An organization is defined by its income and expenses. After all, business success stems from the efficient management of outflow and inflow.
However simple this fact appears in writing, it is a lot more complicated in practice. Each business exists in a fragile ecosystem that is vulnerable to global events, consumer trends, regulation changes and environmental risks, among many, many other challenges.
Spend analytics empowers companies to take a proactive approach when navigating with this intricate ecosystem.
What is spend analytics?
Spend analytics refers to the process of identifying, collecting, cleansing, categorizing and analyzing a company’s spend data. The main benefits of spend analysis in procurement include accurately identifying patterns in addition to gaining greater spend visibility.
The information generated from spend analytics is used by organizations to make proactive, informed decisions to decrease procurement costs and improve efficiencies.
- Big data analytics in procurement can solve the following spend analysis questions:
● Which suppliers have the most major spends? Is their greater expense warranted?
● Is it possible to identify and consolidate tail spend?
● How can maverick spending be realistically minimized?
● What savings opportunities are available? Are there internal functions that are not delivering a strong ROI?
● Is it possible to forecast spending by category or supplier? How can we use this information to better adapt to external changes?
● Are there suppliers that pose a compliance or reputational risk? How can we improve our supply chain network to exercise our ethical commitments?
How is big data analytics used in spend analysis?
Big data analytics is a valuable tool for companies looking to streamline their procurement management processes by enhancing key spend analytics benefits. To put it simply, big data allows organizations to accurately examine broader trends and spending patterns within large amounts of data.
The role of big data analytics is widespread when it comes to spend analysis and securing a higher ROI. How an organization will implement their analysis into big data procurement will ultimately come down to their industry, size and internal capabilities.
Five of the most notable areas where big data analytics can be employed to great success are:
Spend data gathering
Data gathering is a key component of the entire spend analytics process pipeline. Big data analytics involves gathering historic spend data from a wide range of sources, consolidating the information so that it is accessible from one central location.
Spend data cleansing
In order to ensure that the data is accurate and meaningful, data analysts must cleanse the collected data by detecting and fixing any records that are duplicated, corrupt, irrelevant or incorrect.
Spend data categorization
AI driven data analytics can be used to automatically categorize, organize and store data for future use. Spend data categorization also helps companies pinpoint trends and use the identified variables to conduct predictive analytics.
Spend data analysis
The above processes support the various components of spend data analysis by providing a reliable, data-driven knowledge base for:
- Forecasting market and consumer trends
- Automating category management
- Predicting supply chain risk
- Increasing supply chain transparency
- Decreasing procurement costs
- Improving internal efficiencies
Spend data visualization
Spend data visualization describes the process of converting raw data into a visual context, whether that be through a graph, chart or interactive dashboard. Visualization is a critical part of the data analysis process as it transforms large sets of data into a user-friendly format that clearly showcases patterns and outliers.
Humans respond to visual information in a much more emotive way than words or numbers, making data visualization an especially powerful tool for communicating with key stakeholders across any organization.
What is the ROI of spend analytics?
The ROI of big data analytics in procurement will depend on the business model it is applied to and their individual preferences. As savings scale with spending, spend analytics is most economical when used by businesses with high spending, typically exceeding $1 billion. Agile companies tend to secure a higher benefit from spend analytics as they are able to swiftly adapt to real-time information to deliver greater value.
Of course, investing in spend analytics doesn’t necessitate an ‘all or nothing’ mindset. Specific areas of concern may require spend analytics, while others may not. Organizations can scale their efforts to strike a perfect balance between supporting business goals and investing economically in spend analytics.
Before investing in big data procurement and spend analytics, it is recommended that organizations create KPIs to measure the value of their spend analytics. This will allow them to monitor their savings and assess their procurement function’s ability to interpret collected data.
How AI driven big data platforms add value to spend analysis
Spend analysis is a time consuming process that can easily become overwhelming when maintained through paper management.
AI big data tools can work together with data analysts to improve speed and enhance the granularity of the collected data. These AI driven big data platforms provide companies with deeper insights into internal and external events. This can in turn empower companies to purposefully channel their resources into more productive pursuits that actively improve working capital performance.
When used effectively, AI driven data tools can:
Drive efficiencies in spend analysis
Access to AI driven data tools allows companies to spend less time and fewer resources on low-value number crunching. Instead, the capability of employees can be dedicated to conducting high value strategic analysis.
Allocate resources towards critical categories
AI driven data tools are particularly useful for optimizing the allocation of time and resources. By appreciating where and how resources are used, companies can direct available resources to the areas that they will be most constructive.
Identify opportunities for cost savings
Companies can further capitalize on the insights generated by AI driven data tools by identifying opportunities where costs could reasonably be cut without negatively impacting other areas of business.
Manage risk of disruptions
Being able to predict supply chain risks provides companies with the ability to pre-emptively take concerted action to eliminate or reduce risks. Predictive analysis is also valuable for gaining a competitive advantage in the marketplace.
Improve delivery performance and satisfaction
It is difficult to build productive and meaningful solutions to issues without first understanding the root cause of the problem. By using data-driven insights from delivery cycles, companies can recognize areas that are weakened by inefficiencies and build meaningful, targeted solutions that will increase customer satisfaction.
Benchmark internal performance
One of the main benefits of AI-powered big data tools is that they can monitor both internal and external contexts. This means that companies can utilize their big data tools to keep an eye on key performance metrics across business functions and then capitalize on this enhanced insight to identify opportunities for improvement.
Reduce the spend on spend analysis. Try Robobai today.
Capturing meaningful data doesn’t have to come at the cost of time, money or resources.
Robobai gives companies access to a global procurement knowledge base, empowering them to make smart and informed business decisions supported by real-time data. Request a Demo today and discover how Robobai’s innovative AI-powered spend analytics can help your organization optimize its functions.