[et_pb_section fb_built=”1″ admin_label=”section” _builder_version=”4.16″ global_colors_info=”{}”][et_pb_row admin_label=”row” _builder_version=”4.16″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text admin_label=”Text” _builder_version=”4.21.0″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n
Imagine a chef preparing a dish. Even a skilled chef with modern equipment cannot make a great dish from poor-quality ingredients. Fresh ingredients are the foundation of a good dish.<\/span>\u00a0<\/span><\/p>\n AI works in much the same way:<\/span> AI can help forecast demand, improve inventory planning, automate workflows, accelerate decision-making, and uncover patterns hidden in operational data. But just as a chef cannot turn poor ingredients into a consistently excellent dish, AI cannot reliably produce valuable insights from inaccurate, inconsistent, or outdated data. Therefore, before investing in AI, businesses should ask:\u00a0<\/span>\u00a0<\/span><\/p>\n Is our data accurate, consistent, current, and relevant?<\/span><\/i><\/b>\u00a0<\/span><\/p>\n Consider a wholesale distributor preparing an AI-powered demand forecasting pilot. It has years of sales history and an established ERP system. Yet, a closer review reveals that the same product has product codes, packaging sizes have changed, and historical sales records in spreadsheets have not been reconciled with the ERP. Combining these records without correction could distort demand forecasts. The problem is not a lack of data, but poor data hygiene.\u00a0<\/span>\u00a0<\/span><\/p>\n Business data readiness for AI starts with identifying the information a specific initiative requires, correcting inconsistencies, and maintaining reliable records. The good news is that businesses do not necessarily need a new ERP or a company-wide data overhaul. A focused data hygiene review, supported by well-configured systems such as Odoo ERP and appropriate integrations, can help establish a practical foundation for AI adoption.<\/span>\u00a0<\/span><\/p>\n To move from a broad awareness of poor data hygiene to an actionable preparation plan, organizations must stop looking at all their business data and focus exclusively on the pipeline feeding their first AI use case. Here are six practical steps to help prepare your business data for AI.<\/span><\/p>\n Returning to our distributor, a product previously sold in packs of 12 is now sold in packs of 10, and its product code has changed. Older transactions use the previous code, while purchasing spreadsheets record quantities differently. Without reconciling these differences, the AI forecast could misinterpret packaging changes as shifts in customer demand.<\/span>\u00a0<\/span><\/p>\n The problem is not insufficient data, but inconsistent data.<\/span><\/i>\u00a0<\/span><\/p>\n Data hygiene is the ongoing process of keeping information accurate, consistent, current, and relevant. It involves correcting errors, standardizing formats, resolving duplicates, addressing missing information, and reviewing outdated records.<\/span>\u00a0<\/span><\/p>\n These practices support reliable reporting, planning, and operational decisions, even without AI. For AI initiatives, they help establish a more reliable foundation for meaningful outputs.<\/span>\u00a0<\/span><\/p>\n The distributor therefore defines its forecasting requirements before cleaning records across the entire organization.<\/span><\/span><\/span><\/p>\n [\/et_pb_text][et_pb_image src=”http:\/\/www.confianzit.com\/cit-blog\/wp-content\/uploads\/2026\/11\/Before-and-After-Data-Hygiene-1-2.png” _builder_version=”4.21.0″ _module_preset=”default” title_text=”Before and After Data Hygiene (1)” url=”http:\/\/www.confianzit.com\/cit-blog\/wp-content\/uploads\/2026\/11\/Before-and-After-Data-Hygiene-1-2.png” width=”600px” max_width=”100%” module_alignment=”center” hover_enabled=”0″ sticky_enabled=”0″][\/et_pb_image][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” hover_enabled=”0″ sticky_enabled=”0″]<\/p>\n The distributor first considers reviewing all systems and historical records. However, this would take too much time. Instead, the team focuses on the data needed to forecast seasonal demand for selected products. This includes past sales, transaction dates, product codes, units of measurement, and seasonal trends. Stockouts, returns, and promotions can also provide useful information.<\/span>\u00a0<\/span><\/p>\n This focused approach saves time and keeps data preparation aligned with business goals.<\/span>\u00a0<\/span><\/p>\n Different AI initiatives need different types of data. For example, an AI tool that drafts marketing emails may not need access to internal business systems. A document assistant may need approved policies and procedures. Demand forecasting, on the other hand, relies on relevant sales history and clear business measures.<\/span>\u00a0<\/span><\/p>\n Consider these examples:<\/span>\u00a0<\/span><\/p>\n [\/et_pb_text][et_pb_code _builder_version=”4.21.0″ _module_preset=”default” hover_enabled=”0″ sticky_enabled=”0″]<\/p>\n <\/p>\n <\/tr>\n <\/thead>\n <\/p>\n <\/p>\n <\/tr>\n <\/p>\n <\/p>\n <\/tr>\n <\/p>\n <\/p>\n <\/tr>\n <\/p>\n <\/p>\n <\/tr>\n <\/p>\n <\/p>\n <\/tr>\n <\/tbody>\n <\/table>\n [\/et_pb_code][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” hover_enabled=”0″ sticky_enabled=”0″]<\/p>\n The exact requirements depend on the intended workflow and the consequences of incorrect results.<\/span>\u00a0<\/span><\/p>\n For the distributor, the priority is now clear: prepare the records needed for a limited forecasting pilot rather than attempting a company-wide cleanup.<\/span>\u00a0<\/span><\/p>\n The distributor stores sales data in its ERP, CRM, spreadsheets, and archived reports. Each source contains useful information, but some records conflict.<\/span>\u00a0<\/span><\/p>\n For example, older spreadsheets may contain previous packaging details, while the ERP shows current product information. Combining these records without checking them could lead to errors.<\/span>\u00a0<\/span><\/p>\n The team identifies the trusted source for each type of data. It also maps old product codes to current ones while preserving original transaction details. This helps the team understand what was actually sold at the time.<\/span>\u00a0<\/span><\/p>\n Businesses should not automatically replace historical data with current information. They must also remember that system integration does not guarantee accurate data.<\/span>\u00a0<\/span><\/p>\n Before connecting applications, define who owns each data field, which system is the source of truth, when updates should happen, and how transfer errors will be handled. These steps help businesses clean and connect their data more reliably.<\/span><\/p>\n The distributor identifies three main areas for improvement.<\/span>\u00a0<\/span><\/p>\n Product codes, descriptions, dates, and units of measurement must follow consistent rules. If a product changed from packs of 12 to packs of 10, historical quantities should be interpreted using verified packaging information. Otherwise, the forecasting system could mistake a change in packaging for a change in demand.<\/span>\u00a0<\/span><\/p>\n Similar customer or product records should be investigated before merging. Unusual sales quantities should be checked rather than automatically corrected, since they may represent genuine bulk orders or data-entry errors.<\/span>\u00a0<\/span><\/p>\n Missing fields should be assessed according to their importance. A missing promotional detail might not prevent a transaction from being used, while a missing product identifier could make the record unsuitable for the analysis.<\/span>\u00a0<\/span><\/p>\n Old data should not automatically be <\/span>deleted<\/span>. Historical transactions may remain valuable for <\/span>identifying<\/span> seasonal patterns, even when products or processes have changed.<\/span> Instead, distinguish between correction, archiving, and exclusion.<\/span><\/span><\/p>\n [\/et_pb_text][et_pb_code _builder_version=”4.21.0″ _module_preset=”default” hover_enabled=”0″ sticky_enabled=”0″]<\/p>\n <\/p>\n <\/tr>\n <\/thead>\n <\/p>\n <\/p>\n <\/tr>\n <\/p>\n <\/p>\n <\/tr>\n <\/p>\n <\/p>\n <\/tr>\n <\/p>\n <\/p>\n <\/tr>\n <\/p>\n <\/p>\n <\/tr>\n <\/p>\n <\/p>\n <\/tr>\n <\/tbody>\n <\/table>\n [\/et_pb_code][et_pb_text _builder_version=”4.21.0″ _module_preset=”default” hover_enabled=”0″ sticky_enabled=”0″]<\/p>\n The objective is to prepare information for the intended use without losing records needed for business operations, audits, or retention obligations. However, cleaning historical data alone will not prevent new inconsistencies. The distributor must also improve how its systems capture and maintain information.<\/span><\/p>\n1. More Data Doesn’t Always Mean AI Readiness<\/span><\/b>\u00a0<\/span><\/h3>\n
2. Define What Your AI Initiative Actually Needs<\/span><\/b>\u00a0<\/span><\/h3>\n
<\/p>\n<\/p>\n
<\/p>\n <\/p>\n AI initiative<\/th>\n Data preparation priorities<\/th>\n <\/p>\n Demand forecasting<\/td>\n Reconcile product codes, standardize quantities, and review historical sales<\/td>\n <\/p>\n Customer insights<\/td>\n Resolve duplicate customer records and standardize customer information<\/td>\n <\/p>\n Internal document assistant<\/td>\n Identify authoritative documents, review outdated versions, and configure permissions<\/td>\n <\/p>\n Inventory assistance<\/td>\n Validate product identifiers, inventory records, and stock updates<\/td>\n <\/p>\n Invoice processing<\/td>\n Standardize required fields and validate extracted information against source documents<\/td>\n 3. Find the Source of Truth<\/span><\/b>\u00a0<\/span><\/h3>\n
4. Clean the Data Without Losing Valuable History<\/span><\/b><\/h3>\n
1. Standardize records.<\/span><\/b>\u00a0<\/span>\u00a0<\/span><\/h5>\n
2. Resolve duplicates and inaccuracies.<\/span><\/b>\u00a0<\/span>\u00a0<\/span><\/h5>\n
3. Review missing and outdated information. <\/span><\/b>\u00a0<\/span><\/h5>\n
<\/p>\n<\/p>\n
<\/p>\n <\/p>\n Action<\/th>\n Example<\/th>\n <\/p>\n Correct<\/td>\n Fix a confirmed data-entry error<\/td>\n <\/p>\n Standardize<\/td>\n Convert verified quantities into a consistent unit<\/td>\n <\/p>\n Map<\/td>\n Connect historical product codes to current identifiers<\/td>\n <\/p>\n Archive<\/td>\n Retain inactive product records for reference<\/td>\n <\/p>\n Exclude<\/td>\n Leave an unreliable record out of a specific pilot while documenting why<\/td>\n <\/p>\n Retain<\/td>\n Preserve valid historical sales information<\/td>\n 5. Use Your ERP to Build a Stronger Foundation for AI<\/span><\/b><\/h3>\n