This article provides an overview of big data and analytics and its application to the air cargo industry. Combined with your current management methods and tools, analytics adds unique value in decision making by turning raw data into useful knowledge. Analytics can be considered the Global Positioning System (GPS) of decision making in which one can visualize the most efficient “route” to achieve a business goal. Similar to GPS, analytics can adjust the route as conditions change to optimize the best approach in reaching a “destination”. However, the real benefit of analytics is its ability to combine historical and real-time data with statistical analysis to search for key patterns, draw inferences and make predictions about actions in the future.
According to a 2015 survey of 437 organizations by Gartner, Inc., more than 75% of companies are investing or planning to invest in big data within the next two years with approximately two-thirds of big data projects initiated by the Chief Investment Officer or business unit heads.
Three Approaches to Analytics:
• Establishes the parameters to systematically collect, map and analyze relevant data
• Looks at past performance and data to identify patterns and correlations to uncover the reasons behind success or failure
• Develops the narrative to enable you to ask the right questions and frame the problem clearly
Example: Descriptive analytics can help identify your customer base by creating a comprehensive customer profile based on relevant data including key demographics on industry, size, location, shipping history and buying patterns, etc. A specific marketing campaign can then be developed based on customer segmentation with the ability to predict customer behavior and sales. Ultimately, a customer lifetime value can be determined based on longer projections of the amount of future business which would provide a broader view of your customer base and enable you to build stronger customer relationships resulting in potential up-sell opportunities.
• Links historical and real-time data with statistical algorithms to predict the likelihood of future outcomes or alternative scenarios
• Provides the capability to move from reaction to prediction
• Builds supply chain optimization with ability to forecast demand and revenue, allocate resources and minimize risk and uncertainty
Example: Most airlines have a business strategy for cargo demand and capacity. Analytics can maximize revenue and minimize transportation costs by predicting customer demand while satisfying any capacity limitations. The analytic model would incorporate various predictors such as, customer’s buying pattern, seasonal influence, show up behavior and available cargo capacity on a daily, weekly or monthly basis. The analytic results would be continuously updated with actual results. Accordingly, a business strategy would be able to adopt changes by adjusting flight schedules or prices charged during different demand periods. All of which would maximize revenue while increasing customer satisfaction.
• Provides a recommendation (“prescription”) on what actions to take to achieve a business goal by developing a model linking actions to a goal
• Anticipates not only what will happen but also why it will happen
• Prescriptive analytics suggests several decision options and shows the impact of each decision option.
• Considered to be the next step following predictive analytics
Example: An air cargo company needs to implement procedures to ensure the proper temperature controls for transporting pharmaceutical and perishable products globally which is a difficult and complex process. Data mining can explore large data sets using correlation analysis to uncover insights into those factors that contribute to failure in controlling temperature. Such factors may include length of travel in time or distance, number of transfer locations, delays due to weather or missed flights, small versus large shipments, etc. Analytics can generate a model that estimates the probability of a success for adjustments made in each correlation factor either separately or in combination resulting in minimizing the risk of failure and resulting loss revenue.
Benefits and Features of Analytics
• Machine Learning which enables analytics tools to learn each time they collect new data
• Instant feedback which may accelerate a response or solution during unexpected events
• Shifts from a top down approach to a bottoms up approach in strategy and decision making
• More efficient allocation of resources and in setting prices
• Improve customer retention and loyalty by enhancing the customer experience by providing tailored products and services that are valued
• Provides a data driven approach to managing employees in your company including hiring, retaining the right employees and determining the attributes that predict employee performance
• Ability to assess the drivers of financial performance and understand how financial data and non-financial data interact to forecast events under different future financial scenarios
Collecting, Tracking, and Storing Quality Data
There are many sources of data from internal and external to structured and unstructured. Structured data is organized data including tables, charts and graphs which may be integrated into a database management system that is easily searchable by query and other straightforward search engines. Unstructured data includes social media, e-mail, and videos. Although data is never perfect, most companies struggle to get quality data. However, there have been significant improvements in the way data is collected and stored. The first step is to start with the data that you already have and can measure. Although some analytic tools focus on gathering the data into a centralized data warehouse, it’s no longer necessary to have all your data in one place. There are tools available within the Cloud which can extend across multiple platforms to access and store data. The key to analytics is searching for relevant data and eliminate the “noise”. Gathering and storing data takes time, money and expertise. In the end, more data does not necessarily lead to better decisions since the real value of data is context.
Visualization – The Dashboard
Results can be shown by performance reports, surveys, all sorts of graphs and charts but similar to GPS a dashboard is easy to view, share and fast to read. Dashboards are an effective tool for combining and blending data from many sources and transferring data into actionable insights. Well-designed dashboards can reduce the number of reports on key performance indicators (KPIs) and accelerate data driven decisions which can increase the company’s return on investment (ROI).
Incorporating big data and analytics into your business strategy is quickly moving from a luxury to a necessity. It doesn’t matter how much data you collect; data is worth very little unless you can turn it into insights and action. However, analytics, no matter how advanced, does not remove the need for human insights.