How Supply Chain Leaders Can Make Sense of Data Complexity

As companies settle into the new year, making sense of data complexity will become a critical concern. According to the new Data and Analytics Leadership Annual Executive Survey conducted by NewVantage Partners, 93% of Fortune 1000 companies plan to invest in data in 2023, yet only 24% consider their organizations already data driven. The survey explains: “While organizations point to widespread delivery of business value from data and analytics investments, businesses continue to struggle with fundamental structural issues and barriers to success due to the need for long-term organizational transformation and organizational and business process change.”

You probably go to multiple places to get data within your company, with your suppliers, and from other third-party resources. Data visibility can be a challenge for all organizations since data are usually found in separate systems, leading to increased risk exposure, decreased product profitability, and missed opportunities due to a lack of real-time visibility into direct materials spend.

New research by BCG confirms that “companies are still grappling with legacy data sources and technology stacks, and often lack the talent to manage the massive business process changes required to fully utilize available use cases and unlock the data value proposition.”

Fortunately, there is a path forward to ensure that data can be properly utilized. Indeed, data complexity is often the reason to aggregate, consolidate, and augment your data for actionable insights.

 

Common Mistakes to Avoid

You can use many tools to gain data epiphanies. However, tools are costly to implement and sometimes take several quarters to ramp up. By the time implementation is complete, data needs will have morphed from the original issue into something else entirely.

When solving for data complexity, here are some common mistakes to avoid:

“Big Data will give me better insights!”: Big Data means more data and more sources on top of an already large amount of data that are difficult to access, analyze, and understand. Getting deeper into the data weeds does not lead to better insights.

As an article from CMSWire explains, “Big Data is increasingly and overwhelmingly vast and plays a role in the creation of data swamps, which very quickly become difficult to leverage.”

“BI tools can blend the data for me to get awareness!”: Multiple large datasets can be confusing, and it is difficult to know which sets to pull together to generate any meaningful insights (1+1=3), resulting in organized spaghetti, but no real understanding.

An article from VentureBeat sums up the limitations of BI tools: “BI and analysis tools were the promised future, where business users could easily access and transform huge volumes of corporate-wide data to predict business outcomes and future demand. However, the reality is that traditional BI solutions and ERP systems are static and can only provide a snapshot of the present or past.”

“Data Science can figure it out!”: According to Fortune, “The number of data scientist roles is projected to grow 36% between 2021 and 2031, making it one of the fastest-growing occupations in the U.S.” But as Jason Davis, CEO of Simon Data, points out, data scientists must evolve along with the field and keep up with the latest technologies for companies to see value. He says, “Today, many data scientists and data science teams are too disconnected from core business outcomes, focused on interesting experiments instead of programs that deliver measurable revenue.”

While data science can solve complex problems statistically and algorithmically, the methods are based on highly narrow use cases and can prevent the creation of a simple solution, which in most cases is all you need.

“Engineering can build me the exact insight I want to see!”: Detailed specific requirements are needed to ensure accuracy and success, but you often “don’t know what you don’t know” when initially exploring data for insights.

Further, as TDWI reports, due to the “sheer volume of data many engineers are obliged to work with and the pressure to stay up-to-date on a constantly evolving set of tools and technologies…78 percent of data engineers said they wish their jobs came with a therapist to help them manage stress.” With widespread reports of data engineer burnout, simply throwing talent at the problem isn’t an effective solution.

 

How to Harness Data Complexity for Actionable Insights

Complete data visibility is the springboard that you can use to gain a competitive edge by reducing costs, mitigating risks, and keeping up with customer demands. Analysis of large datasets often mistakenly jumps to a conclusion, surfaced on a dashboard, and inadvertently with no corresponding context. This makes it difficult to provide validity, trust, and confidence in the metric, effectively creating a metric on an island. On the other hand, solving for data complexity with small, focused solutions will lead to more accurate conclusions.

 

Here are some best practices:

Baby steps first: Start simple with aggregation and consolidation of a smaller data set you can understand. Only focus on a specific category of parts or a handful of vendors to easily enable awareness and locate variances in the high-value attributes (What are the costs? How long does it take to get the parts? Which are socially diversified, etc.) you have chosen. As you begin to gain traction, it will be easier for everyone to see what’s possible.

Learn what you didn’t know: Supply chain leaders have historically been underserved and are increasingly being asked to do more with less while also being expected to make better decisions. Whether it’s a lack of collaboration or limited access to advanced AI, the siloed approach to decision-making leads to considerable lost opportunities for cost savings and risk mitigation. Advanced AI improves visibility, collaboration, and communication across direct materials sourcing and supply chain organizations, guiding larger implementation of specific insights across more categories and more data in an automated method.

Track over time: Most organizations lack access to good, clean, and easy-to-understand data to support informed decision-making. An AI platform enables you to track attribute values over time and analyze them in intelligent aggregated or classified ways. It can provide a new level of intelligence by building on the data in your company, along with additional third-party insights you normally wouldn’t have access to.

While more than one-third of companies say their technology falls short in providing real-time insights needed to adapt operational strategies to a changing market, Bain notes that “many senior executives are uncertain about how to proceed.”

With integrated insights and intelligence, supply chain leaders can make sense of data complexity and identify immediate savings.