Reinvigorate Your Data Analytics Team with Serverless Computing

Reinvigorate Your Data Analytics Team with Serverless Computing

What is serverless computing?

The funny thing about “serverless computing” is that in reality, it still requires servers. The name serverless computing is used because the server management and capacity planning decisions are completely hidden from the developer or operator. Sometimes referred to as “Function-as-a-Service” (FaaS), serverless computing is the concept of executing your application code as a series of tasks in an environment completely managed for you. Pricing is based on the actual amount of resources consumed by an application, rather than on pre-purchased units of capacity. It is a form of utility computing.

Serverless computing has recently gained a lot of traction, as many cloud services are being made available as “serverless”. According to a recent report, the serverless market is projected to grow 32.7% by 2021. Serverless is no longer a futuristic technology; it’s widely used to run applications with rapid time-to-market and unpredictable scale requirements with ease.

Google is committed to providing its customers with a broad set of serverless products. Google Cloud has always believed in the vision of serverless by debuting Google App Engine in 2008; the first fully serverless compute service. Since then, Google has evolved serverless offerings in both application development and analytics. Today, Google Cloud Platform (GCP) offers several serverless cloud services for data analytics including:

  • BigQuery: Serverless data warehousing services that help deploy advanced cloud data warehousing solutions for the enterprise.
  • DataFlow: Serverless stream and batch data processing service.
  • Cloud ML Engine: Serverless machine learning services that automatically scale, built on custom Google hardware (Tensor Processing Units).

With serverless technology, it’s possible to flip the 80/20 rule to now focus 80% of resources on insights to drive better business outcomes instead of dedicating time and energy to maintaining infrastructure.

Rethinking the Data Analytics Team Structure

Organizations that are taking advantage of serverless cloud services can get their data analytics platforms up and running in a matter of minutes vs. weeks or months with on-premise solutions. With serverless, IT does not have to worry about allocating, configuring, and managing virtual machines and software. The management of servers, storage, and memory is transparent to the consumer and managed by the cloud providers.

Many organizations are migrating to the cloud for data analytics to improve speed to market, reduce costs, and optimize performance.  But, what opportunities are there for data and analytics organizations from a structure perspective? Let’s look at some examples.

  • Software upgrades and regression testing – When working with a client on a DBMS upgrade project that was going poorly, the CIO had daily one-hour meetings with his direct reports (15 IT leaders) to work on bringing the project back on track. In a serverless data analytics world, he wouldn’t have to worry about these types of projects.
  • Just a bunch of admins and coders –  During a strategy engagement for a client, the CIO stated that “one of the struggles we have is that we cannot translate what the business really wants. Right now, I just have a lot of really good admins and heads-down coders. I don’t have people that can translate what the business wants into a technical solution. So many times we don’t: we guess”. Those admins and coders could be refocusing their efforts that drive real business results in a serverless environment.

Serverless computing can help IT leaders and data analytics organizations make a stronger impact to their bottom line. If an organization was running their data and analytics platforms in the cloud using serverless computing, they would not need the following roles:

  • Data Analytics DBAs
  • Software Admins
  • Large testing organizations to support system regression testing

As a result, these resources can be refocused and their talents leveraged for more important efforts. They will already have a good understanding of the organization’s business, so they can leverage data to make an immediate impact on critical business decisions and potentially uncover new business models. With serverless computing for data and analytics, an organization can have the capacity to hire and retrain staff to focus on high-value capabilities, like machine learning, predictive analytics, and impactful dashboarding solutions.

Ultimately, CIOs have the opportunity to reshape their data analytics organization to be more strategic and value-driven. Hiring and managing software admins can be replaced with experts that understand your industry drivers, analytics,  data. The shift to focusing on predictive analytics capabilities and data science has immediate impact on the bottom line.  Data is everywhere – but the power comes from uncovering insights to drive a competitive advantage.

Contact us for a complimentary consulting session on how we can assist your organization take advantage of serverless computing data analytics solutions available.

By | 2018-02-16T14:17:49+00:00 February 14, 2018|Categories: Data & Analytics, Fusion Blog|Tags: , , |

About the Author:

Todd Truesdell
Todd Truesdell is a Managing Director with over 20 years of data and analytics leadership and technical experience. At Maven Wave, he is responsible for data and analytics strategy, solution definition, and delivery management. Prior to Maven Wave, he held leadership positions at Clarity Solution Group, Pivotal Software, Knightsbridge, and PwC focused on value driven data and analytics solutions. Todd has led and successfully delivered several large-scale, enterprise data and analytic solutions for a variety of clients. In addition to his consulting background, Todd has held data and analytic leadership positions for GE and GMAC.