The New Age of Analytics: Artificial Intelligence and Data are Not Enough to Power Your Business

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In this special guest feature, Rosemary Radich, Head of Business Intelligence and Data Analytics for AccuWeather, discusses the importance of the weather data dimension as an adjunct to traditional data sources in capturing the broader picture impacting behavior and results from machine learning. In her critical role overseeing AccuWeather’s dedicated global weather data science and analytics team, Radich identifies marketplace advantages for clients, applying the most accurate, robust and detailed big data sources, AccuWeather’s proprietary IP, and comprehensive, advanced analyses to inform predictive models. She enables clients to maximize ROI in areas such as sales, marketing, supply chain, logistics, and operations through custom weather-based predictive modeling in addition to analyzing internal data within AccuWeather to help increase efficiency and accuracy. Rosemary received her M.A. from Wichita State University

It’s hard to find an article on the future of technology and business without reading phrases like “big data,” “machine learning” and “artificial intelligence.” When we hear these phrases, we get excited about leveraging the power of this new technology, but despite the great availability of data many business leaders don’t know where to start or how to utilize it in a meaningful, practical way.

Business leaders are looking for data to provide insight to make informed decisions that will impact their bottom line, and consumers are looking for products and services that are contextually relevant and seamlessly integrate into their day to help make their lives easier.

Data and algorithms seek to explain human behavior, but in many cases, data sets alone don’t capture the broader picture impacting behavior and results from ‘machine learning’ seem to miss the target. For example, what are consumers buying? What events are people planning? Will I reach my destination safely? All of these questions require several data sets, including one of the most important – weather – and one element that some forget in the headlong rush into ‘artificial intelligence’ – human behavior.

The Influence of Weather

Weather influences what beer people drink, what music they listen to, how many steps they take, and their drive time to work – in other words, virtually every part of their day.

But generic weather data is not enough – the level of accuracy and granularity of your data influences the accuracy of model outputs and the relevancy of the end results. The amount of data and information is always increasing, so finding accurate, granular, and relevant data through the clutter has become more difficult and important. Data scientists and analysts can spend the majority of their time cleaning data, and without clear domain and industry knowledge, ultimately yields limited results.

The process of correctly cleaning data requires knowledge of geo-spatial analytics and time series modeling, but the correct application of these methodologies requires knowledge that can only come from years of experience in forecasting and climatology. Properly cleaning weather data requires more than just identifying outliers and looking for missing values, it requires knowledge of complex and ever-changing climate zones, being able to identify, understand and explain anomalies as either an error in the underlying data or the result of a weather event.

Many enterprises need to navigate through the complexity of weather data with accuracy and levels of detail – applying human expertise as an integral part of the process, which ensures success and continued innovation.

The Importance of Human Intervention

The best data scientists don’t just develop algorithms to produce results independent of industry and business understanding; they work directly with partner businesses to develop algorithms that change the way the world thinks about data and that spirit of innovation and collaboration is what propels insights and new products. In-house talent, with expertise across industries and disciplines, need to work directly with subject matter experts in each business and industry to create tailored ‘artificial intelligence’ solutions.

One example is when Spotify conducted a study to understand the connection between music and weather. This resulted in Climatune, a product that successfully delivers custom music playlists that better meets consumers moods based upon the weather and user preferences based on location.

Delivering on these high expectations requires expertise in meteorology, data science, human behavior, and the most accurate forecasts in the business. Every day engineers work with meteorologists, data scientists, business development leaders, and marketing on cross-functional teams to collaborate with industry-leaders such as Microsoft and Google to revolutionize how we think of big data and analytics.

Having accurate, quality data ensures your algorithms have reliable outputs, but true innovation and creativity requires human talent. When you power life-saving products, partnerships and ideas with human experience and intelligence, you truly revolutionize and leverage ‘artificial intelligence’ in a way that will change lives and your business.


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