In today’s data-driven world, organizations rely heavily on analytics to make informed decisions, drive innovation, and gain a competitive edge. However, as the volume and complexity of data continue to grow exponentially, traditional approaches to data gathering and analysis are no longer sufficient.
GenAI, short for general artificial intelligence, involves the use of AI algorithms and machine learning techniques to analyze vast amounts of data and extract valuable insights. This technology has revolutionized the way organizations collect, process, and utilize data for decision-making. However, the increasing complexity and diversity of data sources present new challenges in terms of data requirements for analytics gathering.
Traditionally, data requirements for analytics gathering have focused on collecting structured data from internal databases or third-party sources. While this approach has been effective in the past, it is no longer adequate for organizations leveraging GenAI technology. GenAI requires a much broader and more diverse set of data sources to train its algorithms and generate accurate insights.
One of the key challenges organizations face when gathering data for GenAI analytics is the sheer volume of data available. With the proliferation of IoT devices, social media platforms, and other digital channels, the amount of data generated on a daily basis is staggering. Traditional data gathering methods are simply not equipped to handle this scale of data.
Another challenge is the diversity of data sources. In addition to structured data from databases, GenAI systems require unstructured data from text, images, videos, and other sources. This presents a significant challenge for organizations in terms of data requirements for analytics gathering. They must be able to collect, process, and analyze a wide range of data types to train their GenAI models effectively.
To address these challenges, organizations need to adopt a new approach to data requirements for analytics gathering in the GenAI era. This approach should focus on the following key principles:
1. Data diversity: Organizations must be able to collect data from a wide range of sources, including structured and unstructured data. They should leverage advanced data collection techniques such as web scraping, data lakes, and APIs to gather diverse data sets for GenAI analytics.
2. Data scalability: With the sheer volume of data generated daily, organizations must invest in scalable data storage and processing solutions. Cloud-based platforms and distributed computing technologies can help organizations handle large volumes of data efficiently.
3. Data quality: To train accurate GenAI models, organizations must ensure the quality and accuracy of the data they collect. This includes data cleansing, normalization, and validation processes to remove noise and errors from the data set if there is reason to remove it. Sometimes noise can be very useful in a genAI environment….
In conclusion, the increasing complexity and diversity of data sources require organizations to adopt a new approach to data requirements for analytics gathering in the GenAI era. By focusing on data diversity, scalability, and quality, organizations can harness the power of GenAI technology to drive innovation and gain a competitive edge.
GenAI data requirements for analytics satisfy new use cases.
Since GenAI use cases differ greatly from data science or business intelligence use cases, the approach to requirements gathering differs greatly. We have published a few data science use cases on our website but our GenAI use cases remain property of our clients.
This article does not deal with content creation and design, where a large corpus of text and image material can provide a flexible and context free base to produce the goods. Neither does it focus on chatbots, programming support, education nor medical diagnosis assistance. We are in the business of analytics: classification, forecasting, optimisation, data and text mining, decision modelling,…
The combination of using the right data types, the proper context and semantics for the tasks at hand makes or breaks your GenAI application for analytics.
But first, a primer on data. There are four types of data: nominal, ordinal, interval and ratio.
• Nominal data are labels like PartyID, Room Number, Car make,… We can’t calculate them. We can’t orden them.
• Ordinal data: we can arrange them but we can’t measure the distance between them, e.g. bad, good, nice, egotistic,…excellent or High School, College, University.
• Interval: have numbers and distances but no ratio, mean, median or absolute zero, e.g. temperature, date, time.
• Ratio: here we have the distance, ratio, mean, median, absolute zero, e.g. weight, height,…
With GenAI’s embeddings you create a low dimensional vector space where items can be projected and used as features for machine learning models, classification and association algorithms and many more. So here’s the catch: embeddings “freeze” a certain semantic meaning for a specific task or a class of tasks. This implies that you can construct different embeddings for the same items, optimised for the task it needs to complete.
You can also fine-tune the model with large input buffers and complex prompts, as well as incorporate external databases with ontologies to make your language model really effective. Therefore you will need to go beyond the aforementioned statistical approach and start building ontologies to classify items and express relationships between them.
Since language models cannot deal with incomplete and contradictory information, heavy investment in data quality with a focus on data consistency seems obvious. But how are you going to detect outliers or signs of fraud if you don’t include these data in the analysis?
For more information visit:
Enterprise Data Architects | Lingua Franca Consulting | The Netherlands
https://www.linguafrancaconsulting.eu/
+31(0)114 700 210
Terneuzen, Netherlands
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