How Are You Going To Get From Business Intelligence to Business Analytics?
Business Intelligence (BI), which like government intelligence sounds faintly like an oxymoron, has been around a long time. The earliest reference to business intelligence appeared in 1865, according to a Wikipedia article. In more modern times, the term started to appear at IBM in the late 1950s but Gartner is quoted as saying BI didn't gain traction in the corporate world until the 1990s. So it appears to be a term coined at the end of the Civil War that then moved into common usage with Decision Support Systems for data-based tactics and strategies, which were developed from 1965 to 1985. Any way you look at it BI is not new technology.
Fast forwarding to the present where massive amounts of data are being gathered from the huge surge in IoT devices and processed by increasingly sophisticated Artificial Intelligence software, the new term is Business Analytics. This is not your grandfather's punch cards being fed into water-cooled mainframe computers. It's not even business analysts working with Lotus 123 on MS-DOS PCs in 1985. Business Analytics is a whole new ballgame and if you want to play in the game, you may need to upgrade your skillset.
How Much Data Is Too Much?
With IoT and other edge computing systems collecting billions and billions of event and process data, some industry watchers are predicting a "Data-apocalypse" where there will be more information than cloud storage can handle or AI can analyze. But a more optimistic view is that the vast amounts of data being gathered globally present an unprecedented opportunity to give business decision makers a wealth of facts organized in applications for developing tactics and strategies. How are products really doing in the marketplace? Are the processes that create those products as efficient and safe as they could be? Who actually wants to buy what you are selling? Business analytics offers the potential to greatly reduce the guesswork that amounted to little more than a coin flip in old time decision making.
From Theory to Applications
Of course, this is not going to magically appear on business users desktops. Applications need to be built to meet the specific business analytics needs of individual companies, departments and groups. There are tools developers can use to get those jobs done, including Microsoft Data Platform technologies such as SQL Server, Azure SQL Server, and even good old Power BI.
There is news this month on updates to some of the Microsoft technologies specifically to help handle big data.
On Nov. 5, Azure Data Studio was released which is designed to provide multi-database, cross-platform desktop environment for data professionals using the family of on-premises and cloud data platforms on Windows, MacOS, and Linux. More information about it is available on this GitHub page.
The recently released SQL Server 2019 (15.x), SQL Server Big data Clusters is designed to "allow you to deploy scalable clusters of SQL Server, Spark, and HDFS containers running on Kubernetes," as explained in a Microsoft post What are SQL Server Big Data Clusters? "These components are running side by side to enable you to read, write, and process big data from Transact-SQL or Spark, allowing you to easily combine and analyze your high-value relational data with high-volume big data. SQL Server Big Data Clusters provide flexibility in how you interact with your big data ... You can query external data sources, store big data in HDFS managed by SQL Server, or query data from multiple external data sources through the cluster. You can then use the data for AI, machine learning, and other analysis tasks."
That Microsoft post provides scenarios for making use of big data for analysis tasks utilizing AI and machine learning.
Get Up to Speed with an Expert
If you are interested in enhancing your Business Analytics skill set including gaining greater knowledge of the Microsoft Data Platform technologies such as SQL Server, Azure SQL Server, and more, there's a workshop for you this month at Visual Studio Live! From Business Intelligence to Business Analytics with the Microsoft Data Platform is an all-day workshop that is part of Visual Studio Live! in Orlando, Florida, Nov. 17 -22. It is being taught by big data expert Jen Stirrup, who is a Microsoft Data Platform MVP.
This workshop will cover real-life scenarios with takeaways that you can apply as soon as you go back to your company.
Here are the topics included:
- Introduction to Analytics with the Microsoft Data Platform
- Essential Business Statistics for Analytics Success: the important statistics that business users use often in business spheres, such as marketing and strategy.
- Business Analytics for your CEO – what information does your CEO really care about, and how can you produce the analytics that she really wants? In this session, we will go through common calculations and discuss how these can be used for business strategy, along with their interpretation.
- Analytics for Marketing – what numbers do they need, why, and what do they say? In this session, we will look at common marketing scenarios for analytics, and how they can be implemented with the Microsoft Data Platform.
- Analytics for Sales – what numbers do they need on a sales dashboard, why, and what do they say? In this session, we will look at common sales scenarios for analytics such as forecasting and 'what if' scenarios, and how they can be implemented with the Microsoft Data Platform.
- Analytics with Python – When you really have difficult data to crunch, Python is your secret Power tool.
- Business Analytics with Big Data – let's look at big data sources and how we can do big data analytics with tools in Microsoft's Data Platform.
You can find out more about Visual Studio Live! in Orlando here.
Posted by Richard Seeley on 11/20/2019