DES MOINES, Iowa – Saying the “hype” about big data can distract from the power of small, real-time data, TMG has published the first in a series of white papers on data-driven strategies for financial institutions.
The paper, Real-Time Data Fuels the Big Data Machine, explores how big and small data strategies can work in concert to deliver exceptional consumer experiences.
“Small, real-time data, when accessed properly, triggers immediate action,” said the paper’s author, TMG CEO Shazia Manus, who was recently named one of the 2016 most influential women in payments. “Financial institutions should consider the key consumer pain points they need to address before they lose out to more enjoyable experiences offered by competitors. These institutions should look at which financial tasks are seen as hassles, and how small data on those tasks can trigger action that will alleviate pain and create memorable experiences.”
The paper examines the ways in which financial institutions can leverage both big and small data to compete with the largest issuers, fintech disruptors and major retailers, each vying for ownership of the financial consumer relationship. Areas of focus include:
- Prioritizing the right capabilities to power big and small data strategies.
- Enabling faster business decisions by utilizing data.
- Running big data and small data plans simultaneously.
- Going beyond what’s visible to the naked eye.
In the paper, Manus uses examples from both in and outside the financial industry to explore the differences between big and small data. She points to uses of small data within the sporting, energy and restaurant fields to illustrate how real-time information can lead to highly engaging moments.
“My personal view on the emerging field of data analytics has been enriched by studying methods outside the financial industry – some of which are just outside my window,” wrote Manus.
Methods for getting started on the development of data-driven strategies are also included in the paper, as Manus puts forth a three-step approach. Further, she recommends starting by solving high-value, low-difficulty problems. “Because the possibilities are so great, you may be tempted to aim high right from the start,” she said. “Give yourself time to learn, relearn and course correct before tackling problems with a higher level of difficulty. Then, address bigger problems and identify new solutions incrementally on a reasonable timeline.”
To download the white paper, visit http://bit.ly/tmgrealtimedata.