Big Data as a Service : the next big thing ?
Big data is large and complex unstructured data (images posted on Facebook, email, text
messages, GPS signals from mobile phones, tweets, and other social media updates, etc.) that
cannot be processed by traditional database tools. Big data has three dimensions: volume,
velocity, and variety.
Three dimensions of Big Data
Big data as a service (BDaaS) is a term typically used to refer to services that offer analysis of
large or complex data sets, using the cloud hosted services. Similar types of services include
software as a service (SaaS) or infrastructure as a service (IaaS), where specific big data as a
service options are used to help businesses handle what the IT world calls big data, or
sophisticated aggregated data sets that provide a lot of value for today’s companies.
In general, big data as a service will offer various kinds of data analytics. For example, a
company could use it to monitor a large SEO or Web content campaign that reaches a broad
audience. In a BDaaS model, these services will commonly be offered over the Internet with key
vendor storage and functionality tools located in the cloud. These setups help to provide agile
services that can perform well, although businesses will not have control over many of the spaces
over which their data traverses.
Experts have identified other common marketing strategies for big data as a service. One of these
is the location of cloud data storage resources in combination with analytics, so that hot or cold
data is stored near where it will be manipulated for analysis. This can help decrease the amount
of effort needed to move data through an analytics program or platform. Other selling points of
BDaaS include specific descriptions of how these tools can help present big data to busy
managers in a cohesive and useful way, where predictive analysis companies are creating many
different kinds of tools to help businesses get actionable results from data.
The ingredients necessary for Bdaas Include:
High-Functioning Service-Oriented Architecture
Cloud Virtualization Capabilities
Complex Event-Driven Processing
Business Intelligence Tools
Businesses are interested in the same functionality for their Data Management needs. Many
companies lack the ability to hire a full staff of Data Scientists, but to remain competitive, they’ll
need access to the same data much larger businesses have. Through Big Data as a Service
(BDaaS), small to medium businesses (SMBs) can have the benefit of Big Data without the
exorbitant expense of a full-time staff member.
As Data Scientists enter the field in larger numbers, many will have a choice of employers. Some
of those choices will be large and mid-sized corporations, but some will be service providers.
The service providers will offer data for a fee, allowing businesses from a wide variety of
industries to have access to their team of data experts.
Differences between BDaaS ,Traditional Big Data and Traditional Database
Big Data as a Service
Scalability on demand through a combination of cloud computing and distributed
Virtualized data storage on a distributed platform.
Structured and unstructured data on cloud environment
Advanced analytics functions with on-demand computing power
Analytical capability derived from out-of-box domain-specific algorithms along with
Traditional Big Data
Scalability in processing and storage achieved through distributed architecture
Data storage on HDFS or distributed platform
Structured and unstructured data
Advanced analytics functions
Analytical capability derived through custom coding
Lack of resources such as computational power and storage capacity.
Integrated hard data storage such as NAS, SAN, and traditional disks
Reporting using tools such as OLAP
Analytical capability derived through custom coding
Big Data as a Service Business Models
Core BDaas :
As Big Data is maturing as a topic business and service models are emerging and we can see the
advantages and differences between the four competing types of Big Data as a Service. The core
BDaaS has been around for a few years and is in use by many companies especially as part of a
larger architecture or for irregular workloads. It has settled as a model supporting the provider’s
wider service architecture.
The feature and performance BDaaS attack the segment with very different value propositions
and there are good reasons for both of them to continue to attract customers. Both will have to
address some features of the other in the long run. For example, the feature BDaaS needs to
proof to be competitive on a performance level though the commoditization and service level
abstraction means that at the end of the day not the model wins that squeezes the most
performance from comparable hardware but on a dollar to dollar basis.
The performance BDaaS, will face business demands from companies that decreasingly are
willing to take on the complex challenges of building their own data architecture and related
SaaS layer, and increasingly want to focus on their value adding domain specific processes. So
while neither of the semi-integrated BDaaS approaches want to square the circle their customer
demand may yet push them to try it.
Not all industries will receive identical benefits from Big Data though. While it’s highly likely
all of this data will be an increasingly important part of day-to-day business, some industries
stand to benefit more than ever. Here are four industries that will likely benefit most from BdaaS.