Year 2025 - We are considered to be at the "quarter mark" of the 21st century, meaning that roughly 25 years of the century have passed.
Somebody else may have an altogether different perspective to the above, and it will vary from human to human. (using the word human deliberately)
Data is one of the most valuable resources to a business. Data in the 21st Century is what oil was in the Industrial Age, i.e., economies will now be run by data, and those who manage this data efficiently will be the ones who succeed.
Why do we need data? Organisations use data to identify potential customers and understand their requirements and preferences. With so much data available around us in so many forms and types, what can be done to move forward? Sometimes, we all know what we want (not at all times), but are unable to delve deep into the sea and make value out of it. We are in the #AI phase of our lives, and everybody is excited to explore the #LLMs to the core. At times, hungry and super excited to integrate AI models.
Let's walk back a bit and take one step at a time!
Yes, I was thinking of jotting down pointers, steps or a workflow if you may call it. And see whether I am able to prep the data before the AI layers come into play and do their magic.
1. Collect & Integrate:
· Identify relevant data sources (structured: databases, spreadsheets, unstructured: text, images, videos).
· Integrate multiple data streams into a centralized repository (data lakes, warehouses, or cloud storage).
· Remove redundant, incomplete, or irrelevant data.
2. Cleanse & Pre-processing:
· Handle missing values (fill, remove, or infer missing data).
· Remove duplicates and inconsistencies to ensure uniformity.
· Convert formats (text to numerical, date formats standardization).
· Handle outliers and anomalies using statistical methods.
3. Structuring & Transformation:
· Convert unstructured data (e.g., PDFs, audio, videos) into structured formats using NLP, OCR, or speech-to-text tools.
· Normalize and standardize variables to ensure comparability.
· Apply feature engineering (creating new meaningful variables).
4. Labelling & Annotation for AI:
· If using Supervised Learning, label data for classification tasks (spam vs. non-spam emails).
· Use Human-in-the-Loop (HITL) for accurate labelling in complex cases.
5. Storage & Governance:
· Ensure data security, compliance (GDPR, HIPAA, etc.), and accessibility.
· Define data versioning and lineage tracking for consistency.
· Implement role-based access to prevent unauthorized modifications.
6. Optimization:
· Identify key features that drive AI model performance.
· Reduce dimensionality.
And finally, split the dataset into training (80%) & testing (20%) subsets. And create cross-validation sets to prevent overfitting.
What can be achieved on a high level by doing the above?
A. Business insights & Predictive Analysis
B. Automation & Process Optimization Opportunities
C. Advanced AI/ ML Applications
D. Decision-making powered by AI
Therefore, before applying AI, ensuring clean, structured, and high-quality data is 80% of the work. Once the data is ready, AI can unlock transformative insights, automation, and efficiency across industries.
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