Big Data Analytics or Data as a Service: How are they different?
Big Data Analytics
According to IDC, the value of the big data market was $6.8 billion in 2012, which is growing at a rate of 40% every year and is predicted to reach $17 billion by 2015 (Source: IDC – PDF). Organisations all across the world have come to realize that the most successful business decisions are not based on intuition, but on facts and data. Conventionally, this data has often been limited to information arising from internal sources, mostly past customer transactions or interactions stored in a customer relationship management system. New sources of data open up many more opportunities for using data from sources such as social media profiles/comments, interaction between customers (or target audience) on forums and blogs, sensor data, clickstream data, mobile-phone call records.
Big data analytics are insights drawn upon these new sources of data, which sometimes happens in near-real time. It’s about uncovering hidden correlations, unknown patterns and valuable information to enable a better understanding of the business environment, in effect leading to superior decision making capability. Companies that employ big data analytics often create new marketing opportunities that contribute towards revenues.
Data as a Service
Just like any other “as-a-service” business model, Data-as-a-Service (DaaS) is based on the premise that just like software, infrastructure or platform, data too can be availed in an on-demand basis. As organisations grow and layers of technological innovations are added, it becomes increasingly difficult for most to have dedicated in-house teams taking care of everything. In some cases it would make more sense to outsource specific tasks and focus on the core business objectives. However for most companies data is increasingly becoming mission-critical – which is all the more reason to have a more specialized and reliable data partner who deal with various complexities for a bunch of clients daily.
Though the initial uses of Data as a service were in creating web mashups, more commercial as well as non-commercial organisations (such as UN, FBI, CIA, research institutes etc.) are increasingly employing DaaS to make the world a better place.
Data-as-a-Service is all about data aggregation and exposure. Most DaaS providers aggregate and manage data sets, and allow access to this data via an API. Usually this is a firehose kind of a setup, though most custom DaaS providers (such as PromptCloud) also function in a similar fashion. The vendor provides the structured data, on top of which companies put an additional layer of analytics, either in-house or through an analytics partner.
Some of the benefits of DaaS are agility, cost-effectiveness and access to superior quality data in a pay-per-use model. As customers don’t need a significant understanding of the data, they can directly put this data to use and as the whole process is outsourced, cost savings are natural.