Data quality is an essential factor in daily business operations and key decision-making processes. Customers spend a large amount of their budget and focus, on ensuring that the data is reliable. In any Product Information Management (PIM) ecosystem, the core foundation of solution implementation is built on the following key aspects of data.
Let us understand what these key aspects are, how they are important to product data, and how IBM Product Master helps customers in strengthening these aspects.
Data accuracy is a vital characteristic of high-quality data, as it ensures that the information which is being collected and used for various purposes, is reliable and trustworthy. In the absence of accurate data, things can be biased or misrepresented, thereby leading to unreliable aftermaths and potentially incorrect decisions being made.
[How IBM Product Master help customers?] Every organization faces the challenge of keeping the product data accurate as per their requirements. IBM Product Master offers an UI-based Rule engine as well as scripting-based rules, which help in defining business rules for validations. The UI-based rule engine allows even a business user to create or manage rules without any coding or scripting experience. Whenever the data undergo any changes, the rules check the sanctity of the data and flag the issues, if any.
Completeness in simple terms is a measure of how comprehensive the data is. If information is incomplete, it may not be usable. More complete datasets help organizations make better decisions by providing them with comprehensive visualizations, relationships, and patterns.
[How IBM Product Master help customers?] IBM Product Master’s Item Completeness feature provides an up-to-date view of product completeness across any channel. Configure the individual channel configuration based on the channel needs and you will have a real-time view of data evolution.
Data can be termed coherent data if it can be combined with other relevant data or dimensions in an accurate manner. Coherence refers to the extent to which the data from a single source is brought together with other data and information, and how they are logically connected and completed. For example, Coherence is utmost essential for building a strong and recognizable brand identity for the organization. If you have multiple products, services, or sub-brands, it becomes even more challenging to achieve coherence across your brand portfolio. Coherence means that your brand elements, as well as your brand vision, mission, values, and nature, are aligned and compatible with each other and with your target customer’s expectations and preferences
[How IBM Product Master help customers?] Let us take an example of a beverage manufacturer say “AAA” who has a brand portfolio that includes varieties of beverages, and a brand architecture that uses a master brand strategy, where all the products are associated with the “AAA” name and logo.
Using IBM Product Master, “AAA” can manage all such attributes related to a master brand strategy under the supervision of a brand manager at a central place. In case of any market realignment or rebranding or any such organization-wide initiatives, the branding can be easily changed in one place and then propagated to all downstream and partner systems.
Data reliability means that when information is collected from multiple sources or over multiple periods, it should be consistent and produce similar results. For data to be considered reliable, it needs to be accurate and up to date. This means collecting data from trusted and verified sources consistently over time to ensure that each piece of information has been collected accurately.
[How IBM Product Master help customers?] The collaborative authoring features of Product Master ensure that the right stakeholders are involved in the data enrichment process with the right access controls. This enables them to make informed decisions and verification on the underlying data thereby improving the data reliability. The necessary set of checks built within the solution can enforce the required quality checks at every stage to accomplish a golden copy of the product data.
Data relevance refers to how pertinent the data is to an application, persona, or business purpose. It should contain the right amount of information for the task at hand. This means only including the information that is applicable and avoiding irrelevant detail or unnecessary duplication of content. Without relevance, accuracy can suffer from distractions such as additional items that are misleading or without importance to the present context. Such a scenario leads to misdirection and suboptimal outcomes.
[How IBM Product Master help customers?] Using the Persona-based user interface and governance features of the IBM Product Master, users are presented with clear, actionable, and relevant information. The use of personalized views, attribute collections, and user-defined tabs on the screen, ensures that the relevant data is available at the user’s disposal. Non-relevant attributes including ones which that a user is not authorised to view or edit, can be easily made hidden by applying the right access controls.
Time is an important factor when it comes to data, especially in a rapidly changing scenario. Data that is not current or up to date can lead to inaccurate results, outdated assumptions, and incorrect decisions.
In today’s digital world, businesses need to have access to accurate and current data to make informed decisions. Data that isn’t updated regularly can become obsolete or irrelevant due to changes in the market or other factors such as the new regulations.
[How IBM Product Master help customers?] Using the various integrations mechanisms available within the IBM Product Master (For example, web services, REST APIs, Java APIs, Kafka, or other connectors, and so on), customers can build a robust, flexible, and up-to-date repository of the product information. This allows them to have a real-time updated view of the data to take critical business decisions.
· Audit Trail
The traceability of changes in data must also be maintained throughout its lifecycle, from collection to storage and analysis. An audit trail is often a regulatory requirement for many compliance activities, and even if not mandated is a business, data security, and privacy best practice. A data audit trail helps organizations answer key questions about data, such as who viewed, modified, or moved data, when was the data changed, how did a user access the data, and whether the changes were approved by someone.
[How IBM Product Master help customers?] IBM Product Master has an inbuilt audit tracking framework that can be configured as per users’ needs. Users can mention the set of events to be applied to a specific set of business objects like Item, Category, User, Role, Catalog, Hierarchy, and so on. Using this configuration, the system captures every minute detail of the changes including what has changed from what, who did the change and when it happened, etc.
Data quality characteristics are an essential part of working with data as they can help determine which data is reliable enough for use in decision-making processes or business operations. Keeping these key characteristics in mind when dealing with data, can help ensure trust and reliability which can ultimately save time by eliminating erroneous results from poor data quality management.
IBM Product Master offers an extensive set of tools and processes which help our customers in taking care of each of these aspects and thus ensuring faster time to market and optimized operations. Keep exploring our new features and changes which we offer in every release to make a robust ecosystem.