Why Retailers Delay Planning When the Data is Messy (and Why You Shouldn’t)
Many retail teams feel stuck in a data dilemma: their data is in terrible shape, so they hesitate to invest in a merchandise planning solution until it’s fixed. It’s an understandable fear, after all, “garbage in, garbage out” applies to planning systems as much as anything. If sales and inventory data don’t even reconcile, how can you trust a new tool to forecast or optimize? This mindset is especially common at growing brands where legacy ERPs have accumulated years of messy data.
The truth: You’re not alone. Nearly every retailer has data issues, and yes, they must be addressed, but you don’t need perfect data to move forward. In fact, waiting for a flawlessly clean dataset is a recipe for perpetual delay. In this post, we’ll unpack the core data pain points holding brands back, share how to start cleaning them up, and explain why you can (and should) pursue a modern planning solution in parallel with data cleanup. By taking action on data quality while leveraging a flexible planning platform, you can break out of analysis-paralysis and start seeing benefits faster.
Core Data Issues Holding Your Planning Back
Let’s first identify the major data problems that derail retail planning. Do any of these sound familiar?
Sales and Inventory Don’t Match. How Do I Reconcile ERP, POS, and E-Commerce?
Sales and inventory don’t reconcile. One planning lead lamented that “sales do not match up to our inventory” in their system, and it drove him crazy. This fundamental disconnect, where the units sold never properly subtract from on-hand inventory, creates confusion. It leads to phantom stock on the books and constant surprises in what’s actually available. Unfortunately, it’s a widespread problem (studies show up to 60% of retailers’ inventory records are inaccurate). When your sell-through data and stock levels don’t align, planners are flying blind. Fixing this is non-negotiable for trust in any plan.
Beginning vs. Ending Inventory Won’t Tie Out. What’s Breaking the Math?
Inconsistent inventory snapshots. Along similar lines, some brands find that inventory “doesn't ever match up” from starting to ending points of a period. Beginning-of-month inventory plus receipts minus sales should equal end-of-month inventory, but in reality, the numbers never tie out. These consistency errors often stem from data timing issues or process gaps (e.g. transfers or returns not properly recorded). The result is confusion and wasted time investigating discrepancies. It’s hard to manage Open-to-Buy or assess turnover if every report yields a different inventory count. Closing those data gaps (for example, ensuring all transactions are logged in one system of record promptly) is critical to restore sanity.
We Don’t Trust Our Numbers. How Do We Rebuild Confidence in the Data?
When data is constantly in question, your team loses confidence in the reports. We’ve heard executives admit they don’t trust the numbers enough to feed them into other systems. This lack of trust becomes a huge barrier: planners double-check everything in Excel, merchants rely on gut instinct over system forecasts, and initiatives like planning tools get put on hold. Building trust means systematically rooting out errors and establishing that data is reliably accurate and up-to-date. Only then will users embrace what the system is telling them.
Our Product Data is a Mess. What’s the Right Retail Product Hierarchy?
Good product data structure (styles & categories) are the foundation of solid merchandise data and key to meaningful planning, yet many brands struggle here. For instance, one fashion brand admitted “we don’t even have smart style numbers” and “don’t have correct categories in the system.” In practice, this means products aren’t categorized or coded in any consistent, useful way. It’s hard to roll up performance by category when your category assignments are wrong or missing. It’s hard to analyze style productivity when your style codes are just random numbers. Best-in-class retailers use a structured hierarchy (department → class → subclass → style) to organize products, making it far easier to plan, track, and analyze performance. If your item master is a free-for-all, part of your cleanup will be defining a proper hierarchy, renumbering or re-tagging styles, and cleaning up category data for every SKU.
We Track Markdowns/Promos In Spreadsheets. How Do We Centralize This?
When the system doesn’t track a key piece of info, teams often manage it manually. A common example is markdown status – perhaps your ERP doesn’t flag which items are on markdown, so the merchandising team keeps a spreadsheet or simply “knows” what’s marked down. As one retail veteran quipped, “Instead of planning, people are formatting spreadsheets.” Every manual data process is not only inefficient, but also prone to error and out-of-date information. If markdowns (or transfers, or promotions) live outside the system, your planning tool will miss them or you’ll spend hours merging spreadsheets. Part of data cleanup is bringing these rogue data sets into the fold: find a way to systematically track markdown status, whether through a new field in the system or an integration, so that all critical data lives in one place.
Our ERP Is Cluttered (Duplicate SKUs, Bad Codes). How Do We Clean System Architecture?
Sometimes the data woes stem from years of poor data governance in the core system. In older ERPs like RLM, it’s common to find duplicate entries, outdated fields, and inconsistent coding conventions, basically an underlying data structure that’s cluttered and convoluted. This “junk drawer” architecture leads to confusion (multiple sources of truth for what should be a single data point) and integration headaches. Cleaning it up might involve purging obsolete SKUs, consolidating duplicate customer or vendor records, standardizing naming (e.g. one “Black” vs multiple spellings), and re-aligning data tables to a consistent structure. It’s not glamorous work, but it can dramatically improve data consistency and system performance. More importantly, it ensures that when you do introduce new tools on top, they’re hitting a clean target.
Each of these issues can be painful on its own; combined, they make reliable planning nearly impossible. The good news is that they can be fixed. It won’t happen overnight, but with a focused effort you can clean up the data foundation. Even better, you don’t have to (and shouldn’t) wait until everything is perfect to start improving your planning process. Next, we’ll outline how to tackle data cleanup pragmatically and why you can pursue a planning solution at the same time.
How to Clean and Organize Your Retail Data (Without Losing Momentum)
Facing a mountain of dirty data can be overwhelming. It’s tempting to declare “project data cleanup” and try to boil the ocean. A better approach is incremental: prioritize the most critical fixes that unlock value for planning, and build from there. Here are some practical steps:
1. Diagnose and Prioritize Your Data Gaps.
Start with a frank assessment of where your data breaks. Map out each core data flow (sales transactions, inventory updates, product master data, etc.) and identify pain points. Is POS sales data not syncing to the ERP in real time? Are e-commerce returns handled outside the system? Is inventory accuracy worst in certain channels or locations? Quantify the scope of issues – e.g., “Our inventory accuracy is 70%” or “30% of our SKUs have no category.” This diagnostic gives you a baseline and helps rank what to fix first (for example, an issue impacting revenue or in-season decisions is higher priority than one that’s merely an annoyance).
2. Fix the Fundamentals First (Sales ↔ Inventory).
Before anything else, get your inventory and sales aligning. If there is one project to devote engineering or IT resources to, it’s this integration/reconciliation piece. Ensure that every sale or return transaction reliably updates inventory in your system of record, ideally in real time or at least daily. This might involve integrating point-of-sale and e-commerce platforms with your ERP, adjusting how timing of shipments vs. sales are recorded, or even a one-time inventory recount to reset a true baseline. The goal is to reach a point where you can confidently say: “Our on-hand inventory in the system is what’s actually in stock.” When you hit that, it’s a huge win , planners and buyers can make decisions knowing the inventory data reflects reality, and one major source of frustration evaporates.
3. Build a Single Source of Product Truth (Item Master).
Next, tackle the product master data. Convene a small task force (merchandising, planning, IT) to design a product hierarchy and coding schema that suits your business. Maybe you need a hierarchy of Department → Class → Subclass → Style → Color. Maybe you just need Category → Style for now. Define it and document it. Then, start cleaning: update each item in your ERP with a correct category, assign new style numbers if necessary (perhaps embedding some intelligence like season or category codes in them), and discontinue old style IDs that don’t fit the new scheme. This is tedious, yes – but it’s largely a one-time sweep that will greatly improve reporting and planning. Going forward, establish a process (and tool, if possible) for new product setup that captures all these attributes correctly from the start. Product data cleanliness will pay off in more insightful analyses (e.g. “tops vs bottoms sales”) and the ability to plan at various levels of the hierarchy.
4. Kill the Rogue Spreadsheets. Automate or Centralize Manual Data.
Identify any important data that currently “lives manual” (outside the system in spreadsheets or someone’s head). Common culprits: markdown status, store transfers, size runs, promotional flags, etc. For each, decide whether it can be systematically managed. Sometimes the fix is as simple as adding a custom field in your ERP or PLM to track that info (and training the team to use it). In other cases, you might integrate a specialized tool or at least an automated Excel import regularly. The aim is to minimize planners’ reliance on side spreadsheets. Not only will this save countless hours, it also reduces errors and misalignment – when everyone is looking at one system for the data, you avoid the “multiple versions of truth” problem that plagues manual work.
5. Establish Lightweight Data Governance.
Sustaining clean data is an ongoing effort, but you don’t need an army of data stewards to start. Assign clear ownership for key data domains: e.g. Inventory team ensures inventory transactions are correct, Merchandising owns product master data accuracy, etc. Set up a few simple data quality checks on a routine: for example, run a weekly report to catch negative inventory or SKUs with missing categories, and have owners correct them. Document the rules (even in a one-page cheat sheet) for things like how to create a new SKU, how to record an inter-store transfer, what the valid category values are, etc. By making data governance part of “business as usual,” you prevent backsliding into chaos. It’s much easier to maintain good data once cleaned, than to do a big cleanup every few years.
6. Take a Phased Approach to Keep Momentum
Notice we didn’t say you must pause everything and clean data for a year. Break the work into manageable chunks. You might tackle the inventory reconciliation in Q1, the product hierarchy in Q2, and so on, while still running your business in the meantime. In fact, many improvements can happen in parallel. The key is to sequence the changes so that you’re always improving your planning capability as you improve data. And as we’ll discuss next, one way to drive this forward is to bring in a modern planning tool sooner rather than later, because it can actually help illuminate remaining data issues and fast-track your planning improvements.
Can We Implement a Merchandise Planning Tool With Messy Data? Yes, Here’s How:
It’s a classic quandary: “We can’t implement a new planning system until our data is clean, but it’s hard to clean the data without modern planning tools.” The reality is you can (and should) do both together. Don’t wait for 100% pristine data to kick off a planning solution search or implementation. Here’s why:
Data Will Never Be “Perfect.”
Retail data is messy by nature – there will always be the next season’s new products to set up, the next acquisition that brings in a new data source, etc. If you wait for a hypothetical state of perfection, you’ll wait forever. Instead, aim for “good enough + continuously improving.” A good planning system can accommodate data imperfections as long as they’re known and managed (for example, you might load sales data that is 98% accurate, and the system can highlight the 2% anomalies for you to fix). Don’t make perfect the enemy of the good when it comes to data quality.
A Planning Solution Can Be a Catalyst for Data Cleanup.
The process of implementing a planning tool often forces the organization to define data more clearly. You’ll need to decide, “What’s our product hierarchy? What’s our calendar? Where is the one source of truth for inventory?” These decisions, made during implementation, actually accelerate the data cleanup that might otherwise languish. It gives you a framework and motivation to tidy up, because you have a real system that needs the inputs. We’ve seen companies use a new planning system rollout as the impetus to finally standardize style codes or reconcile that one troublesome inventory feed – because now it matters for a go-live.
Modern Cloud Planning Platforms Are More Forgiving (and Faster to Launch).
In the past, legacy enterprise planning solutions (think old-school on-premise software) required a huge upfront effort to marshal perfect, well-structured data before you could even start. They also took a year or more to implement, by which time your data (and business) had changed again. Today’s SaaS planning tools are designed to be far more agile. They can ingest data from multiple sources, be configured on the fly, and often include logic to handle exceptions. Traditional enterprise implementations take 6–12+ months, whereas modern platforms can go live in a matter of weeks. This means you can start getting value now, not a year from now. For example, Toolio’s cloud merchandise planning solution plugs into your existing tech stack with minimal IT lift and no lengthy re-platforming – customers are up and running in weeks, even if their data isn’t 100% pristine. The system is built to accommodate real-world data and then help improve it, rather than assuming an ideal scenario.
Quick Wins Build Momentum.
Implementing a planning tool quickly (even in a limited scope, like maybe just financial planning or just one product division to start) can deliver quick wins. Maybe you get better visibility into SKU-level performance, or you free your planners from manual spreadsheet merges each week. Those wins not only provide ROI early; they also build internal confidence in the data initiative. When executives see a new system producing useful insights despite some data imperfections, they gain trust that the data is getting better and the investment is worth it. It creates a positive feedback loop: better tools encourage better data hygiene, which leads to better results, and so on.
Bottom line: You do need to address data issues, but you do NOT need to completely fix everything before improving your planning capabilities. By pursuing a modern, flexible planning solution alongside your data cleanup, you tackle the problem from both ends. The new tool will impose some helpful structure and reveal remaining trouble spots, while your data efforts ensure the tool has quality inputs to work with. Together, this gets you to the finish line faster.
The Payoff: Faster Decisions, Better Forecasts, Fewer Inventory Surprises.
Dirty data may feel like an insurmountable hurdle, but with a focused plan and the right technology, you can overcome it. Clean up the big issues step by step, rather than postponing all progress until some distant “clean slate” day. At the same time, don’t deprive your team of modern planning capabilities just because your data isn’t perfect yet. The competitive retail environment demands agility – you need to make smarter merchandising decisions now, not a year from now.
The good news is that today’s planning solutions are far more adaptable and quicker to implement than the legacy systems of yesterday. For example, Toolio’s planning platform was designed for rapid deployment and flexibility – clients often live in weeks, not months, because the system configures to your business with minimal IT effort. It’s cloud-based and modular, so you’re not locked into a massive, monolithic project. This means you can start with imperfect data and iterate, rather than waiting for a perfect dataset and doing a big-bang implementation. In contrast, older legacy planning solutions often required heavy upfront data preparation and lengthy consulting projects to get off the ground.
By choosing a modern tool that emphasizes speed, configurability, and data integration (versus strict, rigid data requirements), you give yourself the best chance to quickly improve planning outcomes while you continue refining your data. The end result? You’ll start seeing benefits like more accurate forecasts, better inventory alignment, and fewer surprises in the business sooner. And as those benefits accrue, they’ll reinforce the value of keeping your data clean and your planning process robust.
Don’t let messy data be an excuse to stand still. Many retailers have walked this path: acknowledging data weaknesses, fixing what truly matters, and leveraging modern planning technology to leapfrog ahead. You can do the same. Clean data and advanced planning go hand in hand – and together, they empower your team to make confident, informed decisions. Start where you can, secure a quick win, and build on it. Your data may be a mess today, but with the right approach, it will soon become one of your greatest assets, not a hindrance, to driving profitable growth.
Remember, the ultimate goal is trustworthy data feeding a dynamic planning process. Achieve that, and you unlock the ability to respond faster to trends, optimize inventory with precision, and execute on strategy rather than scrambling to reconcile reports. It’s a journey worth starting now. Your planners and executives will thank you when they can finally focus on planning the business – not fixing the spreadsheet.
Toolio helps retailers get there faster with a modern, flexible planning platform. Speak to an expert to see how it could work for your team!
