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2018 Trends & Predictions for Machine Learning & Big Data

Extracting value from data will be one of the key watchwords for Big Data and machine learning in 2018. Rarely before have these capabilities been as optimally tuned to analyze and manipulate data, provide proper quality control, and identify data lineage.

Sila sees the following trends helping organizations become data driven to: conduct business at the speed of data, develop new revenue streams, tailor offerings to individual customers, improve risk posture, deliver business value across multiple functional areas, and solidify regulatory compliance.

Machine learning will be democratized. Machine learning will allow a new audience to apply data science to their business problems. Tools and techniques previously reserved for the largest and most complicated questions will be able to be applied to challenges of all sizes, and by analysts, managers, and engineers of varying backgrounds.

More organizations will move toward a cloud-first strategy. Serverless computing, elasticity, and automation will drive enterprises toward the cost-saving benefits, self-service, and versatility of public cloud offerings.

The importance of data engineering will be reinforced. Data engineering ensures that data is in the right place at the right time in the right format. With a wider audience for analytics and machine learning, data engineering will become the linchpin to enable the business to extract value from data.

Vendor proliferation will continue. The market research and technology vendor selection process within the data and analytics space has been arduous for many organizations, and the array of choices continue to grow. Organizations that desire to accelerate their data and analytics journey will need to pay close attention to lessons learned, gather technical expertise as needed, and develop appropriate partner relationships.