Predictive Insights to Data Science: Emerging Trends & Technologies

About Ronald Van Loon

Ronald Van Loon is one of the foremost thought leaders in the field of data science and digital transformation. Onalytica named him as the world’s #1 influencer in data and analytics, automation, and the future of the economy and technology. He's also a globally recognized influencer in topics pertaining to the Internet of Things, artificial intelligence, and digital transformation. 

In addition to his popularity as a speaker at worldwide events, he’s also an author for numerous leading big data and data science websites and a director at leading data analytics company Advertisement.

Q: What is data science, and what does it do? 

Ronald Van Loon: It may be helpful to start with an analogy to make it a little bit more vivid. Think of a fruit basket that contains all different kinds of fruit from numerous different countries, such as bananas from Brazil or avocados from California or Kiwi from New Zealand.

As a chef, you would like to figure out how to combine all these separate and distinct types of fruit and make a unified dish, like a pie or fruit cake, for example. So they need to be able to look at these different fruits and figure out what nutritional value they offer and what flavors work well together and what other ingredients should be added. Also, what impact will this have on the current restaurant menu? 

So if you look at the data scientists, they have to act in a similar way for their business, and they have to look at tons of different disparate types of data sources and figure out not only how to collect, store, and process it, but also how to distribute it and maintain it in a way where real meaning can be derived from it. 

They have to obtain insights so that companies can act on all the information and support their business codes. A data scientist needs to be able to use the data for the bigger picture, and they need to be able to look at this from separate stars in the night sky and see this constellation, which is a similar methodology.

Q: How does data science differ from business intelligence?

RVL: So, a data scientist’s tasks or job function, whatever you want to call it, may seem similar to performing basic business intelligence (BI), but I think there are some apparent distinctions. BI is intended for analyzing specific data using specific strategies and technologies to offer past, present, and predictive views of a business’s daily operations. 

BI uses structured information, and it falls more under this solidly in the domain of analytics, and it uses a lot of visualization tools and dashboard reports that are built on more standard statistics. It analyzes current information to pinpoint trends. 

If you look at data science, it uses both scripted and unstructured data and is based more on science and mathematics, using various forms of sophisticated statistical and predictive analysis. You can think about machine learning or AI, of course, which combines both past and current data to arrive at these future predictions.

Q: What is the current state of demand for data scientists?

RVL: Highly specialized knowledge and skills are necessary, so there's a gap between the supply and demand of data scientists. Everybody who’s in data science recognizes this. If you look to the US, for example, there's a need right now for more than 150,000 data scientists. There's also a global shortage of data science skills in Europe and Asia.

If you’re a company that really needs experts in this field, it can be very difficult because of the complexity of the data and each of the company's specific data practices. So if you’re a data scientist, you have to be able to handle the technical and be good at communicating, and also, there’s the business aspect of the role—and that's only at the beginning.

It's also interesting to cite research showing that 94 percent of data scientists and graduates have gotten jobs since 2011. Ninety-four percent, so you can feel very comfortable if you're either moving into this direction or you’re already a data scientist that you have very good job potential. This indicates how reliable a career option in data science is now but also moving into the future.

It's a career path that runs parallel to all the digital disruption that's on the horizon. You can see how this is evolving very quickly, capable of growing alongside the changing landscape of technological progress. If we look at this growth in data science right now, it’s also connected to other important factors. You can think about the data increase from IoT or from social data at the edge.  

If we look a little bit more ahead, the US Bureau of Labor Statistics predicts that by 2026—so around six years from now—there will be 11.5 million jobs in data science and analytics. 

Q: How varied are data science roles? Is this field becoming more specialized?

RVL: Companies want sector-specific skills that can help them innovate; if you don't have the set of specific skills, it's tough to support a company with innovation. 

As we now move into 2020 and look toward the future, data science is requiring more specialized skills, so think about what you want to do and what you love and start finding that direction. This specific fork in the road that you, as a data scientist, need to choose, will determine where you want to go and how your career develops. 

Instead of data scientists who can do a little bit of everything, they look for the specialists. For example, data scientists should focus on one specific field of AI and then think about data labeling, or you can think about machine learning or parallel computing.

New job roles and titles are going to be highly specific to match the need for these specialized talents. It's something that we see as a big trend, so if you look to the professionals who are already immersed in the industry, they're altering their hunt for the talent, and they're altering the move to the future. So if you look to the profile of the data scientist, what businesses cite is this lack of relevant skills and talents as a critical internal roadblock for their success. 

The increasing importance of data analytics is causing organizations to reshape and rethink their approach to specific data analytics talent roles.

Q: What developments will drive the need for more data scientists in the future?

RVL: According to Gemalto’s 2018 Data Security Confidence Index, 65 percent of the businesses they surveyed couldn't analyze or categorize all the data they stored. This is a common problem with many companies. They can’t handle the current data that they store, so companies are going to have even more difficult times in managing all of the data that grows spontaneously. 

So, data growth will be a major factor contributing to the ongoing demand for data scientists, while more companies are adopting AI and machine learning. This means AI-specific skill sets are becoming increasingly important and prominent across all sectors. 

Q: How will the automation of data analysis impact the job market for data scientists?

RVL:  Automation is going to call for a big effort in data cleansing but also in integration, offering data management, and resolving any data issues in legacy systems. 

There's another reason why data scientists are in such high demand: the whole data science process itself is repetitive in nature, so it involves the input of many different skillsets and subject matter experts. Companies want to be able to speed up and refine this process, so it's more predictable. They also want to be able to constantly and continuously improve the operation.

Q: What are some of the most important “soft skills” that data scientists should possess?

RVL: One skill which is really important—which I think many data scientists still lack—is communication, both verbal and written. You're not going to be very valuable for a business leader if you're not able to communicate with them effectively, so you have to be able to connect the business side of the data signs with the technical and scientific side. You have to share your ideas and goals with the business users and leaders in a way that they understand. That's a skill that's so valuable that I think your value increases multiple tens of percentage points if you can master this skill. 

You also need to be able to work as a team member, and the communication part helps you with that. As a data scientist, if you're able to share your insight with business users and non-data scientists in a way that they can quickly comprehend, then you're likely to draw some connections and conclusions in a universal way. This will help your other employees to understand what you mean.

Adaptability is another important skill. You also need to love your domain. If you love it, you can drive your innovation because you feel the passion and understand the value of your business.

Q: How should aspiring data scientists prepare for their careers?

RVL: Your career path is based upon your skills, your interests, and your experience. You naturally gain all different kinds of domain knowledge and specialized skills as you are immersed in your everyday practice. 

Companies are always looking for data scientists with specific skills but also, of course, those with the right certification. For those who are already practicing data scientists, this can help you further improve your career and your ongoing education. Pursuing ongoing education and skills in this field, in particular, is going to be a huge asset because technology is evolving so quickly.

If you're not keeping yourself informed and fresh all the time, you're going to find your relevancy and your contribution to your job role really at risk. If an advanced degree isn't an option for you or doesn't interest you, but you still want to focus on specialization through educational resources, you can earn a bachelor's degree in data science and then extend your specialization by taking supplemental courses. 

For example, in programming languages or in a data warehousing function, many will see the data sciences as cross-disciplinary fields, and this can be an advantage because there's so much room for specialization. 

Q: What are some of the key areas of certification for data scientists?

RVL: There are specific areas of education on which to focus as you consider your future career outlook as a data scientist. Companies are doing so many things with their data on the enterprise level since it gives them a foundation. So you can look into educational courses in big data, data mining, or predictive model building; and also Hadoop and Spark. Skills in BI or data science will give you a really good foundation to perform application analysis or data modeling. 

Machine learning in AI is the domain that’s evolving very rapidly right now. Every technology company that’s focused on it has it as a very high, number one or number two priority and education is a must to stay up to date with the latest advances and technological methodologies. Have a look into professional certifications like linguistic programming, natural language processing, deep learning, and predictive analytics.

We see this trend in data management analytics, machine learning, and AI, all becoming part of the enterprise cloud in structure. As this moves quite quickly forward, it becomes much more comfortable now to set up and deploy an end-to-end infrastructure at an enterprise level. However, it still needs a scientist to understand how all the applications deliver one total solution. If you look at certification, you can think about cloud platforms and infrastructure, cloud development, or cloud architecture. 

Q: Aside from training, what else can you do to prepare for a data science career?

RVL: So how do you improve your talents to develop basic literacy and programming skills with hands-on training? Don't try everything by education; just start doing it.

Applied experience is just as necessary as structured learning—it’s knowing, but also it's doing. If you want as much experience as you can get, go out there and start doing it. Also, you need to stay on top of all of the relevant industry trends and innovations, so you’re aware of changes in your field and can adjust your ongoing training and education accordingly.

As we move into 2020 and the coming years, there's going to be a high demand for data scientists. There's more and more need for a highly specific, highly specialized skill set, so think about what your direction will be as you look into shaping your education and your knowledge base. Having a specialized perspective from your experience and thinking about skills that you can build in data ethics will be especially attractive to companies in the coming years.

I genuinely believe that it's imperative for data scientists to get the right degrees and the right certifications not only to jump-start to their career but really to advance them. So remember that domain expertise and experience both are instrumental and helpful for many companies but this can be learned over time as you immerse yourself in practice. 

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