The last few years have seen quite a bit of demand for people that can analyze data. Certainly the Microsoft platform has embraced more ways to import and visualize data with tools like Power BI, more intelligent analysis with SQL Server Machine Learning and Azure Machine Learning. I think this is because there's been quite a demand for data scientists, and with all the media attention, plenty of IT executives are searching for these people.

It's a good time to change to that type of career, if it's what you want. You certainly should investigate what's involved if you have any interest. Buck Woody has a Data Science series that can help you. Microsoft has a Professional Program in Data Science, and there are not shortage of R and Python resources that can help you experiment with data analysis in a new way. SQL queries work well, but you might find that having other tools in your toolbox is helpful.

As much as there is a demand for professionals, Microsoft, Amazon, Google, and others are trying to find ways to reduce the cumbersome nature of the tools so that anyone that understands the science part can do the work. Will this mean the average business analyst be able to leverage tools and platforms to perform complex data analysis? Maybe, but I don't believe these citizen scientists will remove the need for dedicated professionals. There's an argument in this piece that they might, so you'll have to decide what you think.

The danger is that it becomes so easy to perform some analysis and create a visualization that we will likely have lots of people building reports and drawing conclusions without really understanding how they've aggregated or filtered data, perhaps without even understanding the implication of making these changes. I could see all sorts of poor decisions being made because a manager thinks anyone can use a tool to extract information from data, so they let just anyone do so.

Maybe this is where the data scientist steps in. Help users to refine their analysis, understand the problems when data is put together or taken apart with these tools. Certainly they should ensure that users have good, clean data sets. The last thing we want is another IT bottleneck, but perhaps using highly technical people to review other analysis and ensure no fundamental mistakes are being made by the analysts is a good use of IT skills.

This is another place where we need to ensure that different groups inside of a company can work together to be more effective for the organization. That's the DevOps mentality. We get things done, regardless of who does the work or whose responsibility it is on paper.

Join the debate, and respond to today's editorial on the forums

The precision of smalldatetime goes from 1900 to 2079 in years. Actually June 6, 2079.

Ref: Stairway to Advanced T-SQL Level 9: Compare, Modify, Derive and Validate Date and Time Values

Smalldatetime - https://docs.microsoft.com/en-us/sql/t-sql/data-types/smalldatetime-transact-sql

» Discuss this question and answer on the forums