“Traditional analytics can tell you what happened and why, but leading organizations use advanced predictive analytics to understand what could happen and to choose the next best action,” according to a blog post at the IBM Big Data & Analytics Hub.
Big data comes in many forms, notes the article: Structured, as found in data warehouses; and unstructured, as collected from social media, call-center notes, and the like.
Analysis of varied data is important to financial services providers to gain insight into consumer need, and can provide a vision for products, services, and consumer preferences.
“That view is especially important for banks, which increasingly use multiple ways to interact with customers…”
This week, discover that incorporating big data into your planning efforts goes beyond collecting statistics and facts. Crunching the numbers with analysis and intuition can lead to better products and services, anticipation of needs, and member satisfaction.
‘Numbers have life; they’re not just symbols on paper.’ --Shakuntala Devi, Indian writer, and “human computer”
As a practical example of the importance of data collection and analysis, see “How Fares the Financial Services Industry in Implementing Data Management?” at dataversity.net. During the financial crisis, “No one could understand who was financing whom, who was linked to whom, and the entirety of complex financial instruments” and information gaps in quality data made it difficult for decision makers to take efforts that might have lessened the impact of the event.
“There was more data but it wasn’t actionable,” the article notes. The situation was unpredictable and difficult to mitigate.
Data governance programs have evolved since then to facilitate awareness, although obstacles remain: A variety of data sources to manage; business perspective is not always in accord with governmental efforts; a lack of people to manage data; and progress must continue in spite of funding problems, data quality issues, and accountability challenges.
One suggestion for financial entities to realize greater chances for success is to hire an empowered chief data officer to navigate progress.
At this time, data management is in a “transitional phase.” About one-third of participants in a data management benchmarking study had fully operational data management programs, and 43% said such projects were developed but not yet operational.
“The scope of the task is significant and organizational challenges on doing it right is daunting.”
Find one status check on the use of big data in “2015 Big Data Market Update,” at Forbes. The report on the evolution of big data apps reveals:
Another progress report on change in using data specific to finance department teams is provided by FinancialDirector, reporting on a PwC finance function report. “Finance teams are spending more time on analysis and less time on data gathering,” the article notes.
“The best finance professionals today are producing actionable information, not circulating numbers that are likely to be out of date as soon as they’re released.”
One advantage of the change is that efficiencies result in greater economies of scale.
Where does your credit union stand on the spectrum of data analysis progress?
‘You don’t have to be a mathematician to have a feel for numbers.’ --John Forbes Nash, Jr., mathematician
A succinct summation of the definition and role of predictive analytics directed toward financial providers is found in another IBM blog post.
“The great thing about deploying advanced predictive analytics is the insight you can get about your banking customers both on an individual and a categorical basis,” the article notes.
Practitioners will identify consumer trends and patterns and discern perspectives found in varying consumer data like transactions, social media, surveys, interactions, and demographics.
To get a “holistic view,” keep these considerations in mind:
Not sure how to navigate big data? See “Analyzing Big Data: 8 Tips For Finding the Signals Within the Noise,” as outlined at Forbes.
As organizations realize the importance of data, collection of it grows, “doubling in size every two years.” The result is that organizations are “increasingly data rich but insight poor” due to information overload.
“It’s estimated that currently only 0.5% of all data is being analyzed.”
Data only holds value through analysis, and as time passes and collected data grows, take these steps to “amplify the signals in your data”:
Finally, realize that intuition remains important in an environment of big data, per an article at the Graziadio Business Review. Research findings here indicate overreliance on number crunching in decision making can be detrimental as “The reliability of numbers can come into play and concerns about asking the wrong questions can impede the value of the results obtained.”
The “big picture” can become lost in granular-level data, the article notes.
Better decision making can result when tapping of intuition and quantitative approaches coexist.
Achieve taking a broader view in data analysis to involve intuition through these six considerations:
Number crunching alone won’t lead to good decision making. Human elements such as intuition and emotion also come into play when anticipating consumer need and your responses to the need.
Understand the relevance of data collection, and how to use captured data to best effect.
Per Plato, “A good decision is based on knowledge and not on numbers.”