According to Forbes, data scientists spend about 80% of their time on data collection, cleansing, and preparation, while only 20% of it is left for actual data analysis. Organizations that don’t utilize master data management systems or data warehouses to keep their data clean and accurate end up basing crucial business decisions on bad data.
The cost of bad data was recorded to be $3.1 trillion in a Harvard Business Review study. Bad data costs so much because companies produce loads 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 solution for continuous data cleansing.
In this article, I want to share with you some 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 follow strict deadlines – with no consideration of data quality verification – businesses experience critical risks. From market opportunity analysis to customer support, all business operations stress out when poor data is pushed into the systems without any data quality firewall. I have listed some key areas that in my experience are impacted most by poor data:
- With poor data flooding your systems, you are bound to miss crucial business opportunities on multiple fronts, such as identifying potential prospects in a database of leads, uncovering market demand in a competitive landscape, and so on.
- Oftentimes, teams are unable to reach their annual sales and revenue targets since they use outdated or inaccurate data while setting those targets. A decline in annual business revenue can be very dreadful either due to losing customers or financial ambiguities.
- Dirty and inaccurate data must be fixed before it can enter into your BI systems. This leads data analysts to waste a lot of time in duplicate work and manual data quality checks, leading you to experience reduced operational efficiency and productivity across the organization.
- 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, dirty data, brands can never infer reliable insights about their customers. This can lead to reduced customer satisfaction and loyalty.
What does a data cleansing tool do and how?
After reviewing some serious dangers of utilizing dirty data for crucial business processes, leaders wonder about the possible solutions out there. The truth is that in an era where data is generated in large volumes and used across every transaction, adopting a data cleansing tool is imperative for data-driven decision making. A tool that can help prioritize these three concepts:
- High quality data
- Efficient data integration
- Ongoing data cleansing
Some companies use spreadsheets to achieve these goals with their data, while others decide to implement in-house solutions. But both options do not offer the accuracy, speed, and consistency required to keep data clean and standardized over time.
What is a data cleansing tool?
A data cleansing tool helps implement a number of processes that eliminate data discrepancies, such as:
- Integrating and combining data from multiple sources,
- Removing garbage values or noise from your datasets,
- Fixing misspellings and abbreviations,
- Transforming letter cases and patterns to achieve a consistent view,
- Converting values to follow consistent measurement units,
- Matching records to identify records belonging to the same entity,
- Merging records to attain a golden record – free from data quality defects.
5 questions to ask before choosing a data cleansing tool
There are some important questions that you need answers to before you can jump in 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 pull data from will help you analyze the solutions offering the needed integration options.
- Question 2: How will you uncover all data quality defects polluting your data?
Once you have the needed data pulled together, how will you know the data defects present in your datasets? This is where data profiling is an important pre-requisite of data cleansing. It is a process that helps uncover hidden details about your data in terms of incompleteness, lack of standardization, invalid values, and possible noise present in your dataset.
- Question 3: How will you merge duplicate records (if any)?
Many data cleansing tools come with in-built data matching and deduplication features. Such all-in-one solutions can be great to save time and money, and other managerial overhead, since data cleansing and matching is taken care of within the same tool.
- Question 4: How will you ensure continuous data cleansing?
Think about how your organization will continuously keep data clean and matched. Some vendors offer scheduling features that you can use for batch cleansing. Other vendors offer API services that you can integrate in a custom application.
- Question 5: Where will you move your data after the data is cleaned?
After data cleansing and matching is done, you need to move it to a destination source. Find out the different data export or migration options offered by various tools in the market.
Using data cleansing for reliable, accurate data insights
Data cleansing is a basic requirement of enabling a data-driven culture in any organization. When business leaders rush the process of extracting insights, they put their company at the risk of basing crucial decisions on faulty data – and hence, end up spending months and years rectifying the damage done. Investing in a data cleansing solution can make businesses save a great amount of time and money and get the most out of their data with reliable analytics and business insights.
Originally appeared at Intellspot
