According Forbes, Data scientists spend about 80% of their time collecting, cleaning, and preparing data, while only 20% of that time is spent on analyzing the data itself. Organizations that don’t use master data management systems or data warehouses to keep their data clean and accurate end up basing critical business decisions on bad data.

The cost of bad data was recorded as $3.1 trillion in a Harvard Business Review study. Bad data is very expensive because companies produce tons of data every day, but it is quite expensive and time-consuming to rectify data errors with the same frequency. For this reason, business leaders are increasingly realizing the importance of implementing a continuous data cleansing solution.

In this article, I want to share with you some of the serious dangers of using bad data for BI and how a data cleansing tool can help in this regard.

Why clean data is crucial for effective business intelligence?

When data scientists and data analysts are forced to meet strict deadlines – regardless of data quality verification – businesses face critical risks. From analyzing market opportunities to customer support, all business operations are stressed when poor quality data is pushed into systems without any data quality firewalls. I’ve listed a few key areas that, in my experience, are most affected by poor quality data:

  1. With shoddy data flooding your systems, you are forced to miss crucial business opportunities on several fronts, such as identifying potential prospects in a prospect database, discovering market demand in a competitive landscape, etc.
  1. Often teams are unable to meet their annual sales and revenue goals because they use outdated or inaccurate data when setting these goals. A drop in the company’s annual revenue can be very dreadful, either due to loss of customers or financial ambiguities.
  1. Dirty and inaccurate data must be corrected before it can enter your BI systems. This leads data analysts to waste a lot of time in duplicate work and manual data quality checks, which allows you to experience reduced operational efficiency and productivity through the organization.
  1. One of the most important benefits of business intelligence is leveraging personalized customer experiences. Customers want to feel that brands understand their needs and requirements. But with inaccurate and dirty data, brands can never infer reliable information about their customers. This can lead to decrease in customer satisfaction and loyalty.

What does a data cleansing tool do and how?

After reviewing some serious dangers of using dirty data for critical business processes, executives ponder possible solutions. The truth is that in an age where data is generated in large volumes and used for every transaction, adopting a data cleaning tool is imperative for data-driven decision making. A tool that can help prioritize these three concepts:

  1. High quality data
  1. Efficient data integration
  1. Ongoing data cleaning

Some companies use spreadsheets to achieve these goals with their data, while others decide to implement in-house solutions. But both options lack the accuracy, speed, and consistency required to keep data clean and standardized over time.

What is a Data Cleansing Tool?

A data cleansing tool enables a number of processes that eliminate data gaps, such as:

  • Integrate and combine data from multiple sources,
  • Removing unnecessary values ​​or noise from your datasets,
  • Correction of spelling errors and abbreviations,
  • Transform letter case and patterns to get a consistent view,
  • Conversion of values ​​to follow consistent units of measurement,
  • Matching records identify records belonging to the same entity,
  • Merge records to reach a golden record – free from data quality flaws.

5 Questions to Ask Before Choosing a Data Cleansing Tool

You need to answer some important questions before you can get started and select a data cleansing tool. I went ahead and listed them below:

  • Question 1: Which data sources include the required data?

Identifying the sources from which you need to extract data will help you analyze which solutions offer the necessary integration options.

  • Question 2: How will you uncover all the data quality flaws that pollute your data?

Once you’ve gathered the necessary data, how will you know about data flaws in your datasets? It’s here that data profiling is an important prerequisite for data cleansing. It is a process that helps uncover hidden details about your data in terms of incompleteness, lack of normalization, invalid values, and possible noise present in your data set.

  • Question 3: How will you merge duplicate records (if any)?

Many data cleansing tools have built-in data matching and data deduplication capabilities. These all-in-one solutions can be great for saving time and money, as well as other management overhead, because data cleansing and matching are taken care of in the same tool.

  • Question 4: How will you ensure ongoing data cleansing?

Consider how your organization will maintain data cleanliness and consistency at all times. Some vendors offer scheduling features that you can use for batch cleaning. Other vendors offer API services that you can integrate into a custom application.

  • Question 5: Where will you move your data once the data is cleaned?

Once the data has been cleaned and matched, you need to move it to a destination source. Discover the different options for exporting or migrating data offered by the different tools on the market.

Use data cleansing to get reliable and accurate data insights

Data cleansing is a basic requirement to enable a data-driven culture in any organization. When business leaders rush the information-mining process, they put their business at risk of basing critical decisions on flawed data — and therefore end up spending months and years repairing the damage. Investing in a data cleansing solution can save businesses a lot of time and money and get the most out of their data with reliable analytics and business insights.

Originally appeared at Intelspot