Why investing in your data is now more important than ever
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.
A modern data architecture helps businesses create clean, reliable and actionable data to meet the need for business agility, speed and innovation in the new normal. Benefits include:
- Speed up time-to-value for enterprise data-driven initiatives in a multi cloud world.
- Lower data management costs
- Reduced time and effort to deploy and maintain business platforms, intelligent workflows and enterprise experience
3 Key areas for investment to transform data value
While data use cases continue to expand, organizations should consider the following key areas for using data as an enterprise asset.
Data for business platforms – Market making business platforms knocks down the walls between enterprises or industries to connect broad categories of complementary products and services in a way that makes experiences more holistic for customers and enable a non-linear growth. As companies pivot on platform strategies, they need to identify the right set of proprietary and partner data and require distinct capabilities to extract value of data. Businesses look to orchestrate internal and external data generated by ecosystem partners and customers to design business and marketplace platforms to monetize data, expand market opportunities and reinvent competitive positioning.
IBM worked with Yara, a Norway-based corporation producing nitrogen for global customers, to design a digital-farming platform using IBM’s Digital Insights. Yara integrates its data using predictive models to optimize the property by acre, considering the location, climate and probable weather patterns to increase crop yield. With the digital platform, Yara aims to cover 7 percent of all arable land worldwide. Learn more here.
Data for workflows – Businesses can automate processes, empower their workforce, improve customer experience and increase productivity and operational efficiency though redesigning enterprise workflows into intelligent workflows. Intelligent workflows are automated workflows powered by AI and other exponential technologies underpinned by data, that create more cost effective and flexible front-, middle-and back-office processes. Organizations need to harness the right set of enterprise data to activate intelligent workflows and automate processes. IBM works with clients to offer Digital Integration for Intelligent Workflows (DI4IW), first of the kind in the industry to orchestrate data stored in large enterprise applications, such as SAP, Salesforce and Workday through a real-time API. DI4IW can reduce intelligent workflow development time by 50%-70%. By using extracting data from systems and build intelligences, businesses take a key step in their digital transformation journey.
Data for experience – Many companies focus heavily on the improving customer experience. However, organizations also need to evolve and design experience-led use-cases (such as app, AI assistants, etc.) using real-time data to elevate customer, employee and ecosystem partner experience into personalized one.
IBM worked with Royal Bank of Scotland (now NatWest Group) to create an intelligent, AI-powered cloud-based platform that aggregated real-time digital mortgage data. By using both internal data from customers and external data from policies, the bank empowered mortgage call center workers as they supported customers throughout the home-buying process. Since implementing the digital mortgage support tool, RBS had 20% improvement in customer NPS, and a 10% decrease in call duration. Learn more here: https://www.ibm.com/services/client-stories/rbs
Moving forward as a data-driven digital organization
Not only will businesses that do not invest in data miss the benefits of using data, but they also risk losing customers and market share. While these businesses’ competitors likely use insights about improving digital customer experiences, consumers are quickly becoming less satisfied with organizations that aren’t creating personalized experiences.
Five years ago, organizations undergoing digital transformations were on the leading edge of technology. Today, however, being a digital business is the expectation — not a differentiator — organizations with digital experiences will realize growth while other businesses rapidly fall behind. Regardless of where organizations were on the digital transformation journey at the beginning of the pandemic, businesses must quickly move to this new model of operations and serving customers by investing in tools that allow them to use one of their most important assets — their data.