By Kayla Matthews, Productivity Bytes
Data science is considered a relatively new career field.
The industry itself and its various platforms, tools and operations have been around for a few years now.
As a whole, it’s starting to pick up speed. Companies are bringing on more and more data-oriented personnel, and for good reason.
As a data scientist — or someone interested in the field — you know the industry is constantly evolving. If you want to remain competitive, you need to keep up with popular trends.
You also need to understand why data scientists are so prevalent and how the market will be shifting in the near future. And since we’re sitting right on the cusp of 2018, now is as good a time as any to look ahead.
What to Expect for Data Science in 2018
Whether you already have a foothold in the industry, or you’re looking to dive in, your mission should be the same: pay attention to current trends to ensure you’re both prepared and capable of handling what’s coming.
This is a huge one, especially with AI and machine learning growing in popularity. As a whole, automation means quite a few things, namely the steady and autonomous operation of a particular process or system.
A series of software tools and algorithms will be used to ingest, filter and highlight data that can be further analyzed.
You’d be forgiven for thinking automation would make data scientists obsolete. A lot of negative talk surrounds automation, particularly about uprooting jobs and careers. That’s not the case in data science, however.
Experts and experienced scientists will still be needed to highlight, identify and implement actionable insights. You can expect to see a lot of automation platforms crop up in the industry over the coming year.
2. Data Empowerment
Data empowerment is another important movement to keep your eye on. In some ways, “empowerment” sounds bold — even a little ominous. It’s just a buzzword though, used to explain a boost in data effectiveness for many parties.
To put it simply, the data and information that a company or organization is collecting doesn’t just belong hidden on a remote server somewhere, gathering dust. Furthermore, just because a chunk of data is not useful to the collector doesn’t mean it’s not useful to someone else.
Data empowerment is about the alignment or collaboration of everyone involved in a system. It means that everyone has access to the same tools and resources and the same data stores.
More importantly, it means putting data in the hands of the right people — those who can make use of it.
3. Ethics and Influence
The debate over the ethical and social implications of data science, artificial intelligence and even cloud storage will probably never end. Privacy, security and automation have all become increasing concerns in the current landscape, even among consumers.
The point here is not that the ethics or influence of the industry and related systems will change over the coming year, but that discussions will continue. New applications for data science are discovered on an almost daily basis. Data engineers, scientists, analysts and even administrators will need to join the discussion to let others know now this technology can help others.
After all, you are the ones responsible for creating, developing and maintaining these technologies and systems. What is it you have to share with the world? What can you explain or provide insights about?
You could introduce the positive side of data analytics and collection, for instance, by explaining how it’s helping to fight modern battles and keeping America safer. Or how it’s providing modern conveniences to many — conveniences like personalized shopping campaigns, better home security and more.
2018 will be the year that data science professionals become a part of the greater discourse.
4. Data Lakes and Mass Cleanups
At this point, a wide variety of parties and organizations have been collecting and storing data in departmental silos.
This process results in what many like to call a data swamp or even a dump. It’s a mass void of raw data, information and potential insights. The problem is, it needs to be cleaned up, skimmed and organized.
Cleaning a swamp and converting it into a data lake calls for categorizing, attaching relevant metadata and sorting everything into the appropriate storage segments.
Expect a boom in data restructuring as more organizations and parties realize how beneficial data lakes are.
5. Blockchain App Development
Attention for cryptocurrencies like Bitcoin and the underlying mechanics of blockchain have exploded over the past year. That response will continue well into 2018, namely because of the implications blockchain has to a great many industries.
This attention will call for more demand in the development world as teams look to work with blockchain and implement it into their products, services and systems. In the financial and healthcare industries, for example, a lot of work is being done to introduce blockchain-powered platforms.
Clearly, some trends in data science aren't going away in 2018, and they'll probably be around for much longer than that. Keep an eye on the field and watch your company or career evolve.
Bio: Kayla Matthews discusses technology and big data on publications like The Week, The Data Center Journal and VentureBeat, and has been writing for more than five years. To read more posts from Kayla, subscribe to her blog Productivity Bytes.
- 8 Ways to Improve Your Data Science Skills in 2 Years
- Machine Learning Engineer, Data Scientist – top US emerging jobs
- 10 Tools to Help You Learn R