Over the last couple of years, enterprises have realized that they can have a competitive advantage by incorporating big data analytics into their business model. It’s not just marketers who have got into big data, it has had an impact on all industries across the board from finance to healthcare to manufacturing to human resources. As a result, the demand for big data professionals is fierce.
Top talent in this field are extremely hard to find. As a result, there’s an ongoing war to hire and hold on to big data talent. This creates a unique situation where companies have to fight over a small talent pool with corporate giants who are able to throw big money to attract the best.
In the U.S. alone, a data strategy consultant can command a fee between $400 to $1200 per hour. Not many can afford to pay these kinds of wages. Further, it can also be a massive hurdle to get over when you’re trying to incorporate a big data function for the first time.
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According to Gartner, only a third of the global demand for data-related jobs can actually be met. This is mainly due to skills being mismatched. Usually, students who focus on statistics don’t take on any IT related courses. As a result, the required skill set isn’t quite there.
According to McKinsey, by 2018 the U.S. by itself may be faced with a 50%-60% gap between supply and demand for deep analytic talent.
So what do you do?
Think of Outsourcing as an Option
If you look outside of North America, you’ll find that there is an accessible pool of big data talent in Eastern Europe and Asia. So if you don’t have the massive funds required to fund data science in your organization, you might find it to be a good option to look outside your shores.
There are big data scientists that charge up to $90 per hour in countries like Ukraine for the same skill set that would command $400 per hour if you hire someone locally. So it can be an attractive option as you can outsource specific functions offshore or bring someone in temporarily to work in-house (side by side).
The Decision Making Process
To successfully accomplish this, you’ll have to ascertain what you’re trying to accomplish with data analytics. Here’s some food for thought:
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Does your data analytics concern a core competency of your company?
If you answered yes to that question, you’ll need to carefully select your outsourcing partner or bring in the data professional to work with you in-house. Although it’s something that should be approached carefully, it’s not impossible to successfully outsource and achieve your goals.
Is it a peripheral function? Can someone outside do it better?
If you don’t have the necessary skills to handle a peripheral function, it will be a good idea to outsource it. It will also be a much cheaper method to keep your operation on track.
Is Big Data outsourcing cost-effective?
More often than not, outsourcing is a better way to access the required skills and technical infrastructure at a lower cost.
To really make this work, you’ll need to set up clear service standards and explicitly define the consequences of a breach of service. Further, it’s a good idea to clearly identify and outline costs associated with the service.
Intellectual property rights are essential, so make sure that you’re protected. When you draw up the requirements in the contract, you can also add language that clearly states how often you need to communicate and who you need to communicate with.
If the account relationship is acceptable to both parties, you’re ready to move forward with your big data operation. Further, if you want to partner with an offshore company, it's better to focus on those who have been in the business long enough for you to make choices based on reputation.
When you start working on an outsourced big data project, start with clear and specific goals like the following:
- Cut delivery costs
- Increase sales
- Increase output (within a specific function)
If you don’t have well-formulated goals, there’s a good chance that you’ll end up with data that is worthless. If you’re looking to change your business process based on analytics, it has to be strict and precise, so first analyze your needs before you engage a third-party.
The bottom line is that everything is now in the cloud. So the concept of big-data-as-a-service isn’t a farfetched idea. If you have the right approach to outsourcing your company’s data analytics, you can get it done for a lot cheaper and with the same level of quality as in your home country. Further, you won’t even have to fight to attract and keep domestic talent. Doesn't it make sense?