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Data-science services amid a knowledge shortage

Sept. 4, 2018

Within weeks, programs can be off and running. 

As IoT initiatives expand, in-house data science resources can quickly reach capacity, stalling progress.

Bsquare's Dave McCarthy

Hiring more data scientists is typically not an option either, as there is a major shortage in the market. In fact, Gartner predicts this shortfall will hinder 75% of organizations from reaching their full potential with IoT through 2020. Data-science services—or outsourced data-science experts—are helping businesses excel without the trouble of finding, hiring and training in-house experts.

Outsourced data scientists are usually able to get up and running quickly, collaborating with internal subject-matter experts (SMEs) and stakeholders to pinpoint specific business objectives, map available data against desired outcomes, and obtain new data if necessary. With data and business objectives identified, data scientists can begin the analytics process.

Of course, collaboration doesn’t end there. It’s critical that internal SMEs participate throughout the process to provide data scientists with the necessary level of detail regarding operating characteristics of the equipment being analyzed. A data scientist operating without this first-hand insight will almost certainly miss important equipment and operational nuances.

Within weeks or months—depending on scale—programs can be off and running, well on the way to generating ROI.

And the benefits of outsourcing data science can extend far beyond creating extra capacity. This approach can also optimize deployments and deliver better results. Here are four advantages data-science services deliver that in-house resources cannot:

Greater depth and breadth of expertise

Incorporating the knowledge of large pool of data scientists makes it possible to rapidly address a wide range of problems. By applying related experiences to new problems, data scientists can find patterns and look for problems in areas that internal SMEs may overlook. They can also help you avoid common mistakes such as wasting time diagnosing root causes of issues.

Initiatives can be scaled up or down depending on needs

Some stages of IoT require more “heavy lifting” data science, others much less. By employing outsourced data scientists, you can schedule only what you need. You can also avoid the heavier investment of bringing on full-time data scientists, bypassing the expensive hiring processes, salary costs and lengthy training.

Free up in-house data-science resources to focus on the highest priorities

Augmenting in-house data-science capabilities with additional capacity enables teams to do more, better, faster. Internal resources are able to reallocate their time to more pressing matters and add value wherever it’s needed most.

 Enable teams to tackle multiple projects in tandem

With limited data-science resources at your disposal, it’s pretty typical to schedule IoT initiatives by priority, time-to-value, or potential to deliver the greatest ROI. But if you have multiple high-value efforts, there is no reason not to pursue them in tandem. Sometimes, data can be exchanged between projects to speed up progress and/or deliver a greater cumulative value.

As IoT programs mature, they require regular attention from data scientists to monitor and fine-tune systems for accuracy and relevance, resulting in optimal returns. Data-science services enable businesses to maintain their hard-fought IoT momentum.

Dave McCarthy is vice president of Bsquare.  

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