Things To Know
"If the only tool you have is a hammer, it's hard to eat spaghetti."
David Allen - Author of Getting Things Done
Here you will find some useful tidbits of information molded to the business leader to help guide your transformation to better insights.
The term Portfolio is a collection of assets which can be a collection of customers, products, transactions, loans, leads, machine faults, or anything that has, is, or has potential of creating or risking monetary or perceived value of your business.
Examples: A list of all customers that have ever visited your venue. All traffic citations in your district. All historical and potential sales leads, opportunities, and closes. A dataset of actualized faults from an operating machine.
A model is a mathematical representation of a real-life process and patterns to be used for decision making or to yield insights. In the context of Analytical Advantage, it can also be a financial model in which is an assessment of financial returns/effectiveness or an analytical model which is a mathematical representation of patterns found in data.
Examples: Return on marketing campaign. Optimization of products to yield higher revenue. Regression model to predict probability of default. Image recognition to identify cracks in object. Business rules that defines high valued customers.
The original term 'mining' stems from the process of extracting valuable material from the earth, “Data Mining” refers to the extracting anomalies, patterns, and correlations from large amounts of data or data warehouses. It can be as simple as slicing and dicing through large amounts of data to implementing statistical models like decision trees. SAS provides a simple overview in it's article 'Data Mining: What it is and why it matters.'
LinkedIn Learning has defined Financial Modeling as distilling key information regarding cash flow levels and risks which helps decision makers make informed choices based on inputs that move their firms forward. In the case of financial modeling in conjunction with analytics, the goal of modeling is to help understand the financial impact over time from the implementation of prescriptive or actionable outcomes from analytical solutions.
Analytical Maturity Stages
Every company is at a stage in the analytical maturity model, but as technology changes and integration becomes less dependent on infrastructure, in some cases, reaching the chasm can occur simultaneously and if not prior to earlier stages. That is the value of Data Scientist and AutoAI. Here is a great depiction of the different stages of analytical maturity from timeoelliott.com.
AI vs. Machine Learning
Often enough these two terms are confused. Bernard Marr, a Forbes contributor, does a great job of explaining the difference and its history. The important takeaway is that they are not the same and should not be used synonymously.