‘Do what you do best, outsource the rest.’ – Peter Drucker
Over the course of years, companies have been outsourcing all kinds of IT functions to various firms and third parties, widely varying from simple telecalling to strategic decision making tasks. Companies such as Google, Alibaba.com, GitHub are just a few big names that outsource multiple IT functions such as web development and support.
Lately, there has also been a boost in the outsourcing of data analytics which is considered as one of the highly important fields of the current day technology continuum.
According to the prediction by Market Research Report Search Engine, the market for data analytics outsourcing is to be valued at an astounding US $20.68 billion by 2026, recording a CAGR of 29.4%.
But why such a huge rise? Why are companies outsourcing their analytics needs and not hiring in-house?
In the 21st century’s digital ecosystem where there is absolutely no shortage of data that can be translated into litigable insights, it only makes sense to put this data to good use.
Multiple researches have unanimously proved the value of data analytics and business intelligence. But companies often have a certain cultural resistance, they are still picking real-world insights instead of data insights. Not only this, there is a bothersome skill gap of data scientists and analysts. 39% of CIOs and IT professionals from across industries agree that data scientists are extremely difficult to find and recruit.
Apart from these, there are various other reasons that Econolytics team collated from research conducted on a sample of small, medium and large companies, they are very clear and distinctive.
a) For Start-ups, the data scientists are very expensive and they would rather outsource their resource requirements in the initial years, unless it becomes core to the business. When outsourcing data analytics, they will get a committed and experienced team of analysts who are skilled and have a proper knowledge at a lower costs.
b) SMEs always want to be on top of their games but they do not have enough time and expertise/resources to manage the data scientists themselves. Also, the impact of data driven decisions can take time to manifest and therefore, companies would rather wait to see some tangible results before investing full time resources.
c) Even with an in-house analytics professional, the chances of the resource being involved in other tasks are quite high and hence not able to concentrate on analytics. Therefore, to avoid this risk, one should outsource data analytics. The service provider will give proper data analysis and the desired result.
d) For large corporates, mostly consulting companies or KPOs, the requirements for data scientists can be quite seasonal and niche. Therefore, they are always on the lookout for specialized data experts for short term projects.
Outsourcing is cheaper both in terms of time and money. It is more costly if one hires analysts, project managers, and software engineers for performing analytics than outsourcing which minimizes this cost. With such an increased competition, no one can afford to waste time. Outsourcing companies have experts who know what and how to do which reduces the time required to perform analytics.
Outsourcing companies use a variety of special tools to manage, visualize, and analyze data. They are skilled analysts who use their experience with similar past projects to come up with innovative and creative insights and visualizations of data. They also effectively manages the organizing and storing of one’s vital data across different platforms. They also provide high quality service which in turn makes business decision-making easy.
In summary, outsourcing data analytics is useful to provide meaningful insights from data that helps businesses in vital decision making. Bringing in outside partners with analytics as a core competency enables organizations to scale up and scale down while adding critical capabilities.
However, while interviewing 5 CXO level individuals across industries we learnt that companies are also skeptical about parting with their data due to issues like finding quality consultants, data security risks and expectation misalignment with consultants. However, given the above benefits and doubts, they still felt that outsourcing was the best bet to at least get started. The disadvantages could be managed well if they found top rated consultants with credible work, milestone based payment solutions and standardized agreements to protect their interests.
In a time where analytics is soon going to become a lifeline for business’s environment, it is extremely important for every business to start analyzing their data, be it in-house or outsourced.