Most companies work regularly to reduce risk without truly understanding exactly what risk really is, or how much it should be reduced. Some managers will tell you that risk exists, but they won’t be able to tell you where or to what extent. Others can identify where a risk might lie, but can not tell you the impact to the company should the identified risky event occur.

Most technology managers tend to talk about risk in terms of security and disaster recovery. They’ve established firewalls and back-up systems with off-site storage to reduce the risk of loss of our data, but have little understanding of how the costs of these systems measure up against the risk of loss. The primary reason for this is they have little understanding of the real value of the data they are working to protect. This is, in part, because many technology managers don’t understand risk or how it is measured.

Risk, in general, involves loss, catastrophe, or other undesirable outcomes. Having your customer information, including their credit card numbers and details, posted on the web would be one such undesirable outcome. Having all patient records lost in a fire would be another. When asked, most technology managers agree that these things would be “very bad”, and they spend a large portion of their IT budget doing everything they can to prevent these undesirable outcomes. Let’s say as an example a manager is spending $5M on their cyber security strategy including software, hardware, and staffing. Is that the right amount? Too little? Too much?

To effectively evaluate the expenditure in context of its value, technology managers need to quantify risk. In the case of the data environment, to do so means understanding the real value of the data itself. The challenge here is that data, the ones and zeros stored on hard drives, has no real intrinsic value. Data, and the information derived from these data sources, is valuable only when used as the basis of business decisions. Thus to understand the value of the data, one must measure its value in the context of the business. It is necessary to analyze the data, its possible uses, and their value to the business in terms of costs and revenue. Doing so provides the technology manager with the insight necessary to reduce expenditures if the risk is lower than the $5M budget, or increase the budget if the risk is higher. - Dr. Jim

© 2019 Data Science Central ® Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

**Technical**

- Free Books and Resources for DSC Members
- Learn Machine Learning Coding Basics in a weekend
- New Machine Learning Cheat Sheet | Old one
- Advanced Machine Learning with Basic Excel
- 12 Algorithms Every Data Scientist Should Know
- Hitchhiker's Guide to Data Science, Machine Learning, R, Python
- Visualizations: Comparing Tableau, SPSS, R, Excel, Matlab, JS, Pyth...
- How to Automatically Determine the Number of Clusters in your Data
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- Fast Combinatorial Feature Selection with New Definition of Predict...
- 10 types of regressions. Which one to use?
- 40 Techniques Used by Data Scientists
- 15 Deep Learning Tutorials
- R: a survival guide to data science with R

**Non Technical**

- Advanced Analytic Platforms - Incumbents Fall - Challengers Rise
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- How to Become a Data Scientist - On your own
- 16 analytic disciplines compared to data science
- Six categories of Data Scientists
- 21 data science systems used by Amazon to operate its business
- 24 Uses of Statistical Modeling
- 33 unusual problems that can be solved with data science
- 22 Differences Between Junior and Senior Data Scientists
- Why You Should be a Data Science Generalist - and How to Become One
- Becoming a Billionaire Data Scientist vs Struggling to Get a $100k Job
- Why do people with no experience want to become data scientists?

**Articles from top bloggers**

- Kirk Borne | Stephanie Glen | Vincent Granville
- Ajit Jaokar | Ronald van Loon | Bernard Marr
- Steve Miller | Bill Schmarzo | Bill Vorhies

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives**: 2008-2014 | 2015-2016 | 2017-2019 | Book 1 | Book 2 | More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central