A swift increase of Data
Digital data has grown quickly, with the propagation of the internet, smart phones and other devices. Companies recognize the massive potential in using this to drive real value for customers, and improve efficiency.
We are now digitizing content that was created over centuries and collecting numerous new types of data from web logs, mobile devices, sensors, instruments, and transactions. Linking the disparate sources of data that exist across the business can be a challenge.
“At the same time, new technologies are emerging to organize and make sense of this huge raw data”.
Today business is accumulating new data at a rate that exceeds their capacity to extract value from it. The difficulty facing every organization that wants to attract a community is how to use data efficiently — not only their own data, but all of the data that's available and relevant.
Our ability to derive social and economic value from the available data is limited by the lack of expertise. Working with this data requires distinctive new skills and tools. The amount is often too ample to fit on a single computer, to manipulate with traditional databases or statistical tools, or to represent with standard graphics software. The data is also more heterogeneous than past. Working with user-generated data sets also raises risks of privacy, security, and ethics.
Data Science: A real game changer
The field of data science is emerging at the intersection of the fields of social science and statistics, information and computer science, and design.
Data science goes beyond traditional statistics to extract actionable insights from information available - not only the sort of information you might find in a spreadsheet, but everything from emails, images, video, social media data streaming, internet searches, GPS locations and computer logs.
With dominant new techniques, including complex machine-learning algorithms, data science enables us to process data better, faster and cheaper than ever before. From location analytics to predictive marketing to cognitive computing, the array of possibilities is overwhelming, even life-saving sometimes.
A large proportion of the current data projects in banking industry revolve around customers - driving sales, boosting retention, improving service, and identifying needs, so the exact offers can be served up at the precise time.
Banks can model their client’s financial performance on various data sources and scenarios. Data science can help strengthen risk management in areas such as cards fraud detection, credit scoring, stress-testing and cyber analytics.
Data science can translate untapped data potential into business results.
Data science in banking will transform the relationships between financial services firms and their customers in ways that can’t even be imagined today.
By using data science to collect and analyze data, banks can improve, or reinvent, nearly every aspect of banking. Data science optimized transaction processing and personalized wealth management advice. Several innovative firms have already started building predictive models using unconventional data to evaluate credit risk and provide new types of financing.
Whereas banks have historically been good at running analytics at a product level, such as credit cards, or mortgages, looking across inter-connected customer relationships that could offer a business opportunity - when an individual customer works for, supplies or purchases from a company that is also a client of the bank. The evolving field of data science facilitates this seamless view.