May 4, 2009
37 Ways To Get More From Analytics, Version 2.0
As I hoped, there were some very helpful responses to my post listing ways to improve analytic effectiveness. Here’s a second draft incorporating them. Comments continue to be very welcome. I need to finalize this soon.
Analyze more data
- De-anonymize transactions (e.g., retail store loyalty programs) — yes, this is mainly a 1990s idea, but not everybody has implemented it who could
- Capture information about tire-kickers (e.g., online sign-ups of various sorts)
- Find more data about prospects (credit-based, geography-based, etc.)
- Make differentiated offers to test response (e.g., multiple versions of web pages)
- Text that you already have (e.g., incoming email)
- Text on the web
- Sensors in your equipment
- Factory floor
- GPS
- Utility networks
- Temperature/environmental?
- Sensors in your products — RFID
- Web logs
- Network event logs
- Keep history you used to throw out — archive it if you can’t use it yet
Serve more people
- Sales and marketing personnel (in case any remain who aren’t served already)
- Senior execs (with cool displays they appreciate)
- Line workers (with details they can use to do their jobs)
- Generic middle managers (budgeting tools, project analytics, salary analytics, and more)
- Engineers
- Customers
- Suppliers
- Citizens
Help in more ways
- Anti-fraud
- Other security
- Analytics-based price setting
- Interaction customization (e.g., personalized web pages, but it goes further)
- Quality diagnostics
- Production process improvement (the ghost of W. E. Deming waves Hi)
- Business process improvement (opportunity is not just on the factory floor)
- Mini trading floor (e.g., for energy supplies)
Analyze faster
- Faster query response (disk-based)
- Faster query response (memory-based)
- Lower data latency (more batches/no batch/CEP)
- Fully automated analytics (rules engines)
- Fast query response without a database design/administration bottleneck
- Data visualization and exploration
- Quicker budgeting/planning cycles
- Faster data mining
- On more columns of data than before
- Through more cycles of analysis than before
- To do better graph/network analytics
- Because of in-DBMS data mining
- Because of MapReduce
Support decisions better
- Adopt consistent enterprise KPIs
- Abolish or go beyond consistent enterprise KPIs
- (Repeat and/or reframe numerous points from above)
Categories: Analytic technologies, Business intelligence, Data warehousing, Presentations, Web analytics
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4 Responses to “37 Ways To Get More From Analytics, Version 2.0”
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wow, thrilled to see that whole new “serve more people” category – in fact, i was just reading some blurb – think it was walmart (not sure) that has like 500+ internal users/analysts digging at the data (or honestly, it may have been 5,000 total internal users, but a core of 500 analysts)…
forget computer science – stats is the new cs 😉
I almost never see any reference to the meaning of the data itself, I suppose because most folks with a technical orientation take it for granted. All 37 suggestions are incumbent (I think) on the assumption that the people using the analytics tools know a) what individual tokens represent; and b)what combinations of tokens (filters, views and modifiers) mean in their respective verticals.
May I humbly suggest that some method for managing a persistent inventory of typed entities – including ones from other languages – be included.
Thanks.
John O’
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