Business Intelligence Training: What to Study Over Lunch in 2017
If you’re like me, you’re an exceedingly attractive, 50-something data geek with a love for aviation history who’s an amazing ice hockey player. You also spend a lot of lunches eating a sandwich while studying new technologies. This kind of on-the-cheap, self service training helps you keep up with changes in business intelligence / data warehousing / analytics / whatever the kids are calling it these days.
So, what should you study (on the cheap) in 2017? Here are some suggestions…
Try a BI Tool You Know You Won’t Buy
IT is the classic “when you have a hammer, every problem looks like a nail” industry. We invest in learning a technology and then become that technology’s biggest proponent. If you know Business Objects, it’s automatically the best BI tool for all problems. But, if you know Tableau, that’s automatically the best tool.
Here’s the thing; there is no best tool. Each BI tool has a sweet spot, and a group of users for whom it excels. Even Excel excels at some things (sorry, couldn’t resist the word play). Knowing a bit about other tools may open your eyes to other, perhaps better, ways to work with what you have.
Even if you have no budget or intention to adopt a new BI tool, I urge you to try one from time to time. All the major vendors have some sort of ‘free’ offering nowadays. So, here’s your first lunch time task: pick up a demo copy of a different BI package, go through its tutorial, and apply the tool to some of your data. Who knows, maybe you’ll like it so much that you’ll find the budget.
Explore a NoSQL / Big Data Technology
It’s very likely that soon, some or all of the data you work with will be stored in something other than a traditional, relational database. Right now my largest clients are working with Hadoop, MongoDB, and CouchDB. If you’re not there yet, start investigating these, and similar, technologies. I generally recommend that folks start with Hadoop as it gets the most mindshare.
Rather than planning a big project to apply the technology, start by just learning about it – perhaps with a basic business problem in mind. After a bit of study, if you think the technology will fit, try it out on a small prototype. While success would be nice, it’s not essential. What you’re really after is knowledge.
To start learning online, during your lunch breaks, Try IBM’s free Big Data University. Full disclosure: I haven’t worked with it much (yet) but it looks like a good place to learn the basics.
Learn Data Vault Modeling
I am convinced that within a few years all the cool kids are going to be using data vault modeling techniques for their data warehouses / integration layers. Data vault is a data modeling approach that builds on some of the best aspects of normalized and denormalized / star schema data modeling. Practitioners using it have only great things to say.
Some data vault modeling benefits include the facts that it:
- Really does support rapid, agile data warehouse development (really!)
- Provides a logical way to easily integrate data from multiple sources
- Greatly eases history record management
- Is sufficiently flexible that source system changes don’t result in huge amounts of rework
Let me warn you, when you first look at a model done with data vault it’s going to be confusing. Remember, those of us who predated star schemas thought the same about the first stars we saw – they looked very different from the traditional, relational models that we knew and loved. However, over time, we got it – we saw the value and jumped on board, using each approach where it made sense. As a preview, just like star schema models use their own kinds of tables (mainly fact and dimension), data vault models use their own kinds (mainly hub, satellite, and link).
Now’s the time to step into data vault modeling. In fact, as my clients look to upgrade their data warehouses, I’m recommending that they strongly consider using data vault modeling techniques. Everyone in business intelligence and data warehousing should learn at least a little about the concepts.
So, I hope these were some valuable thought starters. I’d love to hear your thoughts, too.
My next post will talk about some bigger-issue items that should be on your 2017 to-do list. Thanks for reading!
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