Ever-evolving analytic software can greatly improve financial institutions’ decision-making.
Business intelligence technology has come a long way from the decision support systems of the 1960s. Today, it can do much more than just mine, analyze and report on data — it can cross-analyze different data sets, forecast future behavior and greatly improve decision-making.
Tools continue to expand their capabilities, providing more value every year. The types of analysis they can perform today stretch the realm of what was possible even five years ago.
The financial industry analyzes its vast store of data in several ways, and evolving BI tools aid in those tasks. Some of the capabilities that executives seek include:
Content analytics: Unstructured data (such as the content found in machine logs, sensor data, audio, video, call center logs, RSS feeds, social media posts and PowerPoint files) is growing more rapidly than any other type of data. Content analytics applies BI to this unstructured data.
By understanding more about the content and how it’s being used, enterprises can determine whether it’s valuable to the business. The content that is deemed valuable can be linked to other data to extrapolate additional insight, such as understanding the cause behind trends and events.
Context analytics: Effective decisions can’t be made without understanding the context of data, and that’s where context analytics comes in. It focuses on surrounding each data point with a historical context about people, places and things, and how each data point relates to other data points.
Business analytics: While traditional BI platforms include executive dashboards that provide key performance metrics, newer tools go further. Business analytics provides a deeper level of statistical and quantitative analysis, allowing financial services organizations to dive deeper to discover trends, relationships, patterns, behaviors and opportunities that are particularly difficult to discern.
Predictive analytics: Predictive analytics is a must-have for many financial services organizations, and for good reason. The process uses a variety of techniques, including statistical analysis, regression analysis, correlation analysis and cluster analysis, along with text mining, data mining and social media analytics, to learn from historical experience what to expect in a given area. Financial services firms can use the resulting models and patterns along with real-time data to develop proactive actions in areas such as loan approval determination and product development.
Cognitive analytics: This type of analytics employs artificial intelligence and machine learning algorithms to learn and build knowledge by experience in their domain, including terminology, processes and preferred methods of interaction. They process natural language and unstructured data and can help experts make better decisions.
Text analytics: This process transforms unstructured data such as email, text messages, web pages, social media, survey responses and charts into text. With this information translated into text, BI systems can better use the data to discover patterns, relationships and root causes.
Social media analytics: From Twitter and Facebook to LinkedIn, YouTube and blogs, it’s clear that social media is an information channel that can’t be ignored. Social media analytics gathers and analyzes data from sites like these in near real time, giving decision-makers access to extremely valuable information that provides insight into customer sentiment.
It also provides a way for financial services companies to quantify market perceptions, track the success of marketing campaigns and product launches, discover insights and trends in customer preferences, and react more quickly