Whether it’s anticipating customer needs or staying ahead of competitors, data plays a pivotal role in business success. Companies across industries have realized its importance and are investing in data. That said, many businesses still struggle to run a smooth data supply chain.
As a result, data quality suffers. A study found that 75% of the business executives surveyed did not have a lot of faith in their data. This affects the faith businesses have in human and automated data-driven decisions. The best way to tackle this – improve the data supply chain.
A company’s data supply chain refers to the processes involved in supporting the flow of data from its point of collection, through transformation to consumption. There are 3 distinct phases in a data supply chain; collection of raw data, transformation, analysis and consumption. Like a product supply chain, data supply chains are as strong as their weakest link and hence attention must be paid to data quality in all three phases.
Measures to improve data supply chain outcomes can be categorized under 3 heads:
The first mile in a data supply chain is the distance traveled between the extraction of raw data to its point of processing. The last mile refers to the process of making verified and standardized data available to data analysts and other end-users. The key challenge here is to ensure that data is captured correctly and moved across the supply chain without any kind of quality degradation.
Some of the steps that can be taken to achieve this include:
While today’s customers are concerned about data privacy, over 80% are willing to share their personal data with brands in exchange for a better customer experience. However, when it comes to data, more is not always better.
To increase the value of your data, ensure that only as much data as is absolutely required, is collected. This minimizes distractions for data analysis and makes it easier to comply with global data privacy regulations.
Using multiple channels makes businesses more accessible to their customers but also increases the risk of collecting data in different formats. For example, in one case, a customer’s identity may be linked to only their first name while in another, it may include their first and last name. Standardizing such data formats makes it easier to correlate data from different departments and also minimizes the risk of having duplicate records.
Customers may unintentionally make typographic errors. Or, fraudsters may intentionally enter incorrect details in order to create false records. Data needs to be verified at every stage of the supply chain. This can be automated with data verification tools. These tools check business data against reliable third-party databases to ensure that it meets the company’s data quality standards.
Data supply chains can be quite complex with the variety of data suppliers, business functions and users involved. The more complex a supply chain, the greater the variation in formats. Hence, reducing complexity can directly improve overall data quality. Some of the steps to do so are:
Businesses must inventory the data available to them and map the system with its internal and external sources accordingly. Each data element must have a singular system for downstream consumption with attributes being maintained in a common structure.
Establishing such a system of record helps meet the goal of simplifying data movement across the supply chain and delegate responsibility for data to all involved with it.
To minimize confusion and keep data from being duplicated, maintaining a central database could be advantageous to businesses. Data must ideally be stored in a central database from where it can be accessed by all users.
Data quality is one of the most important KPIs for any company. Departments within an organization may often employ different methods and frequencies of data quality evaluation depending on the aspect they consider most important. This affects the relevancy of the reports as well as trust in data. Some of the steps that could be taken to combat these issues and improve data reporting are:
The first step to improving data quality reporting is to establish common rules and standards for quality evaluation. Take a customer’s street address for example. While knowing the customer’s PIN code may not be as important for the accounting department as it is for the shipping teams, it must be included in all customer records.
Verifying data only at its points of collection may not be enough to maintain a smooth data supply chain. A survey showed that 22% of customer data can become outdated in a year. This is also known as data decay. To keep this from happening, data existing in the organization’s databases must be regularly validated to ensure it still meets all the quality parameters.
To ensure data quality does not degrade as it moves from source to consumer, businesses must be able to audit the data supply chain. This means they must be able to track the point of origin for all records as well as instances when it was modified in any way until it arrived at its current state.
As organizations continue to rely on data for strategic decision-making, a fragmented or inefficient data supply chain can become a serious liability. By addressing weaknesses at the first and last mile, simplifying complex data flows, and implementing robust data quality monitoring, businesses can safeguard the integrity of their data assets. A well-managed data supply chain not only ensures compliance and operational efficiency—it fuels smarter, faster decisions across the enterprise. Now is the time to optimize your data supply chain and turn it from a risk into a competitive advantage.