5 Reasons Why Businesses Need a Robust PIM Platform

Here are certain telltale signs which indicate that an organization is in dire need of a product information management (PIM) solution:

1. Whose Data Is the ‘Right Data’?

A major shortcoming of working with legacy product data management systems (typically spreadsheets) is the lack of synchronized consolidation. In an extended value chain encompassing suppliers, partners, employees, and other external entities, product information is bound to be altered by various individuals at various points of time. With no centralized platform in place, often a miscommunication between disparate touch-points is likely to happen. For instance, when one employee emails a spreadsheet containing product-specific data to another employee and they simultaneously make changes to it, the latest updated file with both inputs is often lost in the transaction.

This is what leads to inconsistent, obsolete, and inaccurate product information.

2. Employees Are Always Busy Monitoring Product Data

The long-term impact of failing to manage product information directly hits the workforce efficiency. Since data is used by multiple systems in multiple locations and amongst siloed departments, even the loss of one ‘unit’ of information can cause huge chaos. And what could be worse than using unclean data? Any company that has dealt with tight product launches knows the impact of having a product in the market with discrepancies in information about it. Your entire rapport with the customers is on the line if inaccurate information reaches them. As a responsible organization, you must avoid such a situation at any cost. For this, your employees must monitor, track, and record all data transactions and filter out dirty data painstakingly.

This tedious job can be enough to overload your employees and hinder their productivity. Additionally, with more products and even more product-related data, setbacks like these can exhaust your workforce’s time and energy, leading to loss of revenue. Continuous data monitoring might result in employees deviating from their core jobs, and the impact will be visible in your profit charts.

3. Customer Support Ceaselessly Brims with Complaints

The manual process of updating spreadsheets at multiple touch-points is prone to human errors and other unavoidable inaccuracies. The outcome is incorrect product information being disseminated to the customers. The impact, however, is the same in both cases — unhappy customers, poor customer experience (CX), bad feedback, and stacks of complains.

The error can originate from anywhere in the value chain and in any form — it can occur due to old, not updated, expired, or outright wrong data. Inconsistencies in stock-keeping units (SKUs) can be the root cause for minor discrepancies to major blunders in ordering. From inventory management, to order placement, to order processing and ultimately delivery, if you are unable to govern and check data quality at every step, you will not be able to convert potential customers smoothly — or worse, lose the existing ones.

4. Analytics Never Gets It Right

Information and data are the strategic assets of a firm. As a result, unorganized, dirty, and inaccurate data is never healthy for the workflows of any organization. If not identified and rectified at an early stage, data that is erroneous, misleading, and without general formatting can cause serious issues. Unfortunately, no industry, organization, or department is immune to the chances of dealing with bad data.

The relationship between data quality and decision accuracy is tightly knit and quite intricate. It especially affects the departments who are instrumental in strategizing moves and deriving insights from the collated data. Management heavily relies on analytics and data usage to draw business acumen and decide on the required course of action. That’s because, wrong data will never point you in the right direction; in-fact it renders the entire analytics futile, contributing directly to failing profits. Be it prescriptive analysis of historical customer data, buying patterns, cost breakdown, and product catalogs or predictive analysis–correct data is the lifeblood of accurate analysis.

5. It Is Chaotic to Manage Data Across Multiple Geographies

Data management might not seem too big of an issue when managing a small business with limited data resources spread across reachable locations. However, with warehouse expansions, as your enterprise products grow, more information starts getting processed throughout the systems. And when you are functioning across multiple regions, geographies, marketplaces, and storefronts, it can be a tedious task to manage a large number of products, with different SKUs.

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