Data cleansing is the meticulous process of identifying and rectifying (or removing) errors and inconsistencies in data to improve its quality. Mistakes in product data can lead to significant operational inefficiencies, misinformed decisions, and unsatisfied customers. Ensuring the data’s accuracy and consistency guarantees that your business functions seamlessly.

With our state-of-the-art ML algorithms and a dedicated team of data professionals, we:

  •  Remove duplicate entries to present a unified product view.
  •  Detect and rectify poor language.
  •  Identify and add relevant missing data.
  •  Detect and fix inaccurate or corrupt data.


Below, you can find more detailed information on each of these topics


Duplicates refer to two or more identical or very similar entries within a dataset. They can occur in various contexts and for various reasons, but they essentially represent redundant information.

The consequences of duplicate data include:

  •  Unreliable KPIs
  •  Increased costs
  •  Reduction in data integrity
  •  Operational inefficiencies

Our system searches for duplicate names, descriptions, and numbers on a column and row level.

The user is then given the option to edit, delete, or ignore the duplicates.

AICA Product Data Cleansing, De-duplication. Our system identifies and manages duplicate entries within datasets, preventing unreliable KPIs, increased costs, reduction in data integrity, and operational inefficiencies. The screenshot showcases the user interface, allowing editing, deletion, or ignoring of duplicates on a column and row level. Product Data


Language refers to the textual and descriptive content associated with products in a dataset. It encompasses all the linguistic information used to describe, categorise and represent products. The consequences of poor language include:
  •  Miscommunication
  •  Operational inefficiencies
  •  Integration Issues
  •  Decreased trust and credibility
Language involves the automatic detection of possible spelling and abbreviation errors according to the algorithms. Our system presents the user with options to correct or ignore the system’s suggestions. Uppercase, lowercase, and camel case can all be updated in bulk, and custom spelling in a foreign language can also be requested.

Missing Data

Missing Data or “Data Profiling” identifies blank values, field data types, recurring patterns, and other descriptive statistics for an instant 360-degree view of your data. As an example, a data profile can be useful in identifying opportunities for data cleansing and assessing how well your data is being maintained based on various quality dimensions.

The user can drill down and see which product item records are affected, as well as sort, filter, and conceal information about products.

AICA Product Data Cleansing, Missing Data. Our system employs Data Profiling to identify blank values, field data types, recurring patterns, and other statistics, offering a 360-degree view of your data. The screenshot illustrates the user interface allowing users to drill down, identify affected product records, and perform sorting, filtering, and concealing of information for effective data management.

Anomaly Detection

Anomalies, often referred to as outliers, are data points in a dataset that do not conform to expected patterns compared to other data points. Anomalies can be the result of variability in data or potential errors; therefore, their presence is misleading. The consequences of anomalies include:
  •  Inefficient resource allocation
  •  Compromised data analysis
  •  False alarms
  •  Errors in decision-making 
  •  Reduced predictive accuracy
We detect anomalies in product data by using Six Sigma. Six Sigma is a data-driven methodology aimed at improving business processes by reducing defects and ensuring quality. This allows us to minimise variability in processes and achieve near-perfect results. Once completed the user is then given the option to either delete or ignore the anomalies.

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