Intelligent CIO Middle East Issue 113 | Page 74

DISRUPTIVE TECH

techniques, companies can ensure their data is consistent, accurate, and ready for deeper analysis.

Here are some more standard techniques for data transformation.

Data Normalisation

Data normalisation is the process of structuring data into a standard format, reducing redundancy and inconsistency across datasets.

Data Cleansing

Data cleansing is crucial for ensuring data accuracy and consistency by removing or correcting inaccuracies, duplicates, and outdated information.

Here are some important techniques for data transformation.

Data Anonymisation

Data anonymisation is the process of removing or masking personally identifiable information to protect individual privacy, especially when sharing or analysing data externally. This transformation is essential for companies that handle sensitive information, such as customer names, addresses, or social security numbers, and is required to comply with data protection regulations like GDPR and CCPA.

Data for Machine Learning

Preparing data for machine learning requires specialised transformation techniques to ensure it is in a format suitable for training models. This process might involve scaling numeric values, encoding categorical variables, and handling missing data to prevent distortions in model training.

Data Parsing

Data Aggregation

Data aggregation involves collecting, compiling, and summarising information from various sources into more usable formats, such as totals, averages, or other meaningful summaries.

Data Enrichment

Data enrichment is the process of enhancing internal data by incorporating information from external sources, adding valuable context and detail.

Data Filtering

Data filtering is a technique that allows businesses to focus on specific, relevant portions of a dataset by setting criteria to exclude unneeded information.

Data Deduplication

Data deduplication identifies and removes duplicate entries in a dataset, ensuring that each data point is unique and accurate.

Data parsing is the technique of breaking down complex data fields into smaller, more manageable parts to enable easier analysis. Parsing allows businesses to transform unstructured data, like addresses or timestamps, into organised components such as street names or dates.

Data Standardisation

Data standardisation is the process of converting data into a consistent format, allowing seamless integration across systems and easier processing. By setting specific formats for data fields, such as dates, addresses, and measurement units, standardisation ensures that data from different sources aligns in a uniform way.

Data Encoding

Data encoding is the transformation of data into a specific format, often for efficient storage, compatibility, or security.

Data Formatting

Data formatting is a transformation technique used to structure data into a required format that aligns with specific application or system requirements.

Enterprises wanting to succeed with AI and Generative AI use cases should partner with specialised players to help them begin their data transformation and data management strategies. p

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