms, and fields, including in the area of UX Design good practices. All this to convey information as efficiently as possible and to get the most out of it. Do you know why we shouldn’t use pie charts to present values?
It has been proven that the human brain is unable to compare and estimate the areas within a pie chart. If the pie chart is sorted from the largest to the smallest value, this is half the trouble because going clockwise, we move from the biggest to the smallest value. Without this, however, it would be very difficult to say straight away which part of the pie is the biggest. Secondly, it may turn out that there is one dominating value and the others become almost invisible – sometimes interesting conclusions are hidden there. Finally, if there are positive and negative values, it will be very difficult for us to show them at once on a single pie chart. There are visualization selection standards, one of which is the IBCS, which is most often used in accounting and has become more popular recently. It is worth reading about it to know which visualizations not to use according to this standard to avoid misleading recipients.
A link to the IBCS standard pointing to other visuals as alternatives to the pie chart.
What about self-service tools? Don’t we need to have all of this knowledge to use them?
The “self-service” slogan is very catchy. Behind it, there is a tool with great capabilities hidden behind a simple, and thereby accessible to everyone, interface. It’s true… or actually it is only part of the truth. With simple data, we can create a functional report quickly and easily. But the bigger the data model is and the more dependencies there are, the more difficult it is to work with a self-service tool. Underneath, almost invisible to the normal user, all the layers are hidden. They have a big impact on whether the report is user-friendly or not. The way we organize data collection and analytical models is important in terms of performance and the visual (meaning visible to everyone) form of the report. The modelling aspect is not always discussed in the case of slogans promoting self-service tools, and maybe this is why some users believe that Microsoft Power BI will do everything for them. In fact, the greater the amount of data and the scope of information, the more knowledge is needed to organize a data model appropriately.
Will Business Intelligence specialists still be needed?
Once I was conducting Microsoft Power BI training, where shifting the financial year was a problem for the user. When the user loaded a data set (it was a flat table: transaction number, value, normal date and added fiscal date), ignoring part of the modeling – he was reporting from only one table. He had difficulties in creating reports that would switch between the standard and fiscal calendar. We used a simple modification and created a dedicated calendar in the tool itself. This solved all the problems, and the measure or calculation code shrunk from A4 size to two lines. It was enough to know that, apart from the visual layer, there is also a modelling one, and a lot can be improved there.
Is it worth investing in Business Intelligence training?
I support the idea of Microsoft Power BI implementation because it is a very user-friendly tool. However, each implementation should include training as standard, as even 1-2 days of training is eye-opening in terms of the tool’s capabilities. It is only through training (regardless of whether it is Qlik, Tableau or Power BI) that we can say that the Business Intelligence tool has been implemented successfully. Working with a trainer creates an opportunity to ask questions, inquire about functionality and good practices in terms of solving typical problems with data in a given company.
Why there is such a great need for Business Intelligence training?
It sometimes turns out that users who participate in the training were told one day that they will be using Microsoft Power BI. However, despite using a modern tool, their problems with data are not over, because in fact they switched from Excel reports with all their defects to Power BI reports, and due to the lack of knowledge on how to use the tool, they gained little or nothing at all. Leaving users with the tool is not fair, because if we do not improve their level of knowledge, they will make exactly the same mistakes we wanted to eliminate by implementing a modern BI solution. If we do not provide them with this knowledge, they will act as they can, often in good faith, but nevertheless partially wasting the potential of the tool. When complications arise and more sophisticated reports are required, it is very difficult for users to act if they are left to themselves. There are plenty of training materials on the Internet, but this does not address the needs of business users. Moreover, after a few months of using the tool, the next stage of training is recommended. Only then can you see the capabilities of Business Intelligence tools and how many problems they help to solve.
Which industries are most interested in data analysis and BI implementations?
During the COVID-19 pandemic, we have observed a real explosion of interest in reporting systems, regardless of the industry. In March 2020, when the restrictions came into force, many companies without reporting systems were helpless. They didn’t know what tools to use to stay competitive or simply to survive. In turn, companies that already had a mature reporting system and trusted the data had more wiggle room and did not have to react impulsively. Having full control over the margin and the employment periods, they could make conscious decisions and quickly identify areas or elements that were unprofitable. With Business Intelligence tools you can react much better to the expected fall in revenues. Apart from the difficult circumstances at the present time, we receive inquiries about BI systems from both small companies, such as specialized online stores, and the biggest companies in Poland and abroad.
Did the pandemic and the need to control costs turn out to be a trigger for introducing the data-driven approach in organizations?
We can observe massive interest in reporting systems! Data is said to be “the world’s new oil”. But we also need to bear in mind that the data we do not use only generate costs – you pay for disk space, for ever-larger backups, or declining system performance. If we use data to analyze the past, or even better, to predict the future, we are in a privileged position as we join data-driven organizations.
2020 also brought 30% growth in the e-commerce industry. How does the e-commerce sector use Business Intelligence tools such as Power BI?
In fact, the e-commerce sector is a pioneer in using Business Intelligence systems and Machine Learning algorithms on a massive scale. In addition to the obvious examples, such as proposing the best-selling product to go with those that are already in the basket, e-commerce organizations can follow purchasing trends outside their platform, e.g. on social media. Let me illustrate this with an example that probably all of us have experienced: the increased interest in yeast in early March 2020. People didn’t know how long they would be locked down at home for, nor how dangerous the virus was. They believed that they would have to bake bread on their own, which resulted in an increase in demand, among other things, for flour and yeast (up to 300%!). I also learned how to bake sourdough. But by following, for example, social media, trends in search engines (people were looking for recipes for bread) or simply analyzing, a bit post factum, a sharp rise in demand in your system, it was possible to anticipate the competition and try to increase the inventory of the most desirable products. Those who had yeast in stock at the time could decide what the price was and who they sold it to.
How does it work?
Advanced Analytics tools are used here. This is a field that uses advanced techniques going beyond Business Intelligence, such as Machine Learning, Data Mining, or automated text analysis, including recognition of emotions. In marketing activities, by choosing target groups on social media, you can check, based on a small group of recipients, how a given campaign will be perceived before scaling it up. This increases its reach and effectiveness. We monitor the reception of the campaign, we have confirmation in unmoderated conversations, and the algorithms recognizing emotions in the text indicate: yes, it’s a positive review, or: no, the reception is negative, we should take a step back. The use of Advanced Analytics allows you to be at the forefront of companies benefiting from data analysis. By analyzing activity, for example on Twitter, we can inform decision-makers what issue is being discussed more often and suggest which area it is worth investing in.
What are the typical benefits of using analytical tools in e-commerce?
Improved demand planning, expedited decision-making processes, increased situational awareness or discovering trends and the effectiveness of marketing activities. Of course, predicting an event such as the COVID-19 pandemic and taking security measures against it is very difficult, because such events are rare, yet they have an extreme impact. In everyday life, analytical tools come in handy when it comes to developing sales, focusing on products that bring us the highest income and quickly eliminating products which do not bring profits. They also enable us to check the results of related offers of given products – e.g. offering product A with product B – meaning carrying out a basket analysis. These are activities that can significantly increase your income.
What is the most interesting project you have ever carried out and what problem has been solved thanks to the implementation of Power BI?
I love what I do and in every project, almost every day, I do something interesting, so I find it difficult to single out one most interesting project. Discovering Power BI and working with a tool that is developing so rapidly is very interesting. What I am most proud of is the moment when users stop using their own solutions which they have built because they did not have a reporting system which fulfilled their needs.
Which situations do you mean?
I mean so-called shadow reporting, informal reporting. It probably happens in every company, both in Poland and abroad. Somewhere in the background, apart from the official, sometimes inefficient reporting system, employees develop alternative reporting, very often using a tool they are familiar with, i.e. Excel. It is very laborious. In my view, this does not allow them to take advantage of the potential of people who are good analysts or experts in their fields and who could take care of data analysis, formulate hypotheses or draw conclusions to bring the company the real benefits. Instead, every day they struggle to obtain information about the results from yesterday or the previous month.
So how to eliminate shadow reporting?
When implementing solutions, we try to talk to all users so that shadow reporting is not needed. We build cross-sectional systems integrating data from many sources, even those that often could not be easily integrated a couple of years ago. Nowadays, tools have amazing capabilities in terms of transforming unstructured data and improving its quality. Finally, we can deliver a system that meets all needs, and this is when users say, “This is what we really need” and admit, “We don’t have to do it manually anymore”. In every project, there are situations when people say thank you and admit: “It is fantastic to have these new capabilities”.