In an excerpt from our recent ebook The Craft Approach to Supplier Data: A Dynamic Solution for a Changing World, we discuss how digital capabilities are essential to scaling resilience across supply chain operations. But adopting a digital-first strategy for supply chain management isn’t without its challenges. Despite the potential of AI/ML and other innovative digital technologies, many companies are hampered by organizational siloes, the scope of the risk landscape, limitations in supplier data and visibility, and the sheer volume of data to work with. Read how to manage some of these data challenges in our latest post, or download the full ebook here

Data Challenges when Assuming a Digital-First Approach

Data Fragmentation

Organizational siloes can lead to data siloes. When each office or department generates its own data, the data becomes harder to consolidate, compare, and analyze. In other words, data fragmentation results in disparate batches of data that aren’t created with outside uses in mind. 

This problem is further exacerbated by the fact that most organizations don’t have a common data language. Lacking that common language, even when teams share their data, means it isn’t always clear how to use the data effectively.

Lack of Visibility Into Different Data Sources

Chief Procurement Officers (CPOs) consistently rate risk management as a priority. Yet, despite its importance, many organizations and their leaders don’t have access or visibility into different data sources to inform their risk analyses. And some aren’t even aware of the gap. Without end-to-end visibility of supply chain risks, many leaders are missing the full context of their risk landscape.

Lack of Visibility Beyond Tier 2 

Today’s supply chains are complex. It’s no longer enough to focus on just your direct suppliers. Disruption and other risks often start further down your supply network with n-tier suppliers. That’s why procurement leaders need visibility into Tier 2 suppliers and beyond. But many organizations’ data capabilities fall short of this goal.

In fact, according to a study by Deloitte, only 26% of CPOs were able to confidently predict risk within their supply bases. And only 15% had visibility into tier 2 or beyond. Without this visibility, it is difficult for leaders and data analysts to accurately identify risks or opportunities.

Stale or Poor Quality Data

Data can hold a goldmine of insights. But only if the data itself is relevant, fresh, and high-quality. Too often, companies work with data that is outdated or simply low quality. This can include inaccurate data, missing data, or even inconsistent formatting, making it difficult, if not impossible, to analyze effectively. As more companies leverage external data to augment their internal insights, this becomes an even bigger challenge. 

Data Fatigue and Paralysis 

Data is everywhere now. A staggering 2.5 quintillion bytes of data are created every day. While this opens up incredible opportunities, the sheer volume of available data today can be intimidating. Procurement professionals can quickly become overwhelmed with the amount of data they have to work with all while juggling shifting priorities within an organization. This can lead to data fatigue as teams struggle to make sense of their data and use it in a meaningful way.

According to Harvard Business Review, on average, less than half of an organization’s structured data is actively used in making decisions-and less than 1% of its unstructured data is analyzed or used at all.

Without support, data becomes a burden rather than an advantage.

How to Manage These Data Challenges

Good data hygiene and management practices are critical for scaling agility. Supply chains are increasingly complex, which makes it difficult to overcome many of these data challenges. But with the right support, tools, and strategy, you can build an intelligent platform with the right insights from first and third party sources. 

Rely Less on Supplier Surveys

Supplier surveys are the most common method for enterprises to gather supplier data and measure their risk. However, while they provide important context, they also have significant limitations that can put your company at risk, including:

  • Subjectivity and supplier bias
  • Stale data
  • Manual response and analysis
  • Limited scope

Surveys are inherently subjective and biased toward the supplier responding. This means you may be missing key insights into the supplier’s operations and risk management processes. 

Survey data also has a short shelf life. Almost as soon as it’s completed and the data analyzed, then it’s out of date. As such, surveys can only provide a point-in-time snapshot of the supplier’s risk-limiting visibility into emerging risks and making it difficult to predict problems. 

Additionally, analyzing surveys is a highly manual-and, therefore, time-consuming-process. And because the responses are typically qualitative, it can be difficult for teams to assess and collate the data into actionable insights. And even when survey data is effectively managed, you’ll still have blindspots. Supplier surveys simply can’t cover all possible risk domains. 

Companies adopting a digital-first approach will need to expand their data-sourcing methods beyond supplier surveys to ensure a more holistic and accurate view of their supplier landscape.

Use a Combination of Reliable Data Sources 

Though surveys have their limits, they are not useless. Companies should instead take a “Yes, And” approach to data collection that includes a combination of reliable data sources from a variety of risk domains, such as: 

  • Revenue
  • Profit margins
  • Credit ratings
  • Funding rounds
  • Solvency ratios
  • Cybersecurity measures
  • Hiring trends
  • Employee headcounts
  • Leadership turnover

Together, these internal and external data can better contextualize your risk and enable stronger mitigation, forecasting, and compliance. 

Monitoring a broad range of data is particularly important because risk is often interconnected, and traditional metrics may lag behind other risk indicators. For instance, at the start of the Russia-Ukraine war, clinical trials for pharmaceuticals stopped abruptly, disrupting drug manufacturing and their supply chains. However, labor data could have raised alarm bells sooner. As trials were delayed or canceled, recruitment for those studies also froze. If companies were monitoring hiring trends, along with other indicators like falling share prices, they could have taken action earlier on to mitigate the risk. 

Leverage Both Manual Review and ML Technology 

Data is only useful if it is both accurate and contextualized. Otherwise, you’re just working with disjointed information that is largely meaningless. 

To achieve this, enterprises should use a platform that leverages a combination of manual human review and ML technology. Machine learning enhances and accelerates data analysis, enabling teams to focus on other high-value tasks. At the same time, supplier intelligence platforms should also enable manual input and review. This is especially important for pulling together data from internal sources and supplier surveys, which can augment the external data you’re working with. Enabling both capabilities ensures that aggregated data is tailored to your company’s needs.