An estimated 90% of all data is either semi-structured or unstructured. This includes videos, PowerPoint presentations, company records, social media, RSS, documents, and text. However, many organizations are not utilizing this data despite the valuable insights that this data could yield. (Click here to read Part 1 of this series.) The reason for this is simple: the tools needed to analyze such a large scale of data have not existed. However, advancements in machine learning and data visualization tools are now making analysis of semi-structured and unstructured data possible.
Source: Gartner, Market Guide for Supply Chain Analytics Technology, February 2017
Let’s turn our attention to the five broad categories of analytics: descriptive, diagnostic, predictive, prescriptive and cognitive or artificial intelligence.
The simplest method is descriptive analytics which shows what is happening with data. A good example of descriptive analytics is the information you would capture and display in an Excel spreadsheet which shows historical information, like broker fees.
Diagnostic analytics goes a step further by providing insight into potential problems or opportunities as evidenced by the data. For example, your analytical tool might identify a duty savings opportunity and the projected annual savings that could accrue from adopting this change.
When your analysis provides a prediction of the future (e.g., here’s the data, here’s what it means, and here’s a look at the future based on the past), you are applying predictive analysis to the situation. A predictive model will not only identify duty savings but will also forecast your future duty payments based on projected sales volumes.
According to data analytics experts, the three biggest benefits for using Big Data within supply chains are traceability, relationship management (e.g., better customer service), and forecasting/predictability. The benefits of traceability are fairly obvious. Knowing where your goods are located at any point of the supply chain, being able to predict or be notified of supply chain disruptions, and having contingency plans to address these issues have an enormous impact on profitability, resource planning and customer satisfaction. A recent article from the Journal of Commerce indicated that shippers, forwarders, and carriers are looking at artificial intelligence to gather and analyze data to address “issues such as how to pick the best alternative port when the original destination is blocked, better estimating the arrival time of a ship so that logistical resources can be ready. AI is also being tapped to forecast whether a shipper will cancel a booking or its container will get rolled by the carrier, and left on the dock.” 
Prescriptive analytics is where progressive companies are focusing investment as it goes a step beyond prescriptive analytics by suggesting how you should address the future opportunities or problems identified by predictive analytics. Analyzing current data sets for patterns, prescriptive analytics evaluate the possible outcomes of the multiple courses of action. This not only provides decision-makers with multiple options on how to address the issue but also the hypothetical impact of each option. Although only 10% of organizations currently use some form of prescriptive analytics, Gartner predicts that the use of prescriptive analytics will grow to 35% in organizations by 2020.
Finally, and most importantly, when you combine advanced technology, such as artificial intelligence or machine learning with data analysis, you uncover new opportunities to improve supply chain efficiency, planning and forecasting. Gartner’s 2017 Market Guide for Supply Chain Analytics Technology Report predicts that technology advances will transform the supply chain of the future. “Machine learning will become more prevalent in areas like demand forecasting, dynamic pricing and asset maintenance. Prescriptive capabilities, spanning optimization, heuristics and decision analysis will become more dynamic, accounting for more constraints, variables, complex objectives and fast response time requirements. Cognitive analytics capabilities will be offered more broadly in cognitive and artificial intelligence (AI) platforms as well as embedded analytics in supply chain and enterprise applications. Cognitive capabilities will be further leveraged by organizations either as an advisor to business users for augmenting their decision-making capabilities or to automate more dynamic supply chain processes like order configuration or price”.
The data analytics top trends in 2017 would support this statement in that:
- Unstructured data was expected to dominate the analytics landscape 
- Embedded analytics is poised to take off 
- Prescriptive, not predictive, analytics will rule the day, and
- Big Data and analytics are in the top three most popular technology trends (KPMG Technology Trends Index)
One would concur that the benefits of utilizing data analytics to improve supply chain efficiency are extremely varied. For example, data analytics can facilitate:
- Data validation/error checking
- Detecting anomalies in your supply chain
- Benchmarking your operations versus internal or external norms
- Mobile reporting of opportunities or problems
- Global logistics visibility
- Real-time route optimization
- Improved demand forecast
- Improved inventory management
- Increased government audits
For Global Trade Management software providers, the availability of customer dashboards within GTM solutions, known as embedded analytics, will become pervasive. If it doesn’t already, your GTM application will not only monitor your trade compliance efforts versus internal KPIs but also provide you with many of the analytical tools discussed previously on a single dashboard tied to the user’s specific compliance tasks.
The power of data analytics and Big Data has not gone unnoticed by customs authorities; a development that suggests both opportunities and risk.
The WCO recently published a paper authored by a member of their research staff, Yotaro Okazaki, entitled “Implications of Big Data for Customs – How It Can Support Risk Management Capabilities”.
The paper discusses the implications of Big Data use by Customs, particularly in terms of risk management. To ensure that better informed and smarter decisions are taken, some Customs administrations have already embarked on Big Data initiatives, leveraging the power of analytics, ensuring the quality of data (regarding cargos, shipments and conveyances), and widening the scope of data they could use for analytical purposes.
The CPB’s (Customs and Border Protection) Performance & Accountability Report outlines their objective for each calendar year. In their 2016 report, under the objective, ‘Strengthen Comprehensive Trade Enforcement Trade Analytics’, CBP specifically outlined their Big Data strategy. “CBP pursues opportunities with the academic community to take advantage of innovative and creative models to analyze the unprecedented information windfall known as ‘Big Data.’”
With the movement to e-filing and single windows, information can be processed and shared between government agencies more efficiently which should reduce error rates and potentially shorten time of entry. Other potential benefits include improved tax fraud detection, threat prevention and better need assessments for heightened security. However, these benefits need to be balanced with privacy protection and data security.
Faster, deeper insight into customs filings though the application of data analytics could also increase the likelihood and volume of customs audits. Companies who do not have the same analytical tools as Customs at their disposal may find themselves at a disadvantage when responding to customs audits.
Data analytics has the potential to benefit your organization through cost savings and operational efficiency. Equally important, data analytics will benefit trade compliance experts professionally. With the proper analytical tools, your role within can reduce mundane, manual tasks and become a trusted advisor as you increase your focus on strategic planning by implementing your data analysis.
 Journal of Commerce, “Artificial intelligence promises smarter container shipping” Hugh R. Morley, Senior Editor, June 20, 2017
 Innovation Analytics, September 6, 2017