Data is the raw material for digital transformation
The ability to act on data-based insights in real-time has never been more critical than it is now. Over the past six months, The IBM Institute for Business Value report COVID-19 and the Future of Business report found that 59% of companies have accelerated digital transformation – companies that were semi-digital pivoted to become fully digital, while companies already fully digital expanded into new use cases. This is not a short-term shift or a moment in time — the current expectation of a fully data-driven, digital business is a permanent shift in the business environment.
The pandemic revealed new use cases for employing data, and accelerated digital transformation. For example, businesses and governments created COVID-19 health dashboards that contain data from many sources to make critical decisions such as those related to contract tracing and bringing people back to work. Some enterprises shifted to offer the same data-driven, pandemic-related insights to customers to enable smart business decisions, such as predicting changes in demand and providing visibility into supply chains. As the pandemic ends and the business landscape moves into its new reality, we can predict massive, continued investments by companies into data, analytics and AI capabilities.
By leveraging ‘data’ as a strategic enterprise asset, companies can accelerate or scale digital transformation, and also contribute to high revenues and business growth. High-performing organizations are 3X more likely than others to report data and analytics initiatives contributed at least 20% to EBIT (from 2016–19), according to McKinsey.
The need for a modern data architecture
A digital-first enterprise strategy requires a data-first approach. The existing data architecture does not support businesses’ high ambitions for accelerating digital innovation and agility in a post-COVID world. A well-planned data strategy for a digital organization provides business transformation opportunities, cost reduction, improved engagement, and maximum flexibility in a multi-cloud environment.
For instance, businesses can leverage enterprise data to develop advanced AI-based innovations using Natural Language Processing (NLP), machine learning (ML), deep learning, neural networks, speech-to-text and text-to-speech capabilities. However, businesses need to know where to focus vital data curation efforts. IBM estimates that 80 percent of the effort in deploying AI is getting data ready for use per .
Over the past decade, the use of and processes for data evolved significantly — both in terms of technology and use cases. Ten years ago, businesses invested heavily in large numbers of data warehouses using relational databases, which limited the use cases to traditional analytics. While many companies began migrating to data lakes for data science purposes, data warehouses remained a cornerstone.
Despite significant investments in enterprise data, most organizations struggle to integrate siloed and outdated warehouses or data marts. Businesses are also unable to effectively use vast amounts of semi and unstructured data not historically used. According to IBM – Cognitive Enterprise Study, less than 4 in 10 organizations (40%) integrated their data across the enterprise, designed and deployed an enterprise-wide data architecture,
A modern data architecture underpinned by ‘data platform’ approach helps orchestrate large sets of data in a hybrid cloud environment. This approach enables o building automated workflows, business platforms, experiences and scale the value of data to accelerate AI initiatives.
IBM worked with a large U.S.-based health care company with an expensive legacy data warehouse to help the company reduce its expenses and increase the value of its data. In the first six weeks of working with IBM, the company transformed its data warehouse into a data platform capable of running 14 data science permits. With currently close to a thousand models, the company improved the process for administering health care for clients and members by expanding the use of data beyond reporting.