In recent years, the business world has undergone a dramatic transformation in how it relates to data. Whereas data analysis was once seen as the purview of only the largest corporations, today it's the beating heart of every business.
Not only that, but while data used to be collected and processed at yearly or quarterly intervals, today its' collected daily — often in real time. And here's the real kicker — the standards to which the actual work of analysis are held are leagues ahead of where they were only a few years ago.
Today, the expectation is that any employee — regardless of his/her department and specialty — be able to conduct a highly precise analysis covering multiple dimensions and variables on demand. Throw in a slew of increasingly sophisticated data manipulations and it's easy to understand why data command and control is now considered the ultimate key to success.
The more on top of your data, the more lessons you learn and faster you internalize those lessons. That means fewer mistakes and fewer missed opportunities. It also means faster time-to-resolution and greater optimization at every link in your value chain. A few fractions of a second shaved here might translate into a few million of dollars there.
That's why data practices and the push to turn data into insights can feel so much like an arms race. To remain competitive, organizations need to continuously identify new and smarter ways to use and understand their data.
This particular arms race is more of a marathon than a sprint. To make it to the winners circle, you'll need a strategy that plans for the course ahead. In lieu of a crystal ball, the best way to do that is by looking at where we've come from and extrapolating forward.
Looking at the last few decades, we can identify 3 distinct stages in the evolution of data analytics.
The turn of the millennium ushered in the era digitization. Suddenly every business became a digital business — which meant every business now had a digital data trail. It didn't take long for business leaders to recognize this as an incredible opportunity.
Without even trying, the available pool of data that decision makers can draw from grew exponentially. Not only that but, by default, the data was suddenly more structured and better contextualized. That would make the work of turning raw data into real insights much faster and more straight-forward.
With a little bit of effort, every business variable could be captured for further analysis — opening the path to a virtuous circle of improvement. At least that was the popular market sentiment — which was what prompted technology vendors to start developing tools to collect information and put it in the hands of decision makers.
As more and more companies started to deliberately create and collect data, a new challenge emerged. Good data needs to be comprehensive, which means it needs to come from a variety of different sources and angles.
But with each source representing a separate silo and liable to structure, format, and measure data a little bit differently, decision makers found themselves comparing apples to oranges.
At best this resulted in a lot of extra and time-consuming work. At worst, it resulted in faulty analyses and wrong conclusions.
Collecting data from different sources turned out to be a real technological challenge. As the data became available, available didn't always translate into usable.
This led to the second distinct stage in the analytics evolution — defined by aggregation and normalization. Technology vendors stepped up to deliver tools that could:
With data aggregation and normalization, enterprises were able to generate more data insights and become more data responsive — helping inform decisions from big to small on an everyday basis.
With data easily aggregated and normalized, businesses dramatically ramped up their data activities. But with the spigots of data open to full blast, analysts and decision-makers were made to contend with overwhelming amounts of information. Regardless of the how well the data is organized, there just isn't enough time or eyes available for it all to pass human review.
And so a new need was put to technology suppliers. With good visualization and reporting tools, businesses would be able to transform huge data sets into easy to understand bites. That would help data managers review much more information much more quickly and accelerate insights.
It didn't take long for technology vendors to meet this need — flooding the market with all manners of dashboards and smart reporting solutions. Today, these solutions are ubiquitous and provide the backbone for most businesses' routine data activities.
And while most people still think we're in the midsts of the Visualization & Reporting stage of the analytics evolution, it's hardly the final chapter on data progress. In fact, the next chapter is already underway — and it's all about relieving any remaining bottlenecks that might slow the process of turning data into insights.
Using BI and visualization dashboards, information is presented in an organized and easy-to-consume manner. But no matter how fast and efficiently information is taken in, you're still limited by the speed at which it's being pumped back out as clearly defined business imperatives.
That's the difference between reporting and monitoring. Reporting is the stuff populating your data dashboards. But what you do with that stuff — that's monitoring.
For most businesses, reporting is already largely automated. For 99% of businesses though, monitoring is an entirely manual process. And that's where this otherwise well-oiled machine really begins to break down.
When businesses can only make sense of their data through manual means, scale will always present a challenge. With all of the heavy lifting of data interpretation still on human shoulders, the need for experienced data operators is profound. And yet, it's becoming increasingly difficult to source and maintain the type of talent required. This in turn drives labor costs up and compounds the problem.
With only so much available budget, it means fewer eyes on screens responsible for reviewing more and more data. This is the main bottleneck to data insights and it's the source of tremendous inefficiencies.
The next step of the data evolution will be about bringing automation to a new category of tasks: whereas progress has been limited to data preparation and presentation until now, going forward it will extend to explanation as well.
Beyond aggregation, beyond normalization, beyond visualization, there's interpretation. This is where the future lies. And it's what oolo was designed to do. We call it Deep Monitoring.
Using machine learning to collect, connect, and contextualize millions of data points, oolo automatically surfaces business problems and opportunities. When an incident is detected, oolo highlights the root cause, calculates the bottom line impact, and helps direct follow up. In this way, the system streamlines workflows, eliminates operational inefficiencies, and accelerates monetization.
oolo not only helps growth teams get their hands on and heads around the data that matters to them, but helps them instantly get to the bottom of the issue.
Businesses live and die on their data. It's a fact that grows truer with every passing day. And yet even after decades of progress, we still extract value from our data in mostly the same ways we always have.
Translating statistical observations into business imperatives requires an attentive human eye and a well-honed skill set. It's effective, but not particularly efficient. It's also far from comprehensive — only scratching the surface of the available data — and prone to occasional oversights.
But just because something's always been done a certain way, doesn't mean that it always will be done that way. As we enter the next stage in the evolution of data analytics, the way we work with our data will be fundamentally transformed.
In this new era, prescriptive analytics will not only touch but totally tackle the most tedious and time-consuming data monitoring tasks. With more automated monitoring, human operators will be able to shift their focus from inspection & investigation to validation & optimization — breaking bottlenecks, boosting efficiencies, and bolstering morale in one fell swoop.
It's a future worth getting excited about and, for digital publishers, it's a future that's already here!