3 Tips for a Data-Driven Business Strategy

Nulogy VP Christine Barnhart summarizes 3 critical insights from our February 2023 webinar with supply chain leaders from Snowflake, MSI Express, and BlueWorld Supply Chain Consulting.

Christine Barnhart, Chief Marketing & Industry Officer at Nulogy
WRITTEN BY Christine Barnhart

Data integration and connectivity are becoming increasingly important to supply chain providers, and for good reason. As businesses introduce more and more software solutions into their operations to keep up with today’s volatile market, the amount of data being generated within their systems—as well as their partners’—is expanding at a mind-boggling pace. In fact, Okta estimates that large companies deploy as many as 187 apps on average, with smaller companies deploying even as many as 89.

Despite the vast amount of business growth opportunities available to enterprises that are able to harness their data for improved business outcomes, there are continuing challenges with enabling and integrating data into existing business strategies.

For some organizations, ensuring data quality is an ongoing issue. For others, the main challenge lies in converting a vast amount of data into actionable insights. Every business sits in its own data maturity stage, which in turn presents its own unique set of challenges.

Nulogy Connect webinar, screenshot

To discuss and address these challenges, Nulogy hosted a webinar session with leading experts in data analytics and digital networks within the supply chain:

I had the privilege of moderating this discussion amongst these experienced industry leaders, and came away with three key insights that may help you efficiently harness the data within your business:

1. Adopt the “Three Rings” approach

When putting together a plan to integrate data into your business strategy, analysis paralysis often rears its ugly head in the beginning stages. What data should a business focus on and when? How much data is enough to harness for business insights?

To help address this dilemma, Rosemary DeAragon at Snowflake offered a framework for organizing the data that flows through one’s organization. This framework can be described as three concentric rings expanding outwards:

  1. First-party data: The innermost ring, comprising internal data such as operational and financial data.
  2. Second-party data: The middle ring, representing shared data between your business and an external party.
  3. Third-party data: The outermost ring, made up of data generated and driven by external forces.

These three rings, DeAragon says, come with their own respective challenges. For example, in the ring of first-party data, businesses can run into issues regarding data quality or data ownership within the organization. Issues such as these can spiral into larger business challenges such as erosion of trust, as there is reduced buy-in amongst internal stakeholders for leveraging said data.

Challenges encountered within the ring of third-party data are of a different nature entirely. For example, data external to your business and trading partners, such as weather and shipping traffic, can be hugely beneficial for planning and scheduling, but are also immense data sets that require additional resources to parse and act upon.

DeAragon concluded that roadblocks in data strategy are unique to each of these three different types of data. Organizing your data into these three categories can help prioritize where and how to focus your efforts.

2. Crawl, walk, run

Jake Barr of BlueWorld Supply Chain Consulting observed that in his experience, enterprises were sometimes tempted to skip steps when integrating data analytics into their operations, with the goal of quickly transitioning to more data-driven business operations at the expense of proven scalability, leading them toward the pitfall of doing too much, too quickly in uncharted territory.

Barr explained that this fast-tracking of strategy development can be dangerous and myopic, and can lead to wasted capital and human effort without a holistic, scalable, tested approach. Instead, he proposed a “crawl-walk-run” approach to incorporating data into business operations.

For example, using this approach, a consumer brand would select one strategic supplier out of its entire network, then focus on a clear, measurable objective for the data integration project. By sharing data and working together toward this objective, both teams can measure the results and leverage their experience to improve and reiterate the process. This cycle of continual improvement, and the efficiencies gained by both parties, can then be rolled out to additional sites.

By adopting a more gradual strategy, a single, focused project at a single site can scale to additional sites using the experience and knowledge gained from previous data integration projects.

3. Align data with your front line staff

Integration solutions don’t have to be costly, cumbersome projects that require significant IT resources and budget. Today’s advanced integration solutions can offer a simple, low-code yet powerful alternative for enabling connectivity between your organization’s teams as well as your external partners. By taking advantage of a solution such as this, your teams are unburdened from repetitive daily tasks and are free to dedicate their time to high-value projects, making your business run more efficiently.

As someone who has worked closely to integrate data into business operations, David Freed at MSI Express stressed the importance of aligning data teams with business operations teams. For example, at MSI Express, data teams work closely with production floor staff to align on what the production data is saying and why. This strategy is intended to break down silos between teams, and collaborate with a deeper understanding of how their respective team processes impact each other’s output.

By working together in a more holistic manner, for example, data teams within supply chain operations can better understand the significance of metrics such as production output numbers, and troubleshoot roadblocks that may be encountered by teams on the shop floor. Conversely, shop floor staff can put their trust into the data that their output is generating, and gain better direction and insight into how to improve their efficiency and productivity.

David Freed added the final point that, similar to Jake Barr’s view, the use of data needs to be focused on a single realistic, measurable area of the business. Instead of initially setting high-level KPIs such as improving capacity or profitability, it is more insightful to narrow down the measured metrics to a specific date or area of focus for the business, such as improving OTIF for a specific customer during a specific time period. By setting focused goals and involving all the staff involved in the project, businesses will be in a much better position to scale up data integration in other areas of the business.

Access the on-demand webinar for more insights

Data-driven strategies offer a wealth of benefits for improving the efficiency and profitability of businesses; however, building and executing these strategies requires focus, direction, and a measured, organized approach.

It was a pleasure to moderate the discussion between our expert panelists, and I invite you to sit in on the on-demand webinar for even more nuggets of wisdom.

For more insights on leveraging data and connectivity in supply chain, watch the full Nulogy webinar, Leveraging data and connectivity for collaborative supply ecosystems.