Updated June 14, 2023
Introduction to Data Supply Chain
Data has become the greatest asset of a business. The bigger the data, the more complex it becomes to deal with it. It becomes more challenging to manage and analyze the data and get a desirable business understanding from the data. The main objective is to enable business people to make better decisions based on analyzing massive datasets. If the data flow is improper, the business will not be able to derive the maximum benefits from its data. The data should flow easily through an organization and its ecosystems. For this reason, it is essential to create a data supply chain that makes the data work towards the business goals and create an environment to help achieve them.
What is a Data supply chain?
A big data supply chain is a process through which something enters an organization, transforms, and comes as something of value that people can use. Data supply chain analysts will enter the organization from websites, social networks, mobile apps, blogs, CRM, etc. It is more related to the standardization of data.
The key benefits are listed below
- Optimizes operational efficiencies
- Improves business agility
- Reduces data latency
- Easy to accommodate new data sources
- Adjustable to handle extensive data in the future
- Improves data quality and, at the same time, meets the customer demands
- It helps to find out new monetization models where data serves as an asset
- Processes the data fast
- Increases the revenue of the company by allowing them to make better decisions.
- Enhance customer relationship
Why building a Big data supply chain is more important?
Below are mentioned some importance :
The quality of data is more important than quantity.
It is the easiest way to improve the effectiveness of any organization. So companies should always focus on data quality and find more sources from which quality data can be derived.
More data matters a lot.
Search for more data is a process by many companies. Companies should also try to create their data. Creating new sources of data can be a significant advantage to the company.
Focussing on your business goals
The most important thing is that everyone in the company should know the business goals, from staff to CIO. The big data supply chain will help to do so.
Wide use of data
The big data supply chain, acquired from various sources, should be used appropriately within the organization. For this reason, the company has to use multiple strategies and technologies.
- Data Sourcing and Collection – This includes Business Process as a Service, Business process outsourcing, and Crowdsourcing. Crowdsourcing is considered as a substitute for the traditional outsourcing method. Here crowd means people with a common interest. They share solutions for the benefit of the organization, which is called the crowdsourcer.
- Data Quality and Cleansing – High-quality data is a very valuable asset that increases the user experience. To improve such an experience, companies should use custom-built solutions and vendors to give the best results. Data Quality As a Service (DQaaS) must form a major part of data quality as it follows a centralized approach. Open-source tools are the best to work with messy data sets.
- Data Enrichment – Using big data tools like Hadoop, the data enrichment components can process the data faster and deliver quicker and better results.
- Data Management – Advanced-Data warehouse features go beyond the traditional data warehouse and offer successful business intelligence. They are easy and affordable. Open-source clustered file systems like HDFS and others can solve some of the greatest challenges of the data supply chain.
- Data Delivery – Data delivery includes data visualization, classifying databases, social media integration, user-friendly data delivery, and Data As a Service (DaaS)
Data Supply Chain Analyst
Data supply chain analysis is the architecture for the modern data supply chain process. If done in a proper way, the data supply chain analyst will let the companies leverage more data sources and improve the data discovery to a great extent. Data supply chain analysts will help the organization to face three major limitations. They are discussed under the topics of data supply chain analysis:
To get in-depth knowledge of the data, businesses need to derive it from various sources and then use appropriate processing and storage systems. While moving data, there should not be a loss of even a single piece of data, and acceleration helps to do that. It brings accurate data into the organization and makes sure that it can be processed quickly.
The processing of data depends mainly on the volume and type of data. Organizations will expect the system to do computations on the data more quickly than ever. Data supply chain analyst technology will help pre-process the data that comes in and streamlines the data with the organization’s existing data to help make smarter decisions. Data acceleration helps in the quick processing of data by improving the hardware and software components and helps in improving efficiency.
Interactivity means the usability of the data. There are a lot of solutions to help get expected results from given queries. Now there are new programming languages developed to support the systems. Data acceleration helps the users to bridge the gap between the infrastructure and applications. This also helps to deliver the query results quickly.
Steps to Build a Chain
Listed here are the steps to building a chain.
1. Data Service Platform
The first and foremost step in creating a data supply chain is to begin with selecting a data service platform that helps the company to have easy access to data from various sources whenever they need it. Users can access a large pool of data through this data platform. It can be a single data platform or a combination of various platforms provided by multiple vendors.
Today, separate data platforms help derive data from one source. But all these platforms work through a common standard access protocol. Recently many organizations have started to use API management platforms.
2. Accelerating data through the supply chain
The next step in this process is integrating the data from various sources. Previously, companies distinguished between frequently used information and less relevant data. The more relevant data are stored in high-performing systems, and the less relevant are stored in slow-performing systems. But now, organizations can increase the speed of the data. The data is accessible to the people in the organization at an incredible rate, which helps in gaining more knowledge from the data.
3. Advancing data discovery
Traditional BI methods require more details from data scientists or data analytics professionals to get an answer to a prescribed business question. But now, because of the data discovery tools, even before the companies start questioning, they discern the questions that are expected to arise from the companies after getting to know about the data in detail.
4. Realizing data value
The final stage of the data supply chain, which is transformed, can now be shared and accessible. Companies can understand the data better and gain knowledge from it. They can make decisions based on the data. To increase the value of the data, it can be shared with the company’s suppliers, partners, and customers.
5. Cognitive computing
The data supply chain provides a long-term solution. In the older method, a solution can be found for a specific task or single business case. But through machine learning, systems can get more knowledge from data as experience. It can be stored, and they can use it in the future when there exists the same situation.
6. Building a Better Data Supply Chain
An organization with the infrastructure to capture, process, analyze, and distribute the data across the supply chain can manage its inventories while maintaining business opportunities. Customers are hard to be predicted these days. As a result, many enterprises are turning towards demand-driven production. Data supply chains that can identify and respond to the business’s demand will help them achieve their production schedules and distribution models, define their marketing strategies, and so on.
A big challenge with data is accessing and analyzing the data in different formats and structures, whether in the on-premise application or the cloud. The data scientist or the data analyst should be familiar with SQL to bridge the gap between these challenges and solve the complex problems in data.
Supply chain decision-makers also rely more on quality data. Quality data helps to make intelligent decisions based on the accurate information available. The organization should ensure that the data used in the supply chain decision-making process is clean and precise. Supply chain leaders should follow these simple steps to maximize the data’s potential.
7. Work with accurate, real-time data
The main factor in the supply network is to have data consistency. Lack of data consistency is a significant problem that most companies face. One vital method to get accurate data is to analyze the timing of MRP data that enters the organization. Companies can also use data capture and validation workflows to find incomplete records in their system.
Mobile technology helps to enhance real-time data and integrate it with the supply networks. Mobile devices can send and receive data instantly anywhere, anytime.
8. Eliminate unnecessary data and processes
Only complete and necessary data are a good use of time in the supply chain process. The company should have an independent AP automation solution to check the data for three-way matching. This will help segment the unnecessary data across the enterprise and segment the valuable data regularly. As a result, the data will be more consistent and dependable to make better decisions.
9. Centralized Data Solution
The major challenge of the data supply chain network is its increasing amount of information every day. The truth is that more data always does not mean better data. Due to mergers and acquisitions, the data supply chain networks grow frequently. So organizations must find ways to combine data from various sources and suppliers.
The best solution is to implement a supply chain collaboration system to help you view your data strategically. This view can help to sort data into necessary parts and generate reports of real-time information.
The data supply chain will be a major focus of many enterprises in the coming years. Selecting the correct key elements and services of the Data Supply Chain will help to increase productivity and optimize the business for any changes in the market.
We hope that this EDUCBA information on “Data Supply Chain” was beneficial to you. You can view EDUCBA’s recommended articles for more information.