Unrelenting pressure from non-traditional players is driving financial services organizations to digitally transform themselves. To become data-driven enterprises, banks and insurance companies need to address three key data management trends: data volume, ubiquity, and user demands.
Mobile apps and devices generate massive volumes of data from new sources, such as images, audio, and video. Combined with new business models and actors in the value chain that increase the digitalization of financial services, this new data provides enterprises with opportunities to gain additional insight and value.
Today, data is everywhere. And financial services companies need to capture it all: customer information, financial transactions, product and service purchase histories, customer journeys, marketing campaigns, service inquiries, market feeds, social media streams, Internet of Things (IoT) streams, software logs, and text messages (including emails and SMS), plus those newer sources.
User demand for this data is rising. In today’s financial services enterprise, it’s important to recognize that every employee is really an analytics user who needs:
- Decision support, allowing users to base decisions on empirical evidence rather than gut feelings
- Trust in the security and accuracy of data
- The ability to proactively anticipate and influence business outcomes by paying attention to new and increasingly forward-looking signals
- Self-service access to data and easily usable analytics tools
- Speed and intelligent information equivalent to what users experience with personal consumer technology
Impact of rising complexity
These expanding data sources and volumes create a new challenge: an increasingly complex enterprise data management landscape comprising hundreds of silos. It’s not unusual for firms to deploy multiple data lakes, data warehouses, operational applications, mobile apps, online apps, call centers, IoT sensors, and analytics solutions. Data can be located in hybrid environments, on-premises, and in the cloud.
To reduce complexity, companies need to combine their existing and new data into a single data universe. Universal data helps firms enhance visibility, delivering insights that can improve efficiency, automation, and growth. By converting data into insights, organizations can become intelligent enterprises.
For many financial services enterprises, however, a single data universe is still an aspiration. More often, data resides in multiple siloed environments (see Figure 1). Because data is not meaningfully connected across these silos, it has become less accessible – compromising insight into customers, partners, products, sales channels, and financial performance.
Figure 1. Siloed data environments
Worse yet, data silos often are reinforced by organizational silos. For example, the group managing the Hadoop data lakes are not the same people who manage the cloud storage. And too often, teams use different tools and rarely interact with one another.
To overcome the challenge of multiple data silos, financial services companies tend to build large enterprise data warehouses. The reality of rapidly changing, growing data sources means that traditional enterprise data warehouses can no longer keep up with the analytics needs of the business. Here are a few reasons why:
- Solutions often cannot deliver real-time insights, as data capture and production of analytics are processed in batch.
- Data typically is replicated across multiple data marts built for specific reporting purposes, reducing transparency and requiring time-consuming reconciliation efforts.
- Solutions often cannot handle the growth of new data types.
- Data linage is challenging when users have no insight into data origins or any transformations applied, including data replication and consolidation across multiple data marts.
- Responding to business needs is slow and costly, especially considering the growing number of internal customers demanding new analytics and insights.
These challenges are further complicated by the increasing number of data consumption endpoints, the business processes and analytics solutions that require real-time data access to support decision-making.
A critical missing link
When considering data management challenges, financial services companies need to address two types of data:
- Enterprise data – High-quality, structured data with clear governance, security concepts, and lifecycle management practices. It is typically captured in relational database management systems. Examples include customer information, contractual agreements, and financial transactions.
- Big Data – Characterized by high volumes of semi-structured or unstructured data, such as text files (including emails, social media streams, or SMS messages) as well as image, audio, and video files. It is typically captured in data lakes such as Hadoop or cloud object storage systems, which are substantially cheaper than traditional database management systems. However, these platforms typically lack comparable enterprise governance, security, and lifecycle management.
Current data management landscapes often fail to create a link between the enterprise data and Big Data worlds. This makes it difficult to operationalize data science and derive the valuable benefits of data-driven analytics. As a result, users may struggle to search massive haystacks of data to find the hidden needles of actionable insights. This missing link also prevents organizations from delivering data-driven innovations, which are a core ingredient of digital transformation.