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Big Data as a Service: What Can it Do for Your Enterprise?

By   /  August 3, 2017  /  1 Comment

big data as a serviceThe multiple V’s of Big Data require the hardware architecture operating it to be “distributed” and rooted in parallel processing for the most efficient management possible. Although the data sources for Big Data can be anywhere, when the multi-channel data is pulled into the Big Data environment, then the two significant characteristics that distinguish Big Data from other types of data is scale and processing power. The Big Data market was forecasted to grow to about $17 billion by 2015, and considerably larger through 2020. If one goes with that figure, then the Big Data as a Service (BDaaS) market has the scope to grow to about $2.55 billion, which is 15 percent of the Big Data market. Furthermore, the Big Data market may reach upwards of $88 billion in 2021, a significant part of which may be captured by Hadoop as well, because Hadoop offers an open source alternative to expensive hardware architectures.

Big Data as a Service (BDaaS) is the new mantra for Enterprise Data Management. Till recently, the term BDaaS was used to imply a broad range of data services hosted on Cloud platforms. In a typical bundled solution, other related services like Software as a Service (SaaS) or Infrastructure as a Service (IaaS) were used to deliver Big Data enabled Data Management solutions. The biggest drawback, in spite of many business benefits, of such a solution has been the lack of user control over the data path.

Now, with the growing popularity of Hadoop as an enabler for commodity hardware and open source software environments, BDaaS is becoming more accessible, more controlled, and more beneficial through a combination of on-premise and Cloud technologies. Now, data may be stored both on-premise and on Cloud platforms, but the data processing will take place on hosted Cloud servers. That means today’s businesses can store data on premise, have access to external data, and can mix-match data from multiple source for centralized Data Analytics and Business Intelligence (BI).

With the flexible hybrid BDaaS solutions, the businesses can “eat the cake and have it too.” While they can access and process data on hosted solutions for powerful, real-time insights, they need not worry about losing control and privacy over their proprietary data that is stored on premise. So while data is kept intact in their premises, they do not need to make costly investments for advanced data analytics and BI activities, and that can be done on outsourced Cloud platforms. BDaaS will enable the smaller to medium sized businesses to reap the benefits of Big Data Analytics without investing in costly in-house data center setups.

As Big Data allows widely varied, complex, and unstructured data to be analytics ready with reasonably fast and clean data preparation methods, it is getting increasingly popular among the global business world. Thus, the possibility of Big Data as a Service (BDaaS) making rapid strides in the Data Analytics market is strong. As more small and medium sized businesses continue to demand more sophisticated business analytics capabilities from IT service provides, BDaaS will become an ideal, cost-friendly alternative to in-premise data centers requiring heavy financial and infrastructural investments.

BDaaS in Many flavors

Big Data as a Service can be offered as many different options, which are described here:

  1. In a “Core BDaaS” environment, you are likely to get the basic Hadoop platform with some popular services like MapReduce, and Amazon’s EMR services. Users have the flexibility to combine services in core BDaaS and interact with other services.
  2. In a “Performance BDaaS” environment, the optimized hardware architecture forms the significant benefit for users looking to work with high-overhead software systems. The judicious balance of performance and cost distinguishes this environment from other BDaaS offerings.
  3. In a “Feature BDaaS” environment, users have the option of using features that are not available in the core Hadoop ecosystem. The feature set offered in this environment enables users to work efficiently with Big Data. The Software as a Service (SaaS) layer of this offering is strengthened by a fully scalable storage and computing architecture, which can be easily manipulated based on storage and processing needs. The pricing model for this option is also variable, depending on user’s specific needs.
  4. In an “Integrated BDaaS” environment, the combined benefits of Performance and Feature BDaaS are available to the users. If used properly, then this option could offer a high-performance, productive solution for users with advanced BDaaS requirements.

The article titled Big Data as a Service Is the Next Big Thing suggests that BDaaS is nothing but a mashup of Data as a Service (DaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) with up-scaled data. In the recent years, paid Cloud platforms have offered all these services in isolation for Enterprise Data Management, and now a comprehensive solution is available with BDaaS. BDaaS can offer all types of data services and full range of business analytics services from a centralized dashboard. You will find many interesting BDaaS use cases in the above article.

As a full range BDaaS on a Cloud platform would have made the offering very expensive and beyond the reach of most business owners, the presence of Hadoop ha suddenly democratized BDaaS and brought it down to mainstream businesses. The emergence of Hadoop has now scaled down the cost of outsourced business analytics where users have to pay for only a few Cloud services. BDaaS providers have the flexibility of combining Hadoop with Cloud to deliver highly economical data services to businesses of all sizes.

Today, a standard BDaaS solution may have the following layers:

  • PaaS layer – mainly Hadoop processing
  • PaaS and SaaS – Performance layer and software as a service layer (Hadoop and features)
  • IaaS and PaaS – Infrastructure layer combined with Hadoop
  • Iasi, and PaaS, and SaaS – Integrated solution for infrastructure, features, and performance.

Big Data as a Service: On-Premise or Hosted?

In Big Data as a Service for On-Premise and Cloud, the author claims that while on-premise Data Management solutions require significant financial investments and ongoing staffing needs, the Cloud-based BDaaS services often raise questions about data privacy and security. However, a solution like BlueData EPIC combines on-premise and Cloud to create unique Data Management environments for the business clients. In this offering, users can mix match with preferred Big Data platforms and applications. In other words, the vendor can select specific open source and commercial solutions and deliver a perfectly custom solution for the user. The biggest benefit of this solution is the ability to tap into both Cloud-based and on-premise data troves while conducting the analytics on the central Cloud platform.

Benefits of BDaaS to Global Businesses

While Hadoop and Hive have brought open source data processing to BDaaS, the initial costs of implementing a Big Data infrastructure for Enterprise Data Management can be substantial. Thus, when considering a BDaaS solution provider, a business must keep these issues in mind to reap the maximum benefits out of their BDaaS solution:

  1. While IaaS offers solutions for data storage and analysis, the unresolved issues of data ownership, privacy, and security are still there.
  2. The start should be small and manageable, and once the BDaaS framework has been validated for performance, speed, accuracy, and economy, it can be scaled up as per need.
  3. Each organization’s Big Data requirements are different, and those requirements must be aligned with the service offerings of a particular BDaaS solution.
  4. BDaaS can work best for deriving rich insights from massive volumes of unstructured data from disparate sources.

In this DATAVERSITY® article, readers of this article will discover a data service platform that has successfully combined open source data processing technologies with tools for optimizing cost and performance for real-time BI.

Future Big Data Trends for Enterprise Business Analytics

This post titled Gartner Predicts Three Big Data Trends for Business Intelligence highlights the following business analytics trends that all businesses may witness in the future years:

  • By 2017, at least 30 percent of Enterprise Data Management services will be enabled through via intermediary service providers like BDaaS providers. The scale of information that these services will offer to business users via web and social sites will enable the businesses to engage in “competitive BI activities” necessary to survive in a cut-throat business environment.
  • By 2017, over 20 percent front-end information systems will allow sensor-driven product tracking.
  • By 2020, information will drive 80 percent of business processes and products. The businesses have to focus on a “global pool of information assets,” which may include government data sources, social data, and connected IoT data – to get a competitive edge.

This EMC Report claims that the data market has been steadily growing by 40 percent every year since 2012. The Cloud platforms have made it possible for Big Data as a Service to survive and prosper in the new data technology world. This report hints that the businesses that use the best combination of technologies and tools for scaled data analytics at a reasonable cost will ultimately win. On the flip side, service providers who were hitherto providing only hardware infrastructure services and now ready to combine powerful Big Data Analytics services in an integrated package.

Insights as a Service: What’s in Store for Future Businesses?

Insights as a Service (IaaS) is growing at an incredible rate as it offers action plans. IaaS depend on other SaaS solutions for data and insights, but it provides ready solutions for businesses by quickly reviewing multi-function, enterprise data to deliver deep insights. Data ownership, privacy, and security are still live issues in the IaaS environment, but insights as a service is here to stay and grow in the coming years. Markets and Markets released a press release titled Insights as a Service, which claims that this market is expected to grow from $1.16 Billion in 2016 to $3.33 Billion by 2021, at a Compound Annual Growth Rate (CAGR) of 23.5 percent during the forecast period. KDNugget’s Insights as a Service Big Data suggests that as businesses continue to move data to Cloud platforms, IaaS may become the future game changer provided the SaaS service providers can take care of data privacy and security.

Right now, the main services offered hover around Descriptive Analytics, but gradually businesses will demand more Predictive and Prescriptive Analytics from these intermediary service providers or data brokers. With North America remaining at the forefront of this market in 2016, the rest of the world is catching up very fast. The most prominent vendors in this market are Accenture, Deloitte, and Oracle. DATAVERSITY’s Why Insights as a Service describes that the modern businesses that have learned to combine the power of data analytics with Cloud, mobile, or social technologies will lead the digital transformation in Enterprise Data Management.

 

Photo Credit: Konstantin Hermann/Shutterstock.com

About the author

Paramita Ghosh has over two and a half decades of business writing experience, much of which has been writing for technology and business domains. She has written extensively for a broad range of industries, including but not limited to data management and data technologies. Paramita has also contributed to blended learning projects. She received her M.A. degree in English Literature in 1984 from Jadavpur University in India, and embarked on her career in the United States in 1989 after completing professional coursework. Having ghostwritten and authored hundreds of articles, blog posts, white papers, case studies, marketing content, and learning modules, Paramita has included authorship of one or two books on the business of business writing as part of her post-retirement projects. She thinks her professional strength is “lifelong learning.”

  • To me we need to embrace change, big data being the future for enterprises

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