Big Data & Data Process Outsourcing Forecast

January 27, 2016
|By Garegin Hakobyan

While the growth of the IT outsourcing market has generally been flat in the past three years, outsourcing budgets are increasing at a rate similar to overall IT budgets. Looking ahead to 2016, big data and data processing will continue to be major line items in the corporate budget. Spending on business technology is expected to have a positive impact in the US and advanced economies in Europe and Asia Pacific in the next two years.

ABI Research predicts that big data spending will reach $114 billion in 2018. The global data analytics outsourcing market is expected to grow at a compound annual growth rate of 30.54 percent from 2014 to 2019, with major end-users in the following sectors: manufacturing, retail/wholesale, banking, financial services and insurance, healthcare and telecoms.

It is clear that big data and data processing technologies have become must-haves for all industries. As mobile devices, wearable tech and electronics generate massive amounts of data on a daily basis, businesses are finding more uses for the information, from geo-targeted marketing to tracking the buying habits and personal preferences of their customers.

Industry growth.
A recent study by IBISWorld reported that the performance of the data processing and hosting industry will improve over the next five years due to renewed IT spending and increased outsourcing by companies looking to cut IT costs. As the data accumulates and management becomes more complex, the outsourcing of big data and processing services will be more convenient and affordable than maintaining these services in-house. The switch to richer online media and services will also boost data outsourcing revenue above pre-recession figures. There will be greater demand for IT infrastructure management from third parties.

Cost reduction remains the top driver of big data and data processing outsourcing, and this will continue in 2016. Outsourced data services help companies save money through economies of scale and labor arbitrage (offshore providers). Outsourced big data solutions can also generate cost reduction. For example, sophisticated data analytics can help banks improve the accuracy of fraud prevention systems, which translates into cost savings. Analytics also help manufacturing firms reduce maintenance and repair costs by predicting equipment malfunctions and process disruptions.

The retail, BFSI and telecoms sectors are the top consumers of outsourced big data solutions, but opportunities can also be found in specialized or niche areas. According to a CBI factsheet, European buyers will tend to work with service providers specializing in certain industries. Experts recommend that it is better for service providers to master and offer simple parts of big data services rather than offer complex solutions that they do not fully understand.

Cultural similarities and established hubs.
Companies that choose to outsource big data services abroad will choose locations based on cultural similarities. Those that offshore data services will tend to go to established hubs like India, the Philippines and China.

Skills shortage.
Skills shortage remains an issue in the US and Europe. As new technologies emerge and platforms become integrated at a rapid pace, new IT skills are required. Qualified and experienced data scientists, analysts and programmers are in high demand, but these skills are not widely available because of the unpopularity of STEM education in North America and Europe. Even if data scientists are plentiful in your area, finding someone with the right skills to make effective interpretations is always difficult.

The prevailing skills shortage in the West will continue to offer opportunities for big data and data processing service providers in emerging markets with large pools of low-cost IT talent. Data service providers that offer skilled talent at cost-effective rates and have built up big data and data processing capabilities will be in demand in 2016.

Hadoop speeds up.
Hadoop is an open-source software platform for large-volume storage and processing of data sets. It plays a key role in sustaining the current big data movement, but alternative platforms are also coming into the mainstream. As more enterprises adopt Hadoop, clients will demand faster speeds associated with traditional data warehouses. Self-service data preparation systems will also be popular this year due in part to discovery tools that cuts data analysis time. With the different types and formats of data available, businesses will seek help from third parties to simplify the preparation of data for analysis.

Hadoop competitors emerge.
2016 will be the year big data grows up and best practices are adopted by the masses. As self-service data analytics and Hadoop hit mainstream, changes are underway that businesses will leverage. According to a recent survey of over 2,000 Hadoop users, the majority are planning to do more with the platform in the next few months. However, Hadoop is not without competition. Apache Spark is fast becoming the preferred big data platform for many companies, and this trend will continue in the coming years.

NoSQL providers like DataStax and MongoDB have displaced traditional database vendors like IBM and Microsoft in Gartner’s Magic Quadrant for Operational Database Systems, indicating the shift towards unstructured databases within enterprise IT departments. Spark is faster than Hadoop when processing data, and Spark is currently the largest big data open source project. Some big firms like Goldman Sachs are already using Spark as their main big data analytics platform.

Big data, cloud and the Internet of Things converge.
Data from devices in the Internet of Things (IoT) will explode in the coming years. Leading data and cloud companies are expected to offer IoT services to enable information to flow easily into online analytics systems. While the convergence trend is in its earliest stages, more companies will follow suit as data accumulates and become too complex to handle in-house. The bottom line is a growing need for data analysts and fast data analytics and processing. Smaller data service providers will start to extend their capabilities to support the convergence of third platform technologies.

Big data for the masses.
Trained data scientists and data analysts are in high demand globally, and they tend to command high salaries. Analysts expect software companies to work on big data applications to bring the power of data analytics to the masses and reduce the dependence on expensive talent.

Outsourced data storage for IT
This year, in-house IT departments will also start to understand that the benefits of outsourced data storage and archive services outweigh security concerns and issues about control. Outsourced data storage prevents IT teams from having inadequate storage and having too much. Managed data storage solutions also help companies avoid resource-heavy upgrades and migration issues.

Big Data and Data Processing Outsourcing Strategy

Should you outsource data processing in 2016 or keep it in-house? It depends on whether your organization’s data is business critical or performs a support function. Either way, the information must be analyzed. Some data must be mined and analyzed in real time, while others can be stored and analyzed over time. Long-term analysis is a backend process facilitated by platforms like Hadoop, which allows data to be scaled and distributed to processors. Long-term analysis is best suited for the cloud and outsourcing. Meanwhile, real-time or near real-time analysis is facilitated by technologies like Spark and Storm that support faster “stream” processing of smaller bits of data. This type of data processing is best performed in-house.

Getting Started on Data Processing Outsourcing in the Philippines

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