In the age of big data, almost every organization relies on periodic reports to inform operational and strategic decision-making across their functions. But as the volume, variety, and velocity of data moving through these businesses continue to grow, C-suite executives are realizing that historical information no longer provides the benefits that it once did.
The modern operating environment is marked by increasing supply chain complexity, sky-high customer expectations, and a slew of digitally-native brands that are able to deliver their offerings in a far more efficient and personalized manner. With these competitive pressures in place, organizations are looking to free their data from functional silos and transform it into real-time or predictive intelligence that can fuel effective and proactive decision-making for employees at every level. As a result, employees at every level can take proactive measures to improve efficiency and drive revenues in their respective departments.
This demand for cutting-edge data infrastructures has led to a sudden boom in the demand for experienced analytics professionals. Data science expertise is particularly sought after in the following industries.
Healthcare
While the medical services industry has generally been resistant to innovation, a number of emerging trends have led to a sea change in healthcare delivery models. There has been a concerted move to digitize and consolidate patient medical records (EHR). At the same time, rapidly rising insurance costs have caused hospitals to emphasize efficiency in billing and administration, and the Internet has empowered modern consumers to take charge of their health and treatment in a way that just wasn’t possible a decade ago. These have led enterprises to focus their efforts on quality of care and treatment.
All of these objectives are being and will be enabled through the data science profession. Analytics professionals took the role of deploying machine learning models that are able to make sense of the patterns and trends underlying raw statistics. Engineering these capabilities into hospital systems allows them to automate billing, optimize bed or surgical room occupancy, and improve diagnostics for a wide variety of diseases.
Insurance and Financing
In such a volatile business landscape, it can be extremely difficult to make effective underwriting decisions. Before any decision can be made, the lender or insurer must evaluate all possible default risks to identify whether financing can be approved. No matter how comprehensive an assessment is, it can be rendered invalid if the associated borrower provides false or outdated information, or if they simply decide to omit key details that would otherwise impact their viability.
Analytics professionals set up predictive systems extrapolating the default risk of advancing financing to a particular client. This determination is made on the basis of a comprehensive assessment that covers millions of relevant data points derived from internal and external sources. Analytics also allows lessors to draw user-specific data from across the customer journey, providing an understanding of the customer’s financing requirements and spending patterns. With the right insights, sales teams proactively deliver tailored product recommendations and cross-sell relevant services that match up to the customer’s needs.
Utilities
To date, most utilities companies have enjoyed comfortable monopolies in their chosen service areas. However, a number of trends are now reshaping the very nature of this industry.
In recent years, rapid advancements in renewables such as solar and wind energy have created a viable and affordable alternative to grid electricity. At the same time, government agencies across Europe, Asia, and North America have implemented strict new measures governing the usage of so-called dirty fuels, such as coal and furnace oil. Finally, the advent of smart home systems such as Amazon’s Alexa and Sony’s LF-50G has given consumers the ability to monitor and manage their energy usage to a fine degree.
All of these trends have caused margins to tighten across the utilities sector. These changes have made efficiency and delivery optimization the main priority for service providers in natural gas, electricity, and water.
Data science has become a critical differentiator in this regard. Most utility companies are now rolling out smart meter systems that plug directly into comprehensive analytics systems. These devices are augmented by a smart grid infrastructure that can adjust in real-time according to input criteria.
With new insights in hand, utilities will be able to adjust supply levels according to fluctuations in demand across their service areas. They will also be able to monitor output in critical assets and schedule proactive maintenance, thus saving significant sums on repair fees and downtime. Real-time usage data can also be delivered directly to consumers through frontline apps that allow users to maximize their utilities expenditures and cut back on wastage. Analytics professionals play a key role in all of these activities.
Summing It Up
As the need for data science talent grows simultaneously with other sectors emerging with analytics needs, hiring & recruitment managers are being forced to come up with applicable criteria on which they can measure the qualifications of new candidates. This task is made more difficult by the lack of any sort of real consensus over the specific skillset and qualifications that are necessary for success in this still developing field.
At IADSS, we are working with senior executives, researchers, and data science professionals in a range of industries and geographies to develop a standardized set of roles and definitions that would bring clarity to the analytics and data science recruitment process in this critical field. Once applied, these guidelines should significantly improve the quality of analytics and data science talents across every sector.
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