The promise of data and data-driven analytics has been top-of-mind for businesses for several years. In this 3-part vlog series, our team provides their insights and leading best practices around data, analytics, and artificial intelligence borne from hundreds of successful projects with clients across nearly every industry.
In each of these brief videos, Sean Caron, Principal Solutions Architect, interviews Scott Schlesinger, Senior Vice President & Global Head of Data & Analytics and they both exchange information on how they have seen data and analytics be truly transformative for a business.
In Video 1: Sean asks Scott, “the promise of big data and analytics has been around for awhile with the goal of driving value. How can customers derive value from their data?” Scott reinforces the importance of needing a firm data foundation in order to achieve value-added outcomes. Approaching data as an end-to-end program includes ensuring it is built on a strong framework, is clean and reliable, secure, and has governance. They discuss real-life examples of working with clients including how you can unify and migrate data to the cloud and use AI and ML tools to help clients frame better queries to solve complex data problems.
In Video 2: This video expands on the promise of data and touches on the various ways customers have and continue to utilize data lakes. While data lakes were initially focused on the storage of large data, their usage has evolved and more typical uses for a modern data lake include data exploration, data analytics, and machine learning. The unification of data versus in separate silos is also an area that is being used with an example of how leveraging a cloud-based solution (i.e. Redshift, Snowflake), can give access to larger groups of people and provide more analytic value. In the digital operations world, two main data applications have been preeminent users of analytics – the prioritization and assignment of incidents in Incident Management, and the mining for trends and failure predictions of Event Management — including using analytics for predictive asset maintenance of equipment. Even though machines and data are becoming smarter, AI and ML augment (not replace) the human experience and are helping us be more productive and safer.
In Video 3: The video discusses the elements that are vital to consider around a data strategy. Many organizations we talk to have some defined data needs, and often already have some active data projects, but organizations that are truly successful generally have a comprehensive data strategy. A key component is a solid business use cases for leveraging data analytics and AI that has intrinsic value to the enterprise.
Another critical component is data governance. This ensures that the business gets trusted data that they can make informed decisions on, as well as a scalable and sustainable solution that will provide trusted data in the future. As part of governance, clients are increasingly looking to meet data privacy and data compliance issues (i.e. GDPR, CCPA, etc…), and need the data to showcase their compliance.
Ness has vast experience helping our customers with successful data analytics, AI, and ML initiatives. We work with our clients as true partners to build solutions that are based on solid business outcomes that help them succeed. From strategy to execution, and to the extension of value, Ness can assist your organization. Contact us for assistance with your initiatives.