3 Essential Components of a Big Data Strategy


Big Data remains one of the hottest trends in enterprise technology, as organizations strive to get more out of their stored information through the use of advanced analytics software and techniques. IDC forecast annual spending on Big Data and analytics technology to increase by nearly 50 percent between 2015 and 2019, growing from $122 billion USD ($157 billion CAD) at the beginning of the survey period to $187 billion USD ($241 billion CAD) by the end. Notably, the software segment of the sector will only account for a little more than one-fifth of the total sum spent, as IT services will comprise the lion’s share of the market.

Although the bulk of companies have started to discuss the prospect of deploying advanced analytics or are already using the tools, the technology is expected to become a far more important component of strategy in the coming years. Before long, the ability to make more accurate decisions, thanks to powerful insights garnered from data, will separate the winners from the losers in each industry.

Before an enterprise can launch a successful Big Data strategy, it needs to cover these three bases:

1. Infrastructure management

Data volumes are already having a massive impact on the average corporate IT infrastructure, and advanced analytics will further enhance the strain. Volume is certainly one of the key reasons why infrastructure needs fortification ahead of Big Data deployments, but it is not the only factor. Gartner once used the “3 V’s” to describe Big Data – volume, variety and velocity – essentially showing that scale, different formats and speed will all play a role when advanced analytics are implemented.

The needs of the average enterprise related to supporting advanced analytics are evident when looking at the market for relevant solutions. Wise Guy Reports forecast Big Data infrastructure spending to expand at a compound annual growth rate of 33.2 percent between 2016 and 2020. The analysts pointed out that the infrastructure of the future will need to handle every moving part within Big Data, including hardware, mobile sources, enterprise applications and storage.

Enterprises that have not yet embraced modern infrastructure need to do so soon should they want to enjoy the fruits of Big Data.

2. Cloud computing expansion

Many businesses use cloud for their infrastructure, especially in the era of Big Data. However, cloud-based platforms and software will also be critical drivers of Big Data success for several reasons:

  • Accessibility: Antiquated software will not provide the speed and ease of accessibility necessary to truly capitalize on the insights generated by Big Data. Cloud-based options provide superior accessibility.
  • Platforms: Big Data software – as well as all other apps, for that matter – run on platforms. Legacy-based platforms will not provide the power, flexibility or range of support necessary for enterprise apps to function properly, whereas cloud-based options do.
  • Integration: Cloud solutions are easier to integrate across a range of diverse systems. Considering the variety of data sources feeding into analytics technology, interoperability needs to be a priority.

For these reasons and more, cloud and Big Data go hand-in-hand today.

3. Security

Security is a major concern in Big Data.

InfoWorld reported that studies consistently find leaders are not putting enough – if any – effort into Big Data security. The higher volume of information, greater variety of sources and other characteristics of Big Data make security one of the more worrisome matters. Enterprise leaders need to get a tight handle on their security frameworks and enhance controls wherever possible to avoid a major breach stemming from the use of advanced analytics and Big Data.