In just a few years, big data has transformed how companies track consumer trends, spend resources and interact with their customers. Companies that have fully embraced advanced analytics —think Amazon, American Express, Netflix and Starbucks—are pulling ahead. And those failing to harness the potential of big data are at risk of losing out.
To compete in fast-moving industries like retail, telecommunications and banking, data and analytics must be embedded into your company culture. But what does it mean to have a data culture—and how do you achieve it?
Here are three ways that businesses can make analytics the foundation for decision making.
1. See data opportunities everywhere
It’s simple: the more and better data you have, the more you can do. Bigger and better data allow for a clearer view of the big picture, as well as the details.
Companies with a strong data culture are better able to identify and manage multiple data sources. In your organization, think creatively about new uses for the data you already have: customer service logs, hourly factory reports, even social media conversations. And don’t forget to look outside your walls—everything from weather reports to demographic information can also be valuable sources of contextual data.
Of course, quality matters as much as quantity. If your data is bad, it could affect every level of the company. Maintaining and managing data so there is a single source of truth—a central, controlled pool for everyone to draw from—is crucial. Large or older organizations may have to bite the bullet and upgrade from legacy IT architecture.
2. Use next-generation tools to solve a problem
Data analysis has moved far beyond traditional business intelligence. Predictive tools powered by machine learning have changed everything from how telecom providers predict network usage to how retailers forecast sales.
Most of all, these AI-powered tools have transformed how companies engage with their customers. Businesses can personalize customer onboarding, upsell by offering right-sized incentives, and cut churn by identifying at-risk customers early enough to make a save.
It’s key to start by identifying the business opportunity you’re trying to seize, and then design or source the models to solve it. Analytics projects with a clear hypothesis to test tend to result in faster performance improvements—and are also usually better understood by managers.
3. Get everyone onboard
Of course, pools of data and advanced models are worthless if nobody uses them. Getting buy-in typically requires a multi-pronged approach that includes training, incentives, and metrics—as well as executives who lead by example.
A successful data culture must move beyond specialists into the wider organization. Start by ensuring that executives, leaders from marketing, finance and IT, and channel owners truly understand how data can help them make better decisions. Without leadership buy-in, teams will never truly adopt a data-driven mindset.
It can be useful to show the value right away with a small, analytics-based project that delivers quick insights to act on immediately—there’s no better way to show people the power of data than by actually using it in real life.
Building a data culture is easier said than done, but the effort is more than worthwhile. Companies that embrace advanced data analytics make better decisions, seize opportunities and avoid squandering resources. And those that don’t will fall behind. The true value of your data though requires investment in automatic message optimization to both analyze and act on your data.
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