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Making data a first class citizen is imperative to competing and succeeding in the business of today, and definitely tomorrow. Knowledge of data and how to leverage it is power.
Data logistics is a concept used in computer networking that refers to the study of solutions to problems in computer systems contemplating resources and services relating to data movement, data storage and data processing. – Wikipedia
Defining data logistics
There are three main data logistics approaches. Below are definitions, each increasing in maturity, technical complexity, and use of interpretation of results (automation/AI).
- Extract-Transform-Load (webopedia): “ETL is short for extract, transform, load, three database functions that are combined into one tool to pull data out of one database and place it into another database. Extract is the process of reading data from a database. In this stage, the data is collected, often from multiple and different types of sources. Transform is the process of converting the extracted data from its previous form into the form it needs to be in so that it can be placed into another database. Transformation occurs by using rules or lookup tables or by combining the data with other data. Load is the process of writing the data into the target database.”
- Streaming Data (AWS): “Streaming data is data that is generated continuously by thousands of data sources, which typically send in the data records simultaneously, and in small sizes (order of Kilobytes). Streaming data includes a wide variety of data such as log files generated by customers using your mobile or web applications, e-commerce purchases, in-game player activity, information from social networks, financial trading floors, or geospatial services, and telemetry from connected devices or instrumentation in data centers.”
- Data Automation (Learn.org): “Automated data processing is the creation and implementation of technology that automatically processes data. The purpose of automated data processing is to quickly and efficiently process large amounts of information with minimal human interaction and share it with a select audience.” Data automation often enables an ELT (extract, load, transform) approach, moving the logic to change the data – the transformation – to the front.
What problem are you trying to solve?
As with all solutions, it is essential to understand the business context and justification underpinning a data initiative – what problem are you trying to solve, and why?
Below are three key business benefits and simple examples to illustrate the importance of embracing data to enable your business.
1 – Velocity. The automation of systems that intelligently route products or services based on historical data and live streaming of data create improved, efficient, customer experiences. For example, in shipping logistics, a product delivery can be optimized based on data derived from the transportation ecosystem using sensors on vehicles, be it a plane, truck, car, drone, human, or robot.
The data can be enhanced by what has been learned historically and from environmental nodes sharing real-time data – weather conditions, impact of seasonality, time of day, traffic patterns, and when the end customer will be home and how to deliver the goods or services.
This may also include a human interaction during the “last mile” to contextualize and personalize the experience, or require a bike currier to complete the delivery in urban areas. Suburban areas may simply require sensors to deliver the goods or services to a secure location at a home which is available 24/7.
Examples: Groceries being delivered to a home, cars being shipped from overseas to a local port, and then to a dealership, or a local interior designer ordering materials from overseas for a home/office project.
2 – Access to information/data. Data that is created in the course of an individual or business conducting activities should be theirs to own and share as defined by their own definition of conditions related to security and privacy. We as consumers and organizations all permission apps, systems, and 3rd parties to ingest data, and incrementally learn patterns and build profiles from the data.
The resulting metrics, reports, and summary statistics become a data record that is best suited for use and real-time monitoring and response functions by the data owner. This data should be at least accessible, if not shared, with the consumer or organization being serviced.
Examples: 3rd party marketing technology stack silos broken down to inform customer needs (martech stack), broadcast signals/weather advisories based on location, mobile campus security updates.
3 – Productivity. Data can be utilized to enable transparency in production, customer understanding (needs, demands, wants, and wishes), and delivery. When timeliness, safety, perishability, or security of goods is valued, sharing of data between important constituents builds the relationship and trust.
Data analysis unveils patterns that yield improvements and optimization of product development and delivery timeframe. Data automation can be harnessed to reduce supply chain costs by optimizing product or service creation, removing in-process storage or human-intervention, and delivering in a timely, customer-desired manner.
These savings will benefit the entire ecosystems from production, to supply, and all the way to delivery and the consumer (satisfaction with result). Data also allows you to do more with less information, process, and communication friction.
Examples: Creating and delivering a part to fix a car, component to replace a faulty machine in manufacturing plant/process, or binding an insurance policy or financial portfolio to finalize a transaction.
Recommendations & take aways
- Make data a first class citizen; educate your executive team and organization on the value of data. Knowledge of data and how to leverage it is imperative to competing and succeeding as a business. Data gathering and data management is only growing larger as more and more data is collected and stored. Move your organization from being a “data janitor” to a “data analyst.” Learn the science of data, and more importantly how to make it actionable in the business.
- The reliance on IT to access critical business intelligence will lessen in time – plan to be ready. As tools, employee capabilities, and internal team competencies increase, the reliance on the data expert – externally or internally – will decrease. Take steps to make data fluency an organizational competence.
- As an executive, lead by example. Leaning in to take a professional development course in data analytics will pay dividends. Sharpening your data analytics knowledge and business intelligence skills will make you more relevant and valuable to your organization.
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