This is a review of a chapter from ""Competing on Analytics"-
- How will this investment make us more competitive?
- How does the initiative improve our enterprise-wide analytical capabilities?
- What complementary change need to be made in order to take full advantage of new capabilities, such as developing new or enhanced skills; improving IT, training, and processes; or redesigning jobs?
- Does the right data exist? If not, can we get it? Is the data timely, consisten, accurate, and complete?
- Is the technology reliable? Is it cost-effective? Is it scalable? Is this the right approach or tool for the right job?
Some missteps are due primarily to ignorance. Most common errors of omission are:
- Focusing excessively on one dimension of analytical capability (e.g. too much technology)
- Attempting to do everything at once
- Investing excessive resources on analytics that have minimal impact on the business
- INvesting too much or too little in any analytical capability, compared with demand
- Choosing the wrong problem, not understanding the problem sufficiently, using eh wrong analytical technique or the wrong analytical software
- Automating decision-based applications without carefully monitoring outcomes and external conditions to see whether assumptions need to be modified
There is here an element of risk. What will be the expected outcome based on the implementation of analytics? What does the upside look like and what is the downside? Analytics should be looked at like any other investment. What is the return? What costs can be reduced? How much more revenue can we increase? At the heart are always these assumptions. Other considerations are important, too. How much faster could we move? Can we increase throughput? Are our clients demanding better analytics with our product or service?
The costs of delivering analytics has dropped a lot in the past 5 years. Better software and cheaper software. It has become crucial pieces of some industries- while others are just starting to wonder what could be...