From computer science, AI, and predictive analytics to statistics and machine learning, data is being collected every second and much of it never sees the light of day.
But when harnessed and converted into actionable insights, data yields recommendations, and user insights, that are informing solutions of tomorrow.
Michael Emmert, Data Scientist at S44 helps to break down this topic in terms we all understand, and our customers use.
Why Companies Need Data Science
From mitigating risks and fraud to informing product development, data science predicts deliverability, improves customer experience, and anticipates needs. It empowers the decision-making process with data-driven evidence, flags opportunities, and enables identification and refinement of target audiences.
Data science is critically important for fast evolving industry sectors such as mobility and energy.
Guiding Energy Distribution and Security
The energy sector is relied on by every other sector, and every human on the planet. It’s the backbone of modern life and needs to function uninterrupted.
Today, we consume more energy than ever before. In fact, in 2020, the year the world changed, the US consumed 3,902.00 billion kWh of electric energy per year, which is an average of 11,843 kWh per capita. With 33% of the energy consumed by commercial businesses. Why does this matter?
To respond to the increased demand for energy, the energy sector is investing in optimization of electric usage, security, alternative energy source generation, and utilizing big data to understand and predict consumption patterns.
According to Smart Energy International, the U.S. energy sector loses US $6 billion annually due to fraud and theft. Data science sheds light on grid security (for example, detecting fraud, theft, or security breaches) but also gives insights into human demands for and interactions with energy.
Using advanced meters and infrastructure to report energy usage allows energy suppliers to monitor and track energy usage 24/7. Furthermore, data science enables balancing supply and demand, leading to less energy waste and driving fiscal efficiency.
Empowering the Mobility Sector
The mobility sector in the U.S. has been transforming. From autonomous driving, improved connectivity, and electrification to shared mobility. With these advancements comes a set of challenges that only having access to the correct data will solve.
The mobility industry is moving towards a shared model that enhances the type of data being collected. As a result, carshare operators gather increasing amounts of data on user behaviors and vehicle information and use them to inform business and customer decisions.
This data is key to improving both the service and user experience — whether it be increasing the number of vehicles available during crucial times of the day or location data to ensure that enough cars are accessible in a specific area.
Another critical use case for data science is fleet management. For example, operators will know when to clean, maintain, or even recharge a vehicle by monitoring data coming in. This will optimize the time and routes and significantly reduce costs for operators.
With access to thousands of data points throughout the country, mobility operators will continue to improve their offerings. Providing high-quality and efficient services, minimizing costs, and improving infrastructure.
Enabling the Supply Chain and Customization in Automotive
The automotive industry is another one that’s evolving from developing EVs that can travel further and recharge quicker to providing more options for customizations of luxury vehicles.
This evolution takes place because of the value of data science. Through collection and analysis of big data, understanding trends, and gaining deeper insight into user behaviors, OEMs and other businesses within the automotive industry can provide holistic experiences that consumers want.
For example, AutoIntel’s Recommender System is based on machine data collection and inventory information tracking user behaviors across channels. This enables what has been called “an Amazon experience” producing recommendations of other products a customer is likely interested in buying.
The information is collected and used as a complete configuration so that when someone is seeking out a customized vehicle, they will not be shown a basic model that isn’t tailored to their tastes and needs. The idea behind using big data to make recommendations, in this case, is to show users relevant products and not only a default model.
This process also informs future supply-chain decisions — which products or parts to build, modify, replace, and so on.
Challenges of Data Science in Industry
Two overarching challenges are time (how long we must collect data, analyze it and build use cases via meaningful insights) and how we collect, store and report on data (across formats, channels, and data sources).
There are also industry-specific challenges. For example, within automotive data sparsity is the number one challenge. An example of data sparsity is that OEMs and other automotive companies don’t willingly share data across the board. This results in a lack of access to data that helps improve the results that data science can yield. Additionally, there are thousands of possible combinations that can be covered in data science, and this complexity can be tricky to gauge if the data is inconsistent or missing.
Data collection is also a hurdle for some to overcome — across different sources and formats. By automating processes, we can mitigate stagnation and errors, removing barriers to decision-making.
What Does the Future Hold?
Data science isn’t going anywhere, and as it continues to grow, so will technology and business innovation. But unfortunately, unpredictability has led to uncertain markets. Using the impacts of the global pandemic as an example, businesses have had to adapt and pivot to remain competitive.
Data science helps ensure that business leaders make the right decisions and, more so than ever, provides additional scope for future flexibility. Data science isn’t just about understanding what consumers want now but developing what they’ll likely want in 6 months, 12 months, or even two years.
Investing time in data collection and analysis will enhance the perception of the business and products provided. As time goes on, all business functions will provide a wealth of information that can be utilized and enhance opportunities for growth and innovation.
To learn more about the role data science plays in these critical industries and how tech innovation leverages data in digital experience solutions development, get in touch.