BOSTON, MA, May 30, 2014 /24-7PressRelease/ -- Analytics is a powerful tool for any company to understand customer better and make decisions based on facts and numbers, not just emotions or feelings. But how to get it right and get the best out of analytics capabilities? Data experts from major brands shared their tips on best practice work with business analytics.
1. Keep It Simple (10 times more so than is necessary to get your point across).
Alex Uher at L'Oreal Paris advises: "Keep it simple. Ridiculously simple. Ten times more simple than what you think necessary. Just about then, you are actually getting your point across in a way that people are starting to follow you".
2. Hypothesize/Put Problem First
3. Don't Assume Data is Good - Check/Validate!
4. Automate repeat tasks & Carve out time to go exploring
Jonathan Isernhagen at Travelocity believes that "Automate anything you do more than once. It's very easy to fill your time with routine pulls of data which lie just beyond the reach of the visualization tools available to business stakeholders".
5. Set a Data Strategy - don't just collect data for the sake of collecting it
As Farouk Ferchichi at Toyota Financial Services says: "Ensure there is a purpose you understand of why analytics is valuable to the organization. Purpose can be a business sponsor like discovering new ways (i.e. products, markets, etc.) to increase revenue, retention, profit, or control costs. So ask the tough questions and align with executives mandates".
6. In a rapidly expanding field, work with people on the leading edge
As per Anthony Palella at Angies List: "Work with people who are able to work on the leading edge ...the people who are helping expand the envelope".
7. Be a Skeptic about models etc.
Sofia Freyder at MasterCard says: "Double check your results using data from different sources. Make sure it makes sense. In case of discrepancies use it directionally. Reach out to experts to obtain their opinion"
8. Look for the pragmatic and cost effective solutions
Deepak Tiwari makes an example: "you can probably do machine learning and neural networks to solve a lot of problems but a linear regression might sometimes be enough".
9. Don't torture Data - in the end it will confess
10. Think like a Business Owner - what would you like to know?
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