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6 signs you need data science services

Data scientists provide expertise that is essential to increasing ROI for industrial IoT deployments — yet experienced, successful professionals are difficult to find and retain. According to analysts at PwC, positions for data scientists can take twice as long to fill than the national average of 45 days for other jobs requiring a college degree. Fierce competition for qualified candidates also drives up the cost of recruitment and hiring. For these reasons, many businesses are turning to outsourced data science services as an alternative to hiring in-house. Beyond keeping a deployment moving forward, this approach unlocks a number of other key advantages that would not otherwise be possible.

A data science services firm provides quicker access to experts as well as access to the most sophisticated analytics tools. Internal subject matter experts (SMEs) working with the data science firm also have the advantage of combining their institutional knowledge with the outsourced team’s breadth and depth of experience. This partnership enables an enterprise to more quickly and effectively scale deployments and rapidly achieve success, all without burdening additional internal resources.

Think data science services might be what you need to reach the next level? Here are six common signs that a data science services partnership is right for your business:

1. Recruitment for data scientist positions has stalled

This is one of the most obvious signs that you need to take a hard look at data science services instead of seeking an in-house expert. Across industries, businesses are experiencing a shortage of data scientists in the job market. Available experts with a proven track record are rare, and demand is only going up: Gartner predicts that a shortage of data scientists will hinder 75% of organizations from reaching their full potential with IoT through 2020.

It seems that many organizations are faced with a difficult choice of hiring a candidate that will require a steep learning curve or relying on an internal SME with little data science expertise. Data science services have the dual benefit of helping keep IoT initiatives progressing while freeing up internal resources to focus on other areas of the business.

2. The in-house data scientist has reached his maximum output level

Maybe recruiting isn’t the problem, but capacity is. Perhaps there is already an excellent data scientist on staff, one who brings industry expertise and understanding to the table. Congratulations! But if she is working at the maximum level of output and is unable to take on more projects, new initiatives will suffer.

Bringing in additional data science resources through outsourced services can help get new initiatives off the ground, and fine-tune existing deployments to better address the most important issues, all while delivering results a faster timeline. Particularly for IoT initiatives, the benefits of data science services are enormous and include creating the right foundation through goal-setting, reviewing existing data (see #3) and overcoming internal and external roadblocks.

3. It’s difficult to identify available data

Complex industrial environments produce a massive amount of overwhelming, unstructured data. This makes it difficult for any organization to identify an optimal IoT use case, as it is challenging to pinpoint and retrieve the required data for implementation. This data deluge is one of the major problems that hinders the initiation of IoT adoption simply because there is too much information.

Data science services can help identify and optimize data for unique business needs and more effectively apply the resulting insights to enhance operations. As a side note, with limited data science resources available, organizations typically schedule IoT initiatives by priority, time-to-value or their potential to deliver the greatest ROI. Data science services allow businesses to have multiple IoT efforts underway in tandem, sometimes speeding up progress through data exchange between the initiatives. These sorts of collaborative processes often deliver a greater cumulative value.

4. The IoT deployment results don’t match business goals

At the start of any IoT initiative, the designated internal stakeholders and SMEs should clearly outline the company’s unique needs and goals, define the problem and identify the available data needed to solve it. While pairing the right goal with the right data is a good first step, successful initiatives need strategic vision to unlock the true potential of IoT.

Data science services can help an internal SME sift through the data, map out data resources and format requirements, and identify the matching data elements across an organization. Together, the SME and data science services organization can build a machine learning model to validate that the available data supports the IoT use case. This critical step helps prevent unnecessary expense and stalled timelines.

5. Having trouble obtaining maximum ROI for an IoT system

Any IoT initiative is a capital-intensive endeavor, so maximizing time-to-value is important for maintaining stakeholder support and fiscal strength. IoT projects that address key business issues will provide the optimal balance between benefit and efficiency, while helping to streamline deployments — ultimately saving time and money. One of the ways data science services can impact the ROI of an IoT deployment is by tailoring a holistic digital strategy to scale across the organization. And while data science services are designed to help launch IoT initiatives, these services can also help businesses further develop existing installations, overcome a roadblock or enhance underperforming projects.

6. An IoT initiative is underway, but progress has slowed

IoT promises operational insights that can unlock unprecedented value and transform an organization. It’s easy to see why businesses are diving into IoT and, as a result, seeing some really exciting momentum. But as IoT initiatives expand and mature in a business, in-house data science resources can quickly reach capacity, stalling forward progress.

Since data often shares common attributes, regardless of place, industry and organization, utilizing an external data science service means there is little lag time to obtaining the greatest results for an IoT initiative. This puts the business in the best position to extract the insights needed to optimize ROI on an accelerated timeline. Data science services can cut through the operational white noise and harness the full potential of data to maximize the ROI of any IoT investment — all without overtaxing internal resources or hinging on a new hire.

Once you understand the signs that point to whether or not you should consider a data science service engagement, it’s important to find a good vendor match. Take a look at the vendor’s body of work and determine whether or not its client base covers similar markets to your own. Ask about past customer successes, paying special attention to whether those examples demonstrate realistic goals, timelines and results. Most of all, find a partner that listens to your problem, understands your pain points and delivers tailored options to consider.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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