In this fourth article in our series featuring insights on data access, connectivity and integration for the financial services industry, author Matt Gillespie explores the persistence of data silos, their impact on the business and the strategic imperative to eradicate them. Gillespie is a technology writer, research analyst and journalist who writes about cloud, AI, cyber security, software-defined infrastructure and data integration, among other topics.

Data silos are the mortal enemy of data value. Isolated data creates opportunity costs along every axis where a dataset could potentially have interacted with others to yield additional insight. Financial services firms must instead capitalize on those opportunities, recognizing the strategic value of connectivity.

Bolstering connectivity to data wherever in the enterprise it resides enriches applications from analytics to sales automation. A 360-degree view of the account holder, for example, enables cross-selling across the financial services portfolio, from insurance, to banking, to investments. Full visibility into cloud-service usage across business units helps streamline governance, risk and compliance. Usages such as these make systematically enabling data sources for standards-based connectivity a strategic best practice.

Setting Data Free Brings Forth Its Value

Internal data is often isolated in patterns that emulate the business’s organizational structure. This effect can easily arise as each business unit collects and stores data to meet its own specific needs, resulting in multiple (and perhaps redundant) efforts that foster fractured technologies and a lack of connectivity. Compared to that state, recognizing and prioritizing the importance of data connectivity delivers immediate benefits:

  • More generalized access. Providing robust, standards-based connectivity to data sources enables present and future tools and applications to draw on them to generate value.
  • Increased data quality. Connectivity among datasets is a fundamental requirement for creating a single version of the truth, to avoid conflicts among multiple independent silos.
  • Richer context. Interplay and cross-correlation among data sets makes data more valuable; a simple example is the cross-sell opportunity to approach a client with a high deposit-account balance for investment products.

This environment demands that the data connectors (also known as drivers) that establish and maintain connections to data sources are a first-order concern for solution architects. They must be treated as the competitive assets they are, rather than as mere afterthoughts or commodity components.


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Big Data Expands the Competitive Opportunity, with Broad Connectivity Demands

The premise that data’s value necessarily depends on connectivity has implications for connecting to big data, including SaaS and cloud sources. Many of these sources are not designed for querying against directly, as a database is, for example. Simba addresses that challenge by building a SQL engine into its driver layer. Using this functionality, analytics platforms and similar tools can query data in the SaaS and cloud resources, to uncover more of its value.

As machine learning continues to advance in mainstream operational environments, training deep learning models is becoming increasingly common. Access to the broadest possible universe of training data enables these models to become more sophisticated and valuable in emerging usages such as predictive models for increasing sales efficiency and decreasing customer churn.

Such emerging requirements place added importance on the data connectors these models depend on. The dynamic nature and variety of both the data sources and the data itself strain the capacities of many organizations to provide optimized data connectivity, presenting challenges in areas such as the following:

  • Quality and breadth. Baseline requirements call for data connectors to deliver extremely high performance and stability, across every current and potential future data source.
  • Flexibility and integrability. Preparing to use any and all data sources places a premium on being able to connect to novel ones easily and to integrate them rapidly with existing algorithms and models.
  • Stability and longevity. A lifecycle view of the solution shows the need for long-term maintenance of data connectors as data sources evolve and hardware improves to take better advantage of them.

Enabling a United View of Data, in a Post-Silo Reality

Partnering with Simba enhances the reach of applications into the data that empowers them. Simba offers data connectors according to a flexible range of options to meet customer-specific needs.

Simba provides off-the-shelf data connectors for most common data sources, which gives customers a simple path to implementation. Custom development is available for more niche needs such as home-grown or obscure data sources, and Simba also offers customers the ability to use the SimbaEngine SDK that its engineers use in-house to build data connectors. A range of benefits accrues across those offerings:

  • Accelerate time to deployment. Streamlined establishment of connections to novel data sources reduces the cost and complexity of connecting to richer data.
  • Optimize business decisions. Consistent, high-performance access across multiple data sources provides the basis for algorithms to deliver insights for data-driven decision making.
  • Future-proof data connectivity. In addition to ongoing maintenance of the data connectors themselves, Simba flexibility and expertise paves the way for ongoing development.

For its off-the-shelf and custom connectors and those built using the SimbaEngine SDK, end customers effortlessly get early support for new versions of standards such as ODBC 3.8 and JDBC 4.2. Standard interfaces enable tools, applications and services to connect seamlessly to the data they need to deliver maximum value.

Conclusion

Getting the full value out of data assets requires banks and other financial services firms to draw on data sources that are authoritative and connected. Data silos must be eliminated, ensuring that datasets don’t remain isolated. In that environment, data connectors have grown in strategic importance as they have become business-critical. Partnering with Simba provides the means to empower tools, applications and processes with data that makes them more powerful and profitable.

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