The success of any business intelligence (BI) initiative ultimately hinges on speed. Data is only as valuable as it is fresh, accurate, and actionable, and these three qualifiers quickly fade, given the rate at which data enters organizations today.

The good news is that there are some steps companies can take to advance their data strategy and make BI initiatives faster and more accessible, among them:

Accelerate Access to Live Data via Virtualization

It’s impossible to get reliable, up-to-date data if you’re focusing efforts on data pipelines. Accelerating access to live data is key to reducing the time involved in delivering business insights. Intelligent data virtualization is an emerging data integration style meant to address this, augmenting traditional ETL/ELT styles and providing updated access to a variety of data sources.

Invest in Scalable Infrastructure and Processing

The cloud provides the scalability and elasticity companies need to efficiently process large data volumes and deliver the resulting analytics and insights to end users as efficiently as possible.

Remove Data Movement Constraints

Building physical cubes and data marts can lead to significant maintenance and risk, particularly when moving data from one location to another. If the process fails, companies could have an outage lasting hours or even days, causing a data blackout. As such, it’s wise to remove data movement constraints through data virtualization or an alternative solution.

Plan for Volume Growth

Given the rate at which data is growing, many companies can easily expect to see 100 percent year-over-year growth in their data volumes. It’s important to plan for this continued explosion and choose the right storage and processing architecture that can expand to meet the organization’s future data needs.

Build Self-Service Analytics

Perhaps the most critical step in accelerating data-driven insights is ensuring that all business users have access to self-service analytics. In addition to developing or procuring the tools necessary for self-service analytics, it’s also important to establish a shared vocabulary for what each metric means to both machines and humans.

For more on the above insights, check out this article in TDWI.