The core ingredient for Artificial Intelligence deployments is data. Many organisations assume that AI projects centralise around structured data and this has historically been the case. However, with the advances of AI systems over the past 5 years, there are now huge opportunities to leverage images, video, audio and documents and unlock operational efficiencies.
1. Comprehensive Data Assessment
We start all projects with a comprehensive assessment of our client's data landscape. This includes internal data, as well as 3rd party interconnect that the organisation accesses or utilises. Sendient works closely with clients to review additional data repositories that would be beneficial.
Through meticulous data profiling and analysis, we gain deep insights into the structure, completeness, and accuracy of the data, laying the groundwork for effective data integration strategies.
2. Identifying Data Anomalies and Inconsistencies
When building out a data strategy, it is essential to identify and rectify anomalies and inconsistencies that could compromise the integrity of AI models. Sendient’s data scientists utilise advanced data profiling techniques and anomaly detection algorithms to uncover irregularities such as missing values, duplicates, outliers, and inaccuracies. By systematically addressing data anomalies, we ensure that the resulting AI models are built on a solid foundation of accurate and consistent data.
3. Data Enrichment and Standardisation
In many instances, the raw data available to organisations may be incomplete or fragmented. To unlock its full potential, Sendient offers data enrichment services that augment existing data with additional attributes or information from external sources.
In addition, we have tooling and processes that can generate synthetic data which can be used to augment pre-existing data sources. Whether it is enhancing customer profiles with demographic data or enriching product data with industry-specific attributes, Sendient can leverage a range of data enrichment techniques to enhance the depth and breadth of client data.
4. Data Cleansing and De-duplication:
Data quality is instrumental in a successful AI deployment, and cleansing is a critical step in ensuring that data is fit for purpose. Sendient’s data cleansing techniques encompass a range of activities, including data validation, correction, and de-duplication. Through automated data cleansing algorithms and manual validation processes, we identify and rectify errors, inconsistencies, and redundancies in the data, eliminating noise and enhancing the overall quality of the dataset. By ensuring data cleanliness, we mitigate the risk of erroneous insights and erroneous decision-making outcomes.
5. Continuous Data Monitoring and Maintenance
Building a quality dataset is not a one-time activity. It requires ongoing maintenance and monitoring to sustain optimal performance. Sendient mplements robust data monitoring and maintenance processes to ensure that data quality remains strong throughout the AI project lifecycle. Through regular data audits, anomaly detection, and feedback mechanisms, we aim to proactively identify and address emerging data quality issues, By embracing a culture of continuous improvement, Sendient empowers clients to harness the full potential of AI with confidence, knowing that their data remains clean, reliable, and actionable.
If you would like to learn more about how Sendient integrates ethical and responsible AI principles into our solutions, please do not hesitate to get in touch with us.
If you would like to learn more about how Sendient can assist your organisation in ensuring data quality and cleanliness for your AI projects, please do not hesitate to get in touch with us. Your data's integrity and reliability are our top priorities.
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