Quality Data Lays the Foundation

As supply chains become increasingly complex, so does the associated data landscape. Millions of data points must be managed in modern supply chains today due to the wide gamut of systems used. From Enterprise resource planning (ERP) systems that manage an organization's basic processes to customer relationship process (CRM) systems that handle contacts with present or future customers, the gamut of systems that create or manage data is wide. Without a single source of truth, each of these systems could have various versions of the same data element, making cross-departmental collaboration difficult and risky. With so many data silos, numerous versions of data, and inaccuracies from manual entries and erroneous data, this problem will only worsen over time until a solution that uses a single source of truth to improve data quality is implemented. Supply chain data quality is critical for better operational management, whether focusing on improving operations and logistics or searching for ways to monitor customer service levels to promote customer retention. Everything from managing operations and logistics to managing data quality with a single source of truth is covered in supply chain planning best practices. SAPinsider recently invited Ranjan Bakshi, CEO of Prospecta, to discuss the criticality of data quality in digital transformation, understand it from a supply chain digital transformation perspective, and understand the role technology can play in addressing data quality transformation.

Importance of Data Quality in Supply Chains

At the heart of supply chain excellence is data quality. Data captured in the supply chain spans the ecosystem's entire end-to-end breadth of processes and activities, including warehousing and transportation, from planning and procurement to consumer fulfillment. Within these elements, we find vertical use cases of data analytics, such as real-time re-routing, demand/supply planning, and sensing, as well as horizontal use cases, such as master data management (MDM) or intelligent automation, that are more related to the rest of the ecosystem. A truly automated and autonomous supply chain can only be realized with consistent and completely integrated supply chain data that drives intelligence and machine learning and the benefits already mentioned. Inaccurate, obsolete, and inflexible (difficult and time-consuming to update) data hurts operations since it makes insights irrelevant and/or outdated. It necessitates a significant amount of manual labor to merely perform regular supply chain activities. High data quality, accuracy, and flexibility, on the other hand, not only save significant time and manual touchpoints and enables companies to generate additional insights and competitive edge– including time to market and rapid adaptation to changing regulations – but also allow them to generate more insights and gain a significant competitive advantage. "You don't need a business case for a data quality solution. You need quality data if you have billions invested in transformation and other areas. It becomes a prerequisite, not a business case by itself," Bakshi says. "When implementing an Internet of Things or another technology initiative, the first thing your consulting partner will ask is if you have your data right."

Implications of Poor Data Quality in Supply Chains

Implications of bad data quality are profound. Bad data quality keeps many supply chains from developing even foundational supply chain capabilities, which in turn are the first step for true supply chain digital transformation. Some of the key implications of bad data quality are: A suboptimal supply chain: In today's data-driven world, poor data quality means that every choice you make inside your supply chain is more likely to be suboptimal or incorrect. Even the best supply chain information management or planning solutions won't assist if the data is erroneous, incomplete, or outdated. Poor data quality can cost millions of erroneous business choices, inefficient operations, and logistics, missing procurement data, and lost opportunities. Visibility takes a hit: Across your product lines and regions, supply chain data is frequently outdated, incomplete, inaccurate, and fragmented across dozens or even hundreds of systems, including supply chain management software, the plethora of Enterprise Resource Planning (ERP), financial, and other point systems. Spreadsheets, databases, and remote file stores all contain valuable data, each with its structure and data model. This crucial asset accumulates in each business unit, procurement team, and regional office, making it difficult to have a consolidated, complete, and trusted view of all your suppliers and sub-suppliers and respond promptly. Agility and resiliency suffer: Over the last few months, supply chain risk factors and unpredictability have intensified. For some supply chain executives, the ability to assess a supplier's risk while monitoring their compliance or performance is a constant source of frustration. "Also, when you lack a common ground of standards or a collaborative way of doing things, a transformation project also suffers because what your data is communicating must be understood by the other side," Bakshi says. "That's why data quality is now so critical for digital transformations." Customer experience takes a hit: Another critical factor to remember is managing consumer expectations as they make their purchase decisions. For example, major organizations today are focusing their supply chain strategy and design around social sustainability and product traceability, as they see this as an important driver for long-term success and something their customers care about. Without proper data quality, the imperative of delivering the right product in the right quantity and at the right time may not be feasible, and hence customer experience may suffer.

Role of Technology in Data Quality Management

Thankfully, technology is an enabler that organizations can leverage to remediate data quality in their supply chain systems landscape. A best-of-breed data quality management platform can assist with data preparation, cleansing, migration, and assurance. Whether your company is embarking on a transformation project or simply needs to include data governance into day-to-day procedures, a platform like this should be able to meet all your needs. "Solutions like Data Intelligence Workbench (DIW) or even our Master Data Management (MDM) solutions are all centered around users taking more responsibility on data and making it easier where understanding the technological intricacies is not required," Bakshi says. The tool should assist with data-related tasks such as preparation, business rules, cleansing, and enrichment that may be carried out in tandem with other project deliverables. It should also give a reliable starting point for data governance and quality. The platform should also carry out data-related tasks seamlessly to give reliable data for future governance and quality. Master data created by other systems is effortlessly cleansed and moved using best-of-breed techniques, eliminating the need to recreate new data sets. Another beneficial element of these solutions is their ease of implementation and maintenance, which removes the need for full IT support. This is a vital feature in the age of self-service technology because it directly helps to develop data-driven cultures in which everyone involved in the process understands the importance of data quality and the tools that help manage it. Another big differentiator between legacy data quality management tools is AI and ML algorithms. Using machine learning and artificial intelligence to cleanse and enhance data allows for a more predictive and intelligent rule-based approach.

What Does This Mean for SAPinsiders?

There is no doubt that data quality may decide the fate of your supply chain digital transformation journey. However, as SAPinsiders embark on this journey, there are a few critical aspects they need to be aware of or keep in perspective.

• Understand your data. Not only do you need data that is "correct," but you also need data that is "right." To determine whether data is "appropriate" or relevant for your intended use, you must first understand it. What it means, where it came from, and how you may get the most out of it are the aspects you need to be fully cognizant of. Remember that data intelligence is the capacity to comprehend and effectively utilize data. Hence, the most important strategy to improve data quality is correctly describing and connecting data throughout its path. Bakshi says data integrity must be a broad topic. This type of initiative must be driven from the top. "I'd like to see in board meetings one statistic of how good the data is in your organization. You're making all the important decisions in the board meeting based on the strength of your data. Bring it to the forefront with intention."
• Define business needs and assess business impact. Business needs frequently drive data quality enhancement projects. You can prioritize data quality issues based on your business needs and the long-term impact they will have on your company. Measuring business effect aids in setting a target and tracking data quality improvement progress. A constant reference sets the context for developing the approach to data quality to the business needs.
• Promote a data-driven culture. A data-driven culture is a collection of values, practices, and norms that enable the successful use of data across an organization. To realize their involvement in data quality, everyone must, of course, buy-in. Develop a consistent definition of data quality across the organization, specify your specific quality metrics, assure ongoing measurement of the defined metrics, and make a plan for error remediation. Data Governance can also be used to standardize and improve the management of data assets within your firm.
• Focus on training and reminding. A data-driven culture ensures that everyone in the company is working to improve data quality. However, it is equally critical to maintain their interest and contribution by coming up with new ideas. The importance and benefits of data quality will be reinforced with regular training in ideas, measurements, and tool usage. Sharing quality issues and success stories around the organization can serve as pleasant reminders. Providing specialized training to employees is a good way to improve data quality.