Accessing Cloud Data: The Leaner, Meaner Racing Bike Approach

From the June 2013 issue.

Current market research studies indicate more than 50 percent of small-to-medium sized accounting firms plan on using cloud-based applications over the next few years. For companies with more than 50 employees, that number rises to 90 percent.

While the advantages of the cloud – less up-front investment, reduced IT cost, scalability – are substantiated, the gemstone is the vast amount of SaaS application data that can be leveraged for increased business intelligence, financial analysis and more competitive professional services.

Access to SaaS data itself is evolving from its historical time consuming approach of loading an on-premise data warehouse, to new technology in which data can be accessed in real-time, providing accounting firms with the freshest, most up-to-the-minute data. SaaS industry leaders such as Intacct, NetSuite and Intuit are helping to drive the growth of cloud-based applications in financial management and accounting.

The technology of accessing and integrating data has fortunately advanced considerably over the last few years or so to the present day SaaS data platform. The classic use case of SaaS data access is for data synchronization across multiple SaaS applications. In this approach, you are typically moving data between two applications, for example, taking contract data from one application and storing it in Salesforce, or adding a new account to SAP when a Salesforce opportunity converts to an actual purchase.

Data synchronization is useful for process workflows but inherently limited in its broader utility since it is more difficult to maintain consistent data between the two applications. And, there is no means of turning that data into any form of actionable information. In some sense this is analogous to a bike with training wheels.

The second use case for SaaS data access is data warehousing for reporting and analytics and could be likened to the two-wheel bike. In data warehousing, an IT professional will move data from the cloud into an on-premise warehouse. It requires IT time and there is the disadvantage of stale data since the data can no longer be considered real time. Enterprises use connectors from Informatica, Dell-Boomi and others to access and load the data into these warehouses. The cost is usually per connector and/or based on the amount of data moved.

While data warehousing has been the common approach to data access, it has its limitations, and inherent costliness. Building and maintaining a data warehouse can run into the millions of dollars in project costs, with the need to regularly update hardware and software.

A more recent development is the launch of cloud-based data warehouses. While cloud warehousing offers low entry cost, the jury is still out on whether long-term aggregate costs will differ greatly from the more traditional on-premise data warehousing. A warehouse in the cloud does not eliminate the complexity of having to design schemas that serve multiple SaaS applications for the kinds of analytics one desires. And then, there also remains the cost of data migration from the SaaS applications to the cloud warehouse.

We pose the concept of a lower cost, faster time to value alternative – following our metaphor, one we think of as a racing bike, lean, efficient and built to get things done fast and superbly. Cloud data virtualization is the evolution of data access and integration that began with data synchronization and warehousing.

In cloud data virtualization, SaaS data is retrieved from multiple cloud applications in real time and virtualized, eliminating the need for costly interim steps, such as moving the data first into an on-premise warehouse. Cloud data virtualization improves time to value by bypassing the myriad of APIs that slow down data access by virtualizing SaaS data as if it was in a local relational datastore.

This enables a user to instantly combine data in real time from disparate sources into one reporting tool such as Excel. By concealing the complexities of the API and elevating to a standards based approach, cloud data virtualization delivers an order of magnitude improvement in time to value at a substantially lower cost.

Cloud data virtualization begins to fulfill the promise of cloud-based data, that it can be accessed more easily, and used more flexibly. This is in stark contrast to what we commonly refer to as ‘siloed’ data residing in various applications, with different APIs and query language, thus making quick access and integration difficult.

How does this help CPA firms? The need to execute more up-to-the-minute financial analysis and reporting is evident on a daily basis, as enterprises demand the most current and thorough data available in order to make informed decisions.

Accounting and financial services firms are seeking a means of delivering this analysis with reasonable cost. Accessing data from where it resides in the cloud and being able to integrate different data sources offers an economically feasible solution, one that can be executed in a matter of days.

A case in point: One popular data query that Intacct users execute is extracting contract data out of Salesforce and merging it with invoice information from Intacct for audit purposes. Cloud data virtualization creates a virtual record that has a combination of fields from Intacct and Salesforce and pulls this information from these applications in real time.

The Intacct user can populate the query results into Excel, Tableau or any ODBC/JDBC compatible reporting or business intelligence tool and view the analysis results for audit purposes in just seconds.

Without cloud data virtualization, these Intacct users would have to, perhaps manually, move the data from Salesforce and Intacct into a spreadsheet and then perform the analysis, hours or days after they needed it. Being able to quickly get at the relevant underlying data, i.e., Salesforce and Intacct data in this case, is an enormous advantage in forecasting.

As we’re writing this at tax crunch time, we should note that cloud data virtualization enables audits to be completed in days, rather than months. An accountant, or controller within an enterprise, can aggregate data from Salesforce, Intacct and SugarCRM to reconcile contracts with invoices and payments – a job that could take months before cloud data virtualization.

Cloud data virtualization, and the move to cloud-based data warehousing are promising developments for CPAs and any professional in the financial management field.

These technologies address the pain points of having to access SaaS data from various sources and combining this data for business intelligence insights, and for compliance. As accounting firms move more functions to the cloud, innovations such as cloud data virtualization can help make the move more palatable and efficient.

Ajay Singh is CEO of Elastic Intelligence, developer and marketer of Connection Cloud, a PaaS (Platform-as-a-Service) platform enabling real-time access to SaaS data.

 

 

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