Products / systems currently running need to look at their Data Collection techniques to identify more relevant data to perform better analytics. If current systems rely on point in time data, overwrite / archive historical records over a period of time, we will lose all the valuable information
Why Analytics ?
- Predict your future based on your past and present
- Correct your mistakes before it's too late
- Identify and correct poor performing segments of business
How Analytics differs from Business Intelligence ?
- I have worked for ETL, data marts, Schemas for BI projects
- BI helps to summarize compare business performance YoY, QoQ
- Analytics, is next step for BI to look at future trends
Where are we lagging ?
We need analytics but we do not have enough data points / features to perform analytics. Data collection is a key aspect. The underlying blood of Data science is collecting meaningful data and making models out of it. We need to devote sufficient time to collect data, pipeline it, process and aggregate it for Data Analysis, Modelling.
To evolve from a current product to a system with Analytics capabilities we need to change we way we store data, process data. Technical aspects, project deadlines, resistance has to be handled to make things work.
Persist, Persuade, Implement....
Happy Learning!!!
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