4 Career Fields Transformed by Big Data
Last Updated February 25, 2020
Think you don’t need to apply data analytics in your current role? Think again. While your passion for people may have led you to a career in human resources, or your creativity may be the driver for your career in marketing, these are just two of the fields that are rapidly changing in the face of greater availability and applications of data information.
More fields and industries are transforming to adapt to and capitalize on the new normal of big data. For professionals in these industries, understanding and strategically applying data analytics within their roles can keep them competitive.
Here are four career fields where continually applying data analytics is the new standard for a competitive business (and professional) advantage.
1. Sales and Marketing
Adoption of data analytics in sales and marketing is widespread, as this area arguably has the largest impact on the business, regardless of the industry. While it can be tempting to run after the latest digital tools and data dashboards, applying marketing analytics effectively is key to impacting campaign outcomes and overall sales goals.
Demand Forecasting
This form of predictive analytics looks at different categories of sales data through different analytics techniques (i.e. time series analysis of historical sales figures) to estimate performance. Demand forecasting can lay the foundation for other sales and marketing analytics as the organization better understands its demands and the operational and marketing support it will need to influence them.
Brand Trends
Understanding future trends in an industry at large can help marketers and salespeople prepare for surges in demand, disruption and swings in customer sentiment. One key component of that is using all the dispersed data about the brand – in customer service reviews, on blogs, in sales conversations and on social media to assess how customers feel about the company – and its competition.
Personalized Marketing
The variety of ways customers shop (from online e-commerce to table-top ordering systems in restaurants) provides a wealth of customer information. By analyzing this customer purchase data based on audience segmentation, marketers can learn how to best influence different segments of their customer-base and refine their efforts with greater levels of personalized marketing that targets exact customers.
2. Human Resources
Human resource management departments are increasingly looking to data analytics to inform their key people decisions, and thanks to evolving artificial intelligence and machine learning, HR professionals now have even more data-driven tools at their disposal. Understanding the best uses and ramifications of these applications is critical for making smarter hiring and retention decisions.
Recruitment and Applicant Screening
HR professionals are now equipped with artificial intelligence tools to analyze resumes for key terms that indicate a good fit. HR leaders can also use data analytics to understand their brand perception in the market – similar to the approach marketing might take – to correct any poor perceptions that could impede recruiting.
Employee Engagement
Using data to inform promotions and compensation can help with engagement as data-driven decisions can help eliminate perceptions of favoritism or bias. In addition, organizations can use a variety of data sources, including employee surveys or feedback apps, to assess employee sentiment, engagement and to gain insight to issues needing correction.
Retention and Attrition
Using data analytics, HR leaders can spot trends in retention and attrition issues, and isolate them to departments, leaders, or events. They can apply predictive analytics to foresee attrition trends, instead of reacting to attrition issues after they occur.
3. Supply Chain Management
Thanks to advanced analytics, supply chain management professionals can better harness dispersed data sets across the supply chain for greater insight and improved operational efficiency throughout the entirety of the supply chain.
Real-Time Data and Visibility
Supply chain managers no longer have to rely on retroactive reports to understand how the supply chain is evolving. Improved real-time insight, enabling greater flexibility and informed decision making through predictive and prescriptive analytics powering artificial intelligence and machine learning tools can help in forecasting risks and uncertainties. In fact, Accenture Global Operations Megatrends Study found that embedding big data analytics into supply chain operations led to a 4.25x improvement in order-to-cycle delivery times and a 2.6x improvement in supply chain efficiency.
System Automation
As a result of advances in big data and analytics applications, automation is increasingly utilized across an organization’s supply chain, with systems seamlessly working together for order fulfillments, inventory updates or picking and packing products. For example, a sensor-equipped stock management system can predict and trigger restock orders. One study found that using robotics to automate supply chain processes resulted in a 43% reduction in resources for “order-to-cash” processes, including billing, credit, collections, and pricing.
Autonomous Robots
As technology advances, autonomous machines, or “bots,” are increasingly taking on tasks in warehouses, manufacturing plants and providing supply chain managers with greater data insights to support their forecasting, pricing and replenishment planning. These “bots aren’t replacing planners, but are certainly repositioning what a planner does,” according to Peter Bolstorff, executive vice president for corporate development at the Association for Supply Chain Management.
4. Healthcare
Healthcare is rapidly evolving and integrating data-driven applications, which have the potential to improve patient care and drive costs down by tapping into the power of analytics.
Predictive Medicine
Healthcare providers can use historical data to predict future outcomes, as well as apply diagnostic analytics to derive correlations that can help in diagnosing risk factors or early warnings. This can help provide “predictive guidance,” better informing preventative measures, such as correlating specific behaviors to chronic health conditions, or noting how a specific intervention helps (or does not help) patients in managing a condition, and adjusting treatment recommendations accordingly.
Telemedicine/Telehealth
These connected healthcare solutions can provide patients more care options and physicians greater flexibility and the ability to provide more precise care. AI-powered chatbots allow patients to share basic symptoms in a chat, and then be routed to the appropriate care team. Virtual visits can increase healthcare access and help to target treatments. Remote monitoring “via patient-generated health data with AI will make it easier for clinicians to monitor patients from afar and…telemedicine consults will help prescreen patients and potentially avoid the need for in-person attention,” explains Anthony Brooke, vice president of strategy at healthcare technology company GetWellNetwork.
Electronic Health Records (EHR)
Going beyond just being a digital version of patient health records, the data in EHRs enables healthcare providers to have a holistic view of a patient’s ongoing treatment. Not only do EHRs provide real-time visibility for all the healthcare providers and facilities caring for a patient, as well as transparency for the patient, the data can also inform predictive analytics to support diagnostics and treatment plans.
The number of professions and industries being transformed by big data is only expected to grow. Equip yourself to make the data-informed decisions for your organization (and your career) that can help you become a leader in your field.
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