Big Data Drives Finance: Data Scientists Are Here to Help


Financial institutions depend on managing big data in order to drive customer engagement.

Transcribing data is crucial for this industry because it provides valuable information that can put banks in a position to stay ahead of the competition and target a customer.

The approach to transcribing big data must be strategic and organized in order to receive positive results.

“In terms of data strategy, financial services organizations are taking a business-driven approach to big data: business requirements are identified in the first place and then existing internal resources and capacities are aligned to support the business opportunity, before investing in the sources of data and infrastructures.” Springer reports. 

The pools of data found in financial systems are classified into three major categories.

Structured Data

Structured data is data that can be transcribed by both computers and humans, generated through the web and PDF platforms.

This data is any form of information that is seamless and easily searchable by using search engine algorithms.

This can be understood by using:

  • Data points – classified by numbers
  • Dates – the date of the transaction
  • Texts – includes multiple data points

This process of data has benefits for the financial industry because it can be transcribed without extensive processing, which ultimately cuts costs and creates a more agile performance.

Some Financial data structured services include:

  • Trading systems (transaction data)
  • Account systems (data on account holdings and movements)
  • Market data from external providers

Unstructured Data

“Unstructured Data refers to information that is not organized in a pre-defined manner or does not have a pre-defined data model. Unstructured data, typically text heavy.” Amazonaw reports.

Experts say that 80% of financial services hold unstructured data, but don’t put it to use.

The types of unstructured data that banks put to use include:

  • Internal reporting – financial information accumulated by an individual to be communicated with the business.
  • Internal communication – Communication inside the organization
  • External communication – Communication outside the organization (with clients, vendors etc…)
  • Social media data – Social media platforms that banks use to target a specific audience (Twitter, Facebook, Linkedin etc…)
  • Financial regulations – Accurate reporting of data by financial institutions that provides regulations for clients and members.

According to a recent survey conducted by Squirro, internal reporting was the most used unstructured data technique among the financial industry, internal communication was second, and external communication came in third.

“Internal reporting, internal communication (e.g. email), and communication with clients constitute the vast bulk of unstructured data in the surveyed financial institutions. A surprisingly high level of social media data is being used within banks.” The survey reports.

Unstructured data also provides businesses with information that enables them to make everyday decisions by responding in real time.

According to the survey, using structured and unstructured data is not common among participants, except those in the trading industry and finance, although 75% said that combining the two would create real value.

22% of participants revealed that they lack in combining both data tools because they do not have the necessary technology to meet the needs of the operation.

Semi-structured Data

Semi-structured data does not conform to any databases or data tags, instead, it offers its own database model where the schema and data are not separated.

“It is not organized in a complex manner that makes sophisticated access and analysis possible; however, it may have information associated with it, such as metadata tagging, that allows elements contained to be addressed.” Techtarget addresses.

Examples of this data are represented in meta-languages, which is basically the analysis of a different language, in different terms, such as HTML coding.

How Can Data Science Help Big Data?

With the explosion of data in the finance industry becoming more prominent than ever, institutions learning how to manage and transcribe this data is becoming a large concern.

“The bigger challenge, however, is how to effectively navigate through this massive amount of data in search for the right data needed to provide the necessary insights to successfully run the organization.” Financial executives reports. 

Data science is a strategic approach that uses processes, methods, and systems to understand what is composed in different forms of data and why it is important or useful for the institution.

In order to successfully carry out a data science model, it is important to target some goals.

Become Adaptable

Data science is successful when tools like self-learning and automation are introduced.

The data being transcribed is meant to offer valuable information for all professionals in the enterprise, so it should be accessible and reachable.

Leverage Existing IT Members

While making a data science model, it is crucial to make it adaptable, in order to fit into the business structure with ease.

If you have an in-house IT department, it can be utilized in order to achieve optimized results.

“With the integrated structure, a data science team focuses on dataset preparation and model training, while IT specialists take charge of the interfaces and infrastructure supporting deployed models. Combining machine learning expertise with IT resource is the most viable option for constant and scalable machine learning operations.” altexsoft reports. 


FSI Transformation Assembly

Application to attend FSI Transformation Assembly taking place September 14-15, 2017 at

Four Seasons Resort in Palm Beach, FL is now open.

Join experts from North America’s major financial services and insurance organizations like Keynote Speaker Scott Dillon, EVP, CTO and Head of Technology Infrastructure Services at Wells Fargo and Company. Scott will be providing unique insight into how his team provides availability, security, and global interconnectivity for the more than 70 million customers who interact 12 billion times a year in person through the company’s 9,000 stores and 12,000 ATMs, on the phone, or online at

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Big data is exploding in banks, creating opportunities to move toward digitally centric tools that integrate CIO’s and data scientists. The tasks of these professionals involve retrieving important information in data in order to create customer-centric experiences as well as to provide banks with informative and up to date information.

With the emergence of structured, unstructured and semi structured data, financial institutions are depending on data science in order to transcribe this data to further drive customer engagement and carry out operations successfully.

Join like minded professionals to discuss the latest digital trends in the financial industry during FSI Transformation Assembly. Become informed about innovative techniques that will further drive success in the industry and position business leaders to stay ahead of the competition.

This is not just another “Financial Services” event. Spaces are reserved for the best in the business. Apply to attend here!

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