Updated: Jan 5, 2021
A business transformation framework to help grow your business!
by Jeff Hulett, January 1, 2021
Jeff Hulett is a banking industry executive. His roles have included operational leadership, data science, process automation, risk management, and risk consulting. Jeff is a member of James Madison University College of Business Finance and Business Law board and past chair. Jeff lives in the Washington DC area.
Think of this business transformation framework diagram as a view from 10,000 feet. It is useful for understanding key process elements and how those elements fit together. It is not as useful for understanding how those individual elements work. The following article series drills down to add some context.
Keep in mind, my perspective is from the banking industry. (I will use the word "banking" generally to describe all related financial services organizations) In a very real way, banking is in the business of information production. To make a comparison, the auto industry manufactures cars. The auto business raw material includes steel, plastic, computer equipment, and other physical material. The banking business raw material is data. For example, a loan is manufactured by assembling data on a loan applicant and using it to make an informed decision about loan eligibility and loan pricing. Once the loan is made, data is required to provide servicing for the ongoing loan relationship.
To start with the framework, the top part of the framework contains the 5 major transformation steps. Notice a transformation step is moving from one category to the next. For example, the first transformation step is transforming data into information.
Let's start with the data to information transformation step
You would think the banking industry is really good at managing data. After all, it is the core raw material for the product manufacturing process. My experience is the banking industry is good at managing data for its standard production processes. (e.g., making a loan) The industry is not as good at managing data for learning or other activities that occur outside the core production process. Data can be tricky. It exists in various forms, but generally can be described as either structured or unstructured. Structured data is great. It lives in a database, with rows and columns. The column header will be defined so the user understands what it means. This is known as metadata and is required to transform raw data into information. Unstructured data is, well, not so great. This is basically all other data that does not exist in a structured format. It could be a pdf document (like a loan agreement) or a physical piece of paper. It could be the recording of a conversation or it could be customer service notes. The discipline of transforming data into information is critical. To some degree, managing existing structured data is easier, but it still requires the discipline to maintain the databases and the metadata. Also, in a world where outsourced systems are more prevalent, sometimes data is hard to access because they are kept in vendor databases.
Finally, converting unstructured data to structured data is important. Keep in mind -
Data storage space costs are coming down dramatically, especially with cloud capabilities and APIs that facilitate the data transfer process, and,
Improvements are occurring with managing unstructured data, especially with Artificial Intelligence and Machine Learning based capabilities that specifically handle transforming unstructured data to structured data.
Most banks are awash in data, but, they struggle with transforming the data to information. This reminds me of the quote from The Rime Of The Ancient Mariner -
"Water, water, everywhere, Nor any drop to drink."
Please follow this link for the Automation Adaptability Framework. This provides more detail on different loan product types and their related adaptability to automation. The different features can be applied to almost any banking context.
The Data to Information Transformation Summary -
Who does it: Mainly an Information Technology responsibility.
What personality often gets attracted to this activity: Usually some one that get's energy from introversion, that is, converting communication to thought.
What kind of causal inference can be made: At this point, only association between data elements.
What is the customer focus: Mainly managing the collection of customer output and organizing it to understand facts about the customer.