Too many unsolved puzzles in your supply chain data?
To be a supply chain executive nowadays is to be at risk for whiplash. You’ve got to be prepared to have your focus swing dramatically day to day — and sometimes minute to minute — from managing crises to creating strategies for keeping your business free of them.
The common element within all supply chain challenges — delays, disruptions, regulatory requirements, risk mitigation — is data. Data affects every step of the supply chain, including planning, sourcing, production, inventory, and logistics. Within each of these functional areas, data is the key to getting questions answered and critical decisions made.
To have the most value, data must be managed correctly. It has to be aligned with your organization’s goals, systems, and processes; collected, prepared, and stored to maximize its usefulness; and, most importantly, made accessible to stakeholders who would benefit from its insights (consistent with your data access control policy or the principle of least privilege). This is effectively the blueprint for making supply chain data actionable.
Common Culprits of Poor-Quality Data
Organizations are collecting vast amounts of data today, but quantity doesn’t equate to quality. Much of the data being ingested has its value eroded because it quickly falls into at least one of these categories:
Duplicate data – This data monopolizes space in your database and slows processing speed. The different formats make "dupes” hard to pinpoint, cleanse and remove.
Disparate data – A common issue affecting supply chain and procurement departments, “siloed” data across the organization makes access and analysis difficult.
Decayed data – As organizations rapidly shift suppliers and partners amid global disruption, the data you’re keeping quickly deteriorates from inaccurate to obsolete.
Contradictory data – If files or data sets exist with incongruous information, it’s clear your organization is improperly maintaining data. What’s not clear is which data is correct.
Incomplete data – Records and data sets with missing fields can be appended through robust third-party sources, but it will take time.
To convert data into action, supply chain and procurement professionals need to have the right data, organizational structure, and processes in place. Does this sound a little daunting? Maybe — but undertaking a data governance initiative is the right strategy for demystifying your supply chain data and helping it to help you uncover and manage supply chain risks and opportunities.
First Steps: Assessing Your Main Assets
Establishing a data governance framework for your supply chain organization starts with a methodical process of assessing four main components: data, systems, team, and software.
Step 1: Data
The first requirement in any data project is rationalizing the data. Data must be cleansed, harmonized for consistency across sources, and formatted for analysis. It’s also critical to establish data metrics that can be adjusted over time. The goal is to be able to project for tomorrow by collecting and storing the right data today.
Step 2: Systems
Legacy systems can be a major challenge for businesses. They can be difficult to maintain, can be slow, and can be incompatible with newer systems. If you have legacy systems in your business that are hampering your operations and preventing you from making informed decisions, it may be time to consider some upgrades.
Step 3: Team
Assessing your team is as important as assessing your systems. If your data and systems are up to date but something still isn’t working, take a step back to look at your team. Make sure that they have the skills and knowledge they need to use available technologies effectively. It’s also helpful to have a cohort of data users from within your supply chain and procurement teams who can offer practical considerations to the team that executes requests.
Step 4: Software
Most data architects recommend that a combination of skillsets and tools is the best path to success in technology and analytics. Rather than seeking out one end-all, be-all solution, selecting the best solution(s) for your specific needs will help achieve your goals and increase the likelihood of success.
Distinguishing Between Good Data and Big Data
After these initial assessments, you can move on to the next steps of the data governance framework. You’ll need to decide on the rules for archiving data; how much legacy data to keep and how much data your organization can reasonably manage; and which aspects of your data management initiative need better focus or reinforcement.
Supply chain executives have a unique opportunity to bring a fresh perspective to a common problem and to help solve it by making their data more actionable — and in doing so, enable better answers to supply management’s perpetual question: Who are we doing business with? There’s much more detail about establishing a consistent, repeatable process of supply chain data governance in our eBook — Demystifying Supplier Intelligence Data: A Quick Start-Up Guide to Data-Backed Decision-Making for Supply Chain Leaders.
The information provided in articles are suggestions only and based on best practices. Dun & Bradstreet is not liable for the outcome or results of specific programs or tactics. Please contact an attorney or financial/tax professional if you are in need of legal or financial/tax advice.