Succeed in the Intelligent Era with an End-to-End Data Management Framework
The last decade has seen unprecedented advancements in artificial intelligence. We have moved towards a data-centric approach, and data is the center of everything digital. The data collected through different sources is refined, analyzed, and orchestrated with data platforms to generate intelligent insights that can facilitate the growth of any organization.
The spread of these data platforms, coupled with the advancements in artificial intelligence, enable what has come to be known as the intelligent era. Enterprises are now making smart decisions – backed by actionable insights that also give them the guidance they need for the future.
As an SAP partner, I was given the opportunity to explore the SAP Data Hub and get insights into the data management challenges organizations face in the new Intelligent Era.
What to Expect in the Near Future
With bigger horizons and a solid base to build on, we can expect organizations to settle down with data to be a more significant part of this intelligent era. The following points list what can be expected from the future:
Enterprises will be able to generate data from any business, any person, and any device. This will be augmented by the Internet of Things or IoT and the broader 3rd party data economy.
We can expect better applications that are focused on improving the customer experience among many other things.
Enterprises will be able to have access to information essential to their products and services. The sales sphere will be smarter than it is now, with many personalized tools and techniques, marketing to every customer out there.
Humans have surely done the tough part, and they need to be consistent to reap the fruits of AI. However, data management is a necessity that shouldn’t be compromised. The foundation of all intelligence in the future and the benefits we mentioned above lie in the provision of data management. Data is the center of the IT landscape.
Data that is properly structured and is duly managed from its arrival to when it is under process within analytic tools or business processes would generate the best insights and actions. The insights you generate to fuel your enterprise’s intelligence and to take smart decisions will only be as good as the data that you have in your hands. Poor quality data and the lack of the data leads to poor quality insights and bad decision-making. Thus, data management is a crucial facet of an intelligent environment that must not be ignored. Since it plays an important pivotal role in dictating and building the ecosystem of change, data itself requires stringent checking and management throughout the whole analytics and business process.
What Hinders Companies from Performing in the Intelligent Era
Having talked about the shiny side of the intelligent world, there are also some major implications that require due attention as well. The SAP State of Big Data Study, published a year ago, revealed important statistics related to the challenges faced by organizations going smart.
According to the results, almost 74 percent of enterprises felt that their data landscape/ecosystem is too complex for them to understand, so much so that it is believed to limit their productivity and innovation in a way. 86 percent believed they weren’t getting much value out of the data and 84 percent of all CEOs were extremely concerned about the quality of the data and how the insights generated through it could negatively impact decision-making in the future. This skepticism is further fueled by the fact that over $9.7 million are lost every year per average organization due to poor quality data and the resulting bad decisions made based on it.
Enterprises and partners are realizing the benefits and the promise of an intelligent ecosystem by focusing on data transparency, by leveraging existing investments, by building new data processes, and by transforming data landscapes. They do plan to incorporate machine learning and open source technologies for insights, but the challenges cannot be ignored. These challenges include:
Siloed data is perhaps the biggest challenge that enterprises looking to go smart are facing. The presence of large sets of secluded data on public or private clouds such as Hadoop, Microsoft, Google, or Amazon Web Services makes it challenging for organizations to gather the data required for decision making. Data is no longer only the domain of Enterprise systems but it’s being created by devices, 3rd parties, and derived from a myriad of sources and therefore the processing of data also needs to reflect the new data landscape data stores connecting, refining, and utilizing wherever it resides.
Most organizations want to run real-time analytics that gives them results and insights from their data on the go. But with these siloed platforms the data is scattered, inconsistent, and there is little potential for generating enterprise-wide insights in real-time. This concerns organizations and entrepreneurs as they try to search for potential options to help them solve this lack of available company-wide insights.
Due to the Internet of Things with literally billions of connected sensors and devices, mobile data, social data like Facebook, Snapchat and others, new data types arise almost daily.
The amount of data being generated and the need for digestion of this data volume is unheard of. Data volume, data generation velocity and data complexity increase to a level that has never been there before.
It is not only a challenge of completely new data types in structured and unstructured data sources that get generated, but it is also a challenge of volume.
Data Migration and Integration
Data migration, or the integration of all data to a single data store is difficult to perform. The substantial increase in IoT data sources, volume and velocity makes it hard to migrate data to a single source or integrate each source with all analytics and applications. It is often the case that analysis and decisions have to be made on data at the point of capture, at the edge, or on data in transit. Data migration and integrations also require significant expertise in the field of data management, which is scarce.
The combination of these factors makes it complex and difficult to build an intelligent enterprise that uses data to the fullest potential. It is because of the complications involved here that 84 percent of all CEOs believe that their data landscape is particularly complex and requires simplification.
It is simply impossible to handle the amount and complexity without software support: be it data quality management all the way up to machine learning and artificial intelligence. To understand the true meaning of data, help is required.
We have seen companies are challenged by having diverse and separated data landscapes, they are challenged with growing and more complex new data types arising, they have a hard time to ensure data quality, to understand the full meaning of data, and now comes along another phenomenon to handle. Additionally, it is very challenging to ensure consistency of data quality and transformation across many different data silos as data cleansing and enrichment may be performed differently in each implementation without central governance and alignment.
The Need for an End-to-End Data Management Framework
Enterprises drowning in the implications of data need an end-to-end management platform. Managing data on several public and private clouds can be too much to handle. A good end-to-end data management system should:
Unify Data: An organization should be able to orchestrate and unify all their distributed valuable data from public and private clouds with an end-to-end data management framework. This applies to both the data itself but also to the metadata that describes the data consistently which is be easy to find, understand, and utilize.
Simplify Landscape: Perhaps the most important functionality of an end-to-end data management system should be that it simplifies the landscape and makes it easier for enterprises to view and understand their data and work on it in one central place. Business users and IT users need to be able to discover the data and metadata and understand the lineage or how that data was sourced and transformed. Hindrances should be reduced to a minimum.
Handle Complexity: With the landscape already simplified, an end-to-end data management system should also handle the complexity by combining data from disparate data sources and make near real-time analyses easier. This enables faster development of new Analytics and decision processes through re-use, reduced redundancy, and making data before a service to users and processes. This simplification should also support the agility to adjust to new data, analytics, and adoption of new technologies to deliver the next generation of data driven innovations to the business. The ability to re-use existing data integration, data processing, and coding is a must – no customer wants to re-implement productive process and data flows but rather focus to driving new value based on net new innovations.
Centralize Governance: Data governance is a major concern for most managers, especially in the era of GDPR, which is why an end-to-end data management setup should centralize the governance and bring data access control over to the people that matter. Insight into of the metadata, profiles of data in the connected data stores, the data processes executed, and the end-to-end lineage on the interconnectivity is critical. This driving understanding what data is available, defined, used, consumed, and if business and regulatory policies are being complied with.
Data Management: Finally, and perhaps most importantly, an end-to-end solution should help you in managing your data better. It should provide a single solution that gives you full visibility into your data without leaving its original home. Data should flow through a data pipeline where it is collected, orchestrated and cleansed so you do not need to worry about the poor quality of insights and decision-making. This is not limited to the data integration flow but also to the process operations that are executed on data as it flows across the landscape. This A data management framework should manage your data for all future needs and even trigger actions based on predefined parameters.
End-to-End Data Management Systems at Work
The SAP Data Hub provides an end-to-end data management system to augment the efforts of enterprises towards a smarter future. Furthermore, embedded Machine Learning Algorithms and the integration to IoT provide SAP Data Hub with the necessary functionality to enable its customers to become truly intelligent enterprises. Kaeser Compressors SE, a manufacturer of compressed air solutions, is a user of the SAP Data Hub. The manufacturer can now integrate IoT data with customer data and equipment information to generate close to real-time insights allowing the company to adjust their business processes on the fly resulting in a better productivity rate, more agile ticket handling/customer service, and ultimately saving costs for the company
The latest SAP Data Hub release provides customers with a lean deployment and easy installation option offering a complete containerized setup for decentralized data processing. Enhanced Metadata Management and Cataloging functionality that were introduced with the latest Data Hub release allow customers to quickly and easily get an overview of their data sources, under the linage of data and operations, define and manage these in one central place, which is critical when it comes to compliance topics like GDPR. Additionally, SAP Data Hub is previewing new capabilities in integrate ABAP based systems into pipelines, integrate cloud data integration tools, and to integrate SAP Data Hub into BPM designed business processes with the wider data landscape.
SAP’s Hana business data platform carries all the benefits of an end-to-end data management platform mentioned above and builds together with SAP Data Hub the foundation for the Intelligent Enterprise. The HANA data platform together with the functionality enabled through SAP Data Hub helped Kaeser Compressors increase productivity and customer service by taking charge of the complexities involved in the data management process and delivering the insights generated through their data. The biggest benefit of SAP Data Hub that customers have reported is its ability to bring their structured and unstructured data together without the high costs of moving or replicating the data.
SAP’s Data Hub creates a foundation for all intelligent enterprises and augments the efforts of thought leaders in the creation of a smarter world.