How do you define and prioritize your procurement analytics projects and initiatives?

The starting points of valid initiatives MUST be the key objectives of the organisation. Any procurement analytics projects should flow directly from these not be developed and aligned to them later. Initiatives not aligned from inception are merely wheelspin. An example from my own experience is my company (acting as part of a profit sharing alliance in a major capital project) abandoning lowest cost in favour of a “best overall value” by paying extra on the price of a commodity purchased to gain the benefit of a large reduction in the cost incurred by another alliance partner in fitting that commodity into the project build. The benefit came in the form of reduced project cost and consequent greatly enhanced profit share.

What's the value of analytics if it doesn't support the purpose of Procurement? Build a metrics and indicator tree linked to strategic goals, tactical decisions based on insights and operational performance measurements for sourcing, supplier management and operations. Define clear data owners and stakeholders of each analytics initiative and regularly test usage and aptitude of the results for the purpose defined.

I came across a piece of research the other day, over 90% of organisations fail to agree and implement a strategy! For procurement this is like trying to score a goal where the posts are constantly being moved. Problems with organisation vision, cohesion, communication and basic management all play a big part in this. Can procurement help this? Absolutely! This is one of the reasons why procurement needs to show their influence outside the walls of their own function.

In my experience, effective procurement analytics involves: 1) Identifying pain points: Analyze areas with high spend or recurring problems (e.g., maverick buying, delays). 2) Data-driven prioritization: Focus on projects with the biggest cost-saving or efficiency improvement potential. 3) Alignment with goals: Ensure initiatives support strategic objectives (e.g., risk mitigation, supplier performance tracking). 4) Measurable outcomes: Define clear KPIs to track success (e.g., % reduction in maverick spend, improved on-time delivery rates).

Align your analytics projects with your overall business goals and procurement objectives – What are the 𝗸𝗲𝘆 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗮𝗻𝗱 𝗽𝗮𝗶𝗻 𝗽𝗼𝗶𝗻𝘁𝘀 you are trying to address? For example, if your organization goal is to increase savings - focus on projects that help identify spend patterns, optimize sourcing, or negotiate better contracts. If managing supplier risk is a priority - prioritize projects that monitor supplier performance, compliance, and resilience. This is primary step is to align your procurement analytics projects and initiatives with your overall approach.

A thorough assessment of data sources and related quality is a good starting point to develop a plan and determining priorities for analytics work and foundational work needed on data governance, custodianship of standards and infrastructure needs. This includes a view on sourcing data internally and expanding with external data sources from suppliers or market intelligence firms.

In my experience, effective assessment of data availability for procurement analytics requires: 1) Data Mapping: Identify the data needed for your analytics initiatives and map it to existing sources within your procurement systems. 2) Data Quality Assessment: Evaluate the completeness, accuracy, and consistency of the data to ensure reliable insights. 3) Data Accessibility: Analyze ease of access and integration of data from different sources for seamless analysis. By addressing these areas, you can identify data gaps, improve data quality, and ensure your procurement analytics initiatives have access to the information they need for success.

There needs to be a preliminary assessment of all of this as part of identification of goals and challenges before committing resources to an initiative. It’s no good starting without understanding what metrics are needed to determine success and to be fairly sure that good quality data is accessible

This is basically backbone of your analytics - 𝗗𝗮𝘁𝗮 𝗶𝘀 𝘁𝗵𝗲 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀, so ensure you have access to relevant, accurate and timely data sources. Evaluate data completeness, consistency and validity. Data quality issues will definitely affect the reliability of analytics results, so need to double check the source. Consider data governance, security and who owns/manages the data, so compliance and data breach can avoid.

Evaluate the availability, accessibility, and quality of your procurement data. This includes data related to purchasing transactions, supplier performance, contract management, spend analysis, and other relevant metrics. Identify any gaps or limitations in data availability and assess data quality to ensure accuracy, completeness, and consistency.

To define scope, identify the must-haves and the nice-to-haves. Even with more and more digital integration to help with analytics, there is still a minimum amount of human involvement required. To ensure data accuracy and avoid overwhelming staff based on undeliverable analytics, it is critical to identify for your team what’s absolutely necessary and what would be nice. Besides a better understanding project and initiative requirements, it will also prevent team members from feeling overwhelmed and making mistakes. Value is not a set definition when it comes to analytics. Most importantly, we must make sure there is a clear outcome and next step that will lead you to the right outcome.

In my experience, effective analytics scope and value starts with clear scoping: 1) Define Objectives: Identify the specific goals the project aims to achieve. 2) Data Availability: Assess the quality, accessibility, and relevance of existing data sources. 3) Metrics & KPIs: Determine the key performance indicators to measure success. 4) Value Quantification: Estimate the potential cost savings, revenue growth, or other quantifiable benefits. By thoroughly scoping these elements, you ensure your analytics projects deliver measurable value and align with strategic objectives.

Define the scope of your procurement analytics projects by specifying the objectives, metrics, and outcomes you aim to achieve. Clearly articulate the value proposition of each analytics initiative in terms of its potential impact on business goals, such as cost savings, efficiency gains, risk mitigation, or strategic insights.

Prioritize procurement analytics projects and initiatives based on their alignment with business goals, potential value contribution, and feasibility of implementation. Consider factors such as the complexity of analysis, resource requirements, time constraints, and expected ROI. Use prioritization frameworks such as cost-benefit analysis, value/effort matrix, or strategic alignment assessment to rank projects and allocate resources accordingly.

Leverage external data sources where needed Internal data from ERP, accounting, procurement and contract management systems is a good starting point- But external data on suppliers, benchmarks, competitive intelligence and market trends can provide additional valuable insight.

Using analytics as a tower of control and to spot early risks and potential opportunities to reach business goals supports agility in Procurement. Get your data ready to achieve sufficiently robust results and leverage external data sources where specialised data sets or gaps in internal data arise.

Analytics has two sides: what you want to see, and what you want to show. You need to consider the audience in its subgroups, and how active they are in the process of generating, using, and receiving the results of the analytics project and the output generated. A recurring challenge of procurement is to evidence its results in a credible way. Hence engage this receiving audience subgroup to ensure that they'll accept this output. Do it as early as you can, and you'll likely gain their support, endorsement, and possibly obtain resources from them if you make their life simpler and generate value they recognize.