The world is increasingly digital, and this means big data is here to stay. In fact, the importance of big data and data analytics is only going to continue growing in the coming years. It is a fantastic career move and it could be just the type of career you have been trying to find.

Professionals who are working in this field can expect an impressive salary, with the median salary for data scientists being $116,000. Even those who are at the entry level will find high salaries, with average earnings of $92,000. As more and more companies realize the need for specialists in big data and analytics, the number of these jobs will continue to grow. Close to 80% of the data scientists say there is currently a shortage of professionals working in the field.

What Type of Education Is Needed?

Most data scientists – 92% – have an advanced degree. Only eight percent have a bachelor’s degree; 44% have a master’s degree and 48% have a Ph.D. Therefore, it stands to reason that those who want to boost their career and have the best chance for a long and fruitful career with great compensation will work toward getting higher education.

Some of the most common certifications for those in the field include Certified Analytics Professional (CAP), EMC: Data Science Associate (EMCDSA), SAS Certified Predictive Modeler and Cloudera Certified Professional: Data Scientist (CCP-DS). The various certifications are for specific competencies in the field.

Now is a good time to enter the field, as many of the scientists working have only been doing so for less than four years. This is simply because the field is so new. Getting into big data and analytics now is getting in on the ground floor of a vibrant and growing area of technology.

Multiple Job Roles

Many who are working in the field today have more than one role in their job. They may act as researchers, who mine company data for information. They may also be involved with business management. Around 40% work in this capacity. Others work in creative and development roles. Being versatile and being able to take on various roles can make a person more valuable to the team.

Being willing to work in a variety of fields can help, too. While the technology field accounts for 41% of the jobs in data science currently, it is important to other areas too. This includes marketing, corporate, consulting, healthcare, financial services, government, and gaming.

Add More Skills

To become more attractive to companies, those who are in the big data and analytics fields can work to add more skills by taking additional courses. Some of the options to consider include:

⦁ Hadoop and MapReduce

⦁ Real Time Processing

⦁ NoSQL Databases

⦁ GTA Support

⦁ Excel

⦁ Data Science with R

⦁ Data Science with SAS

⦁ Data Science with Python

⦁ Data Visualization – Tableau

⦁ Machine Learning

⦁ Cloudlabs for R and Python

Continuing to take classes will provide you with the edge needed to become a valuable member to any team. It shows initiative and drive, and it makes you more of an asset to companies.

Keep Up With the Changes

The field of big data and analytics is not static. As technology changes and increases, so will the field. It is vital that those who are in the field and who want to remain in the field take the initiative to stay up to date with any changes that could affect the field.

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You probably had some big ideas in mind when you first started thinking about adopting big data solutions for your business. There’s usually a tinge of excitement when it comes to big data, and business owners are eager to tap into all its potential. Hiring a qualified data science team is usually one of the first priorities, along with all the investment in equipment and technology needed to properly collect and analyze all the big data you’ll want. Over time though, that excitement might have worn off. Insights from big data analytics were likely coming in, but not at the pace you were hoping for. Is this a result of your data scientists simply not getting the job done well enough? Is it a case of laziness on their part? As easy as it is to think that big data insights should be reached one after the other in a short amount of time, more than likely the data scientists on your staff are doing everything they can. There are reasons for them not being more inventive, and it has nothing to do with their work ethic.

There’s a lot that goes into a data scientist’s job. Some of their time is spent exploring the vast amounts of data they have to work with. Some of it requires preparations of data visualizations. And still other times they’re working on extract, transform, and load (ETL). While these are all valuable tasks in their own right, chances are most of their time is taken up in something far less glamorous. It’s sometimes referred to as data cleaning, but other terms include data wrangling and data munging. Many data scientists jokingly refer to themselves as data janitors, with a lot of time spent getting rid of the bad data so that they can finally get around to utilizing the good data. After all, bad data can alter results, leading to incorrect and inaccurate insights. The costs of bad data are high, with some research stating it costs a typical business more than $13 million every year. So data cleaning is important, but it’s time-consuming and not all that fun.

The amount of time actually spent on data cleaning varies depending on the survey. 31 percent of data scientists who responded to an O’Reilly Media survey say they spend one to three hours every day doing it. Other reports reveal much larger numbers, with some showing that 50 to 80 percent of their time is taken up in the data cleaning process. Some data scientists even go so far as to say 90 percent of their time involves cleaning up bad data. No matter how you look at it, the results seem to echo what a CrowdFlower survey discovered, where two-thirds of data scientists say data cleaning is among their most time-consuming tasks, and 40 percent say they simply don’t have enough time to actually do big data analysis. The numbers are far from encouraging, revealing the big data bottlenecks that many data scientists have to confront. While some will argue that this isn’t necessarily a waste of their time, many can’t help but think that there are better ways for data scientists to be spending their day. To gain insights from data, the whole data cleaning process will need to be streamlined, freeing up data scientists to engage in more creative and inventive tasks.

So what can you do to help in this effort and empower data scientists? Interestingly enough, the same CrowdFlower survey asked data scientists that question. The number one answer (at 54 percent) was for businesses to provide the right tools to help them do their jobs. One of the most common big data solutions used by organizations to speed up data cleaning involves heading to the cloud and using Big Data as a Service (BDaaS). This involves automating many of the necessary tasks that data scientists find mundane or too time-consuming. By employing the right machine learning solutions to this problem, the efforts will lead to higher quality data and more actionable business intelligence. Businesses should also make sure to hire enough data scientists to actually achieve their goals in good time. If more data scientists are working on a set of data, the results will be reached in a short order.

When insights are slow in coming, it’s not always the fault of your data scientists. They enjoy being inventive and would like nothing more than to spend their time coming up with creative solutions based on the data they analyze. Data cleaning, however, can take up too much of their time. Once you know what the problem is and how to fight against it, you’ll be in a good position to help data scientists reach their full potential and unlock the possibilities of big data.

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Kevin is President of MineThatData, a consultancy that helps CEOs understand the complex relationship between Customers, Advertising, Products, Brands, and Channels. Kevin supports a diverse set of clients, including internet startups, thirty million dollar catalog merchants, international brands, and billion dollar multichannel retailers. Kevin is frequently quoted in the mainstream media, including the New York Times, Boston Globe, and Forbes Magazine. Prior to founding MineThatData, Kevin held various roles at leading multichannel brands, including Vice President of Database Marketing at Nordstrom, Director of Circulation at Eddie Bauer, and Manager of Analytical Services at Lands’ End.

What is the biggest misunderstanding in “big data” and “data science”?

To me, it is the “we’re going to save the world with data” mentality. I like the optimism, that’s good! I do not like the hype.

Describe the three most underrated skills of a good analyst and how does an analyst learn them?

The first underrated skill is selling. An analyst must learn how to sell ideas. My boss sent me to Dale Carnegie training, a course for sales people. The skills I learned in that class are invaluable. The second underrated skill is accuracy. I work with too many analysts who do all of the “big data” stuff, but then run incorrect queries and, as a result, lose credibility with those they are analyzing data for. The third underrated skill is business knowledge. So many analysts put their heart and soul into analyzing stuff. They could put some of their heart and soul into understanding how their business behaves. Knowledge of the business really influences how one approaches analyzing issues.

How do you clearly explain the context of a data problem to a skeptical stakeholder?

To me, this is where knowledge of the business is really important. So many of my mistakes happened when I cared about the data and the analysis, and did not care enough about the business. I once worked for a retail business that only had twenty-four months of data. That was a big problem, given that the company had been in business for fifty years. Nobody, and I mean nobody, cared. I explained repeatedly how I was unable to perform the work I wanted to perform. Nobody cared. When I shifted my message to what I was able to do for a competing retailer who had ten years of data, then people cared. They cared because their business was not competitive with a business they all knew. Then folks wanted to compete, and we were able to build a new database with many years of data.

What is the best question you’ve ever been asked in your professional career?

A high level Vice President once listened to a presentation, and then said to me, “Who cares?” The executive went on to say that I was only sharing trivia. He told me that unless I had facts and information that he could act upon, he didn’t want me to share anything. This is a good lesson. Too often, we share information because we were able to unearth an interesting nugget in the database. But if the information is “nice to know”, it doesn’t help anybody. It is better to share a simple fact that causes people to change than to share interesting facts that nobody can use to improve the business.

What is the best thing – in terms of career acceleration – you’ve ever been told in your professional career?

Ask to be promoted to your next job. I had a boss who, in the 9th year of my career, asked me what I wanted to do next? So I told my boss – the job was outside of my area of experience, to be honest, and the job was a major promotion. I described why I wanted the job, I described how I would do the job differently, and I described my vision for how I would make the company more profitable. Within twelve months, I was promoted into the job. My goodness, were people upset! But it was a major lesson. When somebody asks you what you want to do next in your career, be ready to offer a credible answer. Maybe more important, be ready to share your answer even if nobody asks you the question! Tell people what your next job looks like, tell people your vision for that job, tell people how the company benefits, and then do work that proves you are ready for an audacious promotion!

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Re-identifying the roles in the Data Science Industry

Data Science is a growing field, that goes without saying. Businesses are taking a data driven approach to maximize understanding, output and results within their industry, gaining a competitive advantage. Although, the end goal for all companies is more or less the same, the way data is analysed and used will be differ and so of course you cannot lump all careers in the data scientist field into one category.

Forecast for Data Scientists

The need for professionals in the Data Science industry is growing, in fact the Harvard Business Review stated that having the Data Scientist title is the sexiest job of the 21st century. The demand at this point outpaces supply and as such the salaries in this industry are growing exponentially, perhaps perfect time to think about a career in Data Science?

The Learning Process

In a previous Dataconomy interview we asked Data Scientist Thomas Wiecki, advice he would give the younger generation of Data Scientist and he said “Spend time learning the basics. This will make more advanced concepts much easier to understand as it’s merely an extension of core principles and integrates much better into an existing mental framework.”

So I am interested, what now?

How do you decide where you fit in in the Data Science community or what kind of career to look for? This is where Datacamp comes in, unraveling the mystery and confusion behind the “types” of Data Science careers, salaries and hiring companies with an infographic titled “The Data Science Industry: Who Does What”:

Does one of these profiles fit you or are you ready to change the heading on your business cards to ________!

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According to statistics collected by the job site Dice.com, the demand for data processing skills – NoSQL, Apache Hadoop, Python, etc – has hit an “all time high.” Dice claims that among the skills most sought after, NoSQL experts with experience in unstructured data systems like MongoDB is leading the way — with a 54 percent increase since last year. Apache Hadoop and Python also saw an increase in demand with 46 percent and 16 percent year-over-year gains respectively.

In conjunction with Dice’s statistics, Indeed.com provided details on their findings about which big data skills are most in demand. MongoDB is the most commonly mentioned of the NoSQL variants in job listings, with 4,979 entries as of yesterday. Couchbase, Redis and CouchDB were trailing marginally behind as the most common NoSQL variants.

Both job websites also noticed a major surge in “big data” related jobs, with a 46 percent increase year-over-year. Expertise in SaaS and cloud were up too, showing 20 percent and 27 percent increase respectively. “Dice claims one side effect of a rise in cloud-based analytics is a growing demand for employees with multiple skills in this category — for example, both Hadoop and cloud storage.”

In an interview with InfoWorld, Michael Rappa commented on the rise of big data jobs in 2012:  “big data isn’t a new specialty or suite of tools we have to train people into, as much as it’s a new organizational reality that everyone will need to adjust to occupationally…It would be valuable to develop interdisciplinary curricula around the emerging concept of ‘data science’ as a way of blending elements of math and statistics and computer science.”

Read more on the story here