In short: Data on its own has limited value. It’s what an organization does with the data that gives it significant value. Data is the raw material in a manufacturing process that outputs value. To fully realize data’s full potential, it’s important to understand this end-to-end process.
Action is, in a nutshell, what allows data to move from a still piece of information to something of potentially great value to an organization. Without any action, data is just something to look at with some degree of admiration – a degree that is determined by its reliability and potential impact.
There are various methods for triggering action that stem from data. Thanks to Artificial Intelligence (AI) and Machine Learning (ML), an increasing amount of action happens automatically. For example, realtime personalization engines and artificial intelligence make decisions based on data, with little to no human intervention.
Humans still play a central role in determining what is done, and what is not done, based on data. At the risk of sounding like I’m jealous of an algorithm, I’ll just say, we the people, are underrated here. To expand this point more broadly, organizations and their collective action or inaction on data are a key factor in determining how much value can be extracted from their mountains of data.
Extracting value from data is a process, not too different than ones we’d find in the physical manufacturing world. To start with, the raw materials needs to be picked, mined or excavated whether it’s coffee beans, copper or data. With data, the excavators happen to be analysts, researchers, survey practitioners and the like. The various roles cover quantitative and qualitative methods of data excavation.
From data to good data
The first step in the process of extracting value from data is to make sure that the data is good data. What is good data? In short, good data has had some statistical rigor applied to it.
Without going deep into the weeds – many books and papers do a great job covering this topic in detail – data reliability, sample sizes and statistical significance should be given proper attention, wherever applicable. The cost for not doing so can be great. When unproven paths are pursued, enormous amounts of time and money can be wasted.
From good data to insights
As good data moves through the pipeline, the next and critical juncture is transforming the data into insights. According to popular dictionaries, an insight is defined as a deep understanding of a person or thing. What’s important to understand about insights in this context is that they are the critical link that transforms data into action. Why?
Because the people outside of the immediate research circle – also known as “most people” – generally need more context than just the raw data. Staring at the number 49 can only take you so far. But explaining that a new online experience generated 49% more online orders than the baseline experience is meaningful.
Insights can be communicated in many effective ways. Dr. Selena Fisk PhD does a great job of detailing out both the art and science of data storytelling in her book, I’m Not a Numbers Person: How to make good decisions in a data-rich world. In discussing further with Dr. Fisk on the topic of how to make the most of data, Dr. Fisk had this to say:
“It’s about the numbers, but it’s also not about the numbers. You need skill in being able to see what is important to your organization, what you should be paying attention to, and how these values connect with real people and your business practices. When we can do this, we then need to think about how to engage in data storytelling with others, to lead actual change from the data.”
When you add it all up, insights formulate a “presentation layer” that sits between the data and the audience consuming the information. In this sense, they represent a pivotal stepping stone from data to action.
From insights to the consumption of insights
We’ve gone from data to good data. We’ve gone further to develop insights or data stories. The presentation layer has been assembled. But is there an audience? Is the theater full? Unfortunately, it’s usually emptier than we’d like.
And it’s no one’s fault. People are busy. KPIs are usually not based on how much they learn from other people’s work. Whatever the reasons are, this is where clogs in the data-value pipeline often emerge. Data mounts. Insights are communicated in a variety of formats and communication methods, and their impressions fade into the smoggy air.
In practice, insights are often shared in one off conference calls. But is everyone tuned in and listening? The meeting sometimes get recorded, but does everyone dig into them? Insights are also sent out via email attachments. Sometimes they’re looked at and other times they just get buried into dusty email archives. In the end, collective insight consumption is lacking.
This is why we at LiftCentro are excited about Datatinga™, a framework to make insight communication and consumption more fluid, so that more action can ultimately be taken. By combining a highly customizable repository of quantitative and qualitative insights, with easy methods to take actions, brands have a way to put more people in position to actually do something about the data.
Easier and sustained access to an organization’s cataloged learnings are a step forward, yet it still does not solve the logjam. Insight consumption and understanding must also be easy. With increasing competition for attention, if something is not easy to do, it’s not likely to be done. Consumption of insights is no exception to this rule.
Ease of insight consumption is also central to the design of Datatinga™. Insights are structured in consistent ways so that the eyes and minds of the data consumers are trained on how to approach the information. By reducing cognitive load, all attention for the information consumer can be focused on understanding the essence of the learning itself.
From insights to action
At last, we’ve reached our ultimate destination – action. This is what it’s all about. Remember, if there is no action, it makes much of the upstream research work – the picking, excavating, drilling work – a wasted effort. Data is one thing. But like many things in life, it comes down to the basic question, “whaddaya gonna do about it?”
Datatinga™ allows for the assignment of what we call “actions” – which are tasks that can be assigned, performed and closed out, based on the insights. By tying actions immediately to insights, this important pivot point becomes natural, seamless and easy. Notifications are also triggered into “everyday” contexts to help spur the action.
The action that stems from data and research requires a sense of accountability. A task assignee naturally feels a sense of accountability (hopefully!). When the broader organization sees that they are accountable, our bet is that actions will tend to get done more often. The ability to keep tabs on actions at both the insight level and at the organizational level creates this transparency and accountability.
More action means more value and driving incremental value is something to celebrate. Like with everything else, wins should be recognized and celebrated. As an example, when the person that was assigned an action has completed the action, they subtly move from being a watched assignee to an action hero. And who wouldn’t want a label change like that?
When illuminating good data-informed behavior, it gives organizations a better chance to multiply both the behavior and its impact. We have enough action heroes that have superhuman qualities. What we need in many organizations are action heroes that simply carry the torch through the last and, often the most difficult, leg of the process.
Data people are more generally the ones that produce the data and the stories that emanate from it. Ironically though, once the data is established and understood, the heroes that do something about it, are the ones that generate immense value for the organization. In Datatinga™ we are thrilled to offer up a solution that helps to proliferate actions – and action heroes – across distributed organizations.