Should I Outsource My Analytics? It's a Question of Balance

Should I Outsource My Analytics? It's a Question of Balance

News flash: business analytics tools are constantly improving. We're living in an era of “disruptive outsourcing," observed a recent Deloitte report. For many businesses, outsourcing key functions — such as data analytics and cloud computing— is more cost-effective than investing in building those capabilities in-house.


If your business lacks the budget or expertise to develop its own data analytics tools, partnering with a third-party vendor can be a good solution. While outsourcing often makes sense, it's also important not to be completely dependent on third-party partners for your data analysis. Someone on your team needs to understand both the data and the analytics process. It's a question of balance.

The Rationale for Outsourcing

Companies waste a lot of useful data because they lack the technology needed to mine, process, and analyze large amounts of information — or because they don't have the requisite level of in-house expertise to understand how to make data-driven decisions. Often, both technology and staffing are stumbling blocks.


According to a PwC survey, 59 percent of executives acknowledge that artificial intelligence would improve how their company uses big data. At the same time, Forrester Research estimates that companies analyze only about 12 percent of the data they have.


Whether you're the chief data officer at your company, or simply wear the data analysis hat, you should understand how to extract business insights from the information you collect on everything from operational efficiency to customer experiences.


But note every company can afford to employ a team of full-time data engineers, at a cost of $115,000 or more per year — each. Budget and staffing constraints can make it difficult to properly organize and analyze your data.

That is, unless you outsource. Whats more, working with a partner that uses artificial intelligence (AI) for data analysis can give you access to the best available technology.

Maintaining Control Over Your Data Analysis

Business intelligence software has come a long way since the first wave of apps in the 1990s and early 2000s. Today, advanced tools that use machine learning AI are facilitating automation real-time analysis capabilities—at scale.


Machine learning is a subset of artificial intelligence in which the software teaches itself to perform better analyses. As the software gets exposed to more and more data, it learns to increase its accuracy for target outcomes —such as recommending actions your business should take based on certain customer behavior.


But, like a driver who relies solely on GPS, there are risks that you will lose your way if you remove the human element from your decision-making process. Even when you outsource your analytics, your best fail-safe is to have people in-house who understand how to use your data analysis tools.


Even when you outsource, you still need a chief data officer (CDO) to champion a company-wide strategy for data capture, management, and sharing, notes Katy Ring, research director of cloud & IT services at 451 Research. "The CDO is ultimately responsible for business and IT alignment around data management," she says.


If your data analytics partner does not have someone in-house to work closely with, adds Rings, you'll have limited success for an outsourced approach.


In the final analysis, even when you outsource your data analytics, you should be sure you understand the technology and the process. While an outsourcing partner can provide valuable tools and expertise, every company needs an in-house data guru to keep the ship on course.


Keyence has over 40 years experience with data-driven decision making and how AI can help your data analytics. Download the 3 Key Features Digital Brochure to learn more!



LATEST POST : It's Not You, It's Us: Using Data for Employee Retention