In the last article in our Anatomy of a Decision series, we described how important it is for data-driven organizations to insert analytcs into their daily workflow to increase user adoption.

We also noted that for the use of data and analytics to become pervasive throughout the organization, analytics tools must be easy to access and simple to use for all types of roles. When front-line employees are able to easily retrieve and navigate analytics tools, adoption rates surge. This helps drive greater productivity, faster decision-making, time-to-market, and better business outcomes.

In this article, we’ll examine the importance of scalability and governance in an analytics solution.

As the data sources used by an organization continue to become larger and more varied, analytics solutions must be designed to scale and keep pace providing timely and relevant results to end users while maintaining the governance necessary to ensure that the results are trustworthy. When users are provided with self-service tools, strong controls around data governance should be applied to ensure that the data being used is both secure and suitable for specific employees based on their roles and authorization levels. Setting user permissions at a granular level will ensure that users are only able to access those pieces of data that they’re approved to see, thus mitigating risks.

A now infamous case of data misuse, the Target stores identified a teenager’s pregnancy using data analytics and sent her coupons for baby supplies before the girl had informed her parents—causing a major stir in the industry around data governance and security. Establishing security practices, creating usage policies, and parameters, as well as assessing risk can validate that the proper checks and balances are in place with data analytics efforts.

To help facilitate collaboration between employees and work teams, user access to shared data connections can ascertain that employees are working off the same data. Providing employees access to a central repository of data, dashboards, and predictive models will ensure that employees are working from a single version of the truth, leading to greater accuracy and more consistent decision-making.

Meanwhile, a highly scalable approach to data and analytics can provide small or large teams of users immediate access to large volumes of real-time data for high-performance decision-making. Enhancements in in-memory analytics provide direct access to massive data sets along with the ability to mash up data from a variety of disparate sources for rich data visualizations, smart data discovery, and comprehensive analysis.

Scalable data analytics also provide end users with the self-service capabilities they need while equipping IT with the means to govern and control such deployments, regardless of whether they’re on-premises, cloud-based or hybrid deployments.

Next Steps:

  • To learn more about the factors that are driving self-service data discovery requirements, check out the Blue Hill Research study, “Anatomy of a Decision”.
  • Try Spotfire and start discovering meaningful insights in your own data.
  • Subscribe to our blog to stay up to date on the latest insights and trends in Big Data and Big Data analytics.

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