Business Needs Connected And Intelligent Data: Imperative #2

Tim Hardy

Part 2 in the Trusted Data Imperative series

Business data is never isolated. For example, large enterprises may need to merge and compile data from many different systems to deliver a consolidated view of their organization’s financial performance. The ability to connect data across systems is essential to help digital businesses gain situational awareness and extract comprehensive insights in real time to support decision making and automation. Achieving this is what we call becoming an “intelligent enterprise.”

Data management challenges

The goal is clear: “To predict events and generate responses that can be automated to deliver the best outcome.” What are the challenges for an enterprise in implementing this?

  • We need to identify the impact of an event triggered anywhere in the value chain for it to be consolidated with the least latency and to be available centrally.
  • We must define a platform in which data is integrated across devices and systems and connect it with the systems of record.

Accomplish this, and we can efficiently monitor the propagation of the impact across the enterprise. Real-time embedded insights at each process step would help quantify the impact of any event and make it possible to analyze the impact across the value chain, ending with the impact on financial performance.

As we highlighted in an earlier blog, real-time data is the central component of the intelligent enterprise. Data is the currency of the digital transformation, and data-driven applications are the future of application development. An end-to-end process that, for example, collects sensor data to provide new pay-as-you-go business models (by incorporating spatial analytics, machine learning, blockchain, contracting, operations, and billing) opens up a whole new world of data-driven decision making.

Consider a project that combines business data in real time with data from other business solutions. Such a setup delivers data-driven insights at each decision point in your business process by automatically predicting outcomes on both current and historical data.

Choosing the right data-management strategy can also help your business break system barriers and overcome data complexity. A business must run advanced analytics alongside high-speed transactions on real-time data for accurate, up-to-date responses in a fraction of a second, taking advantage of machine learning, for example, to be able to score data sets in real time for instant responses.

This means your data management solution needs to:

  • Integrate data across devices and systems
  • Connect it with your system of records
  • Be available in real time for your system of intelligence

Paving the way to the intelligent enterprise  

A modern data-management system can help you tame your current data landscape complexity and smooth your path toward becoming an intelligent enterprise. An advanced data platform can connect data and build intelligent and live applications that combine advanced analytics on any data.

Key enabling innovations are derived from capabilities around logical data models and virtualization that work in conjunction with the native power of the platform itself. This allows data to be queried and analyzed with real-time machine learning capabilities wherever it resides, or to have high-value data ingested from various sources with smart data integration capabilities.

For the connected and intelligent data, the data management solution should provide three core capabilities: 1) data access, 2) analytics processing, and 3) application development.

Data access:

  • Gain a complete and accurate view of your business by accessing data from any source – internal or external.
  • Access data where it’s located, integrate or replicate relevant data into an in-memory database, and ensure data quality to increase confidence in decision making.
  • Enhance, clean, and transform data to make it more accurate and useful with a simplified landscape – one environment in which to provision and consume data. Access more data formats, including an open framework for new data sources.
  • Take advantage of logical data models and data virtualization capabilities to query data wherever it resides while you retain the ability to ingest high-value data from various sources with smart data-integration capabilities.

Analytics processing:

  • Gain new insights from advanced analytics processing by leveraging in-memory data processing capabilities (text, predictive, spatial, graph, streaming, and time series) to get answers to business questions and make smart decisions in real time.
  • Get new insights from business data enriched with geospatial data (read more), discover relationships on the fly from connected graph data, operate on live data for real-time insight, and seek out new opportunities or optimizing decisions based on the predictive value of large-scale data sets (with in-database machine learning algorithms with major machine learning frameworks).
  • With streaming techniques, capture, filter, analyze, and act on millions of events per second in real-time from a wide variety of sources.

Application development:

  • Develop next-generation applications that combine analytics and transactions and deploy them on any device.
  • Develop responsive Web applications that run on any device and adapt to screen size automatically, delivering a consistent look and feel across all touch points.
  • Build applications based on a microservices architecture through support for multiple programming languages, including Java, JavaScript, Python, Go language, Node.JS, JSON, and Open Data Protocol (OData).
  • Choose among various open-source development tools, such as Git, GitHub, and Apache Maven, and build enterprise-class non-SQL (NoSQL) applications with the support to store schema-flexible data in JSON format. Combine JSON data with structured data and query or analyze it using SQL.

Learn more

In short, taking advantage of an in-memory multi-model database architecture allows you not only to process structured and unstructured data, but also to avoid predefined aggregates, materialized views, and data duplication between operational and decision-support systems.

Learn more about SAP HANA Data Management Suite and how you can use it alongside your SAP S/4HANA projects to pave the way toward becoming an intelligent enterprise. Your business gains the ability to sense, respond, learn, adapt, and predict in real time using intelligence from all your data assets.

This article originally appeared on the SAP HANA blog and is republished by permission.


Tim Hardy

About Tim Hardy

Tim Hardy is VP, Global Industry & Platform Sales Programs at SAP. This team is responsible for articulating the SAP Database and Data Management propositions. Tim has 25 years of experience with the positioning, architecture, delivery, and support of software- and hardware-based solutions. His background is in the disciplines of data management and business applications. Tim has global experience working with leading organizations, partners, and ISVs across every major industry vertical. Tim has a specific passion for the Internet of Things, and how this can generate new business opportunities. Prior to joining SAP, Tim served as chief architect and CTO at Oracle in the ISV/OEM global business. He has a Masters in Engineering from Warwick University, and a BSc in Mechanical Engineering from the University of Birmingham (UK).