Traditional analytics have been central to business best practices for years now. It began back in the late 90s and early 2000s as companies embarked on their digital journeys, evolving and expanding ever since.
Now, we’re seeing the tail end of traditional analytics as companies realize the limitations of this approach. Operational analytics represents a shift towards a more dynamic, action-oriented methodology that uses the power of data in real time to achieve better outcomes in every aspect of the organization.
The difference might sound vague at first, but it could not be more real. Let’s talk about how operational analytics turn insights into action, and why this matters for all businesses moving forward.
Where Traditional Analytics Fall Short
There’s no need to knock traditional analytics, as these technologies and practices set the stage for modern business in many ways. Traditional analytics includes everything from storing data and organizing it, to structuring it in meaningful ways and detecting useful patterns that can lead to better outcomes.
A perfect example would be customer insights, which become more abundant and diverse as ecommerce develops. We learn so much about customers based on how they browse websites, interact with digital assets, research products, and follow up with companies after a purchase.
All those behaviors generate a massive amount of data, from many different platforms, and can be stored and analyzed via hands-on, manual processes.
But this is where we run into problems. With traditional analytics, it’s not only difficult to streamline that data as it’s collected, but the processes are clunky, non-integrated, slow, and lack visibility across key teams and stakeholders.
This is where we see traditional analytics stumble and fail to deliver value in a quick, actionable way. The stage has been set, but now it’s time to make a substantial leap forward with operational analytics.
The Elements of Operational Analytics
After pinpointing the weak points of traditional analytics, we can start to see how the next generation of technology and techniques will look.
To spell it out in clear terms, let’s walk through the five elements of operational analytics and take note of the improvements across the board.
The expression “time is money” cannot be illustrated any better in the context of traditional vs. operational analytics. The speed of OA is perhaps its most valuable attribute, allowing data teams to skip hours of labor and deliberation and get right to the next actionable step.
That speed is made possible through better integrations between software, eliminating data silos, connecting various teams with improved communications, and tapping into the functionality of tools that would otherwise go unused.
The next major advantage of OA is action, specifically, turning that data into actual, impactful decisions. Far too many traditional analytics projects end up with mountains of data that simply collect dust, don’t serve a purpose, and don’t lead to any action. Simply put, they’re useless.
With OA, action is everything, bridging the gaps between potential and kinetic. This could mean anything from triggering messages and alerts to retargeting ads or streamlining customer support interactions.
Even the most advanced traditional analytics systems require all hands on deck to make use of the data gathered. This is hugely limiting in terms of the volume of data that can be processed, and tends to take away skilled labor from other, more pressing objectives.
The answer that OA provides is automation, taking the pressure off stakeholders who have bigger fish to fry. A sales team, for example, can focus on closing deals, rather than uploading data from Salesforce and syncing it across various business intelligence tools.
The best OA tools make decisions for you, based on data and patterns that only a supercomputer could detect. This frees up data teams to spend time on tasks that require creativity and human insights, rather than crunching numbers.
Traditional analytics simply put the data in front of you, while OA systems do the thinking for you.
Every company struggles with silos between teams, software suites, data pools, and even team members. Operational analytics aim to break those silos down and usher in a new era of connectivity between these critical assets.
This leads to better communication between individuals and teams, and improved coordination when it comes to implementing strategies on a bigger scale.
For example, if customer engagement is the goal for the next quarter, OA systems can be programmed to discover what drives these engagement metrics, what each department can do to boost them, and how those numbers are being met (or not) as time progresses.
Don’t Let Data Hold You Back
With all due respect to traditional analytics, it’s time to move on! Operational analytics is the way of the future, and the time to make the upgrade is now.