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How can organizations benefit from Big Data and Analytics as A Service?

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Big Data has become pivotal to the strategic decision making processes of many enterprises. There are numerous benefits to be derived from the ability to collect and analyze huge data volumes by the second. Due to the boom in big data, the IDC estimates that by 2021, analytics services’ spending will have leaped to $101.9 billion, from $58.8 billion in 2015. Out of these projections, the value of the big data-as-a-service (BDaaS) market could be well over $30 billion by 2021. But what exactly is BDaaS (or analytics as service)? The relatively new concepts is rather broad, encompassing the outsourcing of a range of big data functions to the cloud. These functions include, but are not limited to, data supply, provision of analytical tools for data interrogation (through control panels and dashboards), conducting actual analytics, and creating reports. Some BDaaS packages will also come with advisory or technology consulting services. So why would an organization want to take up BDaaS? One of the greatest challenges many enterprises are facing today as they embrace big data is that of managing the work associated with analytics while still focusing on running their businesses. Read further to learn how BDaaS promises to help businesses overcome this challenge and create value for them.

  1. Speed, Efficiency, and Cost-Effectiveness

Undeniably, big data is being rapidly democratized by the success of Hadoop and other technologies such as Hive and Spark, making it easy for many firms to perform analytics by combining open source software and affordable ready-made hardware. However, when large enterprises embark on big data initiatives to derive insights that will power their business processes, they require immense upfront capital investments for big data infrastructure, and personnel. Funding is not a luxury most companies have. This challenge brings out the first clear advantage of BDaaS: it eliminates the burden of upfront costs in developing in-house big data infrastructure, for instance, the Hadoop cluster. This concept borrows from the popular Software-as-a-Service and Infrastructure-as-a-Service models where all major onsite installations are outsourced to the cloud.


Big data is driven by colossal volumes of both structured and unstructured data collected by businesses from multiple sources. This data is being generated by the second. Data management (filtering, sorting, and storage) becomes a major issue for those firms that are not able to invest heavily on the necessary infrastructure. It’s worth noting that data management also consumes time, which is a critical component in all business processes. With BDaaS, this headache is reduced significantly. The service provider has already set up all the required tools (hardware and software), and hired data scientists for the technical tasks. The business (customer) simply pays for either the time spent using the cloud-based analytics service, or the volume of data analyzed through it. In fact, IT personnel in businesses that take up BDaaS need not concern themselves with the intricacies of analytics. Nor do they need to spend valuable time and energy developing new analytics applications to complement evolving businesses.

One popular illustration of this concept is how social networks outsource their analytics services to big data and analytics firms. When social networks are busy focusing on engaging their customers and providing different services (especially through advertisements), which generates tons of data, the big data firms then come in, collect this data, filter it, refine it, store it, and then perform all the relevant analytics and provide high-value insights that can be sold to thousands of other businesses, at a profit. The result is nothing short of efficient. Since many enterprises would struggle, and possibly fail, if they took up such immense analytics services, BDaaS makes perfect sense when it comes to efficiency, speed, and cost-saving.

  1. Precision Customer Profiling

Building on the Twitter example, we can begin to understand how the ability to analyze millions, and possibly billions of data items from different sources is cost-effective and efficient, especially when the analysis is done offsite. These BDaaS capabilities have helped improve sales and marketing activities for many enterprises, through customer profiling. Firms such as Acxiom, the world’s leading vendor of direct marketing data, have perfected the application of analytics on the tons data it collect independently and on behalf of its clients. Because such firms have invested heavily in big data infrastructure and the talent to match it, they are able to profile customers with precision that most small and medium businesses would otherwise find challenging to achieve. Then this profiles are sold to customers, as a service.

Google’s AdWords, and AdSense and Amazon Web Services (AWS) are some popular services that best demonstrate this concept. Thousands of small and medium enterprises rely on these services to host their big data and analytics infrastructure. Since these particular services from Google and Amazon focus primarily on analytics, they help offload the burden of analytics from the smaller firms. The outcomes is very precise profiles that businesses can use to create targeted marketing campaigns, services, and products in relevant places where potential customers are likely to be found.

  1. Flexible Access in Multi-Tenant Environments

Industry experts note that as organizations evolve, especially in their increased demand for analytics, there is a need to ensure big data is easily and readily accessible by all parties who require it within the enterprise. One effective mechanism of achieving this is by using approaches that both centralize and decentralize data. . Such approaches are able to both service requests for enterprise data resources, and accept input in form of sematic definitions and local data set, from multiple teams. Multi-tenant data architectures are best suited to support hybrid approaches. The tasks of data collection, refining, and creating central semantic layers as wells as analytics data stores, are reserved for the IT specialists.

The IT department will then reproduce the centralized data hubs through virtual copies relevant to different groups within an enterprise-finance, customer support, sales, marketing, and many more. Essentially, what this approach achieves is separating the technical work from the business processes. This way, IT personnel handle the “nuts and bolts” of analytics (including data governance and compliance, security, and semantic rules) and ensure that all the necessary insights are available for all decision makers and departments to act on, in a given enterprise. In such cases, therefore, BDaaS providers are responsible for the data (and the servers it resides on).


Many businesses stand to benefit a lot from outsourcing their big data and analytics tasks to the cloud with the help of technology consulting companies. Not only are service providers advanced in their capacity to perform analytics, but they also effectively lessen the burden of huge resource investments in big data, for small and medium firms, with the added benefit of efficiency. BDaaS thus enables smaller firms to buy analytics on the go, which is very cost-effective. BDaaS is also able to support many businesses by providing refined customer profiles that can be used in sales and marketing activities. When you combine these capabilities with that of being able to provide analytics to a multi-tenant big data environment, when and where it is needed, the outcome is that many enterprises are able to focus on running the business,  by leaving the analytics work with trusted partners.

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