When raw data is collected, processed, and analyzed using various machine learning models and AI-powered algorithms, valuable insights emerge. These insights help organizations improve their understanding of business, identify threats and opportunities, and bring efficiency to operations. Using data analytics, organizations can derive many types of insights from data. Some insights provide straightforward answers to questions, some insights provide confirmation for a hypothesis, and some insights predict the future course of business events. And then, there are insights that stimulate action and accelerate decisions. Such insights are called actionable insights.
What are Actionable Insights?
Actionable insights are insights that deliver immense value, provide a clear direction, and drive instant actions towards achieving business objectives. These insights are the most valuable and provide maximum returns on data analytics investments.
Actionable insights are distilled from raw data in the following way.
- Data: Raw data is collected from multiple sources such as information databases, spreadsheets, plans, social media comments, call logs, and so on. This data can be structured (forms and tables) or unstructured (log files, comments) and qualitative (observations) or quantitative (numbers or units).
- Information: Raw data, when processed, categorized, and organized, is converted into information. This information can be presented in the form of reports, dashboards, charts, and other visualizations.
- Insight: Information is analyzed to identify patterns, summarize findings, and draw conclusions in the form of insights. Insights help to understand business events, answer questions, compare details, spot similarities and differences, group similar items, and track trends.
- Actionable Insight: Among the various insights that emerge from data, actionable insights are crucial. These insights encourage informed actions and decisions based on data-driven recommendations. The control to take action or not rests with the user.
For example, the photo gallery app accesses and collects different media files (raw data) stored in various folders of the phone’s internal memory and extended memory in one place. It classifies this data into categories by format, size, file type, timestamp, or tags and presents them visually in the form of tiles, bar charts, or badges (information). It indicates that some files are old, unclear, very large in size, or duplicate (insight). It offers the option to delete duplicate files, fix unclear or hazy images, and indicates how much space will be saved or gained back after deleting files with large sizes (actionable insight).
Benefits of Actionable Insights
Actionable insights help make sense of business events and take action quickly and confidently. In a fast-changing market, identifying threats and converting opportunities early with actionable insights gives organizations a significant competitive edge.
Enhance Decision Making
Actionable insights give business users the much-needed support to enhance their decision-making. When insights are aligned with an organization’s business objectives and KPIs, they become more actionable and influence better decision-making. For example, knowing every increase or decrease in social media views might be a vanity metric if the marketer’s actual goal is to track click-throughs that lead to the company’s e-commerce website and convert it into sales. Actionable insights provide decision intelligence on what matters most to decision-makers based on their role, work, or goal.
Understand Context Better
Actionable insights go beyond answers to provide context for changing business metrics. For example, the monthly report shows a surge in sales by 20% over the previous month. It may look good now, but it is missing a context. The surge was due to an increase in the price of goods, while the number of transactions actually went down. Such a surge might not help in the long run. This actionable insight alerts the management that the price rise is driving away customers, and they can take measures to win back customers. Actionable insights explain not only what happened but also why it happened and how to proceed ahead.
Gain Clarity
Processing data and extracting insights can be managed with automated data analytics, machine learning models, and AI-powered algorithms. However, presenting the insights clearly in an understandable manner makes them more actionable and relevant. Actionable insights, when presented as interactive charts with multiple visualizations, give users control over how they want to see it. Bite-sized audio-visual data stories make actionable insights not only easily consumable but also engaging.
Get Specific Recommendations
Actionable insights cut through the noise of lengthy reports, stacks of dashboards, and information overload to provide a clear direction. When users receive specific, actionable insights, there are higher chances that they will act on them soon. For example, actionable insight on the increase in demand from a new segment of customers delivered in the form of automated business headlines helps customer success managers devise targeted offerings and create better relationships.
Save Significant Time and Efforts
When users get access to relevant, focused, and personalized insights, they can understand business situations accurately and make quick decisions. With actionable insights, users no longer have to go through volumes of information and unnecessary vanity insights. With modern data analytics platforms’ intuitive search interfaces like MachEye, users can access data easily and get insights in an automated way without investing time and effort in learning complex techniques or depending on experts.
Transform Raw Data into Actionable Insights with Machine Learning
Machine Learning helps in automating time-consuming data processing tasks, unearthing actionable insights, and predicting trends from enterprise data. With the right combination of machine learning models and modern data stacks, organizations can transform raw data into actionable insights efficiently.
Define Clear Objectives
It is important to state the business problems that users are trying to solve with data analytics. Set clear expectations on the outcomes and insights users want to receive from the analysis. Identify and track the KPIs and business metrics that are most relevant for getting actionable insights. Ensure that they are measurable and aligned with the business objectives.
Identify Data Sources and Connect to Data
The data to be analyzed can be present in different data sources and formats, generated and maintained across different departments within an organization. Identify the data that are needed for analysis and integrate it for a comprehensive view. Using a modern analytics platform, data from multiple sources can be connected directly without the trouble of duplicating or frequently loading it. This also ensures that current and relevant data is available to generate the latest and most accurate insights.
Prepare and Model Data for Exploration
Collecting and preparing data accounts for the majority of time and effort in the process of distilling actionable insights from data. Modern analytics platforms such as MachEye not only automate data preparation and curation but also measures its quality and offer recommendations to fix data observability issues. Machine Learning plays a critical role in modeling data to achieve the desired insights. Select and evaluate the right ML models that suit the specific needs of an organization. A modern analytics platform that offers robust models and also supports out-of-the-box or customized models gives organizations greater flexibility and control over their analysis.
Set Benchmarks and Provide Context
Success can be measured differently by different organizations based on their objectives. It can be more store footfalls for some, whereas it could be an increase in staff productivity for others. Set a benchmark for what success criteria look like. Provide a context that explains the significance of numbers or values. Is a 10 % increase good, average, or bad? What parameters should products fulfill to be ranked in the top 5 list? Machine Learning algorithms customized based on such context can yield faster and better results. For example, banks and financial lending institutions use ML algorithms to evaluate a loan applicant’s creditworthiness and send actionable insights to loan officers on whether to approve or reject the application.
Recognize Patterns and Deliver Actionable Insights
Various Machine Learning methods can be employed to recognize patterns, draw analogies, identify anomalies and outliers, trace clusters, and make predictions. Configure these models to address specific insight needs. To make the insights actionable, it is necessary to present them in the right format at the right time so that users can act on them soon. Modern analytics platforms such as MachEye present actionable insights in the form of interactive charts, engaging audio-visual data stories, and automated business headlines. Analytics platforms that learn from their users can deliver personalized and focused insights whenever they happen in data and not just when users search for them.
Conclusion
In conclusion, actionable insights are no longer a luxury restricted to the higher echelons of management. They have become a necessity for every decision-maker across the organization, be it on the frontline, on the shop floor, or in the back office. With modern data analytics platforms powered by AI and ML models and technologies, the extracting, presenting, and consuming of actionable insights has become simplified, personalized, automated, and accessible for everyone.
