The fact is, data is becoming an increasingly important business assets and more and more decisions are based on analysis of data.  It’s infiltrating every department of every business, and that means that the employees and managers who have the skills to deal with data will be in a better position to help their company and move up.

But don’t fret: you don’t need to go out and learn computer programming, database management, or advanced maths if you’re a manager in human resources or sales. What you do need is some basic data literacy so that when these subjects come up in meetings — and they will come up, sooner or later — you can be the one at the table who is not only keeping up, but adding value to the conversation.

1.  You must be ready and willing to conduct experiments. A large portion of data analysis is devising and conducting experiments — as in, if we do this, what will be the outcome? Think back to your days of school science classes about forming a hypothesis and designing a method for testing that hypothesis. This is why Google’s recruiters ask incredible questions like, “How many golf balls fit in a school bus?” during job interviews. They don’t care about the golf balls; they want to see the candidate’s skills at experiment design and analysis in action. 
2. You must understand basic mathematical reasoning and statistics. OK, I just told you that you didn’t need advanced maths, but having a basic understanding of how statistics and mathematical reasoning work will be essential to understanding data sets and drawing conclusions. If your skills are a bit rusty, there are plenty of free opportunities on the net to get a refresher course and improve your mathematical literacy.
3. You must be versed in different visualisation techniques. No matter what your occupation, it’s time to become familiar with the graphing functions in Excel and the visualisation tools in PowerPoint and the like. Why? Because a big part of big data is understanding, interpreting, and explaining it. If you can take a set of numbers and explain them clearly and concisely with basic digital visualisation tools, you will be ready for any meeting or presentation, and head and shoulders above many of your colleagues. Again, seek out free tutorials if you need to improve your skills.
4. You must understand the concept of data security. It may be annoying that the IT department makes you change your passwords every 90 days, but do you understand why it’s important? The more you will be entrusted with data in your daily job, the more you will need to understand and comply with data security measures. Make a point of following security best practices and doing your part to keep the company’s data secure. 
5. You must learn to ask the right questions. Data is dumb; it may have the answers you want, but it will only reveal them if you ask the right questions. It’s important to have a good grasp of what data can do and what it can’t. The biggest problem I see with beginners and lay people is that they make mistakes in logic when considering the data. For example, a company might monitor the length of support calls to measure customer service. But if the operators know the goal is shorter calls, they may be providing less service in order to meet goals. You have to think critically about whether the data you have is answering the question you really want to answer.

Not everyone needs to know how to craft an algorithm or query a database; those skills can be left to a data professional. But everyone in every business will, at some point in the near future, be confronted with a request to prepare, analyse, or interpret data.

The question is not if it will happen, the question is: will you be prepared when it does?

What additional skills would you add to this list? I’d be interested in your thoughts in the comments below.

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