Maybe you are like me and love data and all the “things” it can do under the brainpower of a select group of the data inclined. Maybe rather you are someone who appreciates information and the fact that if you ask a question with the right system you can get an answer back that no human could have given you. Either way data has entered our lives and made demands on our lives, careers, and questioned our often ill-held assumptions. Here I’ll outline 5 keys that you’ll need to ensure that when your number is called you are ready to change the game.
1. Location, Location, Location
Know (exactly) where the true source of the data is coming from. This is simple to say and many times a struggle to get a straight answer from anyone using the data. In this situation it is not good enough to just state “Oh….that’s easy! It comes from the database xyz!”. You need to dig further, the data in the database has to be put there from someone or some tool. So where does that data come from? 9 times out of 10 you’ll find that what you thought was the so called “Source” was just a stepping stone on the path to its final destination. You need to know how the data is initially being created. Examples of this could be from when a new agent creates an application online.
2. Where the river runs…
Know all the places where the data travels. Now that you have discovered the true sources of your data you now should place yourself on top of the data mountain and follow all the rivers that your data can flow. The reason this is so important is that many data projects try to merge data that is already coming from the same source in the first place like two rivers coming off the same mountain that merge later on.
3.The Look ‘n’ Feel
Know how the data is being presented. Now that you know where the data is coming from and where its going to, you need to understand how (and sometimes why) the data is being displayed. Is it in a report? How is the data in the report formatted? Are there calculations in the report? What is the report used for? Extra bonus points go to those who do any of the following: Find errors in the data, Find better/more efficient ways to present the data, or Make the report report faster by creating a more efficient data model)
4. Get in, Complete the Mission, and Get out
Know how to get data out of a data source, manipulate it, and get it into a data source. This is really the magic of what a Data Scientist does. They know great and novel ways to get data from any and all places (think from twitter, stats sites, etc) and then put it in usable and relevant forms and get it into some system (ie database) that others can chew on, admire, or flat deny is bad data. It is quite an accomplishment if you are able to do this in 10 different ways using 10 different tools. This is also a unique key because it requires the use of some good programming skills such as Python, C, and others.
5. Come to Work Packing Heat
Not only have tools to work with data but know how to fully use them (or when not to use them!). This is actually a bigger problem than meets the eye. There are many pro’s out there that will say they know their prospective data tool but once put up against a test aren’t sure either how to complete the task or aren’t able to dictate if the tool can do the task or not. All tools have benefits and every single tool has limitations. We need to know what they can do, what they do well, what they don’t do well, and even what they can’t do.
These 5 keys are only but a few key plays in an entire game but these are ones you’ll come back to a million times over. Remember, Know where the data is, where it goes, what it looks like, how to get it, make it look good, put it back, and all with the tools that are best made to do it.
Joshua is working to become a Data Scientist with focus on Analytics, Big Data, Machine Learning, and Statistics. His passion for Data and Information are second to none. He is a certified IBM Cognos Expert with more than 10 years experience in Business Intelligence & Data Warehousing, Analtyics, IT Management, Software Engineering and Supply Chain Performance Management with Fortune 500 companies. He has specializations in Analytics, Mobile Reporting, Performance Management, and Business Analysis.
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