Uncovering What an Actionable Insight Actually Is

Uncovering What an Actionable Insight Actually Is

Ask any executive if they embrace "actionable, data-driven insights," and they'll most likely shout "YES!" But in reality, most won't really know what an actionable insight is. It's easy to nod your head at the latest buzzword, but knowing what actionable insights are — and putting them to practical use — is another matter.

Today, businesses are using artificial intelligence (AI) to make better, more data-driven decisions. Companies now feed data into AI systems to generate more insights — and faster — than humans ever have. AI helps companies sift through mountains of data in order to make business decisions.

Actionable insights have two main characteristics. First, they're a combination of accurate data collection with reporting and analysis. Second, the insights need to drive actions towards tangible business results.

Your goal is to bridge the gap between raw data and making smarter business decisions. It's a critical step towards becoming a successful data-guided business. And when combined with AI, those insights become even more powerful.

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Distinguishing Data from Insights

It's important to understand how actionable insights depend on data and information. Actionable insights sit atop a pyramid, with data and information as the foundation. It's all too common for businesses to confuse the three, thinking that raw data alone leads to value. You need to understand that information is the necessary intermediary between data and insight.

Here's how you can distinguish between data, information, and insights:

Data: Raw, unprocessed, and unorganized facts are classified as data. An Excel spreadsheet of customer purchase history is an example of raw data. The raw data takes one of two forms. Quantitative data are hard figures or numbers, such as customer purchase history or the amount of time spent on an app. Qualitative data is subjective customer data. How trustworthy customers rate your brand, for


Information: Data becomes information after it has been aggregated, processed, or prepared for human consumption in some way. Dashboards, reports, and  data visualizations are common ways to display information. The information stage is where businesses begin analyzing data and drawing conclusions.Information is valuable, but without analysis it doesn't quite tell businesses what they should actually do.

Insights. An insight takes information and frames it in a way that answers an important question, Insights analyze data and information to identify underlying trends and preferences. Today, AI software with machine learning is increasingly used to handle data analysis. The software's insights grow more

accurate and helpful over time as it learns from past analysis. This helps businesses generate insights at scale, faster and more efficiently.

Actionable insights depend on accurate data collection and intelligent information production. With some AI solutions, now the right people can then take stock of the AI's analysis and decide what to do. A sales manager, for example, doesn't need a data science background when working with AI and machine learning. Businesses can generate tens of thousands of actionable insights across multiple departments with an automated solution.

Generating Insights with AI

Many organizations are sitting on a treasure trove of data, whether they know it or not. The challenge is converting massive amounts of raw data into actionable insights. Often data resides in many locations, such as purchase history from your customer database, surveys, and social media metadata. That siloed data needs to be aggregated.

Business intelligence and AI tools can unearth insights at warp speed. AI software pulls data from multiple sources and produces insights in a matter of hours. Ki is one example of how AI helps turn raw data into actionable insights. Anyone, regardless of their data science background can feed data into Ki — along with business goals and key performance indicators (KPIs) — for the AI to analyze. Ki outputs recommended actions and predicts outcomes for multiple scenarios.

What used to take data scientists months, Ki does in minutes. Ki can tell marketing teams, for instance, how best to reduce customer churn. This could mean running promotions or targeting specific customer segments for email marketing.

The trend towards self-serve AI platforms for non-data scientists is illustrative of its ease of use. Average business users can tell Ki their business goals, and gain access to useful insights from their data.

Saying you're a “data-driven organization" is easy. Doing the dirty work it takes to uncover actionable insights takes more effort. Actionable insights lie deep beneath the surface. Machine learning tools like Ki dig more of them up, in less time, than data scientists ever could.

Putting Insights to Work

Implementing actionable insights is more than looking at reports or graphs. Not every insight is actionable. You'll have to prioritize which actions are most important. Ki is one solution that automates insight generation and shows the probable results for various actions. For example, Ki will help you determine the likely impact of your social media campaign versus a radio advertising campaign in January to increase sales for the month of February.

Generating insights with technology is only one part of the equation. To develop actionable insights that have an impact, you need executive buy-in. Actionable insights require that everyone in the organization be on the same page to reap the benefits of being a data-driven organization. You need a C-Level team that's committed to decisions based on data, not gut instinct.

Moreover, both insights and actions need to align with your goals. Insights tied to key objectives and strategic initiatives drive more action. When possible, associate insights with KPIs that relate to specific levers or tactics that you can change. Linking insights to KPIs informs what actions to take, and measures their effectiveness.

Developing actionable insights is a team effort between man and machine. Your organization has to decide on the goals or challenges it wants to address. Using the right technology for data collection and information analysis is a critical step in the process. Technology like data visualization and AI are useful but aren't effective without human judgement and interpretation. People need to decide on the right data to feed in and how to interpret the results.

Actionable insights are more than buzzwords. They're the hidden treasure of business intelligence, and you'll need AI and machine learning to help with the analysis. AI automates the process of analyzing mountains of data so you don't have to. The key is to know what problems you want to solve, feed in the right data, and then take action to achieve your objectives.

To learn more about how an automated analytics software can supercharge your strategies, download the '4 Step Automated Process Digital Brochure'.

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