Thursday, March 26, 2015

People insights– data driven insights regarding people

Whereas marketing and sales as well as financial departments have been using advanced analytics for quite a while, it seems that HR is still in one of the early maturity phases of analytics usage. This  is a view which seemed to be shared by CEOs. In a recent study CEOs gave their HR department a 5.9 (out of 10) for their analytical skills.  (See CEO niet overtuigd van analytische skills HR )

Whereas HR controls a lot of data (and needs to keep it up to date) it does not seem to be able to use this data to provide strategic advise to the board of directors. HR can only deliver truly added value by providing data-driven insights regarding people that are both compelling to business leaders and actionable by HR. This is a view which is also quite nicely outlined by consultancy firm Inostix in their HR Analytics Value Pyramid (See The HR Analytics Value Pyramid (Part 3) ). To make sure that HR team stays current and viable, they will need to adopt a whole need set of skills of which analytics is just one (See The reskilled HR team – transform HR professionals into skilled business consultants  and the capability gap across the 2015 Human Capital Trends)

In a number of upcoming posts I will delve a little deeper into this topic and will show some practical examples of how you can realize some quick wins without a huge upfront investment.

Related links:

SharePoint Saturday 2015 : How to build your own Delve, combining machine learning, big data and SharePoint

BIWUG is organizing the fifth edition of SharePoint Saturday Belgium – this year in Antwerp – for more information check out the site . Here is the excerpt of the session I will be delivering.

How to build your own Delve: combining machine learning, big data and SharePoint

You are experiencing the benefits of machine learning everyday through product recommendations on Amazon &, credit card fraud prevention, etc… So how can we leverage machine learning together with SharePoint and Yammer. We will first look into the fundamentals of machine learning and big data solutions and next we will explore how we can combine tools such as Windows Azure HDInsight, R, Azure Machine Learning to extend and support collaboration and content management scenarios within your organization.

Related posts:

Wednesday, March 04, 2015

BIWUG session on advanced integration between SharePoint Online and Yammer

On the 19th of March BIWUG ( is organizing its next session – don’t forget to register for BIWUG1903 – we have planned a great speaker and an interesting session

Advanced integration between SharePoint Online and Yammer using Yammer Apps (Speaker: Stephane Eyskens, SharePoint Technical Architect - )

First things first, the session will start describing what are the required steps to bind an Office 365 Tenant with an Enteprise Domain, how to federate on-premises users with Office 365 in order to have a SSO in place and how to bind Yammer to the Office 365 Tenant. Next, developers will learn how to leverage the Yammer App Model in order to build deeper integration between SPO(+on-prem) and Yammer. Business scenarios such as leveraging Yammer's Open Graph in SPO Workflows and associating Yammer Groups to SPO Team sites (& groups) will be covered. Security aspects will be discussed as well : from acting on behalf of a user with his consent to impersonating it completely, we'll see how to manage tokens and discuss some best practices.

Intended audience: The session is primarily intended for developers.

Key benefits: After this session, developers should have a good visibility on how to go beyond the OOTB Yammer App integration with
SPO and what Open Graph is all about.

Also thanks to Xylos for hosting this session

Monday, March 02, 2015

Resetting content index in SharePoint Server 2013: why and how

When you are developing against SharePoint Server 2013 search, you might forced to reset the search index. You can do this using the SharePoint user interface through the screen shown below or using PowerShell. I prefer to use PowerShell since resetting through the user interface seems to give me timeouts especially when the index is a quite large. One of the reasons why you are required to reset your content index is when your Search Service Application got into an unhealthy state because of insufficient disk space (See Fixing the Search Service after the Index Drive fills) but I also noticed that when you are working on your development machine and are making lots of changes to the search schema – it might also be useful to reset the search index for your changes to be picked up. If you want to change it using the user interface go to the Search Administration screen of the Search Service Application and select the “Index Reset” option underneath the crawling section of the left menu.

Don’t just reset your search index in a production environment since this will also impact the analytics processing component (Read Reset the index in SharePoint Server 2013). Listed below is the syntax for the PowerShell command (the snippet below assumes that you only have one SearchServiceApplication)


The SearchServiceApplication.Reset method takes two parameters -  public void Reset(    bool disableAlerts,   bool ignoreUnreachableServer) – I would recommend always setting disableAlerts to true if necessary. The value for the second parameter will depend on your specific case. If you also get a timeout when using the PowerShell cmdlet – you can use the steps outlined in SharePoint 2013 Content Index Reset Timeout – they worked for me.

Friday, February 13, 2015

Mindful apps – putting people at the center supported by data

When preparing for my session The future of business process apps – a Microsoft perspective  last year I got inspired by this great article The future of enterprise apps: moving beyond workflows to mindflows – which introduced the concept of mindful apps. The core message is that if we want to automate the last mile we have to analyze how people work day in and day out and start our system/application design with people at the center. One of the quotes which is mentioned in the article is from Bill Murphy (CTO of Blackstone one of the largest investment funds worldwide) – “We aim to take away as much of the stress as possible from easy stuff, by automating the routine and mundane actions, and give users more time to focus on the higher-end pieces of what they need to do.”

Most of the characteristics which are outlined in the comparison between traditional and mindful apps are not revolutionary (See table above) but there is one one important key message.
Mindful apps will allow us to assess and compare options in decision context, they will allow us to quickly respond to events and make the best decision given a specific context and will provide us with “extended intelligence” by understanding and recognizing patterns within the data at hand. We as humans are good at problem solving, pattern recognition, identifying outliers, making creative leaps and incorporating new information when making decisions. We should be able to focus on these high end tasks by being freed from laborious and menial tasks which can be automated.

There are 3 different trends which will impact how these mindful apps will be shaped:
  • User context matters – make it personal. When we make decisions or work within the context of specific processes, there are a lot of parameters which determine how we react or how we make decisions – these parameters should be integrated into the decision framework driving mindful apps. Our calendar, availability of colleagues to reach out to, input from communications (using e-mail, messaging or other formats), information that we capture from blogs, social networks such as LinkedIn or open data sources together with available information within your organization should be filtered and at your fingertips. Machine learning and cognitive algorithms will drive the second machine age (a term coined by Brynolfson from MIT) but we are only at the start of how these algorithms can drive the future workplace for information workers.
  • Mobile shapes our expectations.  Mobile apps and the user experience they provide is shaping at how we see an ideal enterprise application as well. Mindful apps should strive to combine beauty, simplicity and purpose to create an experience that delights us and that is effortless to use. Mobile apps are easy to understand, when people use a good app for the first time, they intuitively grasp the most important features, why can’t we do the same for enterprise apps. Simplicity rules. The apps should also incorporate necessary logic to evolve as the user grows more comfortable with its use and is exploring more advanced functionality. Apps should learn people’s preferences over time and show the interface which is best suited for the task at hand.
  • (Big) data and advanced analytics are the driving force. There is a lot of hype and confusion around the term Big Data but one thing is for sure – storage costs and processing cost have dropped significantly in the last decade. When you combine this with the rise of new storage platforms such as Hadoop, NoSQL datastores  such as HBase, Cassandra, etc … and new data processing frameworks such as Apache Drill, Dremel, Spark, etc..  new opportunities arise to support users in their decision making processes. While there is a lot of emphasis on the 4 Vs (Volume, Velocity, Variety and Veracity) – there is one more V that you have to think about that is Value (Also see  Big Data beyond the hype, getting to the V that really matters)
  • Cloud will lead the way.  A lot of the innovation which will enable this next generation of apps is coming out of the datacenters of Google, Amazon, LinkedIn, Microsoft, Yahoo, etc… but most organizations don’t have the available capacity (nor the same financial resources) as these internet giants. Luckily the economies of scales which are offered by the cloud allows solution providers to provide you with a data infrastructure which can scale from prototype size to production environments able to handle huge amounts of data. The different major cloud players – IBM, Microsoft, Amazon and Google all seem to make big bets in building out the data analytics platform of the future and this competition will drive prices further down. This competition  will also force them to focus on more innovative solutions which allow them to differentiate from the competition.
The best examples where we – as a consumer - see the power of Big Data, Analytics, Machine Learning and the cloud appear is mobile. The three major players (Microsoft, Apple and Google) are relying quite heavily on the cloud computing power and huge data stores to provide the experience of digital assistants. Microsoft is currently working on Cortana (which has been released in a number of countries worldwide), Apple was definitely the trendsetter with Siri and Google has Google Now.

The future is already here — it's just not very evenly distributed. (William Gibson)

Thursday, February 05, 2015

Microsoft Azure Machine Learning–the power to predict

Microsoft Azure Machine Learning provides Machine Learning as a Service (on Microsoft Azure) and allows you to make your own applications more intelligent. Microsoft Azure Machine Learning was initially started as as an incubation project in Microsoft Research (codename Passau) and is part of the overall Microsoft Data Platform.
The best definition for Machine Learning – in my opinion – is from the excellent book “Introduction to Machine Learning (MIT Press 2014, Ethem Alpaydin)” (Use it as a reference – this is not an easy “how to” book)
The goal of machine learning is to program computers to use example data or past experience to solve a given problem.
In general when we want to solve a problem on a computer, we need an algorithm to transform using a set of instruction into an output. Unfortunately for some problems we do not know how to program such algorithms – such as for e-mail spam detection or predicting customer behavior. In most cases we have the input and output available e.g. a set of e-mails for which some are marked as spam. Based on this data, we would like a computer (or machine) to automatically extract the algorithm necessary to perform the classification. The algorithm does not need to be perfect but needs to be a good and useful approximation.
The term machine learning is tightly coupled to the domain of analytics (or data science – see Data Scientist: the sexiest job of the 21st century ). Analytics is concerned with the discovery and extraction of useful business patterns or mathematical decision models from a specific data set. For this a number techniques can be used, depending on the practitioners background they will probably favor a technique from their respective domain:
  • Regression, General Linear Models (GLMS), decision trees, etc … (originated out of the statistics domain)
  • Machine learning algorithms such as support vector machines, neural networks, Bayesian methods, … (originated out of the computer science domain)
If we focus specifically on machine learning we make a distinction between supervised learning where we try to find a mapping between a set of input variables and a specific output variable using a set of values to train a specific model and unsupervised learning where we try to find patterns in the input data.

But why should you care about machine learning? I think the picture below shows you how the focus is shifting from traditional reporting (hindsight) to more advanced predictive and prescriptive analytics (foresight) which will provide business with more added value but also requires business intelligence specialist new competencies such as machine learning and data mining. Examples across industries vary but in general predictive analytics has the potential to change the way how businesses make decisions (I will take a look a more in depth definitely pick up Predictive Analytics – The power to predict who will click, buy, lie or die from Eric Siegel)

Microsoft Azure Machine Learning distinguishes itself from other platforms and tools by a number of different characteristics:
  • Allows you to jointly build predictive models from anywhere in the world using only a web browser by making use of visual composition canvas  (called Machine Learning Studio) using modules without requiring you to write code (although you can use R code snippets if you want). You can start quickly from existing sample experiments/models or you can share your own data experiments.
  • Collaborative work together with anyone from anywhere using just your browser.
  • Available as a cloud service, eliminating upfront costs fro hardware resources.
  • The different modules allow you to author an end-to-end machine learning workflow starting with reading data, to training and validating your predictive model.
  • Ability to deploy models as web services. You can quickly operationalize your models by converting them into web services and you even the ability to monetize your machine learning models using Azure Data Market.
The start location is the Microsoft Azure Machine Learning homepage - which contains a number of user guides as well as training videos - . Another great way to get started is by looking at the different Azure Machine Learning Samples - such as Azure Machine Learning Sample: Credit risk prediction (predict whether an applicant is a good credit risk based on the German Credit Card UCI dataset) and a clustering algorithm to identify similar companies from companies in the S&P 500, using text in published Wikipedia articles for  these companies.


Wednesday, January 21, 2015

SharePoint Saturday Belgium 2015– Call for speakers

On April 18th 2015 BIWUG ( is organizing its fifth edition of SharePoint Saturday Belgium. We invite you to submit a session  for this year's SharePoint Saturday Belgium using this link  - . It is possible to submit multiple sessions. We will close the call for speakers on February 18th EOD.

SharePoint Saturday Belgium 2015 will take place in Antwerp – for more details check out  If you have any questions or remarks, do not hesitate to contact me.