Do you have a data problem?

Do you have a data problem?

How long does it take you to generate a report or dashboard for presenting information to your boss? Do you have any challenges in accessing good quality data? Most organisations' data is unknown, incorrect or underutilised. If you are affected by the quality of the data used to make your KPI, is it time to consider cleansing your dirty data?

Data cleansing is a way to find missing, inaccurate, incorrect, duplicate, or irrelevant pieces of information in your customer records. At Explora, we process millions of client records to determine that all their data follow the right business rules and are in the right rows and columns. Our business purpose is to help reduce the risk of bad data and positively impact your company's business decisions. With unorganised and scattered data on hand, you can't produce an overview or have a clear insight into your data analytics platform.

Explora's data cleansing cycle (Image: Odilia)


Successful Data Cleansing Use Case of Explora 

Data cleansing is important to ensure that we achieve high data integrity for business decision-making. Usually, a significant amount of work is spent on data preparation for many data-centric projects. A representative case of our advisory consultancy and data cleansing in the financial industry is given below.

Client Background:

Our client is a Hong Kong-based financial company with global knowledge and local expertise in business advising, management consulting, accounting and financial reporting etc. Their goal is to have a dedicated data analytics platform with customer segmentation, churn analysis or fraud detection and analysis.

Challenge In Business:

The data analytics project started with data migration, which loaded the company's historical data over to the new system. As part of this data migration process, a large volume of customer data needed to be cleansed and enriched; this included the fragmented data from different source systems, such as client payments and employee timesheets. The data wasn't well organised, and there were problems in linking the client accounts with payments, which might have resulted in lost opportunities for upselling. Spelling errors, duplicate account names, and incorrect values were detected to be unclean. Business users from different departments weren't using the same set of data. Due to those issues, the data had to be normalised and standardised to ensure consistency across different countries, systems and departments.

Our Solution:

To achieve the client's analytics goal, we provided a data-cleansing service for the customer data sets. Our team analysed the data to understand different data patterns and structures for finding and cleansing the duplicate values in the system. Explora professionals made good use of fuzzy matching tactics to do the deduplication process. Once all data was cleansed, business users could view the same set of information for decision-making.

Example in defining the business rule (Image: Odilia)

Benefit Delivered:

The benefit of using our data cleansing service is to improve the quality of customer data set.The other benefits are following:

  • Enhance their cross-sales capabilities after cleansing their internal data

  • Enable users to create BI dashboards

  • Provide a holistic view of their existing clients

  • Capability of doing client segmentation on gross profit margins per clients

  • Possibility of up-selling and cross-selling

If you want to know more details on Explora’s Data Cleansing Service, please send an email to

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