By Jacob Maslow – Branded Content
Data analysis as a significant part of business operations is far from a new concept in 2022. The analysis, understanding, and implementation of data is something that organizations have been doing for decades and longer now. However, significant advances in trends and technology have made data analysis more efficient. One such advancement is the emergence of operational analytics. In an era that revolves around intelligent technology and innovation, the amount of consumer data available is mind-blowing. However, one of the historical issues with data analysis is that it’s a retrospective strategy. It looks at how the organization performed previously and sometimes provides stale insights. This is what operational analytics aims to solve.
Operational analytics is a strategy that brings data alive, makes it agile, and allows employees across the entire organization to make data-driven decisions based on real-time information and analytics. Granting real-time access to pertinent information for employees in accounting, marketing, advertising, communication, customer support, and so forth enables the non-technical employees of an organization to make data-based decisions with confidence and accuracy.
Operational Analytics Vs. Traditional Analytics
Operational analytics differs from traditional analytics. While conventional data analysis relies on dashboards and other internal tools to relay performance data across various internal KPIs, these reports are time essential and can create quick pivots. These reports serve an essential purpose and shouldn’t be disregarded entirely. A powerful analytics strategy requires both traditional and operational analytics.
In contrast to traditional analytics, operational analytics mainly focuses on delivering real-time information to non-technical users across an organization. The popularity of data warehouses takes an SQL proficient user to access and make sense of the data collected. Operational analytics aims to open up this access, making it possible for non-technical employees to access instantaneous data reports, create automated tasks and workflows, and truly make moment-to-moment data-driven decisions.
In other words, the main difference between operational analytics and traditional analytics is that operational analytics are aimed at just that, influencing actual business operations to improve both the consumer experience and the organization’s bottom line.
The Data Silo Issue
Data silos are a significant headache for companies and organizations, no matter the industry, the size, or the scope of products and services. There have been a series of partial solutions to the data silo issue, and operational analytics is one. The first step was data warehouses that most organizations rely on to centralize data. Even these, though, offered particular challenges.
First of all, in using more than a single data warehouse, the same issue of data silos reemerged. In addition, however, there is also an accessibility challenge that’s presented. Organizations need to bridge various data warehouses, but they also need this data available across their software systems, like SalesForce, Hubspot, and others.
Operational analytics is designed to break down these data silos and accessibility issues and deliver the data to users within an organization who can then utilize it in real-time.
Turning Analytics Into Action
The primary purpose of operational analytics is to take action rather than understand or reflect. This is something that organizations struggle with because the pertinent data is often not available or isn’t accessible. Implementing operational analytics allows marketing professionals, salespeople, accountants, and customer support professionals to improve performance across the entirety of the organization through true-to-form data-driven-decision making.
Operational analytics make it simple, convenient, and easy to turn analytics into action, even allowing for marketing teams to experiment and iterate changes instantaneously freely.
Implementing Operational Analytics
There isn’t a place within an organization where operational analytics doesn’t have an application. Offering tactics and strategies that can impact marketing, advertising, accounting, customer support, and even communications, the sky’s the limit to using operational analytics to make the most out of collected data.
Depending on the user’s role in the organization, the exact implementation of operational analytics may look slightly different. For example, a salesperson might use operational analytics to add a consumer to a specific group of marketing campaigns based on a recent purchase. In contrast, for customer support, professionals may use an automation feature to remind users of an expiring account that needs renewal.
A Few Final Words
The industry of operational analytics isn’t going anywhere anytime soon. Constant innovations are being made year after year, and companies are continually finding new and innovative ways to turn analytics into action with various operational analytics tactics and strategies. For the modern organization looking to connect and engage with consumers while offering a tailored customer experience every step along the way, operational analytics is a must-have.
Photo Courtesy // Jacob Maslow //