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If you do not love data analytics, it will be hard to become an analytics rock star. No significant accomplishments are achieved without passion. For many people, passion does not come naturally; it must be developed. Cultivate passion by setting goals and achieving them. Realize that the best opportunity in your life is the one in front of you right now. Focus on it, grow it, and develop your passion for it! That excitement will become obvious to those around you.

Dig down deeper about the business details of your company. What, exactly, does your company do? What are some of its challenges and opportunities? How would the company benefit from valuable and transformative information you can deliver? Take the time necessary to learn the skills that are valuable for your business and your career. Keep up-to-date with the latest technologies and available analytics tools -- learn and understand their capabilities, functions, and differences.

Deepen your knowledge with the tools that you are currently working on by picking new techniques and methodologies that make you a better professional in the field.

Improve your presentation and speaking skills, even if it is on your own time. Excellent and no-cost presentation training resources are readily available on the internet (for example, at http://www.mindtools.com/page8.html. Practice writing and giving presentations to friends and colleagues that will give you honest feedback. Once you have practiced the basic skills, you need to enhance your skills by improving your

You must be able to explain, justify, and "sell" your ideas to colleagues as well as business management. Organizational change does not happen overnight or as a result of one presentation. You need to be persistent and skillful in taking your ideas all the way up the leadership chain.

Tough analytics problems typically don't have an obvious answer -- that's why they're tough! Take the initiative by digging deep into those problems without being asked. Throw out all the assumptions made so far and follow logical trial and error methodology. First, develop a thesis about possible contributors to the problem at hand. Second, run the analytics to prove the thesis. Learn from that outcome and start over, if needed, until a significant answer is found. You are now well on your way to rock star status.

Schema on read Schema on read works on a different principle and is more common in the Big Data world. The data is not transformed in any way when it is stored, the data store acts as a big bucket. The modelling of the data only occurs when the data is read. Map/Reduce is the clearest example, the mapping is the understanding of the data structure. Hadoop is a large distributed file system, which is very good at storing large volumes of data, this is potential. It is only the mapping of this data that provides value, this is done when the data is read, not written.

New World Order So whereas Business Intelligence used to always be driven by the model, the ETL process to populate the model and the reporting tool to query the model, there is now an approach where the data is collected its raw form, and advanced statistical or analytical tools are used to interrogate the data. An example of one such tool is R.

The driver for which approach to use is often driven by what the user wants to find out. If the question is clearly formed and the sources of data that are required to answer it well understood, for example how many units of a product have we sold, then the traditional schema on write approach is best.

Every company has hundreds of millions of records about their sales, expenses, employees and so on, with dozens of insights yet to be discovered through simple comparison or triangulation of relevant data.

Why don’t we focus on this? I think because it’s very difficult to do – being successful in this “data translator” role requires a unique set of skills and knowledge, the combination of which I call the BASE skillset: Business understanding Ability to synthetize and simplify Storytelling skills Expertise in data visualization

Business Understanding This one seems obvious, but it doesn’t mean simply understanding the financials of a business. Rather, it means truly knowing the operational details, the incentives, the install base, market growth, penetration, the competition, etc. An analyst can’t just know the technical aspect of a report or the math behind the numbers, but what is truly driving a pattern in terms of product quality, competition, incentives and/or offerings. The best analysts are able to mathematically isolate the key levers of a trend and then suggest actions to react to or take advantage of those trends. Ability to Synthetize and Simplify This is, in my opinion, the most underrated and underappreciated skill. Combing through thousands of data points and netting out 3-4 key issues in under 10 minutes, and then communicating these to a group of execs with very different analytical skills, is truly difficult. The key is to make it simple but not simplistic, which means you still capture the complexity even as you get to the few core insights. It requires a very thorough effort to gather all the relevant information before categorizing, prioritizing and deciding if it is significant. After a while, you become an expert and can sniff things out quickly. At the same time, there is the danger of missing anomalies when you jump to conclusions based only on a summary look.

Storytelling Skills There are stages that should be followed when explaining complex ideas, something data translators are frequently expected to do. The best storytellers start by giving context and trying to couple the current discussion to something the audience already knows, ensuring the story is well structured and connected. We have to move from a “buffet style” business review with thousands of numbers packed in tables to a layered approach that will guide the audience to focus first on the most relevant messages, diving deeper only when necessary. Minto Pyramid Principles, which are built around a process for organizing thought and communication, are helpful in making sure you really focus on what is important and relevant, versus being obsessed in telling every fact. Expertise in Data Visualization I am glad to finally see so much focus on Information Visualization and I believe this is correlated to the explosion of data. Traditional methods of organizing data do not facilitate an intuitive understanding of key information points or trends. For instance, the two examples below contain data on car sales across the U.S. The first, an alphabetized list, is much less intuitive than the second, which shows those sales on a map in Power View. With Power View, right away you can identify the states with the highest sales: CA, FL, TX, NY. (Workbook available here)

There is no better way to see patterns or trends than data visualization, making expertise in this area – both technical and analytical – critical for data translators.