Staying Ahead Through the Power of Data
Businesses today face a more challenging and fluid business environment than in the past. But while data and AI-powered insights can help organizations pivot and embrace new opportunities, many are not prepared to leverage the power of their data, says Colin Tan, general manager and technology leader at IBM Singapore.
Responding to queries from CDOTrends, Tan explained that a significant amount of data remains untapped within enterprises, with up to 74% of data not analyzed and up to 82% of enterprises inhibited by data silos.
According to Tan, the root causes for this can be attributed to a lack of data skills, inflexible governance tools, and biased data (among others), and means that many organizations are not getting all the value they could be from their data.
An end-to-end approach
Though organizations have started to analyze their data and embed analytics to transform information into insight and action, forward-thinking organizations remain many steps ahead. Indeed, Tan says top-performing organizations are five times more likely to be using analytics than lower-performing organizations, giving them a crucial competitive advantage.
As businesses rush to strengthen their data capabilities, what are some common missteps? Tan pointed to lack of scale and lack of trusted data as common mistakes his team has seen. Organizations are hampered if they start too small, or if they are forced to work with an outdated data architecture or legacy systems that are ill-suited for modern AI-powered, data-driven decisions.
“Organizations need to take an end-to-end approach and define intelligent workflows to transform their businesses. [This] helps to bring simplicity to organizations through the automation of complex processes… [and] enables employees to focus on high-value tasks,” said Tan.
Organizational challenges can result in companies failing their AI initiatives, cautioned Tan. According to Gartner, just half of AI projects make it from pilot to production, taking an average of nine months to do so. Nonetheless, the undeniable role of AI in business transformation is driving a surge of interest to operationalize AI platforms to solve enterprise problems.
To succeed, Tan says businesses should start with a coherent vision that connects their AI and data strategy with business goals. They must also gain executive sponsorship or line of business involvement in AI projects and focus on driving business outcomes instead of the technology.
Role of the data fabric
One of the most effective ways to harness disparate data repositories is through a data fabric, which is a data architecture that delivers consistent capabilities across a distributed environment. This opens the door to frictionless access and data sharing, enabling self-service data consumption and collaboration.
“A data fabric architecture provides a connective tissue between data endpoints – enabling the full breadth of data management capabilities including integration, discovery, governance, curation, and orchestration,” explained Tan.
Using a data fabric, business users and data scientists can quickly access the trusted data they require for their applications, analytics, and machine learning models, improving the speed of decision making and driving digital transformation. Similarly, technical teams can also leverage a data fabric to radically simplify data management and governance in complex hybrid and multicloud data deployments, even as they reduce costs and risks.
Tan pointed to Dutch financial services giant ING, which has been building vast data lakes for its data but had struggled to make them work. It eventually utilized a data fabric solution from IBM to solve the challenge of enabling high-quality, governed, business- and regulatory audit-ready data across the entire enterprise – and locations.
According to Tan, organizations that rely on data fabrics to dynamically connect, optimize, and automate data management processes will reduce time to integrated data delivery by 30%.
Safeguard your data
As the use of data grows, businesses must also address inherent privacy and security challenges that invariably arise in tandem with the greater accessibility of data. Tan says this can be achieved by incorporating the right governance controls to secure it against unauthorized use.
“We need to balance the benefits of data democratization and at the same time build a sufficient safeguard into the platforms we built. That’s the reason why implementation [and] the need to have automation for governance and data protection is important. There should be a balancing act between both accessing and governing data,” said Tan.
“With a data fabric architecture, organizations can safeguard their data. [A] data fabric embeds automated governance within the data platform. Enabled by active metadata, data fabric ensures automatic policy enforcement for all data access, providing a high level of data protection. That’s why we need platforms that have been built and engineered for the new generation of data projects which has security and governance built into them.”
“Companies do understand that they need to rely more and more on data, rather than gut feeling, to get new opportunities,” summed up Tan. “[And] organizations that go beyond simple regulatory compliance can build trust with customers and stand out from competitors.”
Paul Mah is the editor of DSAITrends. A former system administrator, programmer, and IT lecturer, he enjoys writing both code and prose. You can reach him at [email protected].
Image credit: iStockphoto/spainter_vfx
