Data Supply Chain

Introduction to Data Supply Chain

Data has become the greatest asset of a business. The bigger the data it becomes more complex 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 the analysis of huge datasets.

If the data flow is not proper then the business will not be able to derive the maximum benefits out of their data. The data should flow easily through an organization and its ecosystems.

For this reason, it is important to create a data supply chain that makes the data works towards the business goals and create an environment to help achieve those goals.

What is a Data supply chain?

A big data supply chain is a process through which something enters into an organization, undergoes a transformation and comes as something of value that can be used by the people.

A data supply chain is also the same as any other supply chain where data is entered from one end of the system and in the next step it is transformed using analytics. Finally, it is delivered as a set of useful insights about the organization which can be used for any further improvements in the business. Data supply chain analyst will enter the organization is derived from various sources like websites, social networks, mobile apps, blogs, CRM and others. The Data Supply chain is more related to the standardization of data.

Benefits of Data Supply Chain

The key benefits are listed below

• Optimizes operational efficiencies
• Improves business agility
• Reduces data latency
• Easy to accommodate new data sources
• Adjustable to handle large data in the future
• Improves data quality and on the same hand meets the customer demands
• Helps to find out new monetization models where data serves as an asset
• Processes the data fast
• Increases the revenue of the company by helping them to take better decisions.
• Enhance customer relationship

Why building a Big data supply chain is more important?

• 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 the quality of the data and find out more sources from which quality data can be derived.

• More data matters a lot

Search for more data is in a process by many companies. In addition to this companies should also try to create their own data. Creating new sources of data can be a big advantage to the company.

• Focussing on your business goals

The most important thing is that all the people in the company, from staff to CIO should know the business goals. The data should be directed towards the business goals. The big data supply chain will help to do so.

• Wide use of data

The big data supply chain which is acquired from various sources should be used properly within the organization. For this reason, the company has to use various strategies and technologies.

Components

The important components are given below

• Data Sourcing and Collection – This includes Business Process as a Service, Business process outsourcing and Crowdsourcing. Crowdsourcing is considered as a substitute for traditional outsourcing method. Here crowd means people with a common interest. They share solutions for the benefit of the organization who is called the crowdsourcer
• Data Quality and Cleansing – High-quality data is a very valuable asset that increases the user experience. To improve such experience companies should use custom-built solutions and vendors to give 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 more faster and deliver quicker and better results.
• Data Management – Advanced Data warehouse features goes beyond the traditional data warehouse and offers 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 data supply chain.
• Data Delivery – Data delivery includes data visualization, classifying databases, social media integration, use friendly data delivery and Data As a Service (DaaS)

Data Supply Chain Analyst

Data supply chain analyst 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 analyst will help the organization to face three major limitations. They are discussed under the topics of data supply chain analyst:

• Movement

To get in-depth knowledge of the data businesses to 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 single data and acceleration helps to do that. It brings accurate data into the organization and makes sure that it can be processed quickly.

• Processing

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 to pre-process the data which comes in and streamlines the data with the existing data of the organization 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

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.

5 Steps to build a chain

Listed here are the 5 steps to build 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 the data from various sources whenever they need it. Through this data platform, users can have access to a large pool of data directly. The data platform can be purchased from a vendor. It can be a single data platform or it can be a combination of various platforms provided by various vendors.

Today there are also separate data platforms that help to derive data from one particular 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. In the past companies distinguish between the frequently used information and less relevant data. The more relevant data are stored on 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 a great speed and this helps in gaining more knowledge from the data.

3. Advancing data discovery

Traditional BI methods require more details from the data scientists or data analytics professional to get an answer for a prescribed business question. But now because of the data discovery tools, even before the companies start questioning they discern their own questions that are expected to arise from the companies after getting to know about the data in detail.

4. Realizing data value

In 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. In order to increase the value of the data, it can be shared with the company’s suppliers, partners, and customers.

5. Cognitive computing

Cognitive computing is a method where the machine is taught to leverage the data, learn from it and find out what can be done with it. Data supply chain provides a long-term solution. In older method, a solution can be found out 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.

Building a Better Data Supply Chain

An organization that has the infrastructure to capture, process, analyzes and distribute the data across the supply chain will be able to manage their inventories without losing any 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 demand of the business will help them to achieve their production schedules, distribution models, define their marketing strategies and so on.

The data supply chain must be kept simple and integrated. A big challenge with data is accessing and analyzing the data in different formats and structures which is in the on-premise application or in the cloud. It is the greatest challenge faced by the data analysts in the long run. 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 smart decisions based on the accurate information available. The organization should make sure that the data used in supply chain decision-making process is clean and accurate. To maximize the potential of the data supply chain leaders should follow these simple steps.

• Work with accurate, real-time data

The main factor in supply network is to have a data consistency. Lack of data consistency is a major problem that is faced by most of the companies. One important method to get accurate data is to analyze the timing of MRP data which enters into the organization. Companies can also use data capture and validation workflows to find incomplete records in your system. Frequent auditing can also be done to find out any errors in data.

Mobile technology helps to enhance the real-time data and integrate it with the supply networks. Mobile devices can be used to send and receive data instantly anywhere, anytime.

• Eliminate unnecessary data and processes

Incomplete and unnecessary data are a waste of time in the supply chain process. The company should have an independent AP automation solution to check the data for three-way matching. One way to find out unnecessary data is the evaluate the areas of supply network where multiple processes are used to stream the data into an integrated system. This will help to segment the unnecessary data across the enterprise and segment the valuable data on a regular frequency. As a result, the data will be more consistent and dependable to make better decisions.

• Centralized Data Solution

The major challenge of data supply chain network is its increasing amount of information every day. The truth is that the more data always does not mean better data. Due to the mergers and acquisitions, the data supply chain networks grow frequently. So organizations must find ways to combine data from various sources and from a large amount of suppliers.

The best solution is to implement a supply chain collaboration system which will help you to strategically view your data. This view can help to sort data into necessary parts and generate reports of real-time information.

Conclusion

Data supply chain will be a major focus of many enterprises in the coming years. Selecting the correct key elements and services of Data Supply Chain will help to increase the productivity and optimize the business for any changes in the market.

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