Companies have all the information they need to maintain efficient operations, gain a better understanding of their customers and drive innovations that power growth.
But if leveraging these resources were easy, we’d see a whole lot more businesses getting value from their data.
After all, collecting data — while the first step toward taking advantage of analytics — is nothing without the right infrastructure and culture.
However, most organizations don’t struggle with deployment. User-friendly tools and next-generation technologies offer the potential to streamline the once time-consuming process of supplying a business with analytic models.
The real issue standing in the way of businesses is adoption. To better understand the gap that exists, let’s take a closer look at the difference between deployment and adoption.
Deployment Is Only the First Step
Companies have used traditional business intelligence technologies to varying levels of success for decades. These tools helped organizations evaluate their past decisions and clarified areas on which they should focus their future efforts. What they didn’t do was allow for real-time decision-making or broader data access. Instead, everything was filtered through IT and the analytics team.
Modern-day BI is much more flexible than its predecessor, but experts are still the ones in charge of choosing and implementing the technology. This means there’s little barrier to entry when it comes to finding the software solutions to tap into data. Advanced data analytics solutions like ThoughtSpot make deployment decisions even easier in that they relieve experts of the burden that comes from being the gatekeepers to knowledge, so they can instead focus on higher-level tasks.
These types of tools incorporate subsets of AI, like natural language processing and machine learning algorithms, to allow non-technical employees to find their own answers from company data.
But IT’s deployment responsibilities requires more than choosing a tool, of course. Developing a system for governance and deciding on whether to use a multi-cloud or hybrid cloud strategy are other important details to consider. Considering that experts who know best are in charge, these decisions don’t usually delay organizational developments.
Culture Dictates Adoption
Analytics initiatives often start out with a head of steam, but when it comes time to put infrastructure into regular business use is when struggles typically begin. The reasons companies struggle with data adoption are vast, but it usually boils down to a lack of data literacy in the workplace. The right tools are in place but employees still aren’t interacting with data in their workflows, and they’re surely not collaborating with their teams around data. If a company culture doesn’t have a baseline understanding of how to interpret and communicate about data, they’ll reject technology and resort to less-effective means of decision making, like intuition and hierarchy.
Key leadership roles like the data evangelist and chief data officer, as well as senior analytics professionals, are the ones responsible for creating a data lexicon and educating employees on how to analyze data. While leadership instills the technical skills employees need to use analytics tools, it’s also the time to address the concerns of resistant employees. Because no matter how inefficient some processes may be, many employees prefer stability over change.
But it’s not just employees that may be resistant to new ways of doing business. Non-technical C-suite members and managers might view the analytics program as a one-time project, expecting full value once the end date is reached. Analytics experts must be clear that just the opposite is true; analytics requires continual fine-tuning to be effective. The time and energy invested will all be worthwhile as long as data quality and business use stay consistent.
Developing a streamlined analytics process that serves the entire business requires proper technologies. However, organizations won’t get far if they don’t foster a culture that understands the many benefits that come from using data regularly in their workflows.
