Owning and running a business is not worthwhile if you can’t figure out profit and losses, which are obviously numbers. But to extract these exact numbers, you need to process a lot of information, i.e., data from various departments. And it is not as easy as it sounds, because every business data comprises different formats, sizes, types, and more. This is why you need big data analytics in procurement for a more organized, smooth, and calculated process.
What is Big Data?
Big Data stands for huge, complex, rapidly increasing, and fast-moving sets of data that can’t be managed by traditional database software. You can spot Big Data characteristics with these 5 V’s.
- Volume: Is the amount of data
- Velocity: Is the speed at which data is being created and processed
- Variety: Defines the formats and sources of data
- Veracity: Tells about the accuracy and reliability of data
- Value: It defines the importance and benefits of data, which can be fruitful for the business
There are various Big Data types, such as structured, unstructured, semi-structured, geospatial, machine/operational logging, and open source.
What is Big Data Analytics?
Big Data analytics is a process of studying and comprehending the complicated Big Data to understand patterns, trends, and convert omnisourced information into actionable insights for business growth.
This analysis helps businesses understand what is working right, what is not, and what the room for improvement is. You can build a roadmap and strategic plans to scale up your business.
It is a multistep process that includes collecting, storing or categorizing, cleaning, processing, analyzing, visualizing data, and the final step of decision making.
Impact of Big Data Analytics in Procurement?
Acquiring a pronounced amount of procurement intelligence can be a game-changer.
There are various data points in an organization for procurement, such as supplier performance, purchase order data, and market data. And all of this is gathered from different sources, including external and internal sources, ERP systems, and procurement software.
By using analytical tools and techniques like ML algorithms, predictive analytics, data visualization, etc, it becomes much easier and efficient to identify patterns, shortcomings, make price predictions, and understand market trends based on historical data.
The global procurement analytics market is projected to reach $18.18 billion by 2030 with a CAGR of 23.6% from 2022 to 2030.
Benefits of Big Data in Procurement Management
Cost Efficiency
It helps companies to compare and choose better deals without compromising on quality.
Enhanced Decision Making
Since companies can leverage the right analytical tools, it enables them to turn raw data into actionable insights, which in turn help in making more accurate and data-backed decisions.
Improved Supply Chain Visibility
Since Big Data provides ample information about supply chain operations, including real-time information about goods, geographical information during transportation, and more. All this data analytics in the supply chain gives greater visibility and overall control.
Reduced Risk
Since you have all kinds of information about the procurement process, it gives a great sense to predict future risks and take action to mitigate them.
You gain the ability to diversify contracts to fight against market demands, surging prices, and maintain mutually profitable supplier relationships.
Real-time vendor management helps you with prediction and actual expenditure comparison.
Competitive Intelligence
As you gain insights into market dynamics, an understanding of different supplier plans, industry trends, and pricing benchmarks. Also, you can know how your competitors are dealing with all these factors. All these factors help gain a competitive edge and stay geared up.
Big Data Tools for Procurement Teams
Procurement intelligence tools are not typical data tools or database systems, as they don’t just organize information, but instead they analyze the information to highlight what matters the most, predict what can be next, and suggest steps to be taken.
1. GEP Smart
It is one of the leading AI-powered source-to-pay platforms for direct and indirect procurement. It offers services such as spend analysis, savings tracking, sourcing new suppliers, contract management, supplier chain management, and procure-to-pay with a consumer-like purchasing experience. They also have a varied range of software for different requirements. One of the best features is mobile-ready procurement.
2. JAGGAER
It is a platform that goes beyond just costs and margins in collaborating with suppliers. They work across varied industries such as automotive, manufacturing, public sector, energy, transportation, logistics, and more.
They provide multiple suppliers, including procurement, finance, information technology, and supply chain.
The company is backed by 30 years of experience and offers direct and indirect, upstream and downstream, in settings demanding an intelligent and comprehensive source-to-pay solution. They help in accelerating autonomous commerce.
3. Zip
Zip is a procurement orchestration platform that uses AI powers in automating the entire buying process. It offers an end-to-end solution from employee request to final payment.
Some of the major services of Zip are that it offers centralized purchase requests instead of scattered data; everything starts in one place. Workflow automation and one-click approvals, i.e., after submitting a single request, Zip automatically works it through departments and checks to avoid multiple follow-ups, which leads to speeding up the whole process.
It also offers broader visibility and control over spending, vendor, and supplier management. This indeed helps in improved compliance and risk management. Integration across systems and tools like ERP systems, finance software, and HR tools is also a key feature.
How to Use Big Data in Supply Chain Procurement
Using Big Data or dealing with it is the same across every industry or role; the fundamentals of processing Big Data remain the same. What actually changes is the regard as per the industry or role.
Therefore, in supply chain procurement, steps are the same, but the sources of data, categorization of data, tools used in the process, etc are different.
1. Define Goals
Decide what you wish to achieve from this analytics process. It can be cost reduction, mitigation plans, improving supply chain visibility, supplier management, fraud detection, optimizing inventory, and more.
2. Data Collection
Gather data from multiple sources from your organization, including purchase orders, bids and contracts, sales details, bills and invoices, ERP, and procurement systems.
3. Data Cleaning
Collected information is corrected by removing errors, duplications, filling in missing values, and standardizing names and formats. And later organizing it as per the type, such as structured, unstructured, etc.
4. Integration & Data Storage
The procurement data is scattered across different systems and departments, such as finance, logistics, and vendor management. To get a unified source of information, this data is formatted in a single form. This helps in comprehensive analysis.
Then the integrated data is stored in the data warehouses, lakes, and cloud storage platforms.
5. Analysis and Reporting
This is the main step where all techniques and analytical skills are applied to answer questions like what happened? What will happen? And what should be done?
All this is done by analyzing spending patterns, supplier performance, reading trends, and risk with the help of statistical models and AI tools.
Then this information is converted into visual representations such as graphs, dashboards, chats, etc., to make them easy to understand for managers and stakeholders to figure out cost-saving opportunities, supplier negotiations and issues, spending cycles, and trends.
6. Decision Making
After the reports are presented, required actions are taken, which give this information real business value. These steps can include negotiating with suppliers, finding better deals, switching vendors, and cutting down unwanted spending.
7. Monitoring & Compliance
After the required action is taken, procurement teams must keep an eye on the progress to understand whether it is working or not. Metrics like KPIs, updated data, and refined models are constantly being monitored and improved.
And also, organizations must ensure adherence to data safety and privacy compliance. Any organization’s data is its everything, therefore it must be kept protected and safe from misuse.
Conclusion
Big data analytics in procurement is upending the procurement process from traditional and typical routines to a more strategic, transaction-focused, and value-driven discipline. It helps companies understand everything from what to buy, how to buy, and what not to buy. It not only saves cost but also provides long-term benefits such as better transparency, reduced risks, and enhanced supplier relationships. Data analytics is the fastest-growing sector today in the age of artificial intelligence and the digital-first era. Therefore, adapting to it is no longer optional for businesses but important.
Related: How To Become a Data Scientist
