Data is valuable when collected and used, otherwise it's just junk.

Organisations have many sources of disparate data, that is generated and retained by different systems and applications

These systems and application usually maintain this data in their respective forms within their infrastructure.

We specialise in guiding you through the process of data transformation into a singularity and make that data available across multiple 3rd party systems to improve customer profiling, journey management and automation.

Discover

• Understand sector, business environment, systems, processes
• Advise on process improvements, identify focal integration points and areas where there may be potential security concerns.
• Devise a data strategy by cataloguing identifiers that can accurately identify registered customers vs anonymous users.

Extract

• Review volume, velocity, variety and veracity for mapping exercise, using best in class visualisation tools.
• Data is extracted from source(s) and moved to a staging area. Data sources include relational and non-relational data files e.g. JSO, Flat files and XML.
• Validation where data that fails validation rules will be rejected, then discarded.

Transform

• Identify the methods of integration from ETL or ELT.
• Cleanse corrupt, duplicate, irrelevant, or misrepresented data by replacing, modifying or deleting it.
• Sensitive data is scrubbed, encrypted & protected (under ETL).
• Sorting/filtering, merging, combine/split, normalise schemas, perform basic calcs. Website schema markup and data structuring.

Load

• Under ELT the data is immediately loaded raw to the data lake (e.g. AmazonS3, Azure Data Lake, Google Cloud, Hadoop HDFS, Snowflake)
• Under ETL the data is loaded to its end target for storage and analytics such as a Customer Data Platform (CDP) either built custom or using a 3rd party solution e.g. Salesforce Data Cloud, Telium, Twilio Segment for whom we are a technology partner.
• Rules & constraints defined within the d/b may trigger upon load e.g. filtering duplicates that already exist, rejecting data missing mandatory fields, or perform actions based on set parameters.

Enrich

• All the data that has been collected is brought together and aligned with individual customer profiles based on a segmentation methodology considering audience, experiences, activity and other attributes defined by the data strategy.
• Preferences can follow the customer (e.g. consent) rather than having to establish this at every activation point.

Integrate

• APIs and integrations are established with tools that make up the customer experience e.g. email services, social media platforms, web forms etc.
• These APIs and integrations push and pull data about a customer from a single source of truth (SSOT) in real-time.
• This also means that the customer profiles are constantly evolving and are up-to-date with their experiences.