Data today is one of the most valuable organizational assets and businesses are leveraging data analytics to make sense of the growing volumes of raw information available. Quality operational data is at the heart of organizational decision-making, impacting success and long-term growth.
In an increasingly competitive marketplace, businesses are hard-pressed to optimize their rebate programs for maximized returns. Quality data is always an issue when managing rebates.
When you partner with Rebate.ai, we have the tools like Sancus to help drive better data quality across the organization.
For this, they need to leverage the right strategies that foster collaboration among stakeholders and ensure sustainable revenue growth. Insightful, real-time, non-redundant and reliable data forms the foundation for a rewarding rebate landscape in the increasingly demanding rebate ecosystem. However, maintaining quality data health continues to be a huge challenge. In fact, a survey by Gartner reports that organizations believe poor data quality to be responsible for an average of $15 million per year in losses.
It is crucial to gather, cleanse, validate, wrangle, store and manage data appropriately so that it remains a key enabler to consistent growth. Clearly defined and conscious data management practices can enable equitable and optimized returns for all members of the rebate ecosystem, including buying groups, buyers and suppliers.
Is your rebate management process smart? Is your data functional?
Data management is not limited to just inputs from suppliers, numbers on invoices, codes on products, or feedback from customers. There is a lot more to data management, including data classification, data quality assessment, data catalog operationalization, and data compliance. Encapsulating all these segments into a well-defined data management framework with equal emphasis on each ensures your data is functional and scalable. A centralized rebate management system can support robust downstream analytics, enabling the business with levers of growth and profitability.
Data categorization or classification is the first step in achieving efficiency and resource optimization. This process ensures you can effortlessly access the right data at the right time to facilitate swift decision-making. Data classification can be based on nomenclature, user type and other classification parameters.
Classification based on nomenclature: Managing data) in a rebate management system) in a standardized format following specific nomenclatures helps keep track of and access relevant data easily. Common name-based categories include:
- Customer master data: All business transactions carried out with a particular customer are stored in the customer master data. By maintaining a single central customer data repository, any modification to the customer record is reflected in all corresponding and related entries, eliminating data inconsistencies.
- Supplier master data: All rebate management system’s data corresponding to suppliers, including supplier details, location, products supplied and corresponding prices and rates, legal information, supplier certifications and other related information, are stored in this master data repository.
- Product master data: All data related to a particular product, including product name and description, technical specifications, standards, customer-supplied data, vendor-supplied data, bills, and more, are stored in the product master.
Classification based on user type: Classification based on customers, suppliers, or buying groups with similar products or similar buying patterns provides the opportunity for risk mitigation. Organizations can effortlessly protect sensitive rebate management data by allowing or restricting access to a specific set of stakeholders based on their rebate performance.
Other common classification types: Content-based classification simplifies data search operations and context-based classification filters data based on parameters including creator, product, and rebate type.
Focus on financial drivers that induce growth and profitability in your rebate management platform
The next key step toward leveraging rebate management system’s data for profitability is to identify Key Performance Indicators (KPIs) that drive financial benefit or profitability. Data associated with rebate revenues, pricing, sales, and purchases are interconnected to form a growth chain that when analyzed, structured, and efficiently utilized, drives financial upliftment.
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Is a data quality management tool necessary to optimize rebate management system’s data?
Consider a scenario where you are working with data corresponding to product tracking. Product tracking includes utilizing valid data to identify relevant products and track availability, remove obsolete products, monitor price change and replace or replenish products periodically to ensure uninterrupted and smooth flow of operations. Effective data management in these instances can significantly help increase productivity. However, a process of this magnitude that assures enhanced operational efficiency cannot be achieved without the aid of a data management tool.
Data quality management tools, such as , empower your business with trustworthy data that accelerates your business outcomes. Some of the key capabilities of an AI-driven data management solution include:
Data cleansing: On average, only , while the rest is excluded due to data inconsistencies, duplicates, missing information, etc. The data cleansing feature leverages AI/ML algorithms to de-dupe, identify and capture valuable data for data configuration and standardization. Cleansing can be performed on all product catalogs, material pages and master data files, including customer, supplier and product master data, to create golden records – a single, trusted version of data that captures all necessary and accurate information about the customer, member or asset.
Learn to successfully manage 100s of trading partner relationships with an AI-powered rebate management platform.
Data validation: This feature helps with address validation and corrections related to other personal details using postal directories and 3rd party APIs. To simplify and expedite the process, it also includes an email verification option.
Data enrichment: All data enrichments are carried out through 3rd party partnerships. Material or product enrichment processes such as web scraping and image processing are fully automated.
Data ingestion: This process extracts data from other internal documents for cleansing and consumption by downstream layers. The resultant data can be easily integrated with industry-leading tools and applications, including Oracle, Salesforce, Rebate.ai, etc., for smoother functioning.
Data governance: This process aids in the efficient management of metadata, standardization of data and processes, and compliance to metrics, equipping the platform with insightful and scalable data. It also.
Intelligent rebate management platform holds the key to informed decision-making capability
Backed by operational data, a robust data management solution offers effective downstream analytics at all stages of the rebate management process, including inventory management, supply chain, trading partner relationship management, and customer feedback. It rewards your business with sustainable revenue growth opportunities by facilitating access to actionable insights and enabling quick decisions at the right time.
Sancus, Tredence’s intelligent, AI-powered data quality management solution, aids in maintaining, tracking, and governing data quality effortlessly over time. The solution encompasses all dimensions of data quality management, allowing businesses to leverage data for trustworthy intelligence, actionable insights, and enhanced business benefits driving long-term revenue growth. Speak to our experts to learn more!