Dieter Rams is a German industrial designer most closely associated with the minimalist designs of the consumer brand Braun. Dieter was head of design at Braun for over 30 years, where he became famous for creating an austere aesthetic while focusing on user-friendliness. His philosophy is summed up in his saying, “Weniger, aber besser.” which translates into “Less, but better.” He has won many awards through the years including the World Design Medal and the Ikea Prize.
Dieter's impact reaches beyond his retirement as he is now impacting design in the 21st century, with a company widely considered a leader in technology design, Apple, acknowledging a debt to Dieter as inspiration for many of their design decisions. The Head of Design at Apple wrote, “Rams's work is beyond improvement... Rams's ability to bring form to a product so that it clearly, concisely and immediately communicates its meaning is remarkable.”
Ten principles of good design
As a prolific designer, Dieter formulated ten principles of good design. In this series of articles, I will adapt several of these principles for guidance in creating good analytics. I have selected the sixth principle of good design as the one I consider most important for good analytics.
The sixth principle of good design
6. Is honest - It does not make a product appear more innovative, powerful or valuable than it really is. It does not attempt to manipulate the consumer with promises that cannot be kept.
We’ve taught entire courses on how to design and build dashboards in Tableau over multiple days, so the topic can be quite complex. In this section, you will learn the basic functionality so you can get started. For more advanced dashboards, visit www.Freakalytics.com/examples.
VP of Sales at a cheese maker that sells to the public and to gourmet retailers
Overall Objective of Dashboard Sales updates for monthly review by Sales Vice President (VP)
The Sales VP has four questions:
1. What are sales by state?
2. What were sales by customer contact method in 2013 compared to 2012?
3. What are the actual sales by item versus the target sales?
4. What are the actual sales by customer contact method versus the target sales?
Included in the workbook: • Data source contains two years of sales data for 2012 and 2013. • Four worksheets with views answering the four questions above. • The final dashboard is illustrated at the end of this appendix.
Build the dashboard Open the workbook, and in any one of the worksheets, on the main menu, select Dashboard → New dashboard. In the Dashboard pane on the left side, double-click on each of the four worksheets in the numbered order. Also in the Dashboard pane, in the Dashboard Size section at the bottom, change Desktop to Automatic so the 4 views fit within the workspace. The worksheets are arranged in the order added and the legends for Q1 and Q2 are on the right.
Dashboards are comprised of containers that contain the views, legends and filters and are outlined by solid lines when you click on them. First, move the legends to the left side of the dashboard. Continue reading →
Do you have data with just first names or even just first initials but no information on the person's gender/sex? If you would like better insights on your customers, based on whether they are likely male or female, then this data download is a great way to maximize your ROI! Download it today and begin using it to tailor your messaging and improve future communications.
There are three licenses available for this data- individual, corporate and corporate for multi-company consumers. The individual version is available free (with discount code) for a limited time. Simply select the Individual license for purchase and use discount code discfreepers at the checkout page- this will deduct $3.99 from your purchase price.
The primary table in this data download is First names by Freakalytics with 5164 rows (distinct names and common misspellings). You can use this data to guess if someone is a male or female based on their first name or find the probability that they are male or female based on their first name.
Here is the column information and simple summaries for this table:
Name mixed case
Most likely gender
Count Either Gender
Male Probability Within
Female Probability Within
Name first initial
Name upper case
The top few rows from this table (as a snapshot of the data in Excel 2003 format and in text):
Eileen McDaniel, Ph.D. and Stephen McDaniel This article was originally published in late 2013 in
The Data Warehouse Institute FlashPoint Newsletter
Earlier this year, we presented this topic in a talk to an independent group of data professionals. When we noticed it was mistakenly promoted as “Accidental Analysts: What are they doing TO my data?”, we had to laugh! Unintentional typo or not, we’ve found that data warehousing specialists often wonder what businesspeople are doing on their end. Who are accidental analysts, how do they analyze data, and what aspects of the data warehouse can data professionals evaluate and improve upon so that they are set up for success instead of frustration?
Who are accidental analysts?
Many business analysts either lack formal education in data analysis or took courses that didn’t fully prepare them for the challenges of real-world data analysis. They are asked to quickly answer business questions so that managers, colleagues and clients are able to identify and implement a plan of action. After teaching analysts in many organizations from all skill levels and backgrounds, we discovered that a major obstacle to obtaining good results is that many are uncertain of the steps to take when analyzing their data. They need a plan of attack, regardless of the analysis software that they are using!
The Seven C’s of Data Analysis
The scientific method has been used by scientists for hundreds of years to design and analyze experiments. In our training and books, we adapted this method to fit business analysis,
January 26th, 2014 Stephen McDaniel Chief Data Officer Advisor at Freakalytics, LLC
Finding it hard to make time to keep up with the rapidly changing world of data, data warehousing, analytics, data science, business intelligence and visual analytics? We understand! Here’s a top new story worth reading and that we considered noteworthy enough to add commentary and analysis by Freakalytics (in purple). A summary of the article and excerpts that I comment on are in black.
In this commentary and analysis, we cover the growth of Tableau and QlikView, the opportunities that exist for Microsoft to disrupt the second-generation business intelligence market and how self-service data integration will likely make data scientists & data enthusiasts much more productive- enabling wide swathes of Accidental Analysts to quickly answer tactical business questions.
Five Business Intelligence Predictions For 2014 (from the CEO of Paxata) Summary
The dust is finally beginning to clear from the big data explosion, which is a good thing. One of the problems with big data is that it’s been led by technology, not business requirements. And business requirements will be the focus in the 2014 business intelligence (BI) ecosphere—to enable enterprises to achieve results with data mining and analytics and to prove those results.
Stephen I found this article a fascinating glimpse into the strategic thoughts of a CEO of a promising, second-generation, cloud-based data integration company- Paxata.
There are many ways of measuring the growth of business intelligence vendors. One approach of interest in the era of self-service analytics is to measure the growth in web search volume. Derived from web search volume data from Google, the following analyses can serve as a useful reference to understand which companies/products are growing in popularity and which may be falling out of favor.
The estimates in all of the following analyses are based on simple web search volume indices from the United States through the end of November, 2013. Using historic search volume data, forecasts were built for each company/product and growth rates for 2014 were derived from these forecasts.
I would group these companies into three categories
fast growers- Tableau, PowerPivot, Qlikview, BIRST and GoodData;
the growers- Spotfire and Microstrategy,
and mature products- Oracle BI, SAS, Cognos, SPSS, SQL Server, Actuate and Business Objects. Continue reading →
Thanks for your interest in our newsletter, please forward it to colleagues that may benefit from it. Please join us for our upcoming webinar next Monday on common analytic issues and mistakes in Tableau 8.
Eileen and I are excited to share our new courses on Tableau, Microstrategy Analytics Desktop (a free alternative for visual analytics and dashboards), SAS programming and data exploration and visualization are all available for on-site instruction.
We are booking engagements with clients for on-site training and strategic consulting projects throughout Q1 and into Q2 of 2014, please let us know if we can help you in 2014!
Data Management and Visual Analytics with Tableau (2 days)
January 28th-29th, 2014—Chicago, Illinois
Everyone can benefit from learning a reliable, flexible and repeatable method to analyze real-world data. Combine this with a solid grounding in the flow and core features of Tableau to achieve great returns with this course. In just two days, you will complete multiple real-world case studies with Tableau paired with supporting data management capabilities of Microsoft Excel and Microsoft Access.
Data Management and Visual Analytics with Microstrategy Analytics Desktop (2 days)
Microstrategy Analytics Desktop is an exciting new offering of Microstrategy, an established leader in business intelligence. Available as a free product for both personal and professional use, Freakalytics considers this new product a good alternative to other leading products in visual analytics and analytic dashboards. It is capable of working with local data sources such as Excel, text files and Access databases in addition to remote, big data sources such as SQL Server, Oracle, MySQL and Hadoop.
Understand them, avoid them and correct them
December 23rd, 2013 --- 11 AM Central, Noon Eastern, 9 AM Pacific, 5 PM London Click here to register
Synopsis In this webinar, Stephen reviews common shortcomings and misunderstandings that can prevent effective use of Tableau. These issues can result in misleading or just plain wrong answers being presented to your analysis audience, most without any warning messages or signs.
This webinar is relevant for all experience levels of Tableau users. Attendance at this webinar is free, compliments of Freakalytics and their upcoming, in-person Chicago Workshop in late January, Data Management and Visual Analytics with Tableau. This webinar is planned to run 90 minutes, with the presentation approximately 70 minutes in length and 20 minutes planned for Q&A. Please use an affiliated e-mail address to receive an attendance link.
Freakalytics offers two public and on-site training seminars on Tableau: Data Management and Visual Analytics with Tableau (2 days) and the original Complete Tableau Training (4 days), authored and taught exclusively by Freakalytics since 2009.
The Complete Tableau Training is the course that many early adopters of Tableau attended, both in public venues and on-site, and that Tableau endorsed in 2009, with Freakalytics as the founding Tableau Education Partner. Since Stephen left Tableau as Director of Analytics in 2013, we are now the world’s leading provider of independent Tableau training expertise, able to objectively train and advise clients on how best to utilize Tableau in conjunction with other leading data visualization, data storage and analytic products.
November 11th, 2013 Stephen McDaniel
Chief Data Officer Advisor at Freakalytics, LLC
Finding it hard to make time to keep up with the rapidly changing world of data, data warehousing, analytics, data science, business intelligence and visual analytics? We understand! Here's our curated summary of relevant news that could help with your future data and analytic projects. We also add commentary on the topic, a summary of the article and the link to read the full article.