There is no way around it - data is a business’ key to success these days. More than ever, the collection and use of data are fuelling business decisions. The information gleaned from data provides insights on customer preferences, behaviours, and more. With the golden ticket offered by data, there is no doubt that data cleaning is of utmost importance. Having inaccurate data is like having a car without tyres. You have the means to get where you want to go with data, but without it being right, you will be stuck. Or, even worse, you may increase your business risk.
What is Data Cleaning?
Data cleaning refers to the process of correcting data in a database or deleting inaccurate records. Called “dirty” files, any data that is inaccurate, incomplete or irrelevant, should be cleansed.
The Importance of Data Cleaning
Accurate data matters. Data affects every department within an organisation, and it has financial effects. When you have proper data, your data analysis shows results that are in line with past and current happenings within your business. As such, every department can rely on this information to make decisions.
Marketing: Let’s take an example from marketing. If a marketing department is running tailored ad campaigns, they need to know which demographic likes which product. Then, they can allocate their ad spend towards those campaigns. However, if their data has provided them with the wrong information, they could end up pushing the faulty products to the wrong people. Not only can this cause customer attrition, but it will also end up in spending unnecessarily.
Sales: A quick fact - it costs 5x more to attract a new customer than to retain an existing customer. If a salesperson has the wrong contact information or purchase history for a current customer, they may lose them. Imagine the ripple effect if this happens more than once.
Compliance: This is a big one, especially as regulations are becoming more strict as big data grows. Failing to comply with data privacy and security rules may result in data hacks and breaches. That leaves the company responsible for the consequences, which could end in massive penalties or even business shutdown.
Finance: Having the wrong data at any point during the finance process can be disastrous, from taking the data from its source in legacy systems to sorting and filtering the data for analysis, to creating reconciliations. The results for wrong data could mean making critical decisions about the strategy or cause and set top-level management on an incorrect path.
Operations: Since data is starting to go hand in hand with automation, you may enlist robotic process automation (RPA) to manage back-office tasks. If the information is inaccurate in the first place, then the automation tool is going to continue a process that will end up being wrong.
So, bad data is precisely that - bad. This leads us to define “good” or clean data. High-quality information is described as possessing the following characteristics:
Validity: The data should be relevant to achieving business goals and complete. For example, columns shouldn’t be empty, values within a column should match in type (i.e. all numbers), there should be minimum and maximum values for data, fields must be unique, etc.
Accuracy: How much the value matches the truth.
Consistency: Data can’t be written in a way that violates the defined validity of the dataset.
Completeness: How much the data is filled out to the best of capabilities.
Uniformity: The use of the same units of measurement throughout the database.
Timeliness: The frequency at which data is updated.
Traceability: The ability to locate the data’s source.
The Data Cleaning Process
The data cleaning process spans four main milestones, including inspection, cleaning, verifying and reporting. They are explained as follows:
1. Data Auditing - (Inspection): You must first locate “bad” data to be able to allocate resources or time to fix them. The inspection, or auditing, phase consists of data profiling, visualisations and the usage of software to assist.
Data profiling gives you summary statistics or an overview of the quality of your data. For example, it can show you how many missing values exist or how many unique benefits exist.
Visualisations: provide an easy-to-read and understand the representation of data. It relies on standard statistical values like mean and standard deviation to show outliers or unexpected, and therefore, incorrect values.
Software packages: can show this information in bars, graphs and reports that will quickly pinpoint where errors lie. For example, if you see customers with negative ages, you know there is an error in the “age” column.
2. Workflow Specification - (Cleaning): Once you know the status of your data, you can approach the cleansing step. Depending on what is inaccurate, you may take a different approach for each piece of data. Overall, data cleaning consists of:
Irrelevant data: You can remove rows or columns of data that is irrelevant to the current problem you are trying to solve or question you are asking. For example, if you are only looking at sales in the American market, you won’t need to analyse data from customers in Europe.
Duplicates: Data should be unique. Duplicates can occur when you are sourcing data from multiple touchpoints, or a user submits information more than once, for example. You should remove duplicate data (this is quickly done on spreadsheets by using the “Remove Duplicates” tool or in automation software by doing the same).
Type Conversion: All data within a column should be in the same form. So, numbers should display as numbers, dates as dates, etc. If a value cannot be converted to the right format, the amount should read “N/A” for “not applicable.”
Syntax Errors: Syntax errors include typos and extra spaces, for example, in a string. They can affect how data is displayed and digested so they should be corrected.
Missing Values: If you are missing information in a row, you can approach it in three different ways. The first solution is to drop that row, if this is happening very sporadically, rarely and randomly. The second is to impute (deduce) information based on context and the other information presented by using statistics. The third is to flag it so the system can take missing data into account for reporting.
3. Workflow Execution - (Verifying): Once you’ve resolved the issues mentioned above, look at the data again to ensure that the values still match the right type of information.
4. Post Processing and Controlling - (Reporting): The use of software can generate reports on the quality of your data. Process and control the cleansing process by checking it was accurately performed and successful.
Good Quality Data Sourcing
Take the time to nip bad data in the bud by investing in quality data sourcing. This includes the following methods:
Commitment to data quality culture
Spend money to improve data entry environment
Spend money to enhance application integration
Promote interdepartmental cooperation
Continually measure and improve the quality of data
Benefits of Data Cleaning
Cleaning data promptly will serve your business with a multitude of benefits. The benefits include:
Streamline best practices
Faster sales cycle
Improves the efficiency of customer acquisition activities
There still are challenges with data cleaning to overcome. Here’s a look at some of its pain points:
Error correction and loss of information: The primary solution for missing data is still to delete that row. This results in lost information. If this happens often, it can result in the deletion of costly and expansive data.
Maintenance of cleansed data: Once the data has been cleansed the first time, you won’t want to clean it again and again. Instead, it requires that you keep a lineage of cleansed data and separate the clean data from new and incoming records to reduce costs and time for cleansing practices.
Data cleaning in integrated environments: If data is stored in a mixed environment, then every time that the information is accessed, it must be cleansed and verified. This is timely.
Data cleaning framework: You can’t always guide the data cleaning process in advance, so the framework becomes iterative.
Challenges of Existing Tools / Methods
In the past, many of the tried and true methods for data cleaning by using existing data cleaning tools have come under scrutiny due to the cost, time and security issues with using them. However, with new data automation technology like that of SolveXia, these challenges can be solved by reducing the burden of data cleaning and increasing the speed at which it can happen.
When looking at data tools, the typical considerations for adoption include:
Project Cost: Ideally, data cleaning can cost hundreds to thousands of dollars in human resources or technology upgrades.
Time: Large-scale data cleaning software can take a lot of time, as can the performing of manual controls to check the work of others.
Security: Sharing information and granting access to legacy systems to access data can cause security risks.
With automation tools like SolveXia, you can easily overcome these challenges and let the automation tool do the work for you. Automation tools, not the only partner quickly with your existing tool stack, but also takes very little time to get up and to run. The automation systems come with a library of existing commands and a friendly user interface so anyone in your organisation can benefit from the analytical data.
Automation Can Resolve Challenges with Data Cleaning
To assist the data cleaning process, automation tools are being created to take on this challenge. The use of automation can help to reduce human errors, especially in the data sourcing and input stage. Furthermore, automation will help to reduce the time for cleansing and mapping data, thereby increasing efficiency and increasing compliance. In a business setting, decreased time equals money saved, while increased accuracy and insights equal more precise insights and salary earned.
Automation tools like SolveXia exist to solve the risks and challenges that often plague organisations when it comes to data. These tools overcome compliance risk by providing audit trails, protecting data from breaches and promotes more exceptional internal communication between departments. By integrating with legacy systems and serving as an easy to set up and use the platform, there is no need for costly overhauls of existing technologies. Furthermore, tools like SolveXia are there to support your team throughout the transition and forever after that.
Data Cleaning - Bring It On!
Regardless of how you choose to approach your database management, and therefore data cleaning, you will want to keep it consistent and set a business process to ensure it’s maintained properly. Clean data will help every department within your organisation perform better.
The future of companies, especially within financial departments, is increasingly relying on data automation. Finance teams are no longer just expected to be bookkeepers. They serve as strategic consultants who leverage data and analytics to provide insight into significant decision-making. As such, the process begins with data cleaning and data collection, and automation plays a vital role in this process and then drives the organisation forward with analytics, dashboards and modelling across the business.