With data compliance and security being front of mind, the banking industry has been unwilling to move past private data management infrastructure for a very long time. However, in the face of emerging technologies, alternative business solutions, and a radical change in customer and regulatory expectations, the banking industry has realized that it’s time to catch up and move to the data cloud.
In a recent study commissioned by Google, which surveyed more than 1,300 leaders from the financial services industry globally, 83% of respondents said that their companies were deploying cloud technology as part of their primary computing infrastructures.
Why? It comes down to the immense benefits and value, as well as the crucial resources that the data cloud provides. With the ability to have several innovative products and services to help with data monetization at its fingertips, the financial industry is embracing data cloud technologies. Banks are now able to deliver data intelligence and support data-based decision making, which results in better customer support with reduced costs, while also meeting regulatory demands.
Compelling reasons for the move to the data cloud
Visibility
The data cloud creates an opportunity to consolidate disparate sources and types of data under a single repository. With the mix of data now organized for analytics vs transactions, banking institutions have the data infrastructure to evaluate an end-to-end view of their business domains.
Operational resilience
A move to the data cloud helps increase operational resilience of both single banks and banking networks by mitigating risk of disruptions, such as power outages, hardware failures and more. With the data cloud, banks are now able to respond to problems faster, as well as increase redundancy by spreading and replicating data and services from a single data point to multiple data regions.
Security
The banking industry has stringent standards when it comes to security of data and software. This is where the cloud shines. With the large investments from the data cloud software companies, along with cloud infrastructure providers, banks can now take advantage of multi-layer security elements at both the data layer as well as the infrastructure layer bringing security comfort compared to on-premise environments.
Scaling
An important advantage of the cloud is the ability to scale up or down as needed, resulting in substantial cost savings. With consumption-based economic models for the data cloud, banks are no longer subjected to heavy upfront capital expenditures with the need to continually upgrade and purchase more technology. Banks can quickly respond to changing priorities in the market with minimal cost, giving them better alignment of their spend with expenses.
Personalized Services
Banks can provide and deliver meaningful personalized services by leveraging existing data and everyday customer touch points. There are endless possibilities to tailor services in banking, going beyond traditional offers and creating more customized, relevant end-to-end experiences.
Personalized Services
Banks can provide and deliver meaningful personalized services by leveraging existing data and everyday customer touch points. There are endless possibilities to tailor services in banking, going beyond traditional offers and creating more customized, relevant end-to-end experiences.
Evolve risk assessment
Data analytics can be used to assess the risk profiles of credit applicants in depth, thereby improving credit assessments. Banks can also detect fraud and money laundering activities at an early stage as well as any unusual transactions or activity within an account. Advanced data analytics also enables banks to comply with the ever changing legal and regulatory requirements in the integrity and credit risk domains.
Creating New Business Frontiers
Ubiquitous access
With the ability to share data across multiple constituents in a secure environment, the data cloud enables simultaneous access by different business units. This opens doors for more integrated decision-making. Customer-centric problems are sorted out more quickly through the collaborative use of advanced analytics tools and platforms. With large, connected data sets at their disposal, banks can now leverage complex, sophisticated data analytics and insight to expedite important decisions.
Embracing innovation
Machine learning, AI, augmented reality, natural language processing and image recognition are some of the cloud technologies that are helping banks become more innovative with new experiences, offers, and interactive capabilities. Automation of critical processes creates opportunities for increased revenue, cost efficiency, and it enables a customer-centric service culture.
Talent acquisition
With the adoption of advanced technology in the form of data cloud, data science driven automation and advanced analytics, banks are now able to create an agile, transparent, and connected system of operations that can attract new talent. Traditionally, banks have not been viewed as the ideal environment for advanced talent, however this migration has enabled the alignment of modern tech and new work cultures, providing enormous benefits to the banking industry.
What does it take to move to the data cloud?
There are many reasons organizations choose to move their data to the cloud. But first, organizations need to ask what business outcomes they are looking to achieve. What areas of the business do they wish to transform? And what value would be created? Not all organizations are in the same place regarding analytics capability or cloud maturity.
A carefully planned migration can lead to significant advantages both in the short and long term. Typically, the first step is to assess your organization’s readiness and maturity related to the initiative. Ask questions and search for information to ascertain the truth about the state of your organization in various areas related to data analytics.
Identify a clear business problem in need of solving, and establish a vision of an end state. Assess the complexity and cost, alignment with overall company objectives and desired outcomes, and confirm that the proposed approach fits in with the broader organizational strategy.