Financial services organizations (FSOs) today collect and maintain an abundance of data, both structured and unstructured. For most of these organizations, data can be a double-edged sword. Collecting and maintaining increased amounts of data related to customers and portfolios can provide tremendous opportunities to increase revenue and reduce risk, yet at the same time, too much data can be a cognitive drain on analysts and knowledge workers. Increasing stores of data require FSO stakeholders to organize and search their data in ways that allow analysts to extract meaningful insights. Artificial intelligence (AI) can help FSOs mature from being data driven to being information driven. Enabling knowledge workers to efficiently derive meaningful information and insights from big data is what transforms FSOs into information-driven organizations.
Declining costs for long-term data storage, increased availability of digital content (e.g., documents, publications, email, voice recordings, and images), and regulatory demands have contributed to the explosion of data at FSOs over recent years. From a regulatory perspective, FSOs are required to be more transparent. Regulations such as Dodd-Frank in the United States and MiFID II and the GDPR in the European Union as well as the proposed California Consumer Privacy Act are examples of major regulatory changes that place increased pressure on FSOs to organize and secure their data as well as make it accessible.
As a result of such regulations, FSOs are storing vast amounts of structured and unstructured data. IDC estimates that up to 88% of the content that organizations possess is unstructured. Many FSOs do not have methods by which they can efficiently access and analyze unstructured data.
In response to regulations and other factors, many FSOs have invested in data management solutions, either purchased from vendors or created in-house. A natural result of this, in many instances, is the creation of data silos from which analytical insights are difficult to achieve. These situations create cognitive burdens on analysts or knowledge workers to decipher meaning from the vast amount of data that they have at their disposal. Given the amount of data generated and the value that can be derived from that data, FSOs are increasingly looking at ways to become information driven. Every day, personnel who are overloaded with data make less-than-optimal decisions as they are pressed to make the right connections and weigh the relative importance of data elements. Furthermore, these employees often must access several systems and repositories to gather the data they need for their specific task.
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