When enterprises put advanced analytics at the core of their strategies and operations, they can accelerate their digital business. Dragan Rakovich, DXC Technology’s chief technology officer for analytics and a Distinguished Technologist, shares his take on analytics’ pivotal role in digital transformation here and in this in-depth analytics position paper.
1. Use advanced analytics to accelerate your digital transformation agendas.
The speed of artificial intelligence (AI) adoption will accelerate as the amount of data for AI consumption increases. This will in turn drive better understanding of how to customize AI for the relevant business context and drive digital transformation to new levels. AI will provide instant measures of business performance down to the smallest task, leading to more predictable business outcomes, as well as enhance productivity and 24×7 business operations through automation of business processes and algorithmic work.
Organizations should define their long-term objectives, clearly understand where and how new business value will be created and design their digital journey maps. Once a business outcome and measurable business value is identified, organizations should proceed with developing analytics and AI/machine learning (ML) models and their implementation in business operations. To achieve this, organizations should invest in a data-centric foundation that contains the cleansed enterprise and external data required to build the analytics models and solutions.
2. Build a data-centric foundation.
A data-centric foundation can scale with growing organizational needs, enable innovation, increase predictability, improve forecasting accuracy, detect new behavior patterns, and deliver information insights in context to processes and applications. Companies such as Apple, Amazon, Google, Netflix, Uber and Airbnb have shown how to build such data-centric foundations and disrupt traditional markets.
To build a data-centric foundation, adopt a hybrid data management (HDM) approach and reference architecture, and implement industrialized analytics and AI platforms based on it. HDM involves optimizing traditional business intelligence (BI) and data warehousing, blending in big data analytics, creating analytic and data solutions across the spectrum of edge/cloud/on-premises resources, and embedding prescriptive analytics models into operations and business processes. The HDM Reference Architecture (HDM-RA) addresses recommended technologies and implementation blueprints for each functional domain as well as distributed aspects of the data, such as what data should reside where, so it fully supports the data and analytics needs of a modern, analytics-empowered organization.
3. Aim for an industrialized analytics and AI platform.
An industrialized analytics and AI platform is an integrated infrastructure, software and services solution based on HDM-RA — often with a managed data lake at its center. It manages data and analytics model life cycles and enables business insights in relation to the work being performed, to augment the decision making of creative workers who need timely information to do their jobs. It analyzes all organization-relevant data from any source, in any format and from any location — with extreme speed, security and scale. It also gives organizations the flexibility to move seamlessly between cloud and on-premises deployments to meet an organization’s dynamic data and analytics requirements. As technological complexity increases, new technological components can be rapidly integrated into the platform, thanks to the HDM-RA.