The first half of 2020 is and will continue to be an uncertain time for businesses. We’ve learned how to collaborate remotely and connect with our customers in innovative ways. But we have not discussed how we have to change the ways we measure the impact of these changes. As we adapt to the pressures of a rapidly changing economy, we see three accelerating trends in data and analytics.
Proactive analytics will overtake predictive modeling
With uncertainty being the new standard, trusting predictions are going to be tough for companies. The cost of gathering data to tune new models is restrictive, and the timeline to build new models is long. For that cost, the accuracy you can expect from a new model will be too low to show material ROI. And the rate of change we expect to see from markets and customer behaviors over the next few months make “prediction” challenging.
When the pace of decision-making accelerates, businesses need to act proactively based on their data. Along with expanding a data team’s ability to work effectively with all their data, these new proactive platforms can accelerate the decision-making process.
Cloud-native will overtake hybrid architectures
The hybrid data models have allowed large organizations to bridge the gap between legacy systems and flexible data tools. Unfortunately, the complexity has built a difference between business users and the data they need to drive informed decisions. We see the most productive collaborations between data teams and the business in organizations that have decided to build a fully cloud-native stack from the ground up.
It’s a far simpler architecture, and this simplicity makes it easier to handle massive transactional datasets and secures the information easily accessible to engineering, data science, analytics, and the business.
Exploratory analytics begins to resemble search
A company’s ability to effectively explore its data and find useful facts is a true competitive advantage. But, when analyst resources are at a premium, organizations need tools that can seek out and recommend the impact of the data.
The advantage of exploratory analytics is rooted in the lessons of search. Results from this analysis can be rapidly stack ranked, engaging populations rise to the surface, and competing hypotheses are tested and presented in parallel. The upshot is that analysts now spend more time making the answers actionable and less time digging.
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