The volume of relevant data for enterprises is growing exponentially and projected to reach 158ZB by 2025
[1]. The massive expansion of unstructured data such as IoT images and social data unlocks new opportunities for enterprises to be innovative. It allows them to learn more insights about their customers to create a better customer experience, while operating more efficiently to achieve a step change in productivity. Businesses can now do more with less by applying Machine Learning to automate business processes. Companies can create new business models and revenue streams by monetizing data-driven capabilities.
While data is the new gold and we are currently experiencing a data-driven “gold rush,” enterprises are still struggling to manage data from multiple sources:
Gaining business value from Data: 74% of enterprises say their data landscape is so complex that it limits agility
[2]. Companies need to gain business value from massive amounts of data to improve internal processes, such as fraud prevention, predictive maintenance, and supply chain optimization. However, it is difficult to transform large volumes of data coming from SAP, non-SAP systems and the broader data ecosystem. Additionally, when customers process data disparately, too often customers have redundant data sourcing, refining, and processing that consume precious resources that could be repurposed for something else.
Driving Data Innovation: 86% of enterprises claim that they are not getting the most out of their data
[3]. Companies need actionable data to enhance customer-facing activities such as pricing, customer churn, upselling, and promotion optimization that drive growth.
84% of CEOs are concerned about the quality of the data on which they base their decisions
[4]
Complexity in preparing, cleansing, and delivering quality data from SAP and non-SAP systems for machine learning and IoT use cases is one of the biggest challenges – along with the lack of simple, production-ready tools to automate end-to-end processing of all data, including IoT, social, and raw unstructured data. They must be able to reuse data operations in data-driven applications. Manual “feeds” and siloed data operations are not effective in delivering data to create agile processes that combine existing data assets with new technologies.
Ensuring Data Compliance: $9.7M is the average financial impact of poor data quality on organizations per year
[5].Enforcing a multitude of corporate and regulatory data policies is becoming a burden and risk for enterprise IT. Approximately 4% of enterprise’s annual turnover can be fined by the EU for violating GDPR regulations. Uncontrolled data sourcing and consumption, and insufficient security and governance across the distributed data landscape make it difficult for companies to monitor and control data usage. Agile processes and governed processes should not be orthogonal to each other in the modern data landscape.
SAP Data Hub is the only all-in-one data orchestration solution that orchestrates and governs any type and volume of data – across the customer’s entire distributed data landscape – to rapidly deliver enriched, trustworthy data for driving business processes that underpin the intelligent enterprise. Innovative data pipelines refine and distill a wide variety of data while eliminating the need for mass data movement. Customers gain complete visibility into all their human-, machine-, and application-generated data, and securely discover, consume, and share relevant data.
Customers can deliver the right data, to the right users with the right context, at the right time, with the most trusted, open and flexible data landscape management solution.
SAP Data Hub’s provides compelling business benefits. Companies can,
- Engage digital consumers to make informed decisions based on intelligent data pipeline flows
- Empower digital users to take timely action based on the value and intelligence from all data
- Empower business to act with agility and compliance by using modern and trusted technology that simplify innovation while protecting data security
- Empower business to take advantage of cloud acceleration for faster time-to-results while preserving operational flexibility and provider choice and reusing existing data assets
With version 2.3 we have recently introduced a major new release of SAP Data Hub with a rich set of new capabilities which include:
- Meta Data Governance
- Catalog to discover, define, publish, and understand sources
- Search for Meta Data attributes and tags
- Automated Meta Data Crawling
- Enhanced Connectivity (Databases, Big Data Stores, Cloud-native Technologies) and deployment options
- Tighter Application and 3rd party Integration into e.g. S/4HANA, hybris, Ariba, Cloud pub-sub infrastructures, open source solutions
- Simplified setup on managed clouds (AWS, GCP, Azure) with unified UX (one modelling)
- Further extending SAP Data Hub’s adoption on serverless compute infrastructures and support for scalability and portability.
- Release of SAP Data Network, live insights for workforce
- Enablement of companies to recruit top talent based on data-driven hiring insights, including up-to-the-moment insights about the labor market across industries and geographies.
- Release of SAP Data Hub on Cisco’s Hybrid Cloud Solution
- Native deployment of SAP Data Hub on Cisco’s Kubernetes-based Hybrid Cloud Solution “Cisco Container Platform.” providing customers with a turnkey solution for their own data centers.
It has always been SAP solutions’ value proposition to support end-to-end enterprise scenarios and we are dedicated to extending this to the Big Data world with SAP Data Hub. It is the only all-in-one data orchestration solution that discovers, refines, enriches and governs any type, variety, and volume of data across the customer’s entire distributed data landscape, whether it be on-premise or cloud or a combination of both.
Learn More:
Press Release
Introducing SAP Data Hub 2.3
[1] Source: IDC’s Data Age 2025 study, sponsored by Seagate, April 2017
[2] Source: SAP’s Data 2020: State of Big Data study, October 2017
[3] Source: SAP’s Data 2020: State of Big Data study, October 2017
[4] Source: Forbes: The Data Differentiator: How Improving Data Quality Improves Business, May 2017
[5] Source: Forbes: The Data Differentiator: How Improving Data Quality Improves Business, May 2017
