The promise of big data is both compelling and straightforward — crunch large data volumes (operational, transactional, structured, unstructured) to unlock insight (correlations, trends, outliers, etc. ) that informs timely decisions that boost efficiency, the bottom line and competitiveness — which is why analysts are predicting big spending on big data analytics.
According to IDC, worldwide revenues for big data and business analytics will grow from $130.1 billion in 2016 to more than $203 billion in 2020, at a compound annual growth rate (CAGR) of 11.7%.
The verified, enormous value hidden in big data is driving strong business interest in big data analytics solutions. McKinsey reports that retailers effectively tapping big data can increase operating margins by as much as 60%. Walmart used big data analysis to drive a 10 – 15% increase in completed online sales for $1 billion in incremental revenue. According to a survey conducted by MIT Sloan Management Review, top-performing organizations are twice as likely as lower-performers to apply analytics in their operations.
A data warehouse for the digital era
Today’s IT landscape has become increasingly complex and costly, with companies investing millions of dollars designing, implementing and maintaining multiple databases, datamarts and enterprise data warehouses to accommodate ever-increasing data volume generated by multiple ERP instances, numerous business applications — CRM, SRM, SCM, etc., and the deluge of unstructured data from social media, Web traffic, IoT and other sources now streaming into enterprises.
Traditional databases and their massive disk farms are costly CPU hogs that pose daunting power and cooling challenges. Moreover, relational database management systems (RDBMS) are woefully inadequate for meeting today’s big data analytics and business reporting needs. While good at storing persistent, structured data, traditional RDBMS fail when it comes to quickly analyzing massive quantities of structured and unstructured data to inform critical, timely business decisions. In short, they take too long and consume too many computing resources.
The days of (latent) IT production reports pushed out to managers are quickly coming to an end. No one wants to wait for time-consuming batch processing when far too compelling are the advantages of real-time, fact-based decision-making capabilities powered by intuitive, self-service analytics/BI tools in the hands of marketing, sales, supply chain management, manufacturing, engineering, risk management, finance, HR, frontline workers and line-of-business pros.
Enter SAP BW/4HANA. Announced by SAP in August 2016 and available since September 7, BW/4HANA is a next-generation data warehouse that meets the needs of today’s real-time digital enterprise. Available on-premises or via leading cloud hosting providers such as NTT DATA or SAP’s HANA Enterprise Cloud, BW/4HANA’s flexible deployment options ease implementation and help manage costs.
BW/4HANA is an evolution of the SAP Business Warehouse, or BW as it is commonly known, and is completely (and exclusively) optimized and tailored to run on SAP HANA. Capable of handling massive amounts of data (historical and live / structured and unstructured) residing in a diverse IT landscape (within or outside the enterprise), BW/4HANA provides an open data warehousing environment for rapid application development, a modern user interface, and advanced multi-temperature data handling. Advances in BW/4HANA provide unprecedented flexibility, real-time performance and the ability to overcome the challenges of data silos from highly distributed data sources.
The ability to run SAP’s modernized data warehouse for the real-time analytics that power business will appeal to new and existing SAP customers. With approximately 15,000 installations, BW is a popular SAP product. For these existing BW customers, BW/4HANA could be just the product they’ve been waiting for to begin (or extend) their HANA migration.
BW/4HANA benefits include:
- An end-to-end data management platform for running a live digital business through the automatic generation of views in SAP HANA that combine SQL data logic and application data taken directly from SAP S/4HANA
- Extreme performance through advanced, in-memory analytics and algorithm pushdown
- Faster application development with a fully redesigned user interface for enhanced data flow modeling
- Reduced data management and storage costs through automatic distribution of multi-temperature data
- Efficient data warehouse migration and implementation with unified data load management and full scale-out support, as a de facto standard for customers
- Easy transition for customers with the SAP Business Warehouse application to SAP BW/4HANA through utility features
IoT and predictive analytics for proactive maintenance
The Internet of Things (IoT) — a network of physical objects accessed through the Internet — has big implications for business. As the cloud, mobility and big data analytics collide to spawn a wealth of digital transformation possibilities, IoT represents the cutting edge of innovation in business today.
- Worldwide spending on IoT will grow at a 17% compound annual growth rate (CAGR) from $698.6 billion in 2015 to nearly $1.3 trillion in 2019.
- By 2018, there will be 22 billion IoT devices installed, driving the development of over 200,000 new IoT apps and services.
- IoT will create between nearly $4 trillion to $11 trillion in economic benefits globally in the year 2025.
By itself, raw data from sensors and IoT devices has limited value. However, for organizations that successfully acquire, analyze and act on the data produced by myriad devices and their connections, IoT represents a transformational opportunity.
For example, companies in machine-heavy industries — manufacturing, transportation, construction, etc. — understand that keeping assets up and running optimally is vital to survival. Downtime — and, even worse, system failure — increases overhead greatly, and, combined with crisis maintenance, can result in significant losses.
For companies in asset-intensive industries, predictive analytics applied to data streams from sensors and other IoT devices is driving a tectonic shift in maintenance practices from reactive to proactive, which helps them increase productivity, reduce downtime and maintenance costs, and ensure customer satisfaction.
As reported inhttp://us.nttdata.com/en/Contact-Us, research indicates that predictive maintenance can generate savings of up to 12% over scheduled repairs, leading to a 30% reduction in maintenance costs and a 70% cut in downtime from equipment breakdowns. For a manufacturing plant or a transport company, achieving these results from data-driven decisions can add up to significant operational improvements and savings opportunities.
The SAP Predictive Maintenance and Service solution helps equipment manufacturers, service providers and asset operators leverage IoT and predictive analytics to cut costs, boost reliability, and achieve operational excellence.
SAP Predictive Maintenance and Service is designed to help businesses decrease maintenance and service costs, while boosting availability and reliability of enterprise assets. Using predictive analytics on the SAP HANA platform, the solution processes vast amounts of real-time operational data from sensors and smart assets.
SAP Predictive Maintenance and Service provides built-in capabilities that analyze sensor data and monitor equipment behavior remotely for businesses in asset-intensive industries. The solution provides predictive models for machine learning and closed-loop integration with business systems to trigger corrective actions in service and maintenance systems to avoid unplanned downtimes.
Data quality a must for meaningful analytics
Ensuring data quality ranks among the top challenges business face as they ply analytics to their ever-growing data swamps.
The causes for data problems — all exacerbated by increasing data volumes — are complex and varied. Data residing in numerous, incompatible data sources, duplicated data, lack of visibility into data sources and the lack of a consistent, repeatable way to measure and score data quality top the list.
SAP enterprise information management (EIM) solutions help businesses manage big data for better insights, and data quality solutions from SAP help deliver accurate, trustworthy and timely information across the enterprise. With these solutions, companies can profile, cleanse, enrich and match customer, product, supplier and material data to improve efficiency of business processes and analytical initiatives.
SAP recently announced product innovations in its EIM portfolio to offer improved support for cloud and big data environments, enhancements for more self-service capabilities and comprehensive support for governing enterprise information.
The update to SAP’s EIM portfolio was sweeping, covering five different products and services:
- Added support to SAP Data Services focused for big data access
- Mobile device access for SAP Information Steward
- Expanded self-service and collaboration capabilities in SAP Agile Data Preparation
- Improved SAP HANA smart data integration performance
- A new cloud-based service — SAP Data Quality Management microservices — providing data validation and enrichment for addresses and geocodes within any application or environment
As further testament to its commitment to helping customers ensure data quality, last November SAP was named a Leader for the 11th consecutive year in Gartner’s Magic Quadrant for Data Quality Tools Report.
Contact NTT DATA today to learn how our expertise across SAP Analytics/BI solutions can advance your big data initiatives.
Post Date: 03/03/2017