The actionable analytics process transforms data signals into insights that help businesses make better decisions. It also helps them identify gaps and opportunities for improvement.
For example, a food delivery mobile app might analyze user feedback and find that the number of new customers is dropping. This is an actionable insight that can be addressed by increasing the budget for marketing.
Datadriven insights
Whether you’re collecting NPS feedback or running churn surveys, actionable analytics requires tracking the right data to get the insights that matter. Using analytics tools with massive visualization options and smart analytics capabilities can help you collect the data and translate it into useful knowledge — aka, actionable insights.
However, these technologies are only one part of the equation. It’s equally important to have a process in place that ensures the quality of the data. Otherwise, you’ll end up with “garbage in, garbage out” results.
A good way to prevent this is by creating a data-driven culture. By fostering a data-driven environment, you’ll be able to make informed decisions that will drive positive outcomes specific to your business. This will save time and money and give you a competitive edge over your competitors. This is especially critical for digital transformation initiatives.
Evidence-based decision-making
In actionable analytics, the goal is to present data in a way that can be easily leveraged by business leaders to drive decision-making. This can be done using business intelligence tools, but it also requires smart people who understand the business and know how to find and translate data into meaningful stories of useful knowledge – insights.
Actionable analytics involves moving from descriptive (informative) to diagnostics (predictive) and finally prescriptive analytics. This progression allows companies to move from hindsight to insight and ultimately foresight.
To create actionable insights, you need to first understand the problem and its implications. This could require talking to end users, using questioning techniques such as the 5-whys or Socratic method, and even leveraging data modeling software. Once you have a clear understanding of the problem, you can begin to explore possible solutions and measure their effectiveness. This will help you build a feedback loop and demonstrate the value of your work.
Risk management
In a high-functioning risk management framework, business intelligence can shape into actionable insights that drive strategic decisions. As a result, leaders across departments can pursue paths of sustainable growth over the long term.
While some risks cannot be predicted, such as legal liabilities and compliance issues, the use of advanced analytics can help flag new threats – including those that your team has not yet considered. This gives you a powerful oversight of your entire operation, so you can identify lapses and mitigate risks quickly.
Your risk analysis program should be able to access data from all your business sources. This includes your CRM, HR system and any other business systems you use. You may also want to incorporate qualitative inputs like customer feedback and staff surveys. Make sure your software has natural language processing tools to detect sentiment and create resources that support user issues. These will improve your risk management program’s ability to reduce loss and prevent repetitive issues.
Sustainable growth
The key to actionable analytics is providing the right data to the people who need it. This means making sure that the data is displayed accurately and in a format that can be easily understood. Having access to this data allows businesses to make informed decisions that can help them grow their business.
However, just having access to the right data is not enough. To turn raw data into actionable insights, it is essential to understand the problem well. This can be done through effective questioning techniques such as the 5-whys and the Socratic method.
Moreover, companies should build their actionable analytics ecosystem by ensuring that the infrastructure they use can support growing volumes of data. This is crucial for streamlined decision-making, 360-degree customer view, efficient root-cause analysis, and demand forecasting. It is also a prerequisite for sustainable growth and competitive differentiation.