Introduction
Data in current times is the transformative force reshaping the world as we know it. From large enterprises to small creators, everyone is obsessed with data, and rightfully so. Data has drastically transformed how we do things, from how we communicate to how we make our daily life decisions. Everything these days is influenced by data. In the B2B business, the talks of “data being the most powerful weapon in current times” are aplenty. However, while some organizations have found ways to wield data for their business gains, many others still need to be challenged with harnessing their unprecedented access to data.
A big reason for this is the many sources’ businesses gather data from. Every source has a different format and structure for storing and generating data. As an organization, you want a unified file with accurate, clean, and complete data. This is where the science of data cleansing comes into place. Individuals with titles like data analysts, data scientists, and other self-proclaimed data experts are assigned as custodians of the data for their organizations, and the onus to produce clean, structured, and accurate data lies with them. To assist them in doing so, we have data cleansing techniques and tools to accelerate the data preparation process.
If you wear any of the aforementioned data custodian hats at your organization, this blog is a wealth of information for you. Even if you do not but are looking to learn data cleansing techniques and the fundamentals of data preparation, continue reading. By the end of the blog, you can combat the challenge of inaccurate, dirty, and unstructured data with absolute precision and emerge triumphant in the modern data-driven business landscape. So let us get started.
Table of Contents
What is Data Cleansing?
Simply put, data cleansing is all about identifying and eradicating inaccurate, outdated, irrelevant, and corrupt data from your database. Data cleansing allows you to increase the reliability, consistency, accuracy, and value of your company’s data.
Some of the most common reasons behind corrupt data include inaccurate entries, missing values, and typographical errors. Therefore, depending on the state of your data, you might require data cleansing techniques that will help you correct the data values in your company’s data or remove the corrupt entries altogether. According to a study by Harvard Business Review, only 3% of the total data available to businesses meets the basic standards of data quality. This lack of quality data costs companies in the U.S. over $3 trillion each year. So, if you think that you don’t need data cleansing techniques, think again.
Importance of Data Cleansing
According to 76% of data scientists, data cleansing and preparation is the most tedious part of the entire data preparation process. However, it is also the most important one, as it is essential for developing high-quality insights, building reliable models, and enabling informed business decision-making. Some of the direct benefits that highlight the importance of data cleansing include:
Reliable B2B Database:
Data cleansing eradicates error-prone raw data, making it easier to understand and process for decision-making and analysis.
Informed Business Decision Making:
Accurate and high-quality B2B data allows you to make decisions based on accurate insights derived from clean data, resulting in more effective and consumer-centric decision-making.
Business Agility:
Data cleansing enables clean and accurate data, leading to better processing speed, vital business insights, and accurate data readings, all of which contribute to promoting organizational agility.
Scalability:
With the emergence of Cloud-based data preparation processes, businesses can now leverage tools and data cleansing techniques that allow them to scale their operations up or down as per their requirements.
Data-Driven Culture
Transforming raw datasets into clean and accurate databases through data cleansing helps establish your organization as a truly data-driven entity, enabling the extraction of actual value from the data.
Data Cleansing Techniques for Modern Businesses
When it comes to data cleansing, there are six key steps that you need to take care of in order to prepare the perfect B2B database for your business. These six steps are the most effective data cleansing techniques that will protect your database from erroneous, outdated, and dirty data. So, let’s look at them one by one.
Step 1: Data Collection
Data collection is the foundation of any data cleansing process, making it of utmost importance. It is essential to establish a strict policy regarding the data collection procedure from various data sources. It is advisable to have different business departments measure the accuracy of all files that are integrated into the larger database and ensure consistent data format.
Step 2: Data Cleansing
Data cleaning is all about detecting and eliminating any incompleteness, inaccuracies, or irrelevancies in the data that may have been inherited from the data source. This step will mitigate any errors that might have entered the data and ensure they are fixed.
Step 3: Data Enrichment
Data enrichment is the process of enriching the existing data by adding extra data attributes in the form of demographic data, geographic information, social media profiles, or any other relevant data points. The aim of data enrichment is to boost the value and quality of data by making it more accurate, actionable, and comprehensive.
Step 4: Data Reduction
Data reduction is the process of reducing the amount of redundant data in the database to only the meaningful parts. Some of the most common data reduction techniques used include compression, diagonalization, ordering, and rounding measures.
Step 5: Automated Data Cleansing
Advancements in data cleansing techniques have resulted in the emergence of several automated data cleansing tools, software, and automated scripts, among others. The automated data cleansing process reduces manual effort and ensures consistent data quality for your database.
Step 6: Validating Data Accuracy
Verifying the correctness and accuracy of your data should always be on your list of effective data-cleansing techniques. Whether you are cleansing the data manually or using automated tools, you must validate data such as phone numbers, email addresses, or postal codes to ensure that the data in your system is reliable and valid.
Tools For Data Cleansing
While there are plenty of data cleansing tools that you can leverage for your data cleansing requirements, here are some of the industry-leading tools that can help you get started:
- Alteryx: It helps businesses prepare, discover, analyze, and extract analytics for deeper insights.
- Trifacta: A cloud-based data wrangling platform that is equally compatible with on-premises data platforms.
- Talend: An intuitive self-service platform that allows businesses and IT teams to work cohesively on data-driven tasks.
- SAP: An agile self-service platform that allows seamless data migration, master data management (MDM), and data preparation, enabling successful analytics. It provides both on-premises and cloud deployment options.
- IBM SPSS Data Preparation Tool: A comprehensive tool capable of delivering faster analysis and insights by automating the data preparation process and eliminating tedious manual checks.
Conclusion
And with that, we conclude this insightful post on data cleansing techniques for anyone looking to prepare high-quality data for their business or research. It is worth mentioning that data cleansing is an integral part of any modern business’s day-to-day operations as it allows them to have accurate, reliable, and high-quality data. Furthermore, it enables informed decision-making, boosts operational efficiency, improves customer relationships, and reduces costs through effective data analytics. If you are seeking to make the most of your consumer data, it is highly advised to invest in data cleansing tools or partner with a B2B data cleansing service provider like us. We will help you gain a competitive edge with clean, accurate, and updated data. So, write to us at [email protected], and we will help you get started on your data cleansing journey.
