Spend Analytics Demystified
In Spend Analytics Demystified, Sievo’s source-to-screen experts reveal the hidden world of advanced spend analysis from data extraction to collaborative classification to spend analysis.
Welcome to Spend Analytics Demystified.
Get yourself immersed and lost in the complex world of advanced spend analytics. In this three-part series, Sievo’s source-to-screen experts share the secrets using practical examples. Backed up with deep domain craft in the subject, they share the several key learnings on how one can truly maximize data and use it as a competitive advantage in this ever-evolving dynamic world.
Read. Learn. Enjoy.
Spend analysis brings visibility into spend, and helps untap the potential savings and opportunities. But even though spend data exists in the systems, getting sense of it is often far from straightforward. It requires three steps before it uncovers the data insights on hand. Think about it like oil. It comes from crude oil, raw and unfiltered. The only way to see its potential is to filter and refine it until it becomes gasoline, which is a usable product that powers the whole world as we know it.
It starts with extracting the data from all possible sources, and consolidating it into one central database. Once it gets extracted, it is ready to be enriched and cleansed. Data extraction is the process that makes outdated and messy sources of information into a clean and consolidated format that can be easily understood and ready for analysis.
Next, data has to be classified into clear and defined categories. To make spend analysis effective, a precise data classification is needed, as it makes the heterogeneous spend data easier to address and manage across the organization. This process harmonizes all purchasing transactions to a single taxonomy to enable customers gain visibility to global spending.
After the data has been classified, it is now ready to be analyzed. Spend Analysis gives you that spend visibility to deliver insightful analysis for accelerated opportunity identification, smarter sourcing decisions and full control on your spending. An access to an accurate spend analysis is the key for massive savings and potentially realizing the opportunities.
We all know that nowadays, data is everything. In fact, most efforts are focused on harvesting the data, but without much understanding which data has the most significance. No matter how important it is, unless you can extract the right data for your analysis purposes, you will get garbage in and garbage out. Pulling out large amounts of data out of myriad of sources without a standard interface can be quite a tedious task.
In a big company in a real business world, data extraction is a challenging task, which requires tremendous human and capital investment. On top of that, most systems weren’t built for the intensive load an extract will put on them, especially internal databases that need to continue serving users during the data pull. Because of the unexpected amount of stress put on the servers, considerably big performance problems can occur during the process. The amount of data to extract and the amount of maintenance required result to pulling out data records out of systems longer than the time it is expected to. The good news is, there’s always a better way to do things. The Sievo Data Extractor is designed to connect and extract the most complicated and extensive procurement data from all kinds of data sources and deliver it for further analytical processing.
Here are 5 things on how the Sievo Data Extractor works its magic:
1. Even if extraction can be done in-house, using Data Extractor incredibly shortens the lead time as it takes away the erroneous manual work. It has a pre-configured template for extraction to take the correct data with possibility of additional data fields. The challenge of spend data extraction lies in the fact that you need to collect data from multiple modules of an ERP, in a way that you get a coherent view on spend. Building this logic is trickier than it seems at the outset, but luckily Data Extractor has pre-built capabilities of doing this for most ERPs. Because the tool cuts away a significant amount of time and effort, there is no need to involve too many people in the whole process, like for example heavily involving internal IT which generally means longer lead times.
2. The tool represents an easy way to extract data from multiple systems with complete data security. Many companies like to have a full control over sensitive data, but this tool guarantees a secured way of extracting data, delivering it for further processing with a possibility for customers to still keep control.
3. Installed to a client’s computer within minutes, and with its compatibility to practically any ERP, procurement and finance systems, it seamlessly captures all the data and pushes it to the Sievo cloud. Correct extraction paves the way for conducting quick data cleansing and analysis.
4. The tool allows us to monitor all processes, in cases of exceptions. We have all the tools to proactively fix the issues, and our unrelenting experience allows us to know how to react to errors such as connections not working or only partial data sets extracted. This results to more reliable processes and up-to-date reports with less support needed from the customer.
5. Once the data gets extracted, it is now ready to get enriched and cleansed . After the transformation logic, what you’re left with is a clean, consolidated and filtered format which then makes the rest of the process easier and faster.
In a nutshell, with its trusted and ready-made plug and play templates, the Sievo Data Extractor Tool does the heavy lifting. With massive amounts of data on hand, the best chance of success is to work with the company who does not only have the technical expertise, but also understands the procurement data and has perfected this process . And Sievo systems have the ability to deal with all kinds of idiosyncrasies and challenges that make the extraordinarily dirty data, outdated and messy sources of information into an easy-to-comprehend format ready for analysis.
You may of course the deliver the data to us, but why opt for that when we’d be happy to take them from you. With the powerful tool in our hands, all this can be done without compromising security.
Head of Integration
Effective spend analysis is not possible without precise data classification. Without classification, everything is cluttered and unorganized. Many of the large enterprises Sievo works with have many ERP’s, each having its own structure, material numbers, accounts and a lot more. Let’s take the example of a laptop. In a larger organization, this one item can be called differently depending on how it has been sourced in tens of different ways, it can be Dell X260, Power Laptop, or perhaps Ordenador in Spanish.
Unifying heterogeneous spend data into clearly defined categories makes them easier to address and manage across the whole organization. Even with the example of the pen, knowing exactly how many pens are bought by different departments and subsidiaries of a company and at what price gives procurement the upper hand in negotiations with new vendors. So, in essence, classification is about harmonizing all purchasing transactions to a single taxonomy enabling customers to gain visibility to the global spending in order to make better sourcing decisions.
Once you’ve properly classified your data, results are presented in intuitive reports and dashboards right away. The harmonized data can then show potential savings and opportunities.
The answer to accurate classification is not a black box
Complex data classification solutions are not always simple to implement, but they can be improved over time. What stands out with Sievo’s data classification tool is that it’s not a black box—it is transparent. Customers can see how information has been classified and make exceptions to the classifications where they feel necessary. For example, if one category is classified wrongly, it can be changed effectively as needed. Sievo empowers users to add data categorization based on their own business logic and they can easily classify data through intuitive add-ons and different levels of categorization. They can slice and dice data to smaller manageable junks on any attribute. Hierarchical classification decisions enable them to manage and pinpoint exceptions both at scale and in detail.
The visibility for the customers makes the whole process collaborative as the customers can freely participate and make exceptions if necessary. Once classified, the information goes standard across the organization and users can see based on what data characteristics the classification has been done.
The key to success is collaborative classification
According to Spend Matters, Sievo’s collaborative classification engine is one of the most distinct spend classification platforms in the market today. Not only does it break down spend into categories and sub-categories, the classification can also be assigned to customer category experts. This results in expert system implementation where the knowledge of the expert is capitalized in ensuring deeper level classification within a category or sub-category and resulting to higher level of accuracy.
For organizations with billions of dollars of spend, this level of collaboration is revolutionary. No one person can be expected to know the full organization’s spend inside-out, but individual category managers can contribute to a more accurate level of classification, and the end result can be ground-breaking. The best practice in spend classification suggests at least 90% accuracy to identify spend opportunities, however reaching 95% or better accuracy level is crucial for savings tracking and management. As the further breakdown occurs for a deeper level reporting and tracking management, Sievo’s collaborative classification approach allows spend to be mapped down to the last dimension
100% error free.
The future is human + machine collaboration
Even when category experts can improve classification close to the 100% error free level, there is still the question of prioritization. How many hours of an expert’s time is worth spending improving the classification of less strategic, long tail spend?
The answer to this comes from a relatively new field of computer science called machine learning.
At Sievo, we’re developing an approach of human + machine collaboration, where the advanced knowledge of procurement experts is complemented by our own proprietary machine learning solutions. In practice, we continuously train computers to learn and adapt the classification techniques performed by humans. The more data computers see classified, the more they learn. This approach is currently tested with a limited number of clients, but we believe in the future we can reach even greater levels of precision on data classification by using the best skills of both humans and computers – the next stage in a truly collaborative classification experience for procurement organizations.
Senior Product Owner
Congratulations! Your company recently recognized the great untapped value of your data. The CPO commissioned a project to look for a spend analytics vendor in the ultimate goal of unlocking the opportunities of your procurement data. Be it supplier rationalization, identifying preferred vendors, PO coverage, group buying, supplier performance, your organization has endless possibilities unleashing the value in data so you can stay competitive. Besides, you’ve probably heard the phrase ‘Data is the new oil’.
Analytics is the detailed examination of data and applying proper statistical analysis of data to unravel hidden insights which in turn leads to supporting decision-making, or let me rephrase that, ‘data driven decision making’. Fast forward into the project, data has been extracted from multiple ERPs, standardized, cleansed and now in the process of being properly categorized via your provider’s collaborative classification process. It’s like oil being refined. As that stage in the process finally complete, you are now locked and loaded to do analytics work. The experience can be compared to freshly minted coins in your hands or the great smell of freshly washed and pressed clothes and even the great smell of the pages of that newly purchased bestseller book. You can safely say, now we can start analyzing and unlocking value. So if data is the new oil, then analytics is the combustion engine.
To help you with your first procurement analytics execution, I have listed 8 analytics insights that every procurement professional must have in order to be competitive.
This is the summary of how much spend you have this year, what is it in comparison to last year, last quarter, rolling 12, ytd etc. Where is the spend increasing and why? How is spend trending over time and is this maverick spending or not?
To know how much you are spending and for what reasons is to know how much cost you are incurring, both in indirect or direct categories. The ability to see your spend and how it compares to a particular time frame is a no-brainer. This method has been perfected by stock market analytics platforms that show the price of a stock vs. the day before, wtd, mtd, qtd, ytd etc. The particular KPI visualization indicates trend whether you’re up or down and can respond to the slicing and dicing that you are doing on the data.
Spend distribution by supplier
A classic way to represent spend is by looking to the pareto principle by Vilfredo Pareto. This allows you to see how much of your spend is controlled by your supplier base. It also allows you to find out if certain spending categories are controlled by a few or worse one supplier. Having one supplier commanding a big portion of your categories’ spend exposes you to the risk of losing your leverage during negotiations. If a particular supplier has a majority control of your category spending, it has to be a conscious and duly informed decision; otherwise you would not be able to negotiate from a position of strength. Slicing this by category, geography and for a particular time-period is crucial. Many companies try to do this, but it is a very strenuous task of creating a spreadsheet table, doing a running sum calculation and then a percentage calculation. You need a provider that has an out-of-the box solution to this.
Visualizing the spend-flow
Analyzing procurement data can be daunting. There are so many dimensions you can slice the data with. I have seen many data visualization executions in the hope of making sense out of how an organization is spending. The questions I encounter can range from how much we are spending in what categories, who are my top suppliers for each category, what is the relation of that to my organization units, which geographic suppliers do we spend most on and which sub categories are those.
Trying to interpret this in a visual form is tasking, some will create multiple bar charts sorted descending, pivot tables of top suppliers, categories and also a fully customized spend bar chart where x, y axis can dynamically be changed. Suddenly you end up with a bar chart and table heavy dashboard. A best-practice approach is to visualize spend as a flow-process. Applying data-storytelling allows you to present complex spending pattern into non-intimidating manner to the layman. This would ensure that the insights are provider using an effective delivery method – data visualization.
The beauty of procurement analytics is that it can help you unlock hidden value and one of these is in the price of purchasing for direct spend materials. When you are able to diversify spend for a particular material to not just one but multiple suppliers, you would be able to gauge the price of purchasing the material and compare that across suppliers to arrive at the best supplier price. What’s better is that if your service provider has data benchmark data on the average price for a particular material which doesn’t necessarily need to come from your data.
Looking at price alone is not enough, so my advice for any opportunity related reports should always balance with other factors like on-time delivery, quality, fill rate etc.
The benefit of price opportunity analysis is that you are able to use the information in terms of negotiating prices for sub-optimally priced suppliers so they can match the best supplier price without sacrificing other supplier key performance indicators.
Everybody loves a waterfall
Your CFO or CPO will probably ask you where we have made significant cost reductions due to the savings programs you’ve implemented. Spend reduction can either be good or bad and it is good when it can be tied up to your executed savings plans. However it can also be the result of seasonality, price fluctuation, currency etc. To know where to attribute the spend change will allow you to provide gap analysis and have confidence when explaining the results to your stakeholders. For this I recommend a waterfall chart as shown below.
Remember that very well regarded purchasing process you have? How do you know if all people spending in your company is following it? The first step is to identify spending as it relates to the process. If a particular invoice has been paid and you can see it linkedto a purchase order, then that is spend in control. I can say spend in control is quite vague especially when you make the mistake of just using a PO field linked to an invoice as a measure for your PO coverage or contract number field linked to your invoice.
The better way is figuring out if an invoice was flipped to a PO for most part is very useful information but with the absence of that, you need to be able to match a PO to an invoice properly. This way, you can be assured that that process is stable and trustworthy. By knowing PO coverage, you will be able to identify maverick spending and address it properly since this information can be further sliced and diced to category, business unity and even supplier level.
If you want to navigate the vast ocean of suppliers you have with the hope of optimizing the supplier base, you need to be able to segment the suppliers based on variables which you think are useful for your needs. An example is looking at your biggest suppliers and seeing their growth in spending vs. last year so you can have a matrix of high growth, high spend, low growth, low spend and then cross reference that with your category data.
Many analytics platform will have some sort of geo-spatial analysis capability, however, you will find it frustrating that the use of the mapping data does not bring the added value to the user. One of the things you can improve is with the use of colors and sizes of the data point when you have a mapping object in your analytics canvass. It could be as simple as supplier location spend value (which is size) and then spend growth for supplier vs. last year (color). This way you would be able to say, hey why is Portugal suddenly lighting up on the map, I wonder what they are spending there on? Another example is when you search for a particular material, say corn, then you would be able to see where the suppliers for corn come from and figure out possibilities of supply chain optimization.
These are just a few examples on how to create insight driven analytics, there are tons more. Always remember, the more you can simplify your approach in presenting very complicated concepts through data, the more you will be effective in communicating the insights in the data. The moment you have delivered simplified and palatable analytics insights for the purpose of aiding decision makers in making decisions about procurement, is the moment you achieve competitive advantage. The world around you is drowning in data, reporting pukes and messy spreadsheets flying around you. There are vast spend analytics vendors out there, but there are a few who can deliver the insights you need designed specifically for certain user groups in your organization.
With Sievo, you can be assured that the data is in good hands. Sievo helps you view and understand your procurement data.
Head of Analytics
Sievo is a leading procurement analytics SaaS-based solution company that provides spend visibility, but also goes way beyond that. We help our clients identify opportunities, translate these opportunities into projects, embed created value into budgets and ensure that savings truly hit the bottom line. We speak the language of procurement and also translate numbers into the financial view.
Our solution is used by thousands of users in best-in-class procurement organizations, such as Deutsche Telekom, ISS and Kellogg’s. With our clients, we don’t stop at backward-looking reporting but deliver more by creating forward-looking forecasts and comprehensive analytics. We combine internal information with external data sources. With Sievo, human input and machine learning technologies are integrated together. In short, we translate procurement data into dollars.
Since our founding in 2003, we have experienced rapid, profitable and self-financed growth. Currently we employ more than 100 professionals and have offices in Europe and US.
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