Wealth Access Report FINAL - Flipbook - Page 12
THE GAME PLAN
By: Kiah Lau Haslett
There are three
main components
of a data strategy
that executives
must decide:
Where does the
data live? How
does the data come
in? And what does
the institution
want to do with it?
Financial institutions are inundated with disparate data stored in siloed
systems. The data that populates its financial records, customer transactions and risk management systems poses complications at every step of
the journey, but especially at the start.
How can banks utilize this information to its fullest potential? Institutions should start by
understanding the basics of their data and have a clear idea of how they will use it.
Benjamin Maxim, chief technology officer at $8.2 billion MSU Federal Credit Union, says
there are three main components of a data strategy: understanding where the data lives, how
the data comes in and what the institution wants to do with the information.
Where Will the Data Live?
Data is generated by and lives within many places within a financial institution. Most institutions will opt for a centralized data repository to store information outside of these siloed
systems and the core. Maxim calls that “one point of entry.”
Deciding what tools an institution will use is an important early step — one that executives may need to revisit as the institution grows in its data maturity, Maxim says. Of
course, institutions could use a spreadsheet to hold this information, or they could leverage
existing flexible structures that house a lot of information already, like digital banking platforms.
There are also several types of digital architecture designed to store data. A data lake can
hold data of all types, including raw and unprocessed information that must be scrubbed and
cleaned. By comparison, a data warehouse stores processed, transformed and structured data
that’s ready to be shipped into a model or report. A data lake house combines these two
types of architecture — the lake for unprocessed data and the warehouse with processed and
structured information.
“I often joke there’s a fine line between building a data lake and a data swamp,” says Arjun
Sud, a principal at audit and consulting firm RSM US. “I think clients should be very cog-
10 | FINXTECH INTELLIGENCE REPORT
