Fundamentals of Big Data Supply Chain
February 1, 2021
Data is very important in all businesses. As data becomes larger, it becomes more challenging to manage. A big data supply chain ensures massive data flows appropriately in an organization and within its ecosystems. This is a process by which an organization’s data is transformed into a valuable form for strategic use.
The big data supply chain is essential because it improves strategic decision making in an organization. This article provides the fundamentals of this vital concept. It also explains how a big data supply chain can be set and provides suggestions for optimizing its use.
Introduction to Big Data Supply Chain
Big data is a term used to describe huge volumes of data. Many organizations have experienced data storage problems due to the volume, speed (velocity), and variety of data. This requires an efficient big data supply chain to improve data management and predictive analytics.
A big data supply chain is a lifecycle of how data moves into the organization and gets transformed into a valuable form that can be used for decision making. The big data supply chain consists of processes such as selection, quality assurance, procurement, warehousing, and data management. The data is then transformed and distributed to various users in the organization. A data supply chain used by an organization should be aligned to its underlying goals.
Data supply chain is similar to the concept of the supply chain in the business field, which consists of inputs collected from suppliers and an output distributed to customers. In big data supply chain, input data is collected from various sources such as apps, websites, blogs, and social networks. The output is in the form of resourceful insights that enable managers to make sound decisions. Like a standard supply chain, a data supply chain’s output is distributed to various people (users).
Benefits of a Big Data Supply Chain
- It provides a process in generating meaningful insights for strategic decision making.
- It enables organizations to organize their data, which improves data analytics.
- It enables organizations to accommodate additional data sources.
- It increases the speed at which data is processed.
- It is an effective way of reducing data latency.
- It improves the quality of data in the organization.
Components of Big Data Supply Chain
The following diagram shows the main components of a big data supply chain.
- Data Sourcing: This involves collecting data from various sources. Data sourcing and collection can be optimized through crowd-sourcing, BPaaS (business process as a service), and integrated workflow tools.
- Data Cleansing: This involves modifying the data to remove incomplete, inaccurate, and irrelevant data parts to improve data quality. This can be achieved using automation tools, open-source tools, and DQaaS (data quality as a service).
- Data Enrichment: Here, data enrichment is done to refine and improve data. This can be done using data mining tools and advanced analytics.
- Data Management: In this phase, data is managed using various data warehousing tools. Storage is optimized to solve organizations’ main storage problems (volume, speed, and variety). The data is transformed to provide meaningful insights.
- Data Delivery: Data is delivered to users through data visualization, DaaS (data as a service), and social media integration.
Steps for building a Big Data Supply Chain
The following steps can be used to build a big data supply chain.
1. Selecting a data service platform
Start by choosing a data service platform that your organization can use for accessing data. This platform enables users to have direct access to the data. Choose a vendor that meets the financial capability of your organization. The organization can choose one platform or a combination of many platforms offered by different vendors.
2. Integrating data
This step involves integrating data collected from diverse sources. High performing systems can be used to store the most frequently used (or relevant) data. This will help by increasing the speed at which users can access the data.
3. Data discovery
This step involves using data discovery tools to provide answers to specific prescribed questions. The organization can use business intelligence (BI) tools to optimize queries.
4. Realizing data value
Once the data is transformed, it is ready to be shared and accessed by various users. The organization can use the final data to make valuable decisions (after understanding and gaining the adequate knowledge). The organization can share the final data with other users such as customers, suppliers, partners, and other stakeholders to realize its value.
5. Cognitive computing
In this step, the organization should invest in cognitive computing to teach machines how to leverage the final data and establish the best use. Machine learning systems help the organization to provide data-driven solutions in the long-run. For example, machines can be trained to understand the market demand and provide meaningful insights based off that.
Issues to consider
Data analysts and managers in an organization should follow the considerations below to enhance an efficient and optimal data supply chain.
- Ensure data quality: Organizations can use validation workflows to establish whether there are incomplete records within the system. Frequent auditing can be done to ensure that data is accurate and consistent. Quality data enhance the formulation of valuable decisions that can transform the business.
- Use a centralized data solution: Use a collaborative system that enables you to combine data from diverse sources. A centralized data solution allows you to view data strategically, that improves data sorting and the generation of real-time reports.
- Track the lineage of data: Tracking data lineage can enable the organization to establish data sources and the processes it passed through. This can help manages understand the kind of data one is dealing with.
A big data supply chain is essential to businesses because it provides meaningful insights that improve decision making. This supply chain can eliminate common data problems such as data latency, data redundancy, and inconsistency. The five main components of a big data supply chain include data sourcing, cleansing, enrichment, data management, and delivery. Big data supply chain in an organization can be optimized by ensuring data quality, tracking data lineage, and using centralized data solutions.
Peer Review Contributions by: Lalithnarayan C
About the authorOnesmus Mbaabu
Onesmus Mbaabu is a Ph.D. candidate pursuing a doctoral degree in Management Science and Engineering at the School of Management and Economics, University of Electronic Science and Technology of China (UESTC), Sichuan Province, China. His interests include economics, data science, emerging technologies, and information systems. His hobbies are playing basketball and listening to music.