How To Do Big Data: DIY VS. MSP?
To say you have cloud enabled your big data usage is to say that you’ve taken the first step of many to get what you need out of that data mass.
However, big data is a very particular to the organization that’s using it – it’s not the same everywhere you go. The virtues of big data supported by the cloud are well documented, but that really only represents part of how big value is derived.
As John Moore of MSPMentor notes, big data tools like Apache Hadoop have become increasingly mainstream. However, as he goes on to say, that doesn’t mean that it’s a simple proposition.
This proposes an interesting issue for many companies looking to access big data insights: what path do you take to implement a big data solution? Big data has only recently become an affordable asset thanks in no small part to the cloud. Yet, while the tools are easily accessible via AWS, big data-as-a-service is still complex to get going. As Joe McKendrick notes:
“The ingredients necessary for BDaaS include a high-functioning service-oriented architecture, cloud virtualization capabilities, complex event-driven processing, Hadoop, and business intelligence tools than provide deep analytics.”
On top of this, big data requires specialized expertise to draw analytical value out of the processed heap of data. Data scientists are both difficult to come by and expensive to maintain. Depending on the scale of a business, such a hire might be beyond the scope of reasonability. It’s true that other roles can generate value from the analytics, but those specialized skills are rare at a baseline.
So where does this leave the business looking to access big data insight? There are several paths to take, and in the end the choice will be made by the specific requirements of a company. Outsourcing the infrastructure requirements to a cloud hosting provider or a managed services company can be a smart way to free up resources to bring in a data specialist. Otherwise, some MSPs have big data services that help a business manage the technical requirements.
Streamlining the cloud architecting and implementation process while consolidating costs can open up the financial and time resources to better refocus big data technical strategy for a business. Ultimately, however, outsourcing or doing it yourself, big data is a challenge that has to be balanced with the requirements of the organization.