Data is the lifeblood of the Smart Enterprise. Over time, enterprises have built custom solutions, used SaaS offerings, used Low-code platforms and cloud solutions to solve business problems. This has caused significant fragmentation of data into various silos. From internal data sources of the early 2000s, we now have a proliferation of data in many more places.

In order to achieve the goal of “Empowering employees to make the right decisions based on data”, it is important to invest in the right data platform.

Data Engineering:
Preparing your data for the AI first world involves: Identifying the data, building ETL pipelines. ▸ Design data processing systems
▸ Ingest and process the data
▸ Store the data
▸ Prepare and use data for analysis
▸ Maintain and automate data workloads


Predictive analytics and Machine Learning:
Predictive analytics tools utilize historical and real-time data to forecast future outcomes, identify trends, and guide strategic decision-making.

Generative AI:
Run Generative AI models locally on your server, have full control over your data
Build RAG systems to identify relevance to the incoming query
Use MCP to provide a natural language interface to your employees for interacting with data.

Data Security:
While preparing the data for AI, it is very important to anonymise the data and use tokens. Various encryption methods, hashing and tokenization techniques need to be employed.