Over the past year, we’ve heard the buzz about big data evolve from “What is it?” to learning about the tangible results companies are experiencing by applying data science to large data sets. You don’t have to look far to see the impact of big data — a quick news scan reveals how city sensors collect data to improve urban life and how political parties use big data to target voters.
So, clearly, big data has arrived. But how are organizations responding? What does the cloud mean for big data? Can the cloud make analytic capabilities more accessible? And how can IT better manage big data analytics in the cloud?
Where to Begin?
Big data has played a role in retail and marketing, in which companies use complex point-of-sale and market research data to gauge customer sentiment, deliver better promotions, predict demand, and keep products in stock. But big data is also revolutionizing other industries.
In healthcare, safety, and public institutions, big data is helping reduce risk, cut costs, and boost efficiency. It is also introducing disruptive innovation. For example, big data is helping speed detection of certain cancers; leverage wearable technology to make air traffic control safer and more accurate; detect bank fraud by analyzing free-text payment descriptions; improve energy management through smart grids; and help first responders assess needs, prepare for disaster recovery, and support public health.
Though the possibilities are nearly endless, actually implementing big data solutions can be challenging, as the value needs to be proven. It is best to first establish a high-level strategy, and then demonstrate a quick win before moving on to broader initiatives: Identify the one question or use case that, if addressed, would bring the most value to your organization. Engage a data scientist to identify “dark” data (data already stored in your organization but not currently utilized for analysis) to uncover insights about your business that can be put to use immediately.
IT leaders want to be the first to introduce innovative technology to the business — and big data is no exception — but with the advent of cloud solutions, some line-of-business executives aren’t waiting for IT to come to them.
Big Data Analytics and the Cloud
In October 2014, Computerworld listed eight big trends in big data analytics.1 Number one on the list: Big data analytics in the cloud. The report cites a shift from using frameworks and tools to process very large data sets on clusters of machines to processing data in the cloud. Meanwhile, IDC predicts that “a shortage of analytics and big data technology skills will drive a growing number of buyers toward cloud solutions.”2
CIOs at companies of all sizes are embracing the cloud; 67% have already adopted a hybrid approach to application use, integrating on-premise and cloud-based applications. And 75% of hybrid users believe that their company’s IT processes are less complex after migration.3
Cloud services make it easier for the business to purchase and install analytic solutions, often without the involvement of IT, which can then devote its time and resources to other projects.
Putting complex analytics in the cloud helps organizations make sense of a constant, fast-moving stream of information, sifting out important events and illuminating previously undetected insights.
Getting Your Big Data to the Cloud
The real value of big data lies in the insights it produces when analyzed — discovered patterns, derived meaning, decision indicators, and ultimately the ability to respond to the world with greater intelligence. Putting it in the cloud makes it that much easier to use, and therefore to glean these insights. There are three main scenarios for putting big data in the cloud:
- Infrastructure as a service (IaaS) provides storage and computing power for your big data platform in the cloud. This option is especially valuable for highly innovative and individualized use cases. Costs are based on resources allocated and consumed.
- Software as a service (SaaS) supports fast implementations of standard use cases in which cloud providers manage application infrastructure and platforms; applications are delivered along with content and predefined analytics for use in areas such as basket analysis, churn analysis, and predictive maintenance. Predefined scenarios accelerate time to value and ensure access to up-to-date models and tools. Pricing is typically a subscription model.
- Platform as a service (PaaS) allows application developers to use a cloud computing platform to develop smart extensions of big data applications — such as connecting with social networks or external sources of information — without the cost and complexity of buying and managing the underlying hardware and software layers. Costs are based on resources consumed.
In all three scenarios, putting complex analytics in the cloud helps organizations make sense of a constant, fast-moving stream of information, sifting out important events and illuminating previously undetected insights. And cloud computing’s unparalleled scalability makes it the perfect environment for affordable and accessible big data analytics.
As analytic environments become more complex, however, IT organizations will be looking to simplify the technology landscape. Imagine that your company is running cloud solutions from several vendors, each with its own analytics. Over time, this can create a high level of complexity with numerous tools to manage, as well as possible reconciliation issues. It is best to consolidate the cloud landscape using just a few vendors — ideally only one — that can deliver everything. Another possibility is to use overlay analytics to unify multiple cloud systems.
Several software companies are poised to support the future of big data and analytics in the cloud — and none are making a bigger investment in business intelligence (BI), enterprise performance management (EPM), predictive capabilities, and visualization in the cloud than SAP.
For both new and existing SAP customers, the cloud — specifically SAP HANA Enterprise Cloud — increases deployment flexibility for leading analytic solutions. SAP HANA Enterprise Cloud helps you run SAP in-memory-based applications in a private managed cloud, allowing you to cost-effectively get real-time analytics without having to sink significant resources into on-premise storage. You can get up and running quickly, and then use the speed and power of SAP HANA along with the ease of use of the cloud to make better decisions and predictions based on your data.
Big Data Solutions to Your Challenges
The explosion of interest in big data, analytics, and more recently the Internet of Things is transforming the marketplace and offering many possible avenues for competitive differentiation. Yet big data and analytics are not the same to all companies, and no single future or opportunity fits all.
Big data services from SAP help clients harness big data in the cloud with everything from strategic and cloud advisory services, to roadmaps, implementations, and application management services. Our big data experts and data scientists work to design analytic solutions that respond in the right time for your business, depending on the challenge you are looking to solve.
To learn more about how big data services from SAP can help you determine which big data opportunities are right for you, visit www.sap.com/bigdataservices.
1 Computerworld, “8 Big Trends in Big Data Analytics” (October 23, 2014; www.computerworld.com/article/2690856/8-big-trends-in-big-data-analytics.html). [back]
2 IDC, “Market Analysis, Worldwide Big Data Technology and Services; 2012-2015 Forecast” (March 2012; www.idc.com/research/viewtoc.jsp?containerId=233485). [back]
3 SAP and Wakefield Research, “CIO Survey Results — Hybrid: The Next-Generation Cloud” (April 2013; http://bit.ly/1I5PWvo). [back]