In the burgeoning landscape of Artificial Intelligence , data stands as the paramount pillar determining the success or failure of projects. As AI ventures from the drawing boards into real-world applications, the hurdle of data access looms large.
The recent “Navigating the Path to Successful AI Scaling” report, commissioned by Kyndryl and developed by S&P Global, highlights this challenge, citing that data access and quality are not just barriers but the very gates through which successful AI projects must pass.
We would like to acknowledge Kyndryl as a source of information referenced in this article. For further details, please see their report at – https://www.kyndryl.com/content/dam/kyndrylprogram/cs_ar_as/navigating-path-successful-AI-scaling.pdf
The Data Dilemma
According to Kyndryl, 65% of enterprises face difficulties in accessing their data. This statistic is not just a number—it’s a narrative of the many struggles that organisations encounter, from data silos and privacy concerns to compatibility issues. The graph further illustrates that while a majority of companies have sufficient data quantity, with 90% reporting they have just enough or more than they need, the issue of access is a separate and more pressing concern.
This distinction between availability and access is critical. Data might be abundant, but if it’s locked away, unstructured, or not readily usable, it’s as good as non-existent for AI’s purposes. Access to clean, well-organised, and enriched data is what turns the wheels of AI algorithms, allowing them to learn, predict, and evolve.
The Rise of AI Deployments
Kyndryl states that there is a growth of 73% growth in the number of machine learning models in production from 2021 to 2022. This growth trajectory suggests a rapid adoption of AI technologies across industries. Yet, due to proper data access, this growth may stall, unable to reach its full potential.
The Role of Data Cleansing and Enrichment
Herein lies the importance of data cleansing and enrichment.
Data cleansing purges databases of incorrect, incomplete, or irrelevant information, which can mislead AI models and lead to inaccurate predictions.
Enrichment, on the other hand, enhances data with additional context, providing AI algorithms with a richer training environment that mimics the complexity of the real world.
Cleansed and enriched data sets are like clear, well-marked roads for AI development. They allow for smoother travel towards objectives such as predictive analytics, personalised customer experiences, and intelligent automation. For instance, in e-commerce, enriched product data that includes detailed attributes, classifications, and descriptions can significantly improve recommendation engines, directly impacting sales and customer satisfaction.
Leveraging AICA for Enhanced Data Quality and AI Success
As organisations navigate the intricate landscape of AI deployment, the role of specialised service providers like AICA becomes increasingly significant. Specialising in product and service data cleansing and enrichment, we stand at the forefront of enabling businesses to overcome the barrier of data access and quality.
AICA’s AI-Driven Approach to Data Excellence
AICA leverages proprietary Machine learning algorithms to not only cleanse data but to enrich it with attributes that are critical for nuanced AI decision-making. This process involves meticulously scouring through data sets to eliminate inaccuracies, redundancies, and gaps that could lead to poor AI performance.
Following cleansing, our algorithms undertake the task of enriching the data, imbuing it with long and short descriptions, attributes,classification and translation, therefore readying it for AI consumption.
Conclusion
By transforming raw data into a more structured and meaningful form, we effectively pave the way for AI technologies to realise their full potential, driving forward the success of AI applications across various industries.
For more information about our services and to book a consultation, please visit our website at – https://aicadata.com/.