Turning analytics into actionable insight data
It is a common knowledge that insight data and analytics play a crucial role in a successful business strategy.
Data and analytics are, of course, inextricably linked. However, it is essential for the businesses to place equal emphasis on data capture and analysis. After all, collecting data for the sake of just collecting it is inevitably futile. The primary concern of data capture and analysis should be a specific, strategic undertaking that reveals answers to the carefully considered questions. Therefore, companies need to ensure that their approach to analysis is as comprehensive as their data capture capabilities. Once an organization knows how they want to use this data, they can begin to more effectively leverage its benefits. Here, we explore what constitutes actionable insight data and discuss how to devise an efficient data project.
The key characteristics of actionable insight data
Today, businesses generate enormous amounts of data. Therefore, it is extremely important for the data projects to be focused. Without specific goals, analytics departments are at risk of generating what industry insiders refer to as ‘vanity insights’ – which are basically metrics that seems important but lacks the real use value. Below, there are seven key features of an actionable insight, which enable businesses to separate useful data from the excess information.
Useful data will align with business objectives. If it is difficult to tell how the data informs KPIs, it is likely that analysis has produced a ‘vanity metric’.
It is challenging to act on an insight if there’s no context. Therefore, analysts need to ensure they have benchmarks that illustrate why the metric is significant.
There is a degree of subjectivity when it comes to the relevance of data. As such, analysts need to ensure they deliver information to the right person at the right time. If insight data is not channeled to key decision makers, it is unlikely to be effectively actioned.
The more complete an insight, the more actionable it is likely to be. Although insight data based on broad subjects can reveal interesting anomalies, it often lacks sufficient detail to uncover solutions.
New insights are inevitably more interesting than familiar ones. Therefore, when the first time analysts identify new intelligence, they should ensure what exactly is driving this behavior.
It is important for a broader team to be data literate not to overlook valuable insights. In addition, analysts should strive to create legible visualizations that facilitate action.
Planning a data project
Once an organization has a robust understanding of what constitutes a valuable insight, they can work towards developing an effective data project. Below, there are the three key steps to implementing a data strategy that extracts and exploits actionable customer insights.
1. Understand different analytic methods
Integrating analytics into existing systems is often challenging, so it is important that the entire organization understands the processes involved. As a foundation, the team should understand different types of analytic approaches. These include the following: descriptive analytics that identifies problems; diagnostic analytics that ascertains why the problem has occurred; predictive analytics that projects what may happen; and prescriptive analytics that defines the best course of action.
2. Target relevant data
Unless a specific objective is identified, it is almost impossible to tell which data is relevant. With objectives, comes optimization – which prevents analysts from getting ‘lost in data’, or generating arbitrary metrics. The data team should focus on high-quality data analyzed in real time. Often, a sensible approach is to ask key stakeholders and senior management what exactly they want to find out.
3. Context is key
With greater context comes richer insights. Therefore, the better the team understands the context of their data, the more nuanced their interpretation will be. From here, the wider team will be able to make better-informed business decisions. The data team can establish context by interrogating the significance of data, assigning a value, and evaluating the impact the intelligence will have on the organization.
Why collaboration is essential
As the final point emphasizes, data literacy and collaboration are key to an effective data project. So, whilst data scientists may have the capacity to put together a highly technical report, other staff may have a different approach. These differing interpretations will unlock wide-ranging insights with a greater potential for action. In essence, collecting data without clearly defined objectives is unlikely to generate value. Although data collection capabilities are important, generating an actionable insight requires diverse skill sets. Therefore, the key to ensure that your data project is as useful as possible lies in nurturing a data-driven corporate culture.