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There’s a lot of buzz in the marketplace today about Big Data, business intelligence and data analytics – and a lot of confusion.
One of the biggest misconceptions about analytics – and all of these these can be lumped under the term “analytics” — is that it’s some kind of software package you have to purchase, or that it requires the launch of huge software project. While those things are important for some organizations, they’re not always necessary – nor are they among the first things you’ll want to take on if you’re going to get started with data analytics.
Data analytics is the process of using business data to make informed decisions and to spot new insights about your business. It includes a lot of business concepts you probably already know about – including financial analysis, reporting, and forecasting – and employing it isn’t as hard as you might think.
Don’t be scared off by terms such as “business intelligence,” “data mining,” and “predictive analytics.” Here’s how to get started with data analytics in six steps.
1. Choose the questions you want answered.
Analytics doesn’t start with data. It starts with a question you have about your business.
That question could be as simple as “Are we profitable in all parts of our business?” The better the questions you ask, the better the results.
2. Pick the metrics that can answer your questions.
This is the hard part: deciding how to answer your own question. Someone has to dream up the numbers that, if you had them, would eliminate your uncertainty and turn anecdotes into facts. For example, most businesses define “profit” as “revenue minus cost.” In order to analyze profit, though, you’ll first need to measure revenue and cost in as much detail as you can.
3. Find the data that you need.
This is usually the time consuming part of analytics: finding the data you need to answer your business question. If you’re lucky, you’ve got what you need in an existing report from a single computer system, or in a single spreadsheet. If not, you’ll have to do a little work to pull it together.
This is the part of the analytics process where things can get really complicated and expensive.
4. If you already have reports for those metrics, you’re all set. If not, build them.
With any luck, your accounting system already produces a good profitability report – and if it does, you may be done. If not, your analyst will need to sit down and build a grid of data that answers your question, and a chart or a graph that tells your story in the way you want to see it.
5. Dig deeper into your data with advanced analytics.
This is where the average company might need a little help. A good analytics practitioner has a few tricks up his or her sleeve to use when going through your spreadsheet – or your hundred-million-row database – to find interesting patterns that you otherwise couldn’t find yourself.
In this case, just like an academic researcher would, a well-trained analyst will take your detailed list of product sales and costs and try to find what really drives your company’s profitability, and help you figure out exactly where to look for answers.
6. Make decisions using the information you’ve gathered.
All of this analysis is for naught though if you don’t act on what you’ve learned. The true value of data analytics is in reducing managers’ uncertainty to the point where they can take decisive action and alter their performance for the better, and in helping them find the most effective ways to do so.
Want to learn more about how to get started with data analytics in your industry? Download our new guide to some of data analytics’ most practical and profitable applications:
DataClear is a Baton Rouge-based data analytics consulting firm. Contact us for a free 30-minute consultation and discover how your company can profit from data-driven decision making using tools that won’t break your budget.