Today we are going to continue exploring the 7 phases of a Data Lifecycle Management (DLM) as interpreted from this Bloomberg article written by Malcom Chisholm. The purpose of this 7 part discussion is to lay the foundation for more in depth exploration of data management and use cases, especially in the financial industry.
I’m hoping to get through all 7 of the DLM phases by the end of this week so we can move on to exploring real life challenges and interviewing some of our peers to see what they are doing to optimally manage enterprise data. So onto DLM Phase 3 – Data Synthesis…
What is data synthesis?
Data synthesis is the process used to form a new data set by combining multiple data sets for the purpose of inductive reasoning versus deductive reasoning.
So what is inductive versus deductive reasoning?
- Deductive: all ravens are black birds; this bird is a raven, therefore it is black VS
- Inductive: this swan is black; therefor all swans are black.
The outcome of an inductive reasoning may not have the same validity of the initial assumption but we do it anyway to attempt to predict the probability of an outcome based on synthetic data i.e. predictive analytics.
What is predictive analytics and what is it used for?
According to this SAS article, the common uses of predictive analytics in the enterprise are:
“Detecting fraud. Combining multiple analytics methods can improve pattern detection and prevent criminal behavior. As cyber security becomes a growing concern, high-performance behavioral analytics examines all actions on a network in real time to spot abnormalities that may indicate fraud, zero-day vulnerabilities and advanced persistent threats.
Optimizing marketing campaigns. Predictive analytics are used to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow their most profitable customers.
Improving operations. Many companies use predictive models to forecast inventory and manage resources. Airlines use predictive analytics to set ticket prices. Hotels try to predict the number of guests for any given night to maximize occupancy and increase revenue. Predictive analytics enables organizations to function more efficiently.
Reducing risk. Credit scores are used to assess a buyer’s likelihood of default for purchases and are a well-known example of predictive analytics. A credit score is a number generated by a predictive model that incorporates all data relevant to a person’s creditworthiness. Other risk-related uses include insurance claims and collections.”
So having read all that, in the 7 DLM phases, why is “data synthesis” is a separate phase and not part of “data use” phase?
Good question, we will explore that in our next discussion.
Leave your thoughts on the comments section. Or feel free to contact me at email@example.com
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About the author:
Juliana Carroll is a problem solver, strategic thinker with 15+ years of Fortune 500 consulting experience delivering measurable results that align with both business competitive requirements and regulatory compliance.
Juliana have delivered exceptional results for organizations including Morgan Stanley, Prudential, Merck, Guardian Life Insurance, Blue Cross Blue Shield of Florida, and Deutsche Bank.