Outsourcing Data Analytics: Top 5 Benefits For Businesses
In the past decades, many business organizations were looking to outsource many of their IT activities and business functions to offshore software development companies. Recently, data analytics is increasingly becoming included in the myriad IT activities which companies outsource. And it’s worth noting that data analytics is among one of the more competitively strategic areas of the technology gamut for businesses in the new digital economy.
By outsourcing data analytics, companies hire offshore IT service providers to implement analytics on the data they provide to the offshore outsourcing partner. According to the Global Data Analytics Outsourcing Market Report by Credence Research, Inc., the market valuation for data analytics outsourcing was at USD 2.73 billion in 2018, and is expected to reach USD 25.56 billion by 2027, which results in a Compounded Annual Growth Rate (CAGR) of 21.84% during the forecast period from 2019-2027. Such industry research shows a clear sign of increase in demand for data analytics outsourcing service.
Companies are realizing the various benefits of data analytics for their businesses, especially in maximizing profitability and revenue, as well as other areas such as improving productivity rate, etc. However, not all companies possess the necessary knowledge and resources needed for an effective data analytics implementation. Especially, considering the shortage of skilled data scientists making hiring professionals for data analytics becoming even more challenging. As a solution, Business organizations are turning to offshore IT outsourcing providers to help extract actionable insights out of their data.
Offshore Outsourcing Data Analytics
Generally speaking, with software outsourcing, businesses usually have the options of choosing to either outsource on-shore or off-shore. Thanks to the advancement of technologies, specifically cloud computing, business organizations can now manage their own offshore development team remotely in a highly efficient and effective way. Ultimately, with offshore software outsourcing, business organizations can effectively reduce the cost of data analytics implementation on a long-term basis.
More importantly, today, Artificial Intelligence (AI)/Machine Learning technology is gradually substituting for human on many aspects of the task of data analytics, thus requiring less input from human manual work. The nature of data analytics outsourcing, therefore, would be more about how human can effectively create and train machine learning model rather than how businesses could manage an offshore team effectively.
In this article, we examine a number of benefits from outsourcing data analytics and the related considerations when doing so.
#1 Quick access to large pool of IT talents
As mentioned previously, one of the challenges currently faced by various companies when implementing their data analytic initiatives is the shortage of skilled IT professionals, especially hiring a team of skilled data analysts, data scientists and machine learning engineers in the local market could be quite a challenge. Offshore outsourcing can help fill in the gap providing this kind of expertise very effectively.
Furthermore, as the volume of data keeps growing at a rapid rate like now, it could be a huge problem for businesses to effectively capture and manage a huge volume of business data with a traditional data center. This leads to the requirement of businesses to manage their data estate by using cloud-computing platforms such as AWS (Amazon web services), Microsoft Azure, and Google Cloud Platform. Again, without an IT team of skilled cloud computing engineers, it would be a challenge to manage large amount of business data using this kind of cloud infrastructure approach. Offshore software outsourcing company can provide cloud managed services helping businesses to move from traditional data center into a modern cloud environment.
#2 Capacity to leverage the value of data for profitable business decision making
With data has become one of the most valuable assets for businesses, especially considering the incorporation of advanced AI/Machine Learning technology into big data analytics, the potential to leverage analytics for business advantage could prove to be highly ample.
Having said that, companies who looking to transition into a data-driven businesses but without an in-house advanced data analytics capacity could find themselves to be in need of external outsourcing services. Particularly, outsourcing model could prove to be highly useful to businesses in the circumstances where companies only seek to develop a proof-of-concept or MVP model to test out their data-driven solution.
Nonetheless, selecting the right data analytics outsourcing partner can be a highly difficult task, similar to any other outsourcing endeavor. Even though cost plays an important role in the process of selecting outsourcing vendor, skills and experience as well as cultural fit are just as equally important. Business organizations should seek a long-term partnership that could operate as a part of their daily operations, with effective communications and delivering results that go beyond the just costs reduction.
#3 Deep Industry Knowledge
Different businesses should have different requirements in data analytics as each industry has its own specific requirements. For instance, outsourcing healthcare data analytics can provide healthcare institutions with better market insights, improve clinical-decision making and patient care, and better risk management. Also, healthcare organizations will specifically need data analytics to improve upon their management of medical inventory and optimize drug pricing. Another example of specific analytics requirements are businesses operating in the logistics industry, who will require analytics to improve their operations upon particular areas such as: route optimization, fleet management, and parts requirement forecast, etc.
An outsourcing partner who has deep domain/industry knowledge can offer many competitive advantages to their clients since they already know about the unique industry requirements that allow them to get everything right at first, while also able to avoid any potential pitfalls and doing so at a cheaper and faster rate than others.
Additionally, the outsourcing partner can also provide specific industry metrics as a standards for comparison to help business measure their analytics outcome effectiveness. Having said that, its’ worth mentioning that many organizations are still operating under silos model, which business department keeping their business data and information separately from one another.
To derive maximum benefits out of data analytics, it is required equal contributions from all departments in order to apply advanced analytics across organization. Besides, once a predictive model is developed and integrated into with a business application for production and put into use, it might also need to be fine-tuned and updated to keep pace with the evolving of data and business so that the model could be put into use over time. Thus, this would require continuous responds from users across all departments.
#4 Scalability and a fast-track to in-house analytics capacity
More than just a technology partner that can provide skills and expertise on data analytics, outsourcing services can also assist organizations in setting up an analytics capabilities that could be difficult to do in-house.
Data analytics has evolved beyond just data and business intelligence, advanced analytics is now a critical part of enterprise to keep up with the market and stay ahead of competition. Advanced analytics is complex and requires a certain level of scalability to meet the growing demand. Employing outsourcing services can help organization keep pace with the constantly growing demand of innovations while still ensuring the costs remain within budget. Also, outsourcing data analytics to an offshore vendors can help organizations having access to an array of advanced and innovative technologies that organizations may not be currently considering. For example, migrating from a legacy on-premise business app portfolio and transit to cloud-based tools can help organizations to improve operational efficiencies in a number of ways.
With outsourcing, organizations typically need to give the data over to their outsourcing vendor for the machine learning model creation and training. This lead to business potentially losing control of data storage as well as other intellectual property related issues. Thus, it’s always a good idea that businesses work out all these issues in advance before the engagement. For example, business organizations might want to make sure that when handing over the data, such data will be stored in a secured places meeting all security and regulations requirements.
#5 Compliance of Regulations on Data Security and Protection
As previously mentioned, business data is now being generated at an enormous rate on a daily basis. According to Forbes, 2.5 quintillion bytes of data is being created each day at the current pace, and that pace is accelerating with the growing of the Internet of Things (IoT). The need for efficient management and analyzing of such large amount of data could put businesses into a host of regulations and management issues. In particular, with the General Data Protection Regulation (GDPR) goes into effect, the need for a systematic way of collecting, storing, and sharing of business data is more critical than ever. Such need could be an important factor that urge businesses to seek an outsourcing partner for big data analytics, as this enables for more easily audited data meeting regulations and compliance requirements.
However, not all outsourcing endeavors would go smoothly as expected, a number of potential issues may arise and deter the development progress causing conflicts. There are a certain number of key factors that need to be put into considerations before carrying out data analytics outsourcing, including: data governance, intellectual property protection, SLAs (service level agreement), cooperation models and costs, etc. which could lead to potential conflicts during or at the end of engagement. Again, organizations need to carefully consider such aspects to ensure a smooth data analytics outsourcing process.
Business data is now the new asset, and such large set of data can provide actionable insights to help support business decision making, improving outcomes and profitability. These insights and trends are analyzed and produced through various qualitative & quantitative methods by data analysts and other team of IT professionals. Business organizations are now increasingly realizing the cost-effectiveness aspect of data analytics outsourcing for a data-driven business. Data analytics outsourcing is expected to become even more popular in the forecast future period.
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