1/06/2016

How to Maintain Relevancy in the Data Professional Space with All this Change

Change.  Yep, things sure have changed.

In the olden days, a few decades ago, those of us who worked with data, we had less variables.

The Databases didn't change that much.  Nor did SQL language.  Nor did the reporting tool.  Or the export formatting.  We kind of knew our job.  Didn't change much.

We are going through a hurricane of change.  And this storm isn't nearly through.  Total upheaval.

Databases are no longer the sole source for our reports.  Nor is relational data the only game in town.  The number of reporting tools has multiplied exponentially.  And the users have adopted Self Service.  And the amount of data is exploding every second.

The report writers of the past are having a tough ride.  As their world is being rewritten.  Scanning the Twitterverse, you're guaranteed to find some new advancement every time you log on.

 A new vendor.  A new platform.  A new data type.  Deep learning.  Algorythms.  Artificial Intelligence.  Streaming Data.  Sensors.  IoT.

If you're the type of person who likes things to stay the same, so you have a warm fuzzy feeling every day when you get to work, the new world of data may have you running for cover.

There is no stability in the world of data right now.  Everything is in flux.  Everything is changing.  Technology is moving forward.  It ain't slowing down.  The changes are occurring by the day.

This hurricane of change in the data space is disrupting everything in the data space.

How are developers supposed to keep up with the rapid change?  I seriously don't know.  It has fragmented into multiple technologies, multiple vendors, multiple everything.  From on-premise to cloud to hybrid, to relational data to nosql data to images and pictures and speed recognition, to statistics and mathematics and hypothesis and theories, to data quality and project management and presentation skills and documentation and best practices, to self service and visualizations and data story telling and data mashing, to learning languages such as R and Perl and Python and C#, to Hadoop distributed server architecture to Java Map Reduce to Spark.  The list goes on and on and on.

The rug has been pulled out from under the traditional data professional.  The options are, try to maintain an existence using the current skill set, or hop aboard the train of continual learning for the rest of your IT Career.  I don't see any other options at this point in time.  Maybe become an expert in a specific sub-set of technology or space.  But if your goal is to maintain a senior level of expertise across the entire gamut of knowledge, well, that's a lot to learn.

Either way, good luck!  It's going to be a bumpy ride~!

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