Bridging the data science talent gap with data science as a service

If you’re struggling to fill full time data science experts who are qualified to manage complex analytics, data science as a service, a managed service offering that provides regular analyst-level resources at your disposal, may be the right solution to bridge the growing talent gap.

The Exponential Growth of Data

There are many sources that predict exponential data growth toward 2020 and beyond. Yet they are all in broad agreement that the size of the digital universe will double every two years at least, a 50-fold growth from 2010 to 2020. Human- and machine-generated data is experiencing an overall 10x faster growth rate than traditional business data, and machine data is increasing even more rapidly at 50x the growth rate.

The acquisition and analysis of data and its subsequent transformation into actionable insight is a complex workflow which extends beyond data centers, to the edge, and into the cloud in a seamless hybrid environment. The utilization of edge devices, in situ-computation and analysis, centralized storage and analysis, and deep learning methodologies which accelerate data processing at scale requires a new technological approach. Historically, data processing and analytics systems had specialized features for business analytics and high-performance computing (HPC) workloads. Yet with the advent of big data and industry standard x86-based computing, we are seeing a convergence in big compute, big data, and IoT for analytics. IDC research categorizes this convergence as high-performance data analytics (HPDA).

The march of both consumer and commercial adoption of the Internet of Things (IoT), relatively cheap storage, and improved methods of data capture are all contributing to the growth in scope of Big Data. The Wikibon community of business technology practitioners sized the global Big Data market at $18.3 billion in 2014, predicting it will grow at an annual rate of 14.4%, to hit $92.2 billion by 2026.

The growth of Data Science

Data science is growing at an exponential rate and the general availability of machine learning, cognitive computing and the like continue to be developed so that the masses can take advantage of their power.  However, data science technologies, like IBM Watson CX (customer experience analytics) still require a certain level of training and data science skills that may not be prevelant in many organizations.

Because of the growth of Data Science, rapid adoption and the value proposition it brings, the need for qualified data scientists is outpacing the current employee market.

Data is now creating opportunities for business growth and profit like never before.  In the last decade, the emergence of advanced data technologies like IBM Watson and analytics tools like Tealeaf has made it possible for business operators to reap benefits from their data stores, yet for most they’ve only just scratched the surface of what’s possible. Data Science is allowing companies to successfully leverage that potential like never before.

Why Data Science is so critical:

  • Data Science has the requisite capability to provide accurate solutions to business problems when one needs it and where one needs it
  • Data Science enables better business decisions and accurate study of the impact of such decisions. A past Harvard Business Review study stated that top businesses are generally 6% more profitable than their peers when they rely on data-enabled decisions.
  • Data Science can make more exact predictions about the future when both human intuition and experience fail. With Data Science, businesses do not have to depend on guesswork anymore.
  • Customer tracking has become a reality with highly capable, smart devices and state-of-the analytics platforms. Real-time customer data acquisition helps deliver accurate answers.

A shortage of qualified data science experts

In 2013 a McKinsey report predicted that the business community would suffer an acute shortage of Data Science professionals for at least the next decade, specifically a shortage of  “1.5 million analysts” skilled at deriving competitive intelligence from the vast amounts of static and dynamic (real-time) data. While such a prediction is coming true, a greater focus on marketing the importance of Data Management to enterprises and within higher education institutions is enabling the entire industry to cope with shortages in ways that were not fully understood only a few years ago. The upheavals within the Data Science industry will continue throughout 2017, but so will more growth and more possibility.

Despite the surge in data science related programs (more than 100 in US alone), universities and colleges cannot produce data scientists fast enough to meet the business demands. More importantly, they certainly cannot produce experienced data analysts or scientists from their two or four year programs.

International Data Corporation (IDC) predicts a need for 181,000 people with deep analytical skills in the US by 2018 and a requirement for five times that number of positions with data management and interpretation capabilities. “To complicate matters, there is no clear set of capabilities that define a “data scientist,” because different problems require different skill sets”, the report states. “Some organizations are taking a multi-pronged approach by supplementing campus recruiting with alternatives—from turning to managed analytics to cultivating in-house talent.”

Enter Data Science as a Service

Virtually all mission critical technologies that grow quickly undergo some level of skills shortage during their lifecycle and data science is no exception.  Try to find a great UX designer and you’ll know what I’m referring to.  Given the skills gap, growth of tools and apps and the need to get a handle on data, many companies are beginning to warm up to the idea of Data Science as a Service.  Like most “managed services”, firms offer a structured set of ongoing services, typically delivered remotely, to add deep skills and knowledge without having to hire a full-time staff.

Defining Data Science as a Service

Managed services is the practice of outsourcing on a proactive basis management responsibilities and functions and a strategic method for improving operations and cutting expenses.  It appears as an alternative to the break/fix or on-demand outsourcing model where the service provider performs on-demand services and bills the customer only for the work done.

What’s interesting about data science as a service is not the model per se….the managed service model for analyst-related activities is not new…but rather the type of services being delivered remotely.  Data science analysis as a service means you’re sourcing some or all of the duties of deep data work to a firm who analyzes your data based on vendor expertise and best practices and returns value in the form of business and technical recommendations meant to streamline your operations.

CEARGOALS recently announced it’s own data science as a service offering, which is built around the IBM Watson Customer Experience analytics suite, specifically, IBM’s Tealeaf on cloud solution.  We have seen a high level of interest in this offering and clients are excited to take advantage of a wide array of on-demand, managed services.

The future of data science is bright and the need for resources is great….managed services may be the right move for companies that are struggling to hire experts.