Advancements within BI/Analytics in 2015
I was asked by the Norwegian BI blog biblogg.no to write a post for their February issue. They asked me the following four questions:
In your opinion, what was the most important advancement within BI/Analytics in 2015?
In your opinion, what will be the most important advancement within BI/Analytics in 2016?
People have been talking about big data for a while now. Do you see big data projects becoming mainstream in 2016? Why/why not?
What new buzzword within BI/Analytics will we see in 2016?
In this post, I will cover my answer to the first question.
Disclaimer: As Rehfeld is a Microsoft Gold partner in BI and Data platform, I work primarily with technologies from or closely related to the Microsoft BI technology stack. I am the product manager of Effektor, a self-service data warehouse and BI platform based on this technology stack, and a principal consultant working with data warehouse requirements, capacity planning and data science projects. My answers are therefore biased towards technology and uses of technology for BI/Analytics projects, mainly within or around the Microsoft BI technology stack.
What was the most important advancement within BI/Analytics in 2015?
I see three important advancements, which I will elaborate on below:
Microsoft PowerBI gaining momentum
Microsofts acquisition of Datazen and RevolutionR
The rise in the interest and market of data warehouse automation software
It seems to me that the massive work that Microsoft has put into their self-service BI suite PowerBI is finally paying off. The latest Gartner analysis places Microsoft as a leader in the BI and Analytics Magic Quadrant, finally beating other products like Qlik and Tableau. As part of Microsofts open source strategy, they have opened up for the public to add new visualization components. This is in my opinion a genius move from Microsoft. Web based data visualization techniques and frameworks develop so quickly these years that any visualization software is doomed to lack behind due to the time it takes to include and test new technology to a product.
Read more about PowerBi here:
Read more about (and contribute to) the open source PowerBi plugins here: https://github.com/Microsoft/PowerBI-visuals
Within the area of BI and analytics, Microsoft did two very interesting acquisitions in the spring of 2015: Datazen and RevolutionR. Datazen was on its way to become a replacement for Reporting Services, the reporting component in SQL Server, which has been in the product for more than 15 years. The biggest strength of Datazen is its strong focus on support for all mobile platforms (phones and tablets). RevolutionR is an enterprise-ready version of R, the world’s most popular programming language for statistical computing, data science and predictive analytics. The language has replaced statistical software packages like SPSS or SAS in university courses world-wide. The version from RevolutionR is faster and more scalable than the available open source version, and can therefor used in large data warehouses and/or Hadoop systems. We should expect to see both products integrated into the upcoming release of SQL Server 2016.
Read more about Datazen in SQL Server 2016 here: https://blogs.technet.microsoft.com/dataplatforminsider/2015/10/29/microsoft-business-intelligence-our-reporting-roadmap/
Read more about RevolutionR (branded under the new name SQL Server R Services) in SQL Server 2016 here:
Finally, I have seen a rise in the interest and market of data warehouse automation software and frameworks. Demos and use-cases for the developer-centric product like Mist for the almost de-facto standard Biml (Busienss Intelligence Markup Language) are now shown at all the SQL Server conferences world-wide, where I attend as a speaker. I spend a good deal of my time talking about data warehouse requirements with clients, and the topic of automation comes up more and more. Instead of outsourcing development of data warehouse and ETL work, companies now want to do smart in-sourcing (something we with our product Effektor also calls self-service data warehousing), using automation tools like Mist, Effektor, TimeExtender and WhereScape.
Read more about Biml her:
Read more about TimeExtender here:
Read more about WhereScape here:
Read more about Effektor here: