OLAP can adequately be described as the storage of redundant copies of transactional records from OLTP and other database sources. This redundancy facilitates quick lookups for complex data analysis because data can be found via more and quicker (SQL execution) paths than normalized OLTP. And OLTP after all- should stick to its namesake and what it does best: processing, auditing, and backing up online transactions, no? Leave data analysis to separate OLAP Warehouses.
OLAP data is typically stored in large blocks of redundant data (the data is organized in ways to optimize and accelerate your data mining computations). Measures are derived from the records in the fact table and dimensions are derived from the dimension tables.
Facts are "measurements, metrics or facts of a particular process" -Wikipedia. Facts are the measurement of the record value: "$34.42 for today's closing stock price", "5,976 oranges sold at the market", "package delivered at 4.57pm", etc.
Dimensions are "lists of related names–known as Members–all of which belong to a similar category in the user’s perception of a data". For example, months and quarters; cities, regions, countries, product line, domain business process metrics, etc.
Dimensions give you a set of things that you can measure and visualize in order to get a better pulse and better overall understanding of the current, past and even potential future (via regression models) shape of your data- which can often alert you to things you might not be able to see (certainly not as quickly or easily) in an OLTP-based Data Analysis model which is often tied to production tables out of necessity or "we don't have time or money to implement a Data Warehouse".
Yes, you definitely do need OLAP/DW capabilities if you think long-term about it. Having intimate knowledge of your operations and how you can change variables to better run your business? Why would any business person (excepting those committing fraud) not want that?
I'd say that implementing a true and effective OLAP environment is worth any project investment and would pay itself over and again in the way of better and more specific/actionable metrics that help administrators of operations make the best, data-backed decisions- some very critical decisions involving millions of dollars and sometimes lives. I'd like a better look at the data before making a multi-million dollar or life/death decision.
I'd say that implementing a true and effective OLAP environment is worth any project investment and would pay itself over and again in the way of better and more specific/actionable metrics that help administrators of operations make the best, data-backed decisions- some very critical decisions involving millions of dollars and sometimes lives. I'd like a better look at the data before making a multi-million dollar or life/death decision.
SAS, Hadoop, SSAS, with healthy doses of R and/or Python customization?- whatever data solution you choose, my advice is to go with something that has a great track record of providing the kind of solutions that your business and/or your industry require. Use the tool that your ETL developers can utilize effectively and that helps you to best meet the needs of your constituents with whom you will exchange data with (the company, the customer, industry and/or regional regulation compliance).
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