data analytics

The growth of the global digital economy is genuinely astounding. Its creation, output, and proliferation of information are hard to grasp. For quick context, every minute millions of Google searches are conducted, millions of YouTube videos watched, and hundreds of thousands of tweets sent. The acceleration of this cycle is unavoidable because it’s increasing by the moment.

According to the sixth edition of DOMO’s report: Data Never Sleeps, by 2020 1.7MB of data will be created every second for every person on earth.

While most individuals put little thought into the data they create, companies have long been concerned with how to use the growing amount of it to their advantage. The effects have varied to-date, but it’s never too late to get better.

Consider these four ways to improve your company’s analytic approach.

Become Data Fluent

The first and most important thing any organization can do to reach a transformative level with their business intelligence is to become data fluent. Data-fluent organizations still need tools and processes in place to extract value, but it’s how they communicate and think about data as part of their daily workflows that truly help them achieve that value.

Juice Analytics highlights four significant benefits of achieving data fluency:

  1. Make Informed Decisions
  2. Save Time and Get to the Point
  3. Be Transparent and Accountable
  4. Cultivate a Learning Culture

Everything a company faces is helped through these four areas. Every employee is empowered to find their own answers and dig deeper into the nuances of their role. Meetings are freed from unproductive conversation and biases, based on one truth instead. Data fluent companies don’t just interact with their data. They discuss it, come to conclusions about it, and then take action.

Leverage AI and ML to Uncover More Value

Data continues to grow and so too does its sources. Even with the right analytics team and search tool, it’s impossible to comb every possible insight from datasets.

Data analytics platforms are just another example of the widespread applications of artificial intelligence (AI) and machine learning (ML). But like the other areas that implement intelligent self-learning software, impressive results are achieved when using them on an entire company’s data repository.

This is the case with SpotIQ, a feature from machine learning analytics company ThoughtSpot that alerts users of hidden trends, causal and noncausal relationships, and key performance indicators in data sets automatically.

Another benefit to using AI and ML in a data analytics platform is delivering more personalized findings the more employees use the tool. Employee adoption remains a huge roadblock for companies to get value from their data. Tailored insights can go a long way to make analytics software more engaging to use.

Incorporate Search and Voice Analytics to Accelerate Findings

Having an analytics tool deliver personalized insights provides the best chance at piquing that employee’s curiosity. However, for large companies that have to get thousands of employees to not only buy into the validity of data but also its daily usage, accessibility needs to be a cornerstone of any analytics approaches.

In the past when an employee needed access to information, they’d put in a report request and wait days to receive an answer. Text and voice-search capabilities make it easier to look to data for answers. We obviously already do this in our personal lives with Google. It’s a natural progression that employees won’t be fighting.

Embed Analytics for Wider Collaboration

The same things that characterize a data-fluent company culture, such as learning, saving time, making more informed decisions, offering transparency and accountability — can be maintained with embedded analytics applications. The ability to embed and share visualizations with team members makes it easier to track long-term narratives and easily settle debates. Embedded BI and analytics extend to an organization’s entire ecosystem, too. An enterprise can monetize their data by making sound business decisions off data and sharing it with partners to make the whole ecosystem smarter and efficient.

Your company’s analytics fate hinges on data fluency, but don’t fool yourself — a tool that blends the right balance of artificial intelligence, self-serve accessibility, and shareable insights help close the loop on the process.