Data science, intelligence & data management
By 2027, the global Data Science platform market is predicted to be worth USD$239.92bn and the business intelligence market USD$32.48bn.
Sources: Precedence Research, https://www.precedenceresearch.com/data-science-platforms-market, and Statista, https://www.statista.com/outlook/tmo/software/enterprise-software/business-intelligence-software/worldwide#revenue.
© Bundo Kim
Chief Data Scientist or business analyst intern?
Data Science predicts behaviour, Business Intelligence provides data-driven insights for operational improvement, and Data Management creates and manages the infrastructure to do both.
Despite the hype, the need for in-house data science is actually quite rare. Proprietary AI & ML is only required when a large part of investor value is driven by predictive analytics. In the majority of cases, linear algorithms perform better and cheaper, and when AI is a necessity, third-party algorithms can often be more cost-effective and have little or no impact on valuation at exit.
Since the rise of SaaS and digital-first consumption, in both B2C and B2B markets, the creation and systematic use of business intelligence has become essential: as markets become transient and users become more complex, the ability to mine and exploit exponentially growing volumes of data has ceased to be a competitive advantage – it is now a requirement for company survival, and quite possibly investor success.
User-friendly dashboards are now the visible side of algorithms and data mining, but these rely on the highly technical skills of data management – a hidden world of data architecture, infrastructure design, distributed storage and programmatic manipulation.