See you at Techorama?


Why should you go to Techorama?

Techorama is a yearly international technology conference which takes place at Metropolis Antwerp. We welcome about 1500 attendees, a healthy mix between developers, IT Professionals, Data Professionals and SharePoint professionals.

I’m delighted to announce I’m speaking, and I’d like to take this opportunity to thank the Techorama team for all of their hard work and effort in putting on a great show.


First off, there will be a keynote by Scott Guthrie, EVP of Cloud + Enterprise, Microsoft Corporation – now this is BIG NEWS.

Scott Guthrie, EVP of Cloud + Enterprise, Microsoft Corporation will be keynoting at Techorama 2017 (May 23). In his keynote, “Azure, The Intelligent Cloud”, Scott will open the event with a strategic vision on the Microsoft cloud.

Scott Guthrie will also give another breakout session on May 23 which will be a Q & A session. Come with your questions!




The event itself will be top-notch content for Developers, IT Professionals, Data Professionals and SharePoint Professionals: 11 parallel breakout sessions with top speakers from all over the world: experts in their field, offering meaningful networking opportunities with partners and like-minded people

There will also be a unique conference experience in a movie theatre with lots of surprises!

What will I be talking about? You can find out more here at my dedicated Techorama page.

Data Visualisation Lies and How to Spot them

During the acrimonious US election, both sides used a combination of cherry-picked polls and misleading data visualization to paint different pictures with data. In this session, we will use a range of Microsoft Power BI and SSRS technologies in order to examine how people can mislead with data and how to fix it. We will also look at best practices with data visualisation. We will examine the data with Microsoft SSRS and Power BI so that you can see the differences and similarities in these reporting tools when selecting your own Data Visualisation toolkit.

Whether you are a Trump supporter, a Clinton supporter or you don’t really care, join this session to spot data lies better in order to make up your own mind.

We hope to welcome you at Techorama 2017!


PASS Business Analytics Day, Jan 11, Chicago


PASS’ first Business Analytics Day, which will be held in Chicago on January 11, 2017. You can choose one of two full-day, in-depth sessions for $595: In-Database Analytics with R and SQL Server 2016 and Mastering Power BI Solutions.

These are unique learning opportunities to get more advanced in R or data visualization with Power BI. And as with other PASS events, the goal is to allow you to walk away with real-world analytics knowledge that you can use immediately!

PASS Business Analytics Day

You have two great choices: In-Database Analytics with R and SQL Server 2016 and Mastering Power BI Solutions.

In-Database Analytics with R and SQL Server 2016

With Microsoft SQL Server 2016, data scientists can run in-database analytics using R. This is a “best of both worlds” scenario: delegate database management to SQL Server whilst you create analytics and visualisations in R and Power BI. In this session, we will cover the overall architecture of SQL R Services and go over some best practices. We will look at best practices in analytics and visualisations with a focus on R, and then we delve more in-depth into some practical common use-cases.

David Smith, R Community Lead at Revolution Analytics, a Microsoft Company
Seth Mottaghinejad, Data Scientist, Microsoft

Mastering Power BI Solutions

In this Power BI hands-on Workshop, you will master the “power” of Power BI. Learn to use self-service and enterprise-scale Power BI capabilities; gain valuable skills to integrate, wrangle, shape and visualize data for analysis. Beginning and intermediate level users will learn to address data and reporting challenges with advanced design techniques.

Paul Turley, Mentor with SolidQ, BI Architect, and Microsoft Data Platform MVP

Date: January 11, 2017

Location: Microsoft Technology Center, #200 – 200 East Randolph Drive, Chicago, IL.

We hope you’ll join us!

PASS BA Visual Data Storytelling precon session with Mico Yuk

CEO of BI Brainz | Author | Global Keynote Speaker | BI Influencer | Trainer | Blogger| BI Executive Advisor *@micoyuk*

I am super excited that Mico Yuk is joining us again at PASS BA Conference!

aaeaaqaaaaaaaasjaaaajdfkmzdiywy2lwizmtktnduxyy05zmfilwy1owyzodgyyjzmzaMico Yuk is well known in the Business Intelligence ecosystem as a community leader, BI influencer, controversial blogger, and the founder of the highly rated BI Coaching Series,
the BI Dashboard Formula (aka BIDF). Headquartered in Atlanta, GA, her team of senior coaches and consultants work with Executives to transform their BI teams to meet the challenges in the new era of BI through a series of coaching, training, and consulting services.Mico’s most recent accomplishments include being named one of the Top 50 Analytics Bloggers by SAP and being rated a #1 global keynote speaker at a number of global BI conferences.

A computer engineer by degree, she has been designing and implementing enterprise dashboards for major corporate clients since 2006 and is considered to be one of the top data visualization experts in the world.

First as a consultant and now through her company, she has helped to implement executive dashboard and reporting using the SAP BusinessObjects platform for customers such as Allstate, Pfizer, Aviva Canada, McKesson, Ryder Logistics, Digicel Jamaica, QatarGas, St. Jude Medical, Walgreens, Chiquita, LG, the US Airforce, Medtronics, SAP Global Marketing, Amtrak, Fresh Direct, Bank of America, and Nestle, to name a few.

To find out more about Mico, please visit

Visual Storytelling – How to Tell a “Compelling” Data Story That Matters to Your Users

This business-oriented, hands-on session will provide the foundation necessary to make your data visualizations more intelligent, actionable, and useful! Whether you are a beginner or a data visualization veteran, this session will guide you on telling more compelling stories with your data, from storyboarding fundamentals to more advanced techniques such as how to add smart context and visual cues. Attendees will learn:

• how to create a simple four-part visual storyboard on paper in minutes, not weeks
why visual storytelling is more effective than traditional reporting
• the one element 98% of data visualizations are missing and how it is negatively affecting user adoption

Format: Half-Day Classroom (Afternoon)

Register here


PASS BA Header Mico


My Kibana presentation slide deck from SQLBits

Here are the slides from my first SQLBits XIV session at the Excel in London, March 2015.

I presented with Allan Mitchell on the topic of ElasticSearch and Kibana. I did the Kibana section and talked around the topic of data visualisation, and the slides are here. I whizzed through these first, before giving a demo. I’ll post up the SQLBits video as soon as I have it.

Visualising Data with R: HTMLWidgets for R

I’m interested in analyzing and visualizing data with R, as you know. Thanks to Jen Underwood, she pointed me at this cool series of widgets, called HTMLWidgets, that allows you to do some interesting visualisations with R. I must admit I haven’t touched JavaScript for years, ever since I helped to build websites for well known entertainment sites, but I’m glad I can pick it up again. What does it let you do?

  • Use JavaScript visualization libraries at the R console, just like plots
  • Embed widgets in R Markdown documents and Shiny web applications
  • Develop new widgets using a framework that seamlessly bridges R and JavaScript

Why don’t you head on over to the site and have a look? I’ll be playing with it, and finally finishing my R blog series, as soon as I can. I’ve been busy helping to organize the PASS BA Conference, which is taking place in April 2015 in Santa Clara. Therefore, I hope you’ll excuse the radio silence. It’s on my to-do list, which never goes down, ever!

Day 6: The Data Analysts Toolkit: Why are Excel and R useful together, and how do we connect them?
Why is analytics interesting? Well, companies are starting to view it as profitable. For example, McKinsey showed analytics was worth 100Bn today, and estimated to be over 320Bn by 2020.
When I speak to customers, this is the ‘end goal’ – they want to use their data in order to analyse and predict what their customers are saying to them. However, it seems that folks can be a bit vague on what predictive modelling actually is.

I think that this is why Power BI and Excel are a good mix together. It makes concepts like Predictive Modelling accessible, after a bit of a learning curve. Excel is accessible and user-friendly, and we can enhance our stats delivery using R as well as Excel.

One area of interest is Predictive Modelling. This is the process of using a statistical or model to predict the value of a target variable. What does this actually mean?  Predictive modelling is where we work to the predict values in new data, rather than trying to explain an existing data set. To do this, we work with variables. By their nature, these vary; if they didn’t, they would be called a constant.

One pioneer was Francis Galton, who was a bit of an Indiana Jones in his day.  Although he wrote in the 19th century, his work is considered good and clear enough to read today. Therefore, this research has a long lineage, although it seems to be a new thing. We will start with the simplest: linear regression.

Linear regression compares two variables x and y to answer the question, “How does y change with x?” For predictive modelling, we start out with what are known as ‘predictor variables’; in terms of this question, this would be x. The result is called the target variable. In this question, this would be y. Why would we do this?

  • Machine Learning
  • Statistics
  • Programming with Software
  • Programming with Data 
  • Fun!

Why would businesses work with it at all?

  • to discover new knowledge and patterns in the data
  • to improve business results 
  • to deliver better customised services

If we have only one predictor variable and the response and the predictor variable have a linear relationship, the data can be analyzed with a simple linear model. When there is more than one predictor variable, we would use multiple regression. In this case, our question would be: , “How does y change with multiple x?” 

In fitting statistical models in which some variables are used to predict others, we want to find is that the x and y variables do not vary independently of each other, but that they tend to vary together. We hope to find that y is varying as a straight-line function of x.

If we were to visualise the data, we would hope to find a pleasing line chart which shows y and x  relating to each other in a straight line, with a minimal amount of ‘noise’ in the chart. Visualising the data means that the relationship is very clear; analysing the data means that the data itself is robust and it has been checked.

I think that’s why, in practice, Power BI, Excel and R work well together. R has got some great visualisations, but people are very comfortable with Excel for visualisations. All that loading packages stuff you have to do in R… it doesn’t work for everyone. So we use R and Excel, at a high level, as follows:

  • We cleanse and prepare data with Excel or Power Query
  • We use RODBC to load data into R
  • We analyse and verify the data in R
  • We build models in R
  • We load the data back into Excel using RODBC
  • We visualise the data for results

Excel is, after all, one of the world’s most successful software applications ever, with reputedly over one billion users. Using them both together means that you get the best of both words: R for analysis and model building: Excel is the ‘default’ for munging data around, and visualising it. I’m sure that one of the most popular buttons on software such as Tableau, QlikView et al is the ‘Export to Excel’ or ‘Export to CSV’ functionality. I’d be interested to know in what people think about that!

Building linear regression models in R is very simple; in our next session, we will look at how to do that, and then how to visualise it in Excel. Doing all this is easier than you think, and I will show you how.