Years ago a top networking systems, services, and software company, made the decision to move away from vertical integration and began outsourcing manufacturing to focus efforts on more strategic elements of their business. As a result, the procurement team began analyzing data to guide improvements and automation of their supplier on-boarding process.
Their ultimate goal was to encapsulate tribal knowledge and disparate external data sources into a comprehensive workflow that was both trackable and easy to follow. Together, the procurement and business intelligence teams planned to evolve their workflow organically and needed the flexibility to make changes over time.
Changing Perspectives: New Data Metrics Required
When the company was vertically integrated the business maintained a set of common objectives under a single organizational structure, which meant procurement could easily find and analyze operational data. Manufacturing KPIs integrated with operating metrics that were related to throughput and cost-efficiency. Trends could be investigated by drilling down to shop-floor process data, build times, or purchasing activities, enabling teams to seek out improvement opportunities.
With the switch to de-centralize the organization, this type of data view was no longer available.
A new divide existed between procurement and manufacturing, requiring everyone to learn how to think and act differently when it came to managing suppliers and performance.
What that meant as implications for business intelligence:
The organization was no longer the manufacturing experts – their partners were and procurement had to learn how to work effectively with new partners.
Operations metrics were redefined to incorporate service level agreements that held new partners accountable and fit with contractual agreements.
Forecasting took on a whole different meaning after crossing contractual boundaries.
It took months to refine the new model and multiple iterations through various approaches before they could find a balance that was both effective and manageable. Finding the right data technologies was key to effective collaboration and trust-building in the new approach. Every team member had access to data in customized dashboards within Onspring, their chosen technology partner.
Fast forward many months, the procurement team realized this newfound wealth of data raised questions rooted in the other half of the data battle: managing the human aspect.
When faced with something new or when data shows something other than what is expected, people will suddenly look at data in new ways. This can be very uncomfortable and quite often stressful. As a result, these best practices were created from this experience.
Business intelligence best practices:
#1 – Respond quickly to those who need reassurance with new data
Real-time data visibility means team members who are not familiar with the data points will raise a bevy of questions. With a lack of confidence in the data, these individuals will likely come across defensive. Your role is to guide them into full understanding for reassurance.
Four approaches to creating reassurance:
Quick updates – Data models need to be updated quickly with new inputs as needed or must enable connecting dots across your business in new ways.
Move the bytes – Use reliable tools to collect and manipulate data and ensure team members are savvy in using the tools.
Draw pictures – Quickly generate interactive online reports that allow teams to explore data based on any new idea stemming from concerns or brainstorming sessions.
Trust others – The key enabler to velocity in business intelligence is by partnering with other teams to apply ideas and better decision making.
#2 – Think end-to-end when setting up new metrics
Any effective business intelligence program will utilize a high degree of automation. Plan upfront the disparate sources that must be utilized to pull records to aggregate into digestible data visualizations.
Subject matter experts (SMEs) need to be able to drill down into the metric’s underlying data to find the actionable nuggets. That process must be immediately accessible, not asynchronously.
In fact, SMEs need to do this level work ahead of the metric getting published to reassure leaders when something has gone red for example. SMEs must communicate to leadership the root cause of the issue is already understood and an action plan is already in place.
Often times, well-tested business continuity plans can be utilized when immediate action plans must roll into effect for risk mitigation or supplier assessments.
#3 – Follow the journey of your end-users
To be informed end-users, team members need to develop a basic understanding of the business’ core data models. In many cases, data end-users are also sources of some of the inputs.
For each team member interacting with data, communicate:
From where data records are sourced
The reliability of those data sources
How formulas are calculated to display data findings
Depending on where your end-users sit in the overall journey, their understanding of the findings will differ. Plan to educate end-users to inform them of the who, what, when, where, and how so they can compose the why.
This organization’s de-verticalization resulted in a decade of adapting to change. Their team’s tolerance for adjustment is high, which has helped their ability to respond to events like tariffs and COVID. If your business is new to significant change, using business intelligence will aid in smarter decision-making that enables teams to be more nimble.
Make a list of what data you need to make better decisions and then outline where that data can be sourced – and make this list blue sky. You will be surprised that collecting data is not as hard as it may seem if you know where to look. Once you have your plans plotted, review your toolsets to ensure technology is in place to arm your team with real-time analytics and high amounts of automation.