Data Analytics in a nutshell is the building of models from historical data to predict future activity. This is based on the concept of machine learning, a term famously coined by Arthur Samuel in 1959 to describe the ability of computers to learn without being explicitly programmed.
Let’s dig deeper into this concept. One of the first steps in data analytics is gathering historical data. This can come directly from a company’s activities or several external sources. Then the data is analyzed for patterns. Machine learning and pattern recognition are considered synonymous terms in many scientific circles. Once patterns have been identified, models are created based on recognizing those patterns. These models create certain outcomes, which helps to answer a question(s) about the future.
For example, a retail store gathers data on customers from its checkout software. This data will contain information such as the following:
- Total purchase amount
- Items purchased
- Date and time of purchase
The retailer wants to understand its busiest times and best-selling products, so it builds a data analytics model to accomplish this, or it partners with a retail data firm or market research company that already has such as solution in place.
If all the model did was identify high traffic times and best-selling items, this would simply be an analysis of existing data. It would not have much predictive value. Increasingly, the truly significant value in data analytics is primarily coming from matching together alternative data sets to derive new insights and trends.
As one example, by analyzing real-time transaction data, the retailer can hone their merchandising model using merchandising analytics and predict higher customer demand across various times (seasons, holidays, day of the week, etc.) and other key factors such as location, household income, or other economic dimensions.
These types of data analytics are also translating to large savings for retailers. Innovation Enterprise’s ‘Data Analytics Top Trends For Retail in 2017’ found that by using data properly, retailers can increase operating margins by 60 percent.
Utilization of Data Analytics in Financial Data Analysis
Both financial institutions and fintech companies are leveraging technology to greatly increase their efficiency and ability to develop new and improved processes to help people better manage their financial lives.
For both financial institutions and fintechs, part of leveraging technology means combing through reams of data to understand customer behavior and needs. It’s an analysis of massive amounts of data, requiring advanced algorithms and machine learning to analyze and determine patterns in data.
To understand the importance of big data, Saurabh Sharma of Indus Insights recently said, “Without question, the emergence of Big Data has been and continues to be a key force in a market that, by some estimates, will continue to double in size in each coming year. The availability of information about prospective borrowers and the ability to aggregate it quickly and overlay algorithms to arrive at actionable results has been a boon for P2P lending growth. Big Data analytics platforms enable institutions to develop predictive models that increase the chances of getting better returns, giving them an edge against banks while allowing them to maintain a less administrative process and provide the quick decisions borrowers come to expect in the online environment.”
To understand where financial services are heading, keep an eye on both financial institutions and fintechs, as they will continue to evolve the industry through their use of data analytics.
As we’ve seen, data analytics is key to making use of all the data amassing digitally. Companies that learn to wield this powerful tool will be the leaders of their industry.
Envestnet | Yodlee and Data Analytics
Envestnet | Yodlee data analytics tools leverage de-identified, enriched consumer transaction data from millions of accounts to deliver critical insights in a variety of areas including banking, retail, media, credit-decisioning, fraud reduction, and more.
Here’s a closer look at our data analytics portfolio:
- Transaction Data Enrichment: Extract deep insights into customer needs and behaviors from clear, contextualized transaction data and drive more meaningful interactions.
- Data Analytics for Financial Institutions: Address the key drivers of your business while discovering unprecedented insights about clients and competitors and identifying growth opportunities.
- Credit Risk Modeling: Our de-identified user data offers a holistic view into how millions of consumers are managing and spending money.
- Consumer Shopping Insights: Unlike traditional data sets, Envestnet | Yodlee Data Analytics for Retail reveals shopper insights making retail sales data actionable.