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In order for you warehouse to function on the highest level, you must have reliable data on its day to day operations. You must have reliable data for better decision making, improved operational efficiency, reduced risk, and cost savings. In order to have reliable data, you have to start with clean data. If your data does not start out as clean data, you run the risk of having duplicate data, incorrect numbers, missing characters, missing data, and irrelevant data—all contributing to inaccurate big data and error. Though data cleansing is time consuming and costly, your warehouse cannot function without clean data.

To begin cleansing your data, review the old data and the problems you hope your big data will solve and consider the costs of having to repeatedly clean your data rather than doing it right the first time you gather data. Compare the data you currently have against the data you hope to gather. If the process of cleaning and gathering data seems daunting as a task on the whole, focus on smaller goals which can be accomplished toward the end goal.

Once your data is clean, focus on keeping it clean. You keep your data clean by continually keeping your goals in mind and what you are hoping your big data will accomplish for you. If your data begins to stray from its focus, you run the risk of once again cluttering up your data which means it will have to be cleaned often.

To maintain good, usable data, you should develop data gathering policies. Know what data you want to be gathering and keep it consistent. Agree on the part numbers, model numbers, and serial numbers you deem most important and collect those. The tools and methods you use to collect your data also need to remain consistent throughout your data collection process. Barcode scanners and automatic data capturing systems are the most reliable for this and leave the least room for error. If you do not use consistent methods for gathering data, you run the chance of losing something important to your big data.

Finally, you should review your data to ensure there are no errors or inconsistencies. If you do see errors in your data, be sure to correct them immediately so as to not have incorrect data. Then find out where the errors occurred, and find out how to avoid them the next time you collect data.

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