Presented by SAP
Any technology expert worth their salt will say a successful AI strategy depends on reliable data. In fact, a recent survey of technology leaders found that almost 94% are now more focused on data, driven by the increased interest in AI.
While this should not be a big surprise, things get trickier when organizations attempt to navigate the landscape of vendors out there. With a quickly changing market, it’s difficult to determine which one can best help harness data for strategic transformation, including AI initiatives.
The good news is there are paths to data modernization that companies can take no matter where they are on their journey. Here are three tips for success.
1. Add value to your most important business applications
Enterprises still struggle to unify data across business applications while preserving context and relationships — only 34% of business leaders reported high trust in their data. Without a trusted data foundation, analytics and AI initiatives often stall, as teams spend more time integrating and managing data than leveraging it for business value.
This is where a business data fabric comes in. A business data fabric reduces latency by providing an integrated, semantically-rich data layer over fragmented data landscapes. This architecture simplifies data management and makes it easier to access trusted data. By creating a single source of truth across multiple sources and applications, organizations can more easily set up data governance and self-service data access.
SAP provides this with its SAP Business Data Cloud, a fully managed SaaS solution that unifies and governs SAP data and third-party data.
SAP Business Data Cloud simplifies customers’ complex data landscapes and with its zero copy share approach, which takes the heavy lifting out of harmonizing, federating, and replicating data. This frees up time for organizations to focus on strategic and transformational data projects, like building intelligent applications or providing high quality data for AI initiative.
Moreover, SAP Business Data Cloud sits within and powers the SAP Business Suite, an integrated set of business applications, data, and AI. This helps customers further establish a harmonized data foundation to connect insights across business applications and processes, and fuel AI initiatives.
2. Move from transactional to intelligent applications
With a harmonized data layer in place, companies can start using or building their own intelligent applications. According to Gartner, “while applications can behave intelligently, intelligent applications are intelligent.”
Moving beyond rule-based, prescriptive approaches, intelligent applications are self-learning and self-adapting applications that can ingest and process data from any source. These applications democratize data, taking it out of the realm of data scientists and giving everyone access from a human resources professional to Chief Financial Officer.
But with various vendors offering these applications, how to choose? Intelligence alone won’t drive business results. It’s important to select a company that provides these applications directly within the context of core business processes like supply chain or procurement management, so people use that intelligence in their daily work.
SAP’s unique approach to intelligent applications, which are part of its business data cloud, are grounded in 50-plus years honing its business process, application, and industry expertise. The applications are built from data products, easily consumable information taken from its vast array of solutions and curated to solve specific business problems. Enriched with AI technologies such as knowledge graph (which pinpoints the relationships amongst data points), SAP Intelligent Applications come out-of-the-box with modeling, reporting, predictive, and other capabilities.
More significant, they are embedded across SAP’s application landscape including ERP, human resources, procurement, supply chain, finance, and other solutions. One example is the newly announced People Intelligence package within SAP Business Data Cloud, which connects and transforms people and skills data from the SAP SuccessFactors Human Capital Management suite into readily available workforce insights and AI-driven recommendations.
3. Don’t go it alone: an open ecosystem is the key to success
No one vendor can provide all industry and business data and expertise. That’s why it’s important to select one with an open ecosystem approach.
It’s critical for several reasons. First, to scale the solution and make it available on multiple cloud platforms. Second, to provide access to a wide variety of domain expertise. And third, to build an ecosystem of intelligent applications enriched by industry leaders that specialize in specific data sets, like risk assessment.
Those three reasons encapsulate SAP’s partner strategy. For example, SAP recently announced a partnership with Adobe to build an intelligent application on SAP Business Data Cloud that combines supply chain, financial, and Adobe digital experience data to generate deep insights for joint customers. Additionally, Moody’s will work with SAP to help customers and partners build intelligent applications using Moody’s risk datasets, integrated with SAP’s accounts receivable data, to boost cash flow and default predictions.
Moving forward, organizations will need to treat data and AI as inseparable. Successful AI initiatives rely on good quality, relevant data taken from business applications and beyond. A recent GigaOm study found that companies that treat data as a strategic asset see a 28% higher AI rate of adoption. With SAP Business Data Cloud, organizations gain the missing puzzle piece to help harmonize and streamline data from any source, enabling AI projects that more effectively drives business outcomes.
Jan Bungert is Global Chief Revenue Officer, SAP Business Data Cloud and AI at SAP.
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