What Is Data Cleansing and Why Does It Matter?

What Is Data Cleansing and Why Does It Matter?

What Is Data Cleansing and Why Does It Matter?

Not everyone knows that data can be dirty. Dirty data poses a myriad of problems to businesses all over the world, so they want to know how to clean the data up. As time goes on, data gets dirtier, making the cleaning process more and more challenging. But what is data cleansing, and why does it matter? Read on to find out.

Cleaning Data

So, what do we mean when we talk about “dirty data?” Dirty data is not information about waste management. Instead, this phenomenon refers to data that’s incomplete, duplicated, or inaccurate. Dirty data doesn’t just spring into existence, though—it comes from somewhere. Typically, dirty data originates due to poor communication, user error, or even a bad data strategy.

Whether you know you have dirty data or you just want to play it safe (which is never a bad idea), data cleansing is your go-to solution. Data cleansing is a process that works to filter out the dirty data and clean it up. Essentially, the cleansing process removes or resolves every instance of incomplete, duplicated, or inaccurate data.

Data Quality

All the data in the world does no good if you’re not working with quality data. Dirty data can waste your time and even cost you serious money. When you work with high-quality data, you don’t need to worry about throwing money at a problem that doesn’t exist except within spreadsheets. That’s what low-quality data can do—tell lies through statistics.

Importance of Data Cleansing

Cleansing your data catalog is important because dirty data is a recipe for misinformation. You may not know the truth about your organization’s processes without a little data cleansing to help you! With master data management, you can clean your data easily and efficiently.

Now that you know what data cleansing is and why it matters, make sure you give your data a good cleaning before trying to use it. Otherwise, you may end up with information that isn’t helpful at all.

Price of Bad Data vs Rise of Good Data

Price of Bad Data vs Rise of Good Data

The Price of Bad Data

If the “Customer” data had old or incorrect address, you’d be sending goods ordered to wrong address leading to a dissatisfied Customer, additional shipment charges and administrative/logistic efforts. If “Customer” Master had duplicates, transactions with the Customer would be spread across those multiple duplicate Customer Master records. The whole picture about the Customer (all the transactions) cannot be viewed, as queries would bring only those transactions that happen to be tied to the Master Customer Record. Read loss of cross selling and up selling opportunities. As a Customer Support person, you cannot find the order the Customer is talking about over the phone.

If there were duplicate Item/Material records in the master table, inventory on one of the duplicates would be low, triggering an automatic replenishment, while there was enough stock on hand under a different master record. If Purchasing department ordered on one of the duplicates and Manufacturing department is searching another duplicate’s bin in the warehouse, the situation can lead to productivity loss and lot of shouting over the phone. The purpose of an ERP system can be broken by bad data.

All above confusions can arise in a single ERP system. But generally, organizations have multiple Enterprise Applications in use, with some processes in place to maintain sync of data across the systems. The confusions can grow multifold in this scenario.

The Rise of Good Data

Processes, Data and People run your business. To have good processes, you have a set of Computer Applications to run. You hired the best employees to run your business. Coming to Data, you need to make sure that it is of the highest quality and readily available to all data consumers.

Facilitation of efficient Master data governance with ChainSys

You need to control, monitor and facilitate data creation (Master Data Governance). This will lead to accurate master and transactional data. You should also maintain the quality of data throughout its life on an ongoing basis (Data Quality Management). Quality data is maintained in Operational Systems and as well Data Lakes/Warehouses. The former is called Operational MDM and the later is called Analytical MDM. ChainSys dataZen™ is the right toolset to perform many of the above activities. Unlike the MDM systems and Data Hubs provided by major ERP vendors, dataZen™ is not tied to any one ERP system and is very agile in terms of easily configuring the tool to add meaningful and necessary DQM functions, cross checks on short notice. All systems over time accumulate bad data, which needs periodic cleanup. dataZen™ facilitates pulling a batch of data at a time, subjecting it to established quality checks, getting correction inputs/okays from the right stakeholders and pushing it back into the system. dataZen™ makes MDM fun and easy to do.

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