Decisions
need data and analysis and the ability to make the right decisions,
especially in resource intensive sectors like IT are posing an
interesting challenge. The difference between a right decision and the
wrong decision could run into thousands of dollars or more and added to
that the net cost of the investment gone wrong in terms of business
inefficiencies and business lost.
Some key questions which typically arise are
Value (including “intangible” benefits)?
Risk Management
How do I know whether one IT investment is “better” than another (IT or otherwise)?
How do I know when to stop analyzing, accept some risk, and make a decision?
How do I know viability of approach ?
Applied Information Economics (AIE) is the practical application of mathematical models and scientific measurements in order to optimize decisions in uncertain investment environments.
In IT, measuring what matters most is key to driving quantification of information value. Typical forecasting models like Monte Carlo simulation, box and Jenkins and other models are used.
An article by hubbard research elucidates this point and the article also states that Contrary to popular belief, the value of information can be calculated as a dollar value. Although the term “information” is often used in an ambiguous manner, it can also be used as an unambiguous unit of measure with a well-‐defined value calculation.
This mathematical procedure can be paraphrased as follows:
1. Information Reduces Uncertainty
2. Less Uncertainty Improves Decisions
3. Better Decisions Result in More Effective Actions
4. Effective Actions Improve Profit
Other approaches using modern portfolio theory to also approach the set of potential IT investments as a portfolio and establish rates of return and risk boundaries and evaluated on a risk-return basis.
Traditional approaches of Cost Benefit analysis are also very popular and using NPV, ROI and EVA are comfortable approaches since finance teams understand them and are very conversant with them.
The trick to this approach is that for analysis, numbers are needed and most are subjective estimates in terms of value and are discrete in nature and not spread over a range, hence there are chances of perceptions taking over and skewing the analysis.
A better approach maybe the AIE approach using forecasting models and statistics.
So, are you valuating your data and IT investments ? I would be keen to hear from you.
Some key questions which typically arise are
Value (including “intangible” benefits)?
Risk Management
How do I know whether one IT investment is “better” than another (IT or otherwise)?
How do I know when to stop analyzing, accept some risk, and make a decision?
How do I know viability of approach ?
Applied Information Economics (AIE) is the practical application of mathematical models and scientific measurements in order to optimize decisions in uncertain investment environments.
In IT, measuring what matters most is key to driving quantification of information value. Typical forecasting models like Monte Carlo simulation, box and Jenkins and other models are used.
An article by hubbard research elucidates this point and the article also states that Contrary to popular belief, the value of information can be calculated as a dollar value. Although the term “information” is often used in an ambiguous manner, it can also be used as an unambiguous unit of measure with a well-‐defined value calculation.
This mathematical procedure can be paraphrased as follows:
1. Information Reduces Uncertainty
2. Less Uncertainty Improves Decisions
3. Better Decisions Result in More Effective Actions
4. Effective Actions Improve Profit
Other approaches using modern portfolio theory to also approach the set of potential IT investments as a portfolio and establish rates of return and risk boundaries and evaluated on a risk-return basis.
Traditional approaches of Cost Benefit analysis are also very popular and using NPV, ROI and EVA are comfortable approaches since finance teams understand them and are very conversant with them.
The trick to this approach is that for analysis, numbers are needed and most are subjective estimates in terms of value and are discrete in nature and not spread over a range, hence there are chances of perceptions taking over and skewing the analysis.
A better approach maybe the AIE approach using forecasting models and statistics.
So, are you valuating your data and IT investments ? I would be keen to hear from you.
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