We’ve had software as a service, platform as a service and data as a service. Now, by mixing them all together and massively upscaling the amount of data involved, we’ve arrived at Big Data as a Service (or BDaaS, and it’s pronounced how you will).
It might not be a term you’re familiar with yet, but it suitably describes a fast-growing new market. In the last few years many businesses have sprung up offering cloud-based Big Data services to help other companies and organizations solve their data dilemmas.
Some estimate that the portion of business IT spending that is cloud-based, x-as-a-service activity will increase from about 15% today to 35% by 2021. Given that it is estimated that the global Big Data market will be worth $88 billion by that point, we can see that the forecast value of the BDaaS market could be $30 billion.
So, here I will attempt to give a brief(ish) overview of the concept, as well as examples of how it is being put into practice in real life businesses and organizations around the world.
What is BDaaS?
Big Data refers to the ever-growing amount of information we are creating and storing, and the analysis and use of this data. In a business sense, it particularly refers to applying insights gleaned from this analysis in order to drive business growth.
At the moment, BDaaS is a somewhat nebulous term often used to describe a wide variety of outsourcing of various Big Data functions to the cloud. This can range from the supply of data, to the supply of analytical tools with which to interrogate the data (often through a web dashboard or control panel) to carrying out the actual analysis and providing reports. Some BDaaS providers also include consulting and advisory services within their BDaaS packages.
Why is BDaaS useful?
There are several advantages to outsourcing or virtualizing your analytics activities involving large datasets.
The popularity of Hadoop has to some extent democratized Big Data – anyone can use cheap off-the-shelf hardware and open source software to analyze data, if they invest time learning how. But most commercial Big Data initiatives will still involve money being spent up front on components and infrastructure. When a large company launches a major initiative, this is likely to be substantial.
On top of upfront costs, storing and managing large quantities of information requires an ongoing investment of time and resources. When you use BDaaS, all of the techy “nuts and bolts” are, in theory, out of sight and out of mind, leaving you free to concentrate on business issues.
BDaaS providers generally take this on for the customer – they have everything set up and ready to go – and you simply rent the use of their cloud-based storage and analytics engines and pay either for the time you use them or the amount of data crunched. BDaaS providers often take on the cost of compliance and data protection. When the data is stored on their servers, they are (generally) responsible for it.
A good example is IBM’s Analytics for Twitter service, which provides businesses with access to data and analytics on Twitter’s 500 million tweets per day and 280 million monthly active users. The service provides analytical tools and applications for making sense of that messy, unstructured data and has trained 4,000 consultants to help businesses put plans into action to profit from them.
Another is agricultural manufacturers John Deere, which fits all of its tractors with sensors that stream data about the machinery as well as soil and crop conditions to the MyJohnDeere.com and Farmsight services. Farmers can subscribe to access analytical intelligence on everything from when to order spare parts to where to plant crops.
The arrival of Apple’s Watch – perhaps the device that will bring consumer wearables into the mainstream – will doubtlessly bring with it a tsunami of new BDaaS apps. They will soak up the data from the presumed millions of people who will soon be using it for everything from monitoring their heart rate to arranging their social calendar to remote controlling their home entertainment. Then they will find innovative ways to package it and sell it back to us. Apple and IBM have just announced their collaboration on a big data health platform.
In sales and marketing, BDaaS is increasingly playing its part, too. Many companies now offer customer profiling services, including Acxiom – the world’s biggest seller of direct marketing data. By applying analytics to the massive amount of personal data they collect, they can more effectively profile us as consumers and hand their own customers potential leads.
Amazon’s AWS as well as Google’s AdSense and AdWords are better known services that would also fall under the banner. They are all used by thousands of small to medium-sized businesses to host data infrastructure, and target their marketing at relevant niches where potential customers could be lurking.
The term ‘Big Data as a Service’ may be rather unwieldy and inelegant but the concept is rock solid. As more and more companies realize the worth of implementing Big Data strategies, more services will emerge to support them. Data analysis can and generally does bring positive change to any organization that takes it seriously, and this includes smaller scale operations which won’t have the expertise (or budget to develop that expertise) to do it themselves.
With the growth in popularity of software as a service, we are increasingly used to working in a virtualized environment via a web interface, and integrating analytics into this process is a natural next step. We can already see that it is making Big Data projects viable for many businesses that previously would have considered them out of reach – and I think it is something we will see and hear a lot more about in the near future.
This article was written by Bernard Marr from Forbes and was legally licensed through the NewsCred publisher network.