Why To Consider Outsourcing Our Data Science Needs

For years, companies have been outsourcing a variety of business, and IT functions to external organizations. However, the tasks that were being outsourced a decade ago, and the tasks that are being outsourced today are vastly different. These changes can be attributed to the advent of big data and the digital transformation that the industry has gone through. Today, data science is the hottest technology trend and no surprises, the data science outsourcing market is increasing too.
Recent data shows that the market for outsourcing data analytics has been growing at a tremendous pace. The Market Research Report Search Engine has predicted that the market for data analytics outsourcing will be valued at a staggering $20.68 billion by 2026 with an incredibly high CAGR of 29.4% (Express Analytics). 
Outsourcing Data Science work is advantageous for companies since building internal capabilities for data science can be time-consuming and quite expensive. Also, because data scientists are in high demand and there is only a limited pool of them, outsourcing data science requirements can significantly speed up an organization’s journey to build strong data science capabilities. 

Why to consider Outsourcing Data Science

Why-to-consider-Outsourcing-Data-Science
Data scientists tend to be talented individuals who are experts in the field of math and computers. They use their technical abilities to extract insights from large data sets and have highly developed trend spotting skills. Due to their niche skills and abilities, data scientists are in extremely high demand. As a result, forming an in-house data science team is not only exceedingly difficult, but it is also quite time consuming. Therefore, despite the high control and lower compliance risks associated with using an in-house team to assess your own data, companies still decide to outsource some or all of the work involved in their projects to external parties. Let us have a look at some of the advantages of outsourcing your company’s data science capabilities:

Best Practices

A professional outsourcing company has its own best practices built with experience for handling and executing data. For example, at Mindbowser, when working with clients, we set the right KPIs at the start of the project in collaboration with the client and deep dive to understand the reports and the insights that are desired as an outcome of the project. This way the project remains highly predictable and on track. 

Greater ROI

Data analytics is an integral part of your business operations, but it still is an outlying function. Often, going through all the effort to hire a talented data science team, may not be worth your time. Since most third-party data analytics service providers’ offer specialized services that meet global standards, you might be better off outsourcing it. The service provider who is working with you on your project is accountable for producing good results. Therefore, when you are collaborating with a third-party, your ROI is clearly defined. Remember to assess your data analytics vendor by the business results they produce.

Cost-Efficient

Cost is a critical factor in any business. But even if your company can afford to hire a full-time data scientist or analyst, it is going to be an expensive affair. In addition to their salaries, you also have to pay taxes and employee benefits. Additionally, a single data scientist may not be able to create an impact. Usually the scientist can create a data architecture and then there will be need for programmers as well. This is why hiring a full-time data scientist in most cases, is not a good business strategy. Outsourcing can provide a ready team with data scientists, architects and programmers. Hence, outsourcing your data analytics needs is a much more cost-efficient and sensible approach. Generally, the benefits of outsourcing data analytics will significantly outweigh your costs.

Find the Right Candidate

In the United States alone, the number of data scientists has grown by an incredible 650% since 2012, according to LinkedIn’s 2017 U.S. Emerging Jobs Report . Since data science is a profession that is highly in demand, there is a limited supply of data scientists. Today, there are more data science jobs in the market than eligible candidates. As a result, many companies are struggling to find the candidate with the right skills. Even after you put in time and effort to hire a qualified candidate, you may have to end up with an entry level candidate because the experienced ones are already off the job market hired by the likes of Amazon and Google. If you only require a data scientist for a few months, you can even outsource one on a per-project basis.

Ready tools

By hiring an outsourced team, you may avoid redesigning the wheel. A data specialized company would be having some of the algorithms and programmes already created that can be reused. Also at many companies, like at Mindbowser, there are domain experts available who have spent countless hours already in a particular field and hence are able to provide insights into the data project.
Ever since the advent of big data, analysis of data has provided tremendous advantages to companies in various industries. Data analytics is absolutely vital for every customer-centric company. By outsourcing your data science needs, you will be able to save time and cost, while increasing your company’s productivity.

Why Mindbowser For Your Data Science Project

Our lean and agile team of full-stack data scientists, engineers, and application developers accelerate innovation and implementation of custom machine learning and AI products. We bring extensive cross-industry expertise backed by scientific rigor and deep knowledge of state-of-the-art techniques to design, build, and deploy bespoke AI solutions.

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