Democratizing Data Science in Your Organization – SPONSOR CONTENT FROM DELOITTE

As cognitive and IoT technologies generate ever larger and more varied data sets, companies face the challenge of unlocking the value of that data. And those that are failing to effectively apply data science may be putting themselves at a competitive disadvantage. Data scientist is one of the hottest job titles today, and battles for that talent are fierce.

Since nearly every major company is actively looking for data science talent, the demand has rapidly outpaced the supply of people with required skills. Based on current demand and supply dynamics, the United States alone is projected to face a shortfall of some 250,000 data scientists by 2024. Data science and analytics jobs typically take 45 days to fill, five days longer than the US market average, according to one study.

The skills gap and longer hiring times can cause project delays and higher costs, hindering enterprises’ data analytics efforts. But a number of recent trends may change how companies acquire and apply data science capabilities, presenting savvy companies with ways for alleviating the talent bottleneck.

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. And an array of training resources is helping professionals with diverse backgrounds gain relevant data science skills. For the foreseeable future, elite data scientists will be in high demand. But five factors are beginning to democratize data science, helping to put this critical capability in the hands of more professionals.

Automated machine learning. By some estimates, data scientists spend around 80 percent of their time on repetitive and tedious tasks that can be fully or partially automated. These tasks might include data preparation, feature engineering and selection, and algorithm selection and evaluation. Various tools and techniques designed to automate such tasks have been introduced by both established vendors and startups. Automating the work of data scientists helps make them more productive and more effective.

App development without coding. Low-code and no-code software development platforms offer graphical user interfaces, drag-and-drop modules, and other user-friendly structures to help both IT and nontechnical staff accelerate AI app development and delivery. For example, using a no-code platform, salespeople can build a machine learning-based tool themselves to provide product recommendations to customers based on cross-sell opportunities. These platforms have the potential to make software development up to 10 times faster than with traditional methods.

Pre-trained AI models. Developing and training machine learning modules are core activities of data scientists. Now, key AI software vendors as well as several startups have launched pre-trained AI models, effectively packaging machine learning expertise and turning it into products. These solutions can slash the time and effort required for training, or even start producing specific insights right away. Pre-trained models are available for use cases related to image, video, audio, or text analysis such as sentiment analysis, sales opportunity workflow automation, customer service, automated equipment inspection, and interactive advertising.

Self-service data analytics. Increasingly, business or nontechnical users have tools at their disposal that can deliver data-based insights without involving analytics specialists, including data scientists. Self-service analytics tools offered by many business intelligence and analytics vendors now include features to augment data analytics and discovery. Some automate the process of developing and deploying machine learning models. Features such as natural language query and search, visual data discovery, and natural language generation help users automatically find, visualize, and narrate data findings like correlations, exceptions, clusters, links, and predictions.

Accelerated learning. Data science and AI-related training courses and boot camps are proliferating. These training programs are aimed at professionals with basic mathematics and coding backgrounds and can impart basic data science skills in a period ranging from a couple of days to a couple of months. Such courses are intended to enable professionals to bring basic data science skills to projects quickly.

Many organizations don’t recognize the mix of talent and skills required to be successful when applying data science. Some put great faith in data scientists but fail to reckon with the importance of business and functional expertise to the success of a project.

A properly staffed initiative may include individuals with design-thinking skills, to help conceptualize a solution; those with functional domain knowledge, to help identify high-value use cases and shape the solution; those with business skills, to articulate a compelling business case; those with data engineering skills, to provide access to the right data in the form needed; and, for AI projects, those with AI skills, to drive execution of a variety of AI technologies. Success depends on more than technology talent—it requires the right mix of skills and expertise.

Eventually, the democratization of data science will enable greater collaboration between business and data science experts in building data-centered solutions. Some companies have started effectively expanding their data science efforts by providing data science automation tools to a mix of professionals including data scientists, data engineers, statisticians, and business users.

Others find that breaking down the data science role into a collection of more specialized roles with overlapping skills makes it easier to get the mix of skills required to staff projects.

Democratizing Data Science In Your Organization
Companies seeking to develop data science capabilities face a tight market for talent. To avoid a labor shortage, they should consider a multi-pronged approach, including employing automated tools and pre-trained models, empowering nontechnical users with no-code tools and self-service analytics, and investing in training their own staff in data science by selecting a high-quality, accelerated training option from among the many currently available.

Companies should also explore hybrid staffing models for their data science projects. Rather than overburden data scientists with all the analytics work, they can assemble combinations of experts such as data engineers, statisticians, and business analysts and equip them with relevant data science automation and self-service tools.

Subject matter experts who can “speak data” to data scientists while “speaking business” to executives can be valuable additions to the teams working on data science projects. This helps foster a culture of collaboration between data science experts and business users, enabling data scientists to focus more on advanced and complex processes while reducing time to access actionable insights for business users.

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. 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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