This comprehensive guide explains what data management is in procurement. It further provides insight on tools, techniques, and trends on:
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Procurement data management is the process of gathering, organizing, assessing, and maintaining all the data generated by a procurement organization. In addition, it also includes many other methods and functions to make this data accessible, accurate, analyzable, and thus, actionable. However, most of the data management work is done by the IT and data management teams, but inputs from business users are also required to ensure that the data meets their requirements.
Effective data management uses the right technology at every step of the data transformation journey to equip procurement professionals, business leaders, and other users with information to drive strategic planning and decision-making.
Therefore, data is seen as an asset used to make informed business decisions, optimize business operations, reduce costs, and increase profits.
In terms of procurement, the more strategic it becomes, the higher is the demand for real-time assertive decisions. Besides this demand, the size, range, and speed of data that impact procurement decision-making are expanding exponentially — the hallmarks of big data: larger volumes and a wider variety of data types. Without a sound data management system in place, such environments can become awkward and hard to navigate.
The idea of data management is not new - it has, in fact, significantly increased with time. Even in the world of procurement, the concept of data is undergoing constant evolution and transformation. More and more procurement organizations now regard data as central to their strategy and necessary for their core competitiveness and success.
If you ask us, data and managing it in ways that enable it to be used effectively is not easy but, when done correctly, is beyond valuable. That is why we have created this guide, to help you sail through all the complexities and queries around the spend data management topic and make your procurement more efficient.
So continue reading to learn everything you need to know about data management in procurement.
Big data is at the heart of the future of procurement. As the name suggests, the term "big data" is attributed to the size of the data and the speed required to manipulate and analyze it. Therefore, the definition of big data is also known as 3 V's definition, with a fourth V, Veracity, added to define it accurately.
Big data in procurement represents an information asset characterized by the ability to consolidate, aggregate, and slice more data coming in High Volume and Velocity, from multiple sources (Variety) - both internal (e.g., ERPs, or any other information systems) and external (e.g., IoT sensors and 3rd party data providers) - for its transformation (Veracity) into value.
In simple terms, big data in procurement mean data that is:
Many consider big data just a buzzword and think they can afford to miss out on it. To their astonishment, this latest International Data Corporation (IDC) report states that the Global Datasphere will grow from 33 Zettabytes (ZB) in 2018 to 175 ZB by 2025.
A trillion gigabyte makes one zettabyte, and that is an extraordinarily high figure. If this level of data is made available to AI- and machine learning-enabled procurement, foreseeing every scenario and ascertaining every action would get possible.
So, what does it mean for those procurement leaders who are yet not able to beat the intimidation factor of big data? To truly stack the deck in your favor, you need big data. Else, it will soon become hard for you to stay viable and competitive in the market.
"The emergence of intelligent data is reversing procurement's reliance on looking backward at the money spent or supplier's past performance. The increased use of 'big data,' the cloud, and analytics enables procurement to work with information, data, and models that predict – providing knowledge at your fingertips!" The Procurement Value Proposition, Gerard Chick, Robert Handfield.
Imagine yourself in a long, dark room with obstructions and blocks all along the way; if you were asked to reach the other end of that room without hitting any barrier or stumbling over, how far do you think your gut instinct can take you?
Once again, imagine yourself in the same room but this time, with the lights on, so you see every obstacle and can easily avoid them. Now, reaching the other end of the room will be a cakewalk. Agree?
If procurement is that long, dark room, data management is your light.
But, surprisingly, many procurement organizations still choose the former or the 'gut feels' option to walk through that room and thus, fail to answer the following questions:
These questions have a deeper purpose. The information that you miss without the light of data management is crucial and can bring you closer to a horizon with endless opportunities like:
The opportunities data management brings are significant indeed; still, many big challenges are laid along this way.
The purpose of big data is to collect sufficient data (but not too much; to avoid analysis paralysis) to consider maximum variables and recognize the best decisions for a business's continued success.
However, in the dynamics of procurement, this is not an easy task. There are three fundamental aspects of procurement transformation: people, technology, and processes. The success or failure of big data depends on how an organization reconfigures its processes, technologies, and people's behavior. Because in procurement and supply chain, behavior, values, and social rules underpin the decision-making process.
Not just that, often, the traditional procurement tools are ill-equipped to handle such voluminous and diverse data. Analyzing complex data and cracking insights and interrelationships is another challenge that requires people capable of programming and data analysis. These facts make the use of big data in procurement a complicated task.
Below is a table enumerating the top challenges faced by procurement teams in managing big data:
These and many other challenges call for procurement organizations to adapt and implement new data management and analytics strategies.
Evaluating big and complex data in procurement opens unprecedented opportunities for adding value, lowering cost, and avoiding risks and frauds. Managing and transforming such massive information into valuable and reliable resources while preventing premature and improper conclusions requires procurement to invest in technology, organizational process changes, and strategies.
A well-executed data management strategy helps companies gain potential competitive advantages by ensuring data accuracy and compliance. But combining strategy with processes and technology is equally important to make sure teams adapt and advance set initiatives aptly.
Data management makes procurement organizations agile and allows them to spot market trends and take advantage of new business opportunities. But maintaining an ongoing data quality effort is easier said than done. Most procurement organizations may not have the capacity, resources, or budget to invest and build data management strategies. As a result, these seemingly unachievable data challenges spook leaders into inaction.
Finding the right technology partner who can build and manage a process to give companies access to accurate and timely data can empower procurement leaders to establish their value to the business as a whole.
But, how to figure out which strategies to focus on?
According to McAfee and Brynjolfsson (2012), teams that set decisions more clearly aligned to their objectives ask the right questions and consequently bring the most assertive answers to the challenges posed.
Hence, coming up with and later optimizing those strategies require cross-functional collaborations and alignment with decision-makers. The result? A data-driven culture that promotes data sharing best practices with clear data management and data governance policies.
The ability to collect and manage trustworthy data lays the foundation for building a world-class procurement organization. Reliable information is imperative to optimize leverage, make informed decisions, and align initiatives to overall organizational strategy. Despite its endless benefits and technological advancements to procurement leaders often deal with questions like:
To answer these pertinent questions, too many CPOs, CFOs, and procurement organizations have looked at technology as a panacea. Many get pressured by their stakeholders to digitally transform their procurement functions while dealing with a shrinking budget. All this and more force procurement to embark on a digital journey without having a full understanding of:
Make use of the concept of the Golden Circle.
Technology is crucial but centering your strategy on the whats instead of the whys lower the chances of using technology as an enabler and receiving its full potential.
Data management means a set of practices for collecting, cleaning, and managing data to make informed business decisions. The core idea behind the entire process is to extract value from data and treat it as an asset.
To generate and maintain high-quality procurement data, identify gaps, and formulate strategies, it is vital to understand the process around data generation. This detailed understanding of the process further helps organizations define a clear data cleaning strategy and provides accurate data for detailed analysis.
Implementing a long-term procurement data strategy mainly involves the following two stages:
In both the above stages, activities are more or less similar.
Any procurement analysis begins with understanding the spend structure of a company and merging its data with analytics to generate value. Following are the foundational steps involved in the :
The first step in the spend management process is gathering, cleaning, and processing the information to create a functional and structured database. Gathering procurement data is particularly challenging because the information is usually scattered across multiple systems and sources.
Procurement teams spend endless hours on exporting and extracting data from several systems and entities like Excel files, paper invoices, purchasing software, etc., and gathering it in a single location.
That is the reason why procurement data quality and structure are grossly uneven - bad, dirty, inaccurate, or missing data - as teams lack skills to fill the gaps in data. Businesses that lack these skills eventually pay the price in cost savings, compliance, and risks.
Following are the activities required to have a clean and accurate procurement database:
Today, large amounts of data are generated in procurement. Depending upon the organization, data size can range from basic invoice-level data to a vast array of data from internal and external sources. Irrespective of the quality and size of your data, putting it to use can help you uncover important insights into your spend. However, the more detailed the data, higher is the scope for better insights.
Below are some of the most common sources of procurement data:
Pulling the widely dispersed and diverse procurement data together is called extracting, transforming, and loading or ETL. In procurement, data is multi-dimensional and with varying levels of details about suppliers, goods, and services. For instance, when the supplier master data is collected from different data sources, it needs to be consolidated, clustered, and laid hierarchically. This way, it becomes easier to detect duplicate supplier information and group them accordingly. Likewise, the material master data is standardized by clustering similar materials and so on.
Here, the focus is on total consolidated spend, the suppliers, and the items or services purchased. The data cleansing rules are defined on a per field basis such as currency, cost center, dates, etc.
It means taking out irrelevant and false information and replacing it with an accurate one to make the data usable for analysis.
The crux to effective spend management lies in extracting, transforming, and managing the data accurately.
After collecting the data, it must be processed to meet the quality standards needed for practical analysis. The clean and transformed spend data is then combined and enriched with the master data of the organization to make it suitable for analysis and reporting purposes.
Classifying the spend data and doing so accurately is one of the most crucial factors for ensuring perfect transparency and, thus, identifying the right opportunity. It refers to classifying as many invoices, orders, and transactional data as possible.
Mastering data classification depends on many criteria like:
Cloud-based spend data management solutions use AI-based, self-learning classification systems to harmonize spend categories and facilitate comparison and transparency in strategic procurement
When talking about challenges in spend analysis, data visualization comes second in line. To identify sourcing opportunities, visibility to the current and past spending is a must. Unfortunately, most organizations do not have an end-to-end view of their spending.
However, as per the following AMR Research, the need for spend visibility is apparent: From recent interviews with 70 supply management executives, they found that those that do not utilize spend analytics have 55% on average of untapped spend, miss 10 % to 15 % in savings in categories that have never been examined, and miss at least 6 % in savings per category currently managed (Spend Visibility: Do You Have the Correct Lenses?, M.N. Rizza, AMR Research Inc., December 2009).
To address the data visualization issue, procurement teams use a spend analysis dashboard that gives them a holistic view of the procurement and supplier information.
Tools that equip companies with easy-to-use, drag-and-drop dashboard functionality simplify the complex, often inaccessible spend information and present it in a visually compelling way to help procurement management take timely and factual decisions.
Such tools also provide procurement teams with several dashboards to:
In this and many other ways, dashboards that enhance spend and performance visibility help procurement teams uncover the following opportunities:
The axiom of bad data in, bad data out is old but still holds. That is because if the quality of the data is too poor, no amount of analysis can yield any insights. Therefore, before initiating data analysis, it is vital to ensure that the information is error-free and up-to-date.
How do We Define Clean Data
Clean data simply mean accurate catalog fields that minimize transactional discrepancies, encourage in-depth reporting, and help in implementing standardized categories across the organization. Clean data is always an ongoing activity and, contrary to the common belief, is not an outcome.
So what happens when the quality of the data is poor?
When the quality of master data is poor, it results in inaccurate analysis and flawed decision-making. And the result is uncertainty, risks, and sub-optimal performance.
The issue of bad data in procurement is widespread. Though there are many drivers of bad procurement data they usually revolve around the following main areas:
This issue is too common with non-stocked items. Lack of basic item master policies that include things like product series consistency, units of measurement, descriptions, etc., are the reason.
Too frequently, the same supplier is represented as multiple suppliers because of irregular data entry. For example, 'Apple Inc' can be used as 'Apple,' 'Apple Europe,' 'AppleEUR' and so on.
This is too prevalent and stems from two separate issues: non-classification of data and inconsistent data hierarchies. To understand this better, let us look at how the spending is broken down. For instance, look at this classification: Office solutions- office machines - printers - printer consumables.
Way too often, many buyers create their own classifications. For example, a printer cartridge can be classified as a part of the printer or printer cartridge itself.
When this classification is not applied consistently, it becomes difficult to arrange spend by hierarchies. On other extremes, spend is not categorized at all and is often grouped under 'others' or 'miscellaneous' headings.
Companies have long struggled with the bad data problem, the effects of which are not just limited to performance erosion and inefficiencies.
The good news for companies who want to take charge of the quality of their data is that they can look towards sophisticated data cleansing software available as a service to improve the condition of their procurement data.
Below, we have enlisted some of the strategies that can be implemented to find the answers to and fix the bad spend data problems:
Most of the time, invoices and purchase orders lack the relevant fields to analyze the spend. To make sure the decisive information is present, the following recommendations can be helpful:
Refining taxonomies is essential to reflect the goods or services purchased aptly. With accurate taxonomies in place, it becomes possible to identify correct spend categories.
The United Nations Standard Products and Services Code or UNSPSC is a well-known taxonomy of products and services. It is extensively used for the taxonomy of products and services. But to fit these taxonomies as per your organizational needs, you need to follow a blended approach.
For better taxonomies, we recommend the following steps:
Utilizing a cloud-based solution to move towards better data classification and quality is an excellent way to maximize your data management efforts. Breaking the bad data problem is intimidating, and a technology partner can help you break the problem into small bits and drive the impetus you need for change.
These data classification tools:
So, combining a best-of-breed tool with stringent review processes ensures the completeness and quality of your procurement data and lets you take a swift drive towards the data-driven decision-making road.
Data is not just crucial for competitive advantage; it is the life and blood of any business entity, especially procurement. In terms of procurement - irrespective of the size and type of an organization - cost-saving is a perpetual priority. And the art of cost savings relies on turning data into information, information into insights, and insights into actions. This unprecedented significance of data is not attributed to a singular benefit but its ability to contribute to the broader organizational goals.
The actions you take dictates whether you succeed or fail as an organization. Hence, they should be factual and must be derived from accurate and reliable information.
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