4 Things You Can Do to Prepare for the Data Science Disruption

Introduction to Data Science Disruption

In the next two years, about 20 billion to 30 billion devices will be digitally connected through the Internet of Things (IoT). The most profound impact of this will be the amount of data it will generate. Data-oriented roles – data scientists, advanced analysts, data systems developers, data-driven decision-makers and data specialists among others – will proliferate. As people take centre stage in the evolving environment, the best strategy for organisations looking to stay ahead is to focus on developing and promoting data skills.

Here are 4 ways to prepare for the change:

#1 Create organisation-wide data literacy initiatives:

While tools and technology are important, creating a culture that fosters the use of data for critical business activities is equally crucial. This should start with the leaders and team managers replacing instinct-based decision-making with solid data-driven resolutions. Experts recommend adopting “data moments” during meetings to discuss data that can help make sound decisions. It’s also important to democratize data where employees can directly access information without the help of intermediaries such as IT professionals. Creating a unified interface for viewing and sharing data and inculcating the necessary skills to accurately interpret data is a must. After all, the risks of misinterpreting information can be high.

#2 Provide access to upskilling programs: 

Organisations are taking advantage of massive open online courses (MOOCs) to upskill their employees. Companies are partnering with online course providers to impart training through generic or customized programs. Ericsson, for one, is a top user of MOOCs. About 3,500 of its employees have already accessed MOOCs on technologies such as data analytics. Industry reports estimate that 59% of today’s data scientists acquired their skills through MOOCs. Organisations can leverage this trend to build relevant capabilities by sponsoring such learning initiatives.

#3 Training for ongoing skill development:

Without a strategic approach, mere access to vanilla courses around data sciences will not help create data-driven professionals. To create robust intellectual capabilities, organisations need to invest in programs that help employees appreciate the macro-level business problem and then move on to analytics – filtering relevant data, composing insights using the right methods, and finally, proposing solutions. Domain knowledge is absolutely mandatory for a data scientist, so much so, that depending on the industry, the specific course requirement may vary. The skill-set required in healthcare, government or science, may be very distinct from the one needed for marketing. A PWC study titled ‘What’s next for the data science and analytics job market?’ indicates that as of today 67% 67% of the job openings are analytics-enabled and require functional or domain expertise outside of data science at the core.  Partnering with ed-tech companies is a great way to provide such domain-specific training.

#4 Propagate data ownership:

The next logical step is to decide who owns the data. Ownership indicates ‘managing’ data in terms of how the information will be used and what kind of business requirement will be considered to interpret a particular set of data. With just 1 per cent of Internet of Things (IoT) data in use today, the key question enterprises will face in 2018 is structuring data ownership. For instance, businesses can put product owners in charge of their own data, wherein they outline the kind of analysis that will be done and decide what kind of developments they will monitor in case of a new product feature release. Discrete ownership of data helps step up accountability in maintaining, updating and even protecting data.

The key to success is to decentralize data – from a skill that belongs to just one team to knowledge that every department can leverage. Making data available to teams on a ‘self-service’ model will drive faster decision-making. Most of all, organisations need to be mindful of the impending skill crunch. Currently, India has 1,00,000 jobs available for data scientists while relevant skills to fill these vacancies are scarce. Strengthening industry-academia connections and adding data science to the training curriculum is a good starting point to building a strong resource pool.