From Data Overload to Data-Driven Decision Making: Three Key Trends

As they seek to keep pace with rapidly evolving environments and demands, it continues to get more critical for enterprise leaders to employ data-driven decision making. This blog post examines the key trends that are driving this change

Introduction: The Move to Data-driven Decision Making

When is too much of a good thing a bad thing? When it comes to data, many organizations have long surpassed that point. Many enterprise decision makers are struggling to keep up with the volume, velocity, and variety of data in their organizations—and the challenges only get tougher. While data is integral to every key business outcome, more isn’t necessarily a good thing.

In response, the way enterprise leaders are approaching data-driven decision making is fundamentally changing. We need new ways to store, analyze, and report on data, employing approaches that are augmented by AI and machine learning and that tie back to business outcomes. Let’s take a look at some of the key trends forcing change in this area.

The Key Drivers for Data-driven Decision Making

#1 Contending with Spiraling Data Volumes

Technology environments continue to grow more dynamic, composed of more disparate yet interrelated systems and elements. Environments now tend to feature a diverse mix of virtualization technologies, clouds, containers, microservices, orchestration systems, big data and business intelligence platforms, and more. Increasing layers and types of security technologies continue to be implemented as well.

For all these reasons, the volume, variety, and velocity of data that needs to be correlated and analyzed continues to grow dramatically. Decision makers need to accelerate their ability to sift through massive amounts of data, and gain the actionable insights they need to optimize performance, service levels, applications, and investments.

Relying on siloed point solutions, teams are drowning in data, while lacking real insights into how technology performance ultimately equates to business outcomes. If your teams haven’t reached a tipping point, with trends like IoT and 5G making their presence known, they will soon.

#2 Adopting a BizOps Approach

To meet their agility imperatives, technology and business leaders are adopting an emerging methodology called BizOps. IDC analysts define BizOps as “a data-driven decision-support mechanism that connects business and technology functions together to drive business outcomes.”

Through BizOps, teams can ensure that digital infrastructures and software provide the operational characteristics—including speed, scale, efficiency, and agility—that business services require.

Teams that employ BizOps practices need to create a data-driven decision-support mechanism that connects business and technology functions together to effectively achieve desired business outcomes. This isn’t easy: Enterprise leaders pursuing BizOps objectives are stymied by organizational silos that separate people, processes, and technologies. The result is that the data required is also locked in silos—stifling collaboration, insights, and innovation.

#3 Employing SRE Models

While the SRE model has been around for more than ten years, the reality is that some enterprises are just now beginning to pursue this approach. Within these organizations, IT leaders are wrestling with ways to maximize agility, and the SRE model is emerging as a key enabler.

As teams seek to pursue SRE initiatives, the tools in place can offer significant support—or pose a massive detriment. The reality is that many organizations looking to adopt SRE models are either employing in-house developed open-source tools or loosely connected toolchains. Quite often, these decisions are made at departmental level. This results in tool sprawl and, due to the ensuing heterogeneity, makes it harder for staff to find a single source of truth for problem solving. In addition, by introducing a multitude of tools, integrations, and administrative privileges that are difficult to monitor and manage, these approaches can pose significant security risks.

Data-driven decision making. Done right.

Today, enterprise decision making processes need to undergo fundamental transformation, growing increasingly event-driven and automated. Teams need to embrace a new AI-driven software intelligence platform that provides breakthroughs in the way decision making is accelerated and automated within an enterprise. Through these capabilities, enterprises can establish the continuous insights and collective intelligence to optimize decision making.