Imagine you’re a logistics provider that transports trainers from manufacturers to stores worldwide. You receive multitudes of information, including forecasted sales data, freight and vehicle conditions, warehouse capacity, and routes available. You make the delivery arrangements, and everything is ready. But unexpectedly, the customer demand skyrockets due to your latest shoe going viral on TikTok. 

User-generated content (UGC) adds a whole new dimension to marketing, reducing the predictability of sales and movements across the supply chain. The vast amount of data sources, from social media platforms to the internet of things (IoT), retailers, and manufacturers, make daily forecasting and planning decisions exceedingly challenging for logistics leaders.

Augmented data is ‘synthesized’ or ‘artificial’ information generated from existing data to train machine learning (ML) platforms with potential scenarios that are yet to happen. While data cleansing reduces the representability of data, by forming new and different examples, the machine can define more data classifications in the cleaning process and increase predictability.

Augmented data management (ADM) applies artificial intelligence (AI) to automate data gathering, cleansing, quality checking, and analyzing tasks. It supports data talent in forecasting, spotting anomalies in large datasets, resolving data quality issues, and tracing data to its source.

However, the complexity of data is pushing highly technical positions (such as data curators, data stewards, and data quality experts) to the hot list. They decide what information to collect, where to find it, how to format it, and must recognize and understand the AI algorithms when inconsistencies arise. And these aren’t just setup issues. Customer needs and expectations change continuously, along with the datasets that best express them.

So how can ADM facilitate and accelerate the role of logistics data talent? Let’s find out.

Challenges for Logistics Providers

Shipping, freight, and road delivery companies all have one thing in common – distributing supply to demand. Their biggest challenge of all is demand volatility. Today, volumes rise and fall within minutes, and customers expect their orders to be delivered or canceled at the last minute too. 

Then, there’s the supply side – manufacturers need to know how many products to produce, store, and the best locations for storage based on demand. Add a little politics – sanctions, road closures, business closures, government bans – and logistics providers tackle missing parts stranded across borders, over-stocked locations with no store, and stores with demand and no access routes. 

By the time a human brain has put together the golden equation, the data has changed again, and we’re back to the drawing board – and that’s if the data was of quality.

Data Quality and Steward

In ADM, the data steward is responsible for maintaining the overall data vision, data assets, and the problems that might arise with the collection and use of data. It’s the steward that will lay the foundation for data governance, access controls, and develop strategic plans that align with broader business needs. 

The number one priority in ADM is quality. Quality data requires oversight from data governance regulations. The data stewards spend about 75%Opens a new window of their time defining quality standards, actively monitoring data, and recommending policy on behalf of the organization. For example, any data that needs processing in a country must follow the region’s rules. 

Say the business goal was to improve last-mile deliveries from a warehouse in Amsterdam to homes across the Netherlands, and logistics providers use ADM to create optimal routes. To represent real asset availability scenarios, the truck data fed to the ADM must consider Amsterdam’s regulations for zero-emission vehicles. The steward is responsible for ensuring the algorithm only allocates trucks that meet this standard.

To apply augmented data to ML models, ADM teams need to understand the industry and objective and modify real data from transactions to empower the models with realistic possibilities. The steward defines the strategy and ensures the required data sources can be accessed following legal requirements. They set strict data recording processes and standards within the company, standardize data quality checks, and monitor anomalies, adding in further checks if necessary. 

But, how can logistics providers ensure all the internal and external demand variables are discovered? This is where the curator comes in. 

See More: Quality Over Quantity: Understanding the Importance of Data QualityOpens a new window

Data Discovery and Curators

Data discovery is discovering, classifying, and tracking sensitive data across a company’s entire data infrastructure – including the network, servers, hardware, cloud applications, and external sources. The larger the number of assets and connection points (which get significantly large across a supply chain), the more risk of data inaccuracies and inconsistencies.

ADM converts raw data, numerical, audio, video, or textual, into a numerical format that the AI algorithm understands so that logistics providers can read vast amounts of information in real-time. While temperatures of engines and dimensions of shipping containers can be rounded off and cleansed fairly simply, other cases take a more artistic approach. For example, ADM for a robot that counts the number of pallets in a warehouse will need to consider boxes of different shapes, sizes, and potential beaten corners. 

Curations are where humans add their knowledge to what the machine has automated. Take our TikTok example. AI can identify data imbalances and notify curators, while humans are able to see the bigger picture and recognize trends on social platforms outside of the previous algorithm. The curator is responsible for making this data discoverableOpens a new window to the AI. 

The curator will augment imagery data to represent different angles of the trainer to be recognized in UGC videos. And in cases where companies don’t want to expose sensitive data, they can augment sample files and data extractsOpens a new window . This way, curators can enhance ML forecasting capabilities with more expansive, reliable data sets while complying with the steward’s security and privacy requirements. Their goal is to ensure that the correct person receives the data discovered in a format they know how to utilize. 

The role of the steward and the curator in ADM go hand in hand. While data stewards define and monitor security, privacy, and quality check standards, curators discover the data points, cleanse the formatting, and make it accessible to ensure the algorithms function. 

How To Implement ADM

There is no successful algorithm without intelligent minds (data scientists) supporting it.

However, whether hiring data stewards, curators or integrating with ADM platform providers, there are a few steps to follow:

1. Know your objectives – there are various ADM platforms for data governance, data tracking, inventory versus asset discovery, and risk management. Their usefulness will depend on your business’s existing strengths. 
2. Look for prospects or providers with industry knowledge – the challenges and data points even vary between shipping parcels versus containers; the more the candidate knows about the industry, the better prepared they will be when handling new anomalies.
3. Ensure value cohesion – a job candidate or ADM platform provider that shares your values, understands your objectives, and believes in your goals is more likely to provide the required longevity and support.

Data discovery, data quality, curation, and stewardship are all integrated. New data comes through, and information relevance needs reviewing. Al allows logistics providers and ADM experts to read multitudes of data in seconds, calculate patterns, and add their cunning strategies to the mix to better forecast and improve the efficiency of their processes. However, ADM teams must frequently check data compliance with regulations and, in turn, modify privacy policies. 

When businesses orchestrate a data lifecycle from discovery (algorithm) to the curator (human) to quality checks (algorithm) to the stewards (human), they can improve data management, cost reduction, compliance, and business value.

What steps have you followed to reach the golden equation in Logistics Augmented Data Management? Let us know on FacebookOpens a new window , TwitterOpens a new window , and LinkedInOpens a new window .

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