Companies that fail to apply data science will be at a competitive disadvantage, according to Deloitte. Here's how to take advantage of new tools, staffing models, and training strategies.
The massive amounts of data generated from cognitive and Internet of Things (IoT) technologies have led to a major challenge for companies: Unlock the value hidden in the numbers, or be placed at a competitive disadvantage, according to a Thursday report from Deloitte. With demand for data scientists at an all-time high, companies must find new tools, staffing models, and training strategies to ensure they can gather actionable business insights from their information, and not fall behind in the market, the report noted.
Data science automation and employee training tools hold promise for democratizing data science across organizations, according to the report. More than 40% of data science tasks are expected to be automated by 2020, and early adopters of data science automation tools across industries have reported significant time and cost savings along with revenue gains.
Meanwhile, the market for low-code development platforms that can make basic data science functions available to non-programmers is booming, and several training courses and boot camps have launched in this area as well, the report noted.
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"Most vendors in the data science and analytics market have made tool simplification a top goal; they are aiming to broaden and accelerate the adoption of data science and analytics capabilities," the report said. "And an array of training resources is helping professionals with diverse backgrounds gain relevant data science skills."
While top data scientists will continue to be in high demand for the foreseeable future, the following five factors are beginning to democratize data science and help put these skills in the hands of more professionals, according to Deloitte.
1. Automated machine learning
Data scientists spend up to 80% of their time on repetitive and tedious tasks-- like data preparation and algorithm selection --that can be fully or partially automated, according to some estimates. A number of automation tools from established vendors and startups are now on the market.
"Automating the work of data scientists helps make them more productive and more effective," the report stated. "Organizations can make aggressive use of data science automation to empower and leverage oversubscribed talent."
2. App development without coding
Low-code and no-code software development platforms offer user-friendly drag-and-drop structures to help both IT and non-technical staff build apps and tools for their business. With the market for these platforms growing rapidly, it's likely that more companies are taking advantage of such tools amid a talent shortage for developers and data scientists, the report noted.
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3. Pre-trained AI models
Data scientists are often responsible for developing and training machine learning modules, the report said. However, several vendors have launched pre-trained AI models, which can cut the time and effort required for training and can be used for image, video, audio, or text analysis.
"We can expect more pretrained models to come to market in coming months," the report said.
4. Self-service data analytics
Business and non-IT users can now access tools that deliver data-based insights without having a data scientist or analyst on staff, the report found. Some of these tools aid the process of developing and deploying machine learning models, for example, allowing business users to perform complex data analysis tasks and gain insights without relying on data scientists.
5. Accelerated learning
Data science training courses and boot camps continue to grow in number, aimed at teaching professionals with basic math or coding backgrounds primary data science skills in a short period of time, the report said.
"Such courses are intended to enable professionals to bring basic data science skills to projects quickly," it stated.
Companies should take a multi-pronged approach to data science work amid a shortage of qualified talent that includes several of the above methods, the report recommended.
"Those enterprises that seek to build armies of data scientists may continue to struggle to hire the desired talent, end up overspending on salaries, and get stuck with excess human capital in coming years," the report stated. "Those that leverage new automation, self-service, and training solutions may be able to mitigate the data scientist shortage without going on a hiring binge."
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