Big Data
IT Consulting

Data Cleaning Techniques and Why Your Organization Needs Them

Your organization’s data is dirty and damaging, unless you’ve cleaned it recently. Learn about some data cleaning techniques that every organization can employ.

The analogy of data as the new oil is troublesome. It has driven some polarizing opinion pieces in journals the world over, but in some ways, the comparison holds. Like oil, data is precious to organizations and enterprises, but of little use to anyone until it has been refined.

Worse though, dirty data harms its users, of which your organization is one. That's why you're doing your stakeholders a disservice if you ignore the need to eradicate dirty data and ensure that yours is as clean as it possibly can be.

Global data management survey 2018

But how do you make sure your data is clean, or, more to the point, how do you get it cleaned up in the first place?

If that’s the question forming in your mind right now, you’ll be glad to know that there are techniques aplenty to turn dangerously dirty data into a much more beneficial resource, and you’ll find some of them explained in the text that follows.

Like oil, data is precious to organizations and enterprises, but of little use to anyone until it has been refined

Data Cleaning – Not Just Important, But Critical

Before exploring some of the data cleaning techniques typically employed or recommended in data science consulting, let’s dig a little into some of the whys and wherefores. After all, it’s rarely helpful to know how to do something without knowing why you would want or need to do it.

Indeed, if more organizations understand the impact of dirty data on productivity, compliance, efficiency, and even reputation, it will remain the stealthy cost driver that it currently is. According to Salesforce, data-quality issues cost the average company some 30% of its revenue. If you need only one statistic that highlights the importance of data cleaning, a saving of nearly one-third of your enterprise's income should suffice.

If you should need more persuasion, though, consider the bottom-line impact of just one type of data commonly used by enterprises—delivery addresses.

Inaccurate customer addresses are responsible for nearly 20% of failed deliveries, each of which wastes a portion of a company's logistics budget, generates costs for redelivery, or, in the worst cases, results in a lost sale and adds up the cost of returning products through the supply chain.

It would be easy to continue to list dirty-data impacts here. However, several of them will be covered elsewhere in this article, with the emphasis on the benefits of preventing them by way of data cleaning.

Before moving on, though, let’s clarify clean data’s importance. More often than not, you will rely on your business data to drive the decisions you make—and bad data means bad decisions.

So When Should You Clean Your Data?

Data cleaning is not one of those tasks for which there is any right time. Quite simply, there is no better time than the present. There is a high chance that it will need plenty of work to clean it up because, without attention, data quality only deteriorates over time. It won’t get better on its own.

The State of Data Quality 2020

Moreover, the application of data cleaning techniques should not be a one-time exercise. It bears repeating—left unattended, data will get dirty as time goes on.

Once you have undertaken an initial project to get your data clean, regular revisits will save you from much more expensive cleaning projects in the future. They will also be faster, less costly, and less labor-intensive than the first-time effort.

There’s no better time for cleaning your data than now.

The Benefits of Data Cleaning

Having delved a little into the grim realities of grubby data, let’s pivot toward the positives of dealing with it. Whether you launch an internally resourced project, or engage specialists with sophisticated algorithmic cleaning tools, the benefits of cleaning your data are easily as compelling as the consequences of neglect.

Clean business data brings with it the following rewards for your enterprise:

  • Accurate, rather than flawed, insights will drive your decisions.
  •  You can target your marketing campaigns more accurately.
  • Your business processes will be more efficient.
  • Your staff will be more productive.
  • Your customers will be less frustrated.
  • Your business reputation and credibility will improve.
  • Your cost-to-serve will reduce, and revenue will increase.

Clean Data: An Impossible Standard?

That’s quite a list of business advantages, but if it seems improbably positive, just think about how bad data can impact decision-making, marketing, productivity, efficiency, customer experience, and operating costs.

If your business has not applied data cleaning techniques, it’s probable that duplicate, missing, invalid or inappropriate data is hampering all of the above business elements to some degree. While it would be wrong to suggest that your data will ever be spotless, its impact on business performance is proportionate to its completeness, accuracy, validity, and appropriateness.

There is such a thing as good enough, and that is a standard that can be measured. If you haven’t measured it, the chances are that, right now, it’s not good enough.

How to Clean Your Business Data

This article has, so far, focused a lot on the importance of data in decision-making. Of course, not all decisions require data to drive them. One such choice is how to approach your selection and application of data cleaning techniques. It will come down to the size of your enterprise and the volume of data captured and used by your business-critical systems.

There are essentially three approaches you can take:

  1. Extract your data from your systems into spreadsheets and clean it with Excel.
  2. Procure an enterprise-level data cleaning application.
  3. Hire a data science consulting company to manage and execute your data cleaning project.

In the remaining sections of this article, you’ll get some advice about basic data cleaning techniques using Excel. If yours is a larger enterprise or organization, you’ll also get to know about some of the most popular tools in use for data cleaning on a significant scale.

Put your data in the safe hands
of Iflexion’s data science consultants.

Data Cleaning Techniques Using Excel

If your company is already a participant in the world of big data, the use of Microsoft Excel for cleaning purposes will be impractical. However, as already stressed, all enterprises owe it to themselves to keep data clean. For the smaller business, Excel is a readily available tool. Indeed, you probably use it extensively as of now.

When you clean data with Microsoft Excel, there are a few rules that you will need to follow. They will help you to clean your data safely and without undue risk. It’s essential to take precautions if you decide to clean your data without external help. After all, it’s all too possible to make mistakes and introduce errors into your data without realizing it—which is not ideal when the idea is to eliminate data problems.

Three Golden Rules of Data Cleaning in Excel

The first thing you will do when using Excel as a data-cleaning tool, is to extract the data from its source. Once you have that data in an Excel file, apply the following three rules when performing the cleanup:

  1. Copy the data into a new spreadsheet, and complete the data cleaning in the copied version. That way, if you do make any errors, you can start again by taking another copy from the original spreadsheet, which will not have been affected by any errors.
  2. Before working on a column of data, copy that column into a separate worksheet. Then perform the cleaning, and copy the results back to the column in the first worksheet. That’s an extra safeguard to avoid introducing errors into the data as you clean it.
  3. After all the data in the spreadsheet is clean, load the entire data set back to the source system.

The Excel Data Cleaning Techniques

When it comes to the task of cleaning your data, the fundamental techniques to know are those that highlight and identify the typical characteristics of dirty data, and those that save time and effort in the cleaning process. Here are some examples of those techniques:

1. Get Rid of Spaces

Any spaces existing between characters and over and above the single space between words, can throw a spanner in the works of your business information systems. Use the “trim” function in Excel to remove all unwanted spaces.

2. Deal with Missing Values

Blank cells in your spreadsheet typically indicate missing data values. You can highlight them using Excel’s “find and replace” function. Then you need to decide what to do about them. For example,

  • You may want to delete the records with missing values.
  • You might prefer to impute the values using an average or a calculation.
  • You could use a flag-value such as “0” or “missing” to highlight the fact that the value is blank.

3. Normalize the Data

For example, numbers entered as text are a common data-quality issue. Use Excel’s formatting functions to ensure all numerical data is formatted as a number. Similarly, you might wish to make sure that all text is in upper, lower, or proper case. To do this, you can use Excel’s UPPER, LOWER, and PROPER formulas.

4. Identify Duplicate Entries

You can use the “Remove Duplicates” function in Excel to highlight where entire records are duplicated. This is one of the few tasks you should probably complete before applying the #2 golden rule described in the previous section of this article. You can’t check entire records entered in columns and rows while cleaning column-by-column. To highlight duplicate values for removal from individual columns, you can use conditional formatting.

Other tasks that Excel can help you to complete quickly and with minimal effort include checking for spelling errors and typos and removing outlying values that might overly distort analysis results.

Big Data Cleaning Techniques

Cleaning data for a large organization, especially one with multiple systems and platforms, will always be labor-intensive and time-consuming, especially the first time. There are several tools available, though, that make the task somewhat less onerous and are ideal for executing regular cleanliness checks and cleanups.

Some of these applications, such as OpenRefine, are even free to use. Other specialized software packages are for use only with specific business systems like Salesforce or SAP. The following five are among the most popular with businesses cleaning large volumes of data:


An easy-to-use, powerful data profiling application that can find specific characteristics, discern patterns, and find duplicates and missing values in your data sets. It lets you create your own cleaning rules and save them as scenarios, making it ideal if you have several databases to manage.


A free, open-source data cleaning tool, OpenRefine is surprisingly versatile for an application with no price tag. Not only can it clean up thousands of rows of data, but it also allows you to make bulk adjustments to data formats and make enhancements via web services.

Informatica Master Data Management

Informatica is a sophisticated data management software product. It can automate specific tasks such as data profiling and matching, and provides users with a 360-degree view of database relationships. If you need a tool that can simplify consolidation, validation, and standardization of large data quantities in a range of different environments, this application should suit your organization well.


One of the more specialized data cleaning applications, Cloudingo is suitable only for use with the Salesforce CRM platform. You can use it to track down and remove duplicate data, standardize and validate addresses, delete obsolete data, and perform bulk record updates.


A world-leader in data cleaning software, WinPure is the tool of choice for renowned brands such as BBC, Microsoft, and Vodafone, so its credentials and capabilities are indubitable. Perhaps not as sophisticated as some of its competitors, it’s simple to get to grips with and suitable for small businesses and the most prominent organizations alike.

Here are at least 5 effective tools to be used for big data cleaning.

When to Outsource Your Data Cleaning

The State of Data Quality Survey 2020

Outsourced data cleaning is not so much a technique as a strategy—one that saves you from being over-concerned with technique. If your enterprise operates internationally, has a wide range of IT solutions deployed, or is involved with AI or IoT technology, clean data is especially critical.

In these situations, outsourcing can be more cost-effective than trying to keep on top of regular checking and cleaning of massive volumes of data. You can leave your data science consulting partner to manage the quality of your data, and focus on its use as a valuable commercial resource.

There’s No Excuse for Dirty Data Use

Small business or commercial giant. Local medical practice or national healthcare system. Small public organization or global governing body. It doesn’t matter how big an entity you are or how much data you use—it is a risk to you if you don’t keep it clean.

You can choose to employ simple tools to vet and clean your data, procure products to clean it automatically and in bulk, or bring in an external partner to keep it clean for you.

Any of the techniques discussed in this article, or any other methods, are worthy of execution—and every moment wasted could be harming your business. Unlike oil, your data is not a refined product when it gets to you. It will stay dirty and dangerous until your hygiene program is underway.

Content type
Let Iflexion take care of your data quality.
Big or small, we’ll get it fixed.


It’s simple!

Attach file
Up to 5 attachments. File must be less than 5 MB.
By submitting this form I give my consent for Iflexion to process my personal data pursuant to Iflexion Privacy and Cookies Policy.