Successful business analytics depend on high-quality connections to data sources across a vast and growing datasphere. High-performance data connectors for many of those sources are built into popular analytics tools, while others may need to be installed as stand-alone drivers. In this article, technology writer and analyst Matt Gillespie shares some key requirements and strategies for pulling data together from every data source to generate business insights.
In many cases, connecting to a data source from an analytics tool is no more complicated than selecting from the list of published data sources in, for example, Tableau’s Search for Data dialogue. Once you have access to the data in your analytics tool of choice, you can filter, search and otherwise manipulate it along with data from other sources to generate and output information and insights as reports, visualizations, dashboards and machine-to-machine communications.
Equipping Business Units with Open-Ended Data Connectivity
Business units such as marketing and finance often have evolving needs to connect to data that’s housed in a broad range of sources. They may satisfy the vast majority with a handful of sources such as Hadoop for big data, plus Snowflake and Redshift data warehouses and a NOSQL database or two like MongoDB and Couchbase.
At the same time, novel requirements often exist for taking advantage of additional data. Data analytics applications need the ability to find patterns, trends and anomalies both within and among all those data sources – whether popular, niche or custom.
- Reporting across data sets. A sales and marketing department might use both Marketo and Salesforce, and their challenge might be in connecting the dots between those two sources and generating reports in one visualization tool, using individual point-to-point connections between the established analytics engine and each source.
- Reaching into additional data. New data connections will inevitably be needed, and in some cases, the destination app will lack the data connectors it needs. For example, finance or internal audit may want access to travel and expense data in SAP Concur for a Tableau visualization or dashboard.
Taken together, these requirements show the value of being able to connect to a comprehensive set of data, wherever that data resides. In the finance and audit example above, Tableau doesn’t ship with a Concur driver, so it can’t connect to that data source without an additional data connector.
The good news is that just as it provides many of the built-in drivers in Tableau, Simba also offers an off-the-shelf driver for Concur that the customer can put in place to add that functionality. In this and many other scenarios, customers can partner with Simba to cover the data connectivity needs that business units will raise as time goes on, even in edge cases where the answer turns out to be a purpose-built custom driver for bespoke data configurations.
Enabling Rich Analytics for the Long Haul
Establishing both internal and external data connections in the first place can be a challenge, but enterprise IT may find the greater challenge lies in tuning and maintaining those connections over time. Meeting evolving requirements for a given data source and taking advantage of new properties such as emerging data standards and security capabilities may require updates to data connectors.
- Optimized performance and reliability. For many analytics uses, high throughput and low latency are critical, especially when data sets get very large. In those cases, Simba can often advise customers on how to improve data connector performance, as well as improving reliability for business-critical implementations.
- Long-term maintenance. Simba maintains its data connectors to stay current with changes such as early adoption of standards that include ODBC 3.8 and JDBC 4.2. In cases where application vendors lag in implementing these updated drivers, customers may get faster access to them by working directly with Simba.
In these cases, the driver functionality in question may exist, but it may fall to the end customer to seek it out and put it in place. It’s also possible that bespoke customization is needed. Simba can cover both cases, and even when an enterprise shop wants to take on customization itself in-house, Simba provides the SimbaEngine SDK for that purpose … it’s the same, time-tested toolkit that Simba developers use internally to create their industry-leading drivers. Customers may also come up against limitations when they need to query against data from sources that lack SQL capability, such as NoSQL databases or the many SaaS platforms that are increasingly as vital to analytics as they are to doing business in general. In these cases, Simba drivers can enrich the functionality of data connections by mapping from the source to a relational schema as required by the analytics application.
That capability is often central to getting the full value out of the data source, in order to effectively use it in conjunction with the organization’s broader universe of data.
When your data analytics tool of choice doesn’t have a built-in driver under the hood to connect to a specific data source, or when those drivers don’t fully meet your needs, engaging with Simba can be the solution of choice. In addition to providing many of the drivers that built into your favorite business analytics apps, the Simba team and technology are responsible for a great number of the ODBC drivers available today. To extend those capabilities to any data source and requirement you can imagine, Simba has you covered.
- Find all the business and technical documentation you need in the Simba resource library.
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