The rise in digital transformation has led to a massive increase in data as the systems used at a company continue to grow. In fact, the 2019 Deloitte Global CPO Survey showed that 59% of CPOs believe analytics in procurement will have the most impact on their businesses over the next two years.

As data grows exponentially, procurement leaders today are dealing with:

• Increasing data complexities
• Poor master data quality
• The inability to generate analytics and insights across systems

Procurement teams need the right tools to interpret and visualize their spend data so there’s true value from their efforts of digitization. Not only that, but it’s also crucial to work across systems and in collaboration with multiple functions to analyze spend.

This is where spend analytics comes into play.

According to Spend Matters, however, traditional rules-driven analytics is no longer enough. To best manage your spend data, you need a spend analytics solution that can also provide prescriptive analytics.

How to address data quality

Data quality is one of the biggest challenges that procurement organizations face today. Good quality data is important to perform spend analytics but companies often struggle with missing or incomplete data. Indirect procurement teams especially struggle with this problem as most of their data is submitted over the phone or by email, and only very basic data is captured.

So how do you know if your spend data is high-quality and relevant?

Here are 5 parameters to measure data quality:

1. Consistency – Is the data consistent across different systems and sources?
2. Completeness – Are records complete? Do we have all the data points we need?
3. Accuracy – Is the data correct?
4. Relevance – Do we have the right data?
5. Timeliness – Is the data up-to-date and relevant right now?

3 functions a good spend analytics tool should perform

Once you have a better understanding of the quality of your data, you can start to address it by ensuring you have the right processes and tools in place to capture, cleanse, and enrich the data.

A good spend analytics tool should perform these 3 functions:

1. Identify data sources

It’s important to identify and map all relevant data sources to examine the data quality. Data sources typically include ERPs, financial systems, and/or procurement systems for purchase order and invoice data, but can also include data from p-card providers and suppliers.

Most modern spend analytics solutions have no issue with large amounts of data or different formats of data, so don’t hold back on adding additional relevant data sources. Even if you lack some of the necessary data, a good spend analytics tool will help you identify the gaps.

2. Cleanse & normalize the data

At a minimum, your spend analytics tool should cleanse and correct misspellings and duplicates in spend data, and normalize it into a single format.

A really good spend analytics tool will also support data enrichment, which means adding relevant data to improve usability as more granular information is needed to uncover opportunities.

3. Provide prescriptive analytics

Ensure your spend analytics tool doesn’t just provide you with pretty dashboards. It should also be prescriptive, providing you with actionable insights into opportunities and risks and recommend actions that humans might miss.

Having good analytics support for procurement and sourcing is a foundational capability for a modern procurement organization today. With a spend analytics tool that can perform these three functions listed above, the benefits are enormous.

Read the full whitepaper, written by Spend Matters, to understand the benefits of a good spend analytics tool and learn their perspective on our Spend Analytics solution.