Data Tool Soup? How Unifying Your Data Science Stack Can Help

data soup.jpg

We examine the benefits of selecting an Enterprise Cloud Platform to manage your Big Data stack, from Machine Learning, AI, Predictive Analytics, and Insight, through to Implementation.

by Alex White

Senior Partner, Talent & Research 11th Jan 2017

'Platforms lead to better data science results', say Insights Leaders who leverage a fully functional platform approach to their data science technology stack. By method of vendor solutions, or a combination of open source and custom coding, by harnessing platforms to unify their technology and infrastructure, this provides a solid foundation for iterating rapidly on solutions.

Platforms also help institutionalise knowledge and promote collaboration, which is critical in a market with widespread talent scarcity and retention concerns. Adoption of a single platform for managing the life cycle of data science work is likely to rise from 26% to 69% over the next two years as firms start to realize the potential benefits.

The ability to effectively turn data into action is increasingly becoming a competitive differentiator. To succeed in this insights-driven landscape, organizations should consider steering data science technology into a single, unified platform. Insights Leaders recognise that competitive advantage often comes from the speed at which they can quickly optimise insight applications.

Platforms unify the tools data scientists need to develop and deploy these. Thinking about your data science tools as a connected platform and taking steps toward integrating and unifying them is a strong step in the right direction for any firm. Vendors recognize this need, and there are many that offer better integrated tool chains. For example, improve integration between data science notebooks, predictive analytics coding languages, and machine learning libraries.

By making data science transparent and part of the business decision making. Many data scientists we work with report a common frustration — businesses hire them with the expectation of magic, and then isolate them in organizational silos expecting the magic to just happen. But data scientists are not magicians; they are professionals with an esoteric skill. Firms must integrate data science activities into the larger processes of strategic business decision making. Hand in hand with this, take steps to create transparent data science input, discovery, and output processes. This will give your business executives more comfort with the results of data science efforts, and it will bolster the prestige of your data scientists, which is important for talent retention.

Regardless of the technology, data, and top-down support, expect high staff turnover in your data science teams. Combat this by putting in place collaboration tools and processes that institutionalise knowledge, including the data sources and provenance for analytic models, the computations performed on derived data and insight, the process and application insights derived from data science, and the implementation and governance of analytic models. Implementing capabilities to manage this knowledge will accelerate onboarding of new talent and ensure team operations are not disrupted as talent comes and goes.

Companies should treat data science platforms as a strategic, transformative investment. Buyers are sometimes confused by insight platforms (of which data science platforms are a segment) because they can appear similar to other analytics packages or big data management platforms. Don’t make this mistake, or you’ll miss the opportunity to transform your business with data science. To leverage a data science platform like an insights-driven business, capabilities need to be made broadly available to teams like R&D, product, and marketing that can use data science to optimise products and customer interactions — in addition to typical users like IT and executive management.

Alex White