Banks and financial institutions are actively using data science to overcome their multiple challenges. Banks are also deploying predictive and prescriptive analytics methodologies to generate powerful and relevant insights from their customer data.
There has been explosive growth in digital touchpoints in the BFSI industry. Banks can access large volumes of data generated from mobile apps, social media platforms, IVR systems, and ATMs. Data Science is deployed in multiple banking use cases, including personalized marketing, risk modelling, fraud management, financial inclusion, and more.
Digital technologies like Big Data, Analytics, and Data Science management will drive the future of banking. This is where a technology company like Emergys Solutions can help deliver the most benefits from data science initiatives in the BFSI sector.
First, let us look at some of the major data science challenges and how efficiently Data Science Managed Services can help.
4 Major Challenges in Data Science
As data emerges as the new “fuel” of modern economies, business organizations face challenges in leveraging its benefits fully for their growth.
Here is a look at the 5 major challenges in data science:
1. Multiple data sources
More banks and financial companies are now collecting data about their business operations and customers from multiple data sources. With the right data technology, this can be easier due to unstructured (or semi-structured) data formats. As a result, data scientists find it challenging to analyze relevant information from heterogeneous data sources, leading to problems like low-quality data or human errors.
Banks also rely on manual data systems that are time-consuming and can contain duplicate data entries. The need of the hour is a centralized data management strategy that seamlessly integrates all data sources and produces meaningful insights.
2. Data security
The growing volume of cyberattacks in the BFSI sector has highlighted genuine concerns about data security and privacy among banking customers. In response, there has been an increase in data regulations and policies around matters like customer consent and data utilization.
Among the major challenges of data science in the banking industry, companies need to follow the 3 basic tenets of data security: confidentiality, integrity, and accessibility. To meet their growing data security challenges, banks and financial institutions are adopting technologies in the form of AI-enabled security platforms and predictive fraud detection models.
3. Business Value
Most data experts believe data science must sync with business and decision-making processes to be relevant to any organization. For instance, how can effective data offer business value to banks? Through objectives like understanding their customers better, helping banks target the right customer base, or offering personalized banking services.
To deliver better business value, data scientists and experts must communicate with non-technical stakeholders who may need help understanding the technical aspects of the data technologies but recognize the business problem that needs to be addressed.
4. Undefined KPIs and metrics
With a clear understanding of the business value of data science among business stakeholders, data experts can often handle undefined KPIs and metrics, which can impact their work. Banks need:
- Well-defined metrics that can measure the accuracy of the generated data analytics
- KPIs that can measure the business impact of data science and analytics
Along with focusing on the business problem being addressed, business metrics can be identified from a clear business vision that is quantifiable to track the progress of any data science project.
Can Data Science Managed Services help in addressing these challenges?
How Data Science Managed Services can help the BFSI sector
An efficient managed services framework can help BFSI companies meet their desired business objectives and KPIs. A data science managed service (or DSMS) can enhance the customer experience and provide real-time insights leveraging predictive analytics.
DSMS options facilitate the work of data scientists and other data professionals by addressing tasks like:
- Requirements management by understanding the needs of the business stakeholders and extracting information from real-world problems the banking company faces.
- Time and Resource management in resolving business complexities and estimating the data science project costs.
- Big Data implementation roadmap framework that can provide clarity to all stakeholders, including the project timeline, budget, and business vision.
Using Big Data managed services; the Emergys team has the right skills to provide services in data science projects. Let us see why.
How Emergys can help with your Data Science initiatives
By using the data management services provided by Emergys, banking organizations can streamline their data science operations. As a Big Data specialist, our technical team is dedicated to solving issues like improper and unstructured data management. Our cloud-based data management solutions are designed to deliver business users the fastest mode of accessing and comprehending information and driving improved decision-making.
With the Big Data implementation roadmap, Emergys can provide BFSI companies with the right tools to improve their success rate in a competitive environment. Our data solutions are customized for banks and financial institutions yet to leverage the benefits of Big Data science technology.
Other data services from Emergys include performance tuning, cluster sizing, and Hadoop migration designed to achieve any business objectives.
