We are on the IIEX Europe from 18.-19. February as an exhibitor in Amsterdam. The IIEX – Insight Innovation EXchange – is the premier fair for innovation in market research. Of course, we should not miss that, since the possibilities that DataLion offers at the press of a button for creating tables and reports – paired with KI algorithms – are certainly among the most exciting innovations in the industry right now.
The full program can be found here . We have our own exhibitor kiosk on site. As usually, there are the latest features in our software, our new DataLion brochure, postcards, adhesive tattoos < / a> and Lion . In addition, a nice raffle for anyone who is willing to stick our adhesive tattoo.
DataLion at the BigDataParis in Paris
From 11-12. March meet us as an exhibitor in Paris. We are part of the Startup Villages . With more than 17,000 visitors, BigDataParis is the central European trade fair for big data. You can still sign up for free here . We would be happy to meet you there!
Wir sind auf der IIEX Europe vom 18.-19. Februar als Aussteller in Amsterdam. Die IIEX – Insight Innovation EXchange – ist die wichtigste Messe für Innovationen in der Marktforschung. Da dürfen wir natürlich nicht fehlen, da die Möglichkeiten, die DataLion auf Knopfdruck zur Erstellung von Tabellen und Reports bietet – gepaart mit KI-Algorithmen – sicherlich zu den spannendsten Innovationen der Branche aktuell gehört.
Das komplette Programm kann hier nachgelesen werden. Wir sind mit einem eigenen Aussteller-Kiosk vor Ort vertreten. Es gibt am Stand wie immer die neuesten Features in unserer Software, unsere neue DataLion-Broschüre, Postkarten, Klebetattoos und Lion. Außerdem noch ein nettes Gewinnspiel für jeden, der sich unser Klebetattoo stechen lässt.
DataLion auf der BigDataParis in Paris
Vom 11-12. März treffen Sie uns als Aussteller in Paris. Wir sind Teil des Startup-Villages. Die BigDataParis ist mit mehr als 17.000 Besuchern die zentrale europäische Messe rund um das Thema Big Data. Noch können Sie sich als Besucher für die Messe kostenlos hier registrieren. Wir würden uns freuen Sie dort zu treffen!
Key performance indicators or KPIs are not just a big issue in most companies since the growing importance of business intelligence software. However, it is very controversial which KPIs actually lead to success. Which are superfluous? Which indicators are more misleading than help? Even dashboard solutions are an indispensable part of KPIs, as they are the only ones that develop their full potential here. Imagine you are still late in the office finishing your presentation for the next morning’s board meeting. That’s where it helps if you can easily pull up the real-time KPI dashboard in BI system.
On the one hand, the up-to-dateness and availability of the data can be continuously ensured by means of dashboards. On the other hand, the development of indicators over the course of time can also be presented in an attractive and supportive manner. Many KPIs perform their purpose much better, especially in marketing and market research, but also in management and sales. The sole use of static reports increasingly loses its right to exist in times of “real-time data”.
Targets are the foundation for every performance indicator
But which KPI should you choose for managing your business? This question is difficult to answer as the choice depends on several factors. It is worthwhile, however, to answer this question in principle for the company, as it simplifies internal discussions immensely. Otherwise, you waste a lot of time discussing different key figures, which may show different trends. Rather than better thinking about what the company needs now.
The right set of KPIs is essential
A key point in choosing adequate KPIs is the objective you pursue with your dashboard and the KPIs shown. A KPI should basically map what its business or a specific department wants to achieve, e.g. contacts with new customers (leads), reach or customer satisfaction. For example, sales operations should be supported in generating new customers as efficiently as possible. By contrast, marketing should achieve the highest possible return on investment (ROI) with regard to the use of the advertising budget. And in customer service, the number of complaints should settle to a low level.
Use of indicators is not an end in itself
Even this small discussion shows how different the goals of KPIs dashboards are. A key success factor is therefore that the use of indicators does not become an end in itself. The chosen KPIs must fit exactly to the objective and be able to represent the appropriate level of target achievement. Another mistake sometimes made is that the KPI chosen is the measure that is easily available or easiest to measure. An example would be e.g. use the number of demo account registries as KPI for leads, without checking if and how much the demo was actually used. In addition, it must be clear to all persons involved what the key figures and their characteristics mean and what blurriness is associated with the corresponding indicator. Even if one speaks frequently of hard numbers, you will be usually amazed, how much blur is also in seemingly “hard” controlling numbers.
Customized KPIs can make more sense
No matter which KPIs you choose: Make sure that the key figures are actually relevant for you and all stakeholders are involved. Sometimes a meaningful indicator only results from the combination of two key figures. And in most cases, individual KPIs make more sense than popular indicators such as the Net Promoter Score (NPS). Everyone benefits, but their informative value can still be low for your concrete goal achievement. In any case, it pays to invest time in planning and to coordinate this with all stakeholders. Another important success factor is the appropriate presentation of the key figures. By mapping past values in the form of progressions, KPI developments can be presented very effectively. Good dashboard solutions offer extensive design options and make positive and critical developments visible at a glance. For time series the database must be updated regularly. The best is a real-time connection to the database via an interface, if one exists. Such a link has the additional advantage that the cost of maintaining the dashboard in the future is very low. In general, the more up-to-date the KPI is, the greater the informative value.
KPI dashboards become even more effective if the possibility of filtering exists within the figures and gradients. Thus, the knowledge gain is often greater, if users can spend depending on the topic personalized KPI developments over certain periods, target groups, cost types, etc. Also the possibility to compare the filter selections usually brings a considerable added value. Last but not least, the motto “less is more” undoubtedly also applies to key performance indicators. A good dashboard, only features KPIs that are actually relevant to users. Even though there is no general rule on numbers, more than ten indicators and time series are more likely to confuse rather than achieve goals. Here are some examples of KPIs from business practice:
The net profit is the difference between revenues and total costs. It can be calculated for the entire company or even for individual products and is not only an important key figure in sales, but also a must in the management dashboard. It symbolizes the success or failure of the entire enterprise.
Being able to see graphically how much the company is growing (or even shrinking) in terms of its sales and revenues is an important support for decision-makers. Do processes have to be changed or optimized? This metric is also a must-have for businesses, but only if it’s always up-to-date. The revenue growth rates should be configured in the dashboard so that you can individually change the period for calculating the rate.
Customer acquisition costs (CAC)
Customer acquisition is a painful and expensive topic in many companies. Keeping an eye on the specific costs is also essential and says a lot about the long-term success of the company. Properly presented in a dashboard, acquisition costs regularly provide for wholesome surprises, as they are often underestimated.
Market research oriented KPIs
Customer Loyalty & CX
In customer-centric organizations, an up-to-date customer satisfaction KPI should be prominently placed on each employee’s dashboard. One challenge, however, is the meaningful measurement of customer satisfaction and its dimensions. Not only in large service departments representations in dashboards help to detect and eliminate grievances.
Net Promoter Score (NPS)
The Net Promotor Score (NPS) is a common measure of the likelihood of a customer recommending a company or brand. In some industries, this metric correlates with business success and is therefore standard on many market research dashboards.
Customer Lifetime Value (CLV)
Often used is the so-called Customer Lifetime Value (CLV). The CLV describes the value of a customer for a company, throughout the life of the customer relationship. What revenue is expected of the customer, what is his potential? By means of the CLV one can e.g. justify why you give different customers different care.
Just as important and challenging in measurement as customer satisfaction is the brand image. How well or badly is your brand perceived among your potential customers? The perception within the individual target groups plays a decisive role in purchasing decisions. With the help of a dashboard solution, you can identify important developments easily and early.
In the age of big data, business intelligence and artificial intelligence, data plays an increasingly important role for companies. Dashboards are now widely used, bringing data to the point and thus becoming an important basis for decision-making.
But the optimal visualization of data still is a challenge. With the following six tips you will be able to build a great dashboard and save time.
1. Have a clear goal in mind
What sounds trivial is a common reason for overburdened and meaningless dashboards. First of all, ask yourself the following questions:
What do you want to achieve with your dashboard?
Who is the target audience for the dashboard?
What data do you need to display to get your target audience attracted?
It makes a huge difference whether you create a dashboard for your finance department, consumer research or marketing team. Although the database may be the same, users’ interests and priorities can vary a lot. Market researchers usually want details and with many drill-down options, whereas marketing colleagues often have more interest in central KPIs. A dashboard for the management board usually needs a lower level of detail than a dashboard for special departments.
It’s better to create different versions of a dashboard instead of trying to make everyone happy with one view. In modern dashboard tools you can quickly and easily adapt existing visualizations and then offer different versions separately.
2. Create stories with your data
Data and results have a special impact when told as a story. Focus on the questions your target audience will have and visualize the data in a logical and thrilling order. A dashboard needs a leitmotif. The order of charts should be based on the interest of the target group.
How should the user proceed from result A to result B? What data must be shown first to set the context for the following data? Storytelling within reports and dashboards is a very powerful way to get your target audience attracted. Further useful may be e.g. appropriate headings, interpretations and explanations. Pictures can help to clarify your story.
3. Focus, focus and focus
A very common mistake derives from the idea to provide as much data and results as possible. Overloaded dashboards with unattractive information for the target person, have no right to exist. The added value of a good dashboard lies precisely in bringing important information to the point. Simplicity and clarity are king. And if the user wants more information, you may provide him with some drill-down options.
But usually, it’s much more difficult to get things straight to the point than to get lost in details. Unfortunately, this applies to dashboards as well as to all other forms of data visualization.
4. Not only bar plots: choose an appealing design
Undoubtedly one of the most important success factors of good dashboards is a clear and appealing design. First of all, it’s about choosing the right visualization: bar charts can be very useful, but there are many other types of graphs to present data. Depending on the topic and objective of the dashboard, it could also make sense to use pictures or text elements. In addition, the corporate identity should be clearly visible.
Don’t worry: you don’t have to reinvent the wheel, there are many templates and examples of good dashboards out there.
Even with dashboards, orient to visual standards that users are already familiar with. Use known icons and navigation elements. A good UX design is key for making the dashboard an important tool in the company. Therefore, test the dashboard to some users before going live. But don’t worry: you don’t need a complex usability test scenario. But go out and watch your target audience during use and take the feedback to improve the dashboard.
5. Let the user play
A major benefit of online dashboards is that they are interactive, not static as PowerPoint reports or Excel spreadsheets. Provide users with the opportunity to delve deeper into data by offering filters or different target groups to choose from. So users can customize the dashboard for their purposes and “play” with data. This interactivity increases the added value of a dashboard and ensures greater use.
Keep in mind, however, that the more possibilities of analysis and customization you provide, the more you have to train users beforehand. Otherwise there is a risk of mis-interpretation. Finding the right balance between too much and too little data in a dashboard is an art.
6. Choose the right dashboard tool
Using a good dashboard software the key factors mentioned above should be implemented easily. But there are also some technical requirements to consider. Of course, the dashboard should also work on a smartphone or tablet device. And ideally in all common browser versions. Since data volumes are getting bigger and the time of your target group ever shorter, loading times must also be as short as possible. Flexibility in data sources is another feature that can decide between success and failure.
It is also good if a dashboard offers export interfaces to Powerpoint and Excel, so that results can be quickly compiled for Executive Board / offline reports as needed. And last but not least: most data is sensitive. Data security should be guaranteed and data protection rules have to apply.
Datalion’s dashboard solution provides the perfect tool to build good and successful dashboards.
We are beyond thrilled that our CEO and founder Dr. Benedikt Köhler is on the list of the Top 50 Data Science Influencers. The list was published by Cognilytica in mid-August 2018. Let’s follow Benedikt on twitter: @furukama.
Experience more lion power – Embrace our new features!
There’s exciting news from DataLion: we released version 1.9 which includes numerous new features we’ve developed in close cooperation with clients & users. We have incorporated your feedback and suggestions gathered in the past months. Among new features like visualizations and export functions, we have also implemented several optimizations (e.g. loading time) and bug fixes.
In short, DataLion is faster, more flexible and more productive than ever. We hope you will love it!
Let’s have a closer look at some highlights:
Yes, more chart types!
Stacked column chart with option to display the mean or the sample size (Example):
Extended donut chart e. g. for the comparison of Key Performance Indicators (Example):
New exports, New interfaces! Let’s check out the new export functions
Export of a dashboard or a report with more than one dashboard and charts as PowerPoint or Excel document
Export of an excel report with multiple tabs in an individually defined format:
Overview of our new features
New chart type: stacked column chart with means and case numbers
New chart type: extended donut chart with means e.g. to compare KPI Values
Sort charts by mean value
Reverse order of stacked column chart segments
Top5, Top10 etc. for time series charts
Hide empty values and categories in time series charts
Separate filter dropdowns for each tab of a report
Export of wordclouds as image
Reports and Dashboards
Placeholder element in report/dashboard title
Read-only mode for read-only access profiles
Selected global filter remains across tabs
Automatic selection of comparison operators depending on the selected report filter
Individual dropdown-menus per dashboard
Adjustable precision of average values
Back-end definition of missing values
Set alignment and spacing in chart settings
Back-end configuration of pptx-exports and masters
Export of an entire report with multiple dashboards and charts as PPTX
Export of an entire report with multiple dashboards and charts as Excel
Export of an excel report in a pre-defined format
Metadata and import
Add of text fields via codebook
Automated incremental data update (e.g. backfill of last month, week)
Export of the codebook including special fields and text elements
Back-end function to delete and replace entire codebook
Various bug fixes and optimizations (load time, exports, imports)
In mid-June, numerous specialists and interested parties from agencies, institutes and companies got together in Munich in order to discuss the latest developments in digitization.
We were very much excited to be there as well: our CEO and founder Dr. Benedikt Köhler introduced the new world of data science. In a live example he demonstrated how one can use Foursquare to avoid being marked as a tourist when visiting an unknown new city.
The speeches and presentations where up-to-date and rather widespread thematically. The participants were presented with examples of the applications of AI and Blockchain, as well as Best and Worst Cases of Influencer Marketing.
Johannes Ceh´s keynote topped off the conference by highlighting the opportunities and risks of digitization. The networking afterwards lived up to the expectations and the speeches were discussed thoroughly at the After Show Party.
“Data is power”: by now an established fact. “Music is power”: a universal truth. So what do we get, when we creatively combine the plethora of available data on music with analyzing techniques and powerful software? We gain knowledge, insight and tremendous inspiration. We have gathered some really interesting and inspiring interactive and non-interactive visualizations on the evolution of music as well as on current distinctive trends. Enjoy!
Using data from the Billboard Top 100 Data 1958 – 2016, The Pudding´s Matt Daniels has visualized the evolution of music taste on a month-to-month basis over the years: every top 5 song from 1958 to 2016 in the U.S.. Headphones required – it´s a “soundalized” visualization!
Kaylin Pavlik, on the on the other hand, in her Blog post „50 Years of Pop Music” has used R and data from the Billboard Year-End Hot 100 to offer a more quantitative insight into the evolution of Pop from 1965 to 2015 in the U.S..
Colin Morris (again of The Pudding) is putting one hypothesis into question: Are Pop Lyrics Getting More Repetitive? . The author used the Lempel-Ziv algorithm to measure repetitive lyrics with compression. The higher the compressibility of a song, the more repetitive its lyrics are. So seems like Rihanna´s Lyrics aren’t as much of a poetry as Frank Sinatra´s used to be! Wait, we did see that coming, didn´t we?
On a more international level, Spotify and open source platform CARTO have created the “Musical Map of the World”. The interactive map is making use of Spotify´s data from cities all over the world to give them their special “musical character”. You want to know how Germany sounds like? Click on the country to listen to its distinctive music!
Brady Fowler of Decibels and Decimals is not a friend of classifying music through genres. So he used Spotify data and Python iGraph to visualize the connection between artists and their music. The beautiful graph is the outcome of clustering the artists into groups, based on the listening habits of Spotify users.
Did you find the visualizations as interesting as we did? What would you add on the list? We are looking forward to your input.
We are proud to be holding speeches in two events in just one week. It all starts on Monday, the 11th of June at the Travel Industry Club. Our CEO and founder Dr. Benedikt Köhler will be talking about how the travel industry can make use of smart data. The event is taking place at Schweiger´s Kochschule and starts at 06.30 pm. You can find more information here.
AI, AR/VR, Blockchain, Influencer Marketing, GDPR – all these current trends will be discussed during the “Digital Challenge 2018” on Thursday, the 14 th of June. DataLion is right in the middle of it, with Dr. Benedikt Köhler talking about Alien Data Science (no worries, E.T. will not be showing up ;)). The location is the Freiheizhalle, starting at 9 am. Read more about it here.
The best things in life aren’t things – but free books. At least if you want to spend the next few weeks to take your machine learning to another level.
We’ve selected five great books that help you to understand one important aspect in machine learning in a very profound way. Thanks to the Open Access initiative, all of these works are available for free:
The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman
If you need a refresher on your machine learning methods, ESL is the book to go to. From Lasso to boosted trees and ensemble learning – this beautiful typeset work covers all the bases for your professional life as a data scientist.
Convex Optimization by Stephen Boyd and Lieven Vandenberghe
If you have spend some years in machine learning, the probability is very high, that you’ve stumbled upon convex optimization problems. The theory and methods around convex optimization has been around a long time. But until a few decades, they were thought to be mostly of theoretical value. Today, convex optimization is e.g. an important part of Deep Learning and many smart things around are powered by these algorithms.
Group Representations in Probability and Statistics by Persi Diaconis
This book goes back to Diaconis’ lecture notes for his course on this topic at Harvard in the 1980s. There are a lot of situations where data scientists have to deal with rankings, e.g. consumers having to rank products in a survey. These mathematical problems can be solved by applying group theory.
But wait, there’s more: As Diaconis is also a magician, the shuffling of cards also plays quite a role in this work.
Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman and Jeff Ullman
And finally, for something more accessible: This book is already a classic on big data techniques for processing large data sets. If you haven’t already, you really take a look at the 2nd edition of the book that also includes mining large graphs and map-reduce programming. You can also take a look at the first chapters of the upcoming 3rd edition.