It’s no secret that our digital ecosystem creates an overwhelming amount of data. For consumers, this proliferation is an afterthought. For companies, it’s a chance to understand customers better, improve the health of their operation, and innovate for long-term growth.

Opportunity and action don’t always align, though. While many enterprises are fully on board with the idea of generating more – and deeper – insights from the data they collect, it’s often a struggle to get data initiatives off the ground.

Why haven’t businesses been able to move forward in their digital transformations despite an array of helpful resources at their disposal? Like analytics, much of the information that exists is complex and hard to put into action.

If you’re looking for straightforward, actionable tips regarding data analytics, consider the following advice.

Focus on Culture, Not Tech

The importance placed on data has ushered in a wave of next-generation tools to collect, store, and deliver insights through data. But if all organizations needed to do were implement a tool, there wouldn’t be so many companies struggling to get value from data. Tools are undoubtedly crucial to facilitating knowledge throughout the workplace. However, if a culture can’t communicate about data in a shared language, companies will fall well short of enlightenment.

Establishing a data fluent culture isn’t easy, but it is straightforward (when done correctly).

The C-suite must set the example and trickle it down to department heads and managers. Organizations need to create a shared lexicon of data, set and communicate the right metrics to evaluate performance, connect workflows and tools to prevent silos, and empower employees to be their own data analysts. This last step is where the right tech comes into play, but it doesn’t mean much without following through on the rest of the steps.

Prioritize Governance Without Sacrificing the End-User Experience

It’s a goal for any business to grow its operation. No matter what stage a company is at when they begin structuring their analytics program, data governance and the end-user experience are both essential elements on opposite ends of the spectrum. As organizations’ data collections and user bases grow, information needs to be accessible without being compromised and shared too widely.

At the same time, for insights to be worthwhile (read: actionable), they need to be speedy. Governance must walk a fine line between security and accessibility. This is one case in which having the right tech makes all the difference. For example, data analytics software tool ThoughtSpot makes it easy to set custom permissions for each user down to specific data rows. This ensures every employee has access to what they need without inundating them with irrelevant information.

Be Decisive with Your Strategy

Of course, just because blunders can be corrected doesn’t mean a half-thought-out strategy should be acted upon. Decision-makers can’t be wishy-washy with their approach. Being decisive means that any faults committed can be learned from as soon as issues arise.

If a company isn’t clear on their blueprint, however, they’ll have a harder time realizing when mistakes have been made, and they won’t be able to learn from the oversight. Committing to a strategy also means there’s less of a chance for balls to be dropped or data roles to blend. Laying out the steps ahead of time and who’s responsible for what will prevent a data analyst from dealing with something that an engineer should be handling, and vice versa.

Don’t Let a Fear Mistakes Prevent You from Getting Started

With so much complexity surrounding analytics, it’s easy to get overwhelmed with decisions and never get the program off the ground. Other organizations hesitate because they don’t want to make mistakes. Both of these thought patterns are fear-based. For any business to get past the initial implementation roadblocks, they’ll have to accept that hiccups are inevitable. The days of analytics programs involving legacy systems and expensive on-premises architecture are long gone. The cloud is more resilient and fluid when it comes to correcting course.

Getting real value from data requires a lot of time and effort, but it’s undoubtedly achievable – especially when companies don’t make the process more complicated than it needs to be. Consider these pieces of advice as you plan your analytics implementation and be prepared to adjust if any issues arise.