Actionable data insight is more than an empty buzzword. These insights translate into concrete actions that make an impact.
Getting truly actionable insights is not straightforward- many organizations struggle to get regular actionable insights. In fact, Forrester reports 74% of firms say they want to be “data-driven,” but only 29% are actually successful at connecting analytics to action. So what is the missing link from data to actionable insights?
What is an Actionable Data Insight?
Generally, data insights enable informed decisions based on data rather than intuition and gut instinct. Data can be used to confirm suspicions, quantify the importance of existing knowledge, or provide deeper context to a problem. But not all insights are created equal, and actionable insights don’t come from having more data.
"And, Therefore" Test
One easy way know if a data insight is potentially actionable is to use the "and therefore" test- add the phrase "and therefore" at the end of a finding and then complete the sentence. If you can do this that data insight is likely an actionable insight.
However, it's not that simple. Again, not all insights are created equal, and getting to an actionable insight can be complicated. There are four types of data insights. Each answer a different question that may reveal critical information on what action should be taken next.
- Descriptive Analytics - What happened
- Diagnostic Analytics - Why something happened
- Predictive Analytics- What is likely to happen
- Prescriptive Analytics- What action to take
Are you asking the right questions?
Just grabbing data and having data is not enough. To ensure your data answers questions that inspire action, the data you pull needs to be more relevant to your organization's business strategy and your technology strategy must be able to support your inquiries.
To figure out what is relevant by ensuring your data stack makes facilitates access to data, up-to-date synchronized updates for accurate data, and easy data exploration.
3 Components Your Tech Stack Needs to Facilitate Actionable Data Insights
1. Your data is easy to access
You can't get actionable insights if you can't access your data. Once you can access your data you can know what questions can be asked and what data is missing to gain an insight. The first step to perform the descriptive analytics and answer what happened is to gather all available data.
You'd be surprised at how much data lives scattered across diverse sources and is unavailable across multiple teams. In fact, a major pain point for many organizations is how much money and time is wasted to connecting or figuring out which data sources to connect to. According to an estimate by IBM - 80% of data collected is dormant or unused data insights.
Data engineers are often required to uncover and extract data from their original sources using specialized programs and even custom coding. But new tools are becoming available that make it easier to extract data and make it available for data insights both for conventional data pipelines as well as single data pipelines.
Historical data is paramount to perform analysis and move from answering descriptive analytics questions of 'what happened' to prescriptive analytics questions of 'what action should I take?'
2. Your data is up to date and synchronized
It's obvious that accurate data is essential when it comes to getting actionable insights, but it's not enough to have historically accurate data, it must be accurate and available as soon as possible.
As with any other data connection problem, it can be difficult and time-consuming to ensure data integrity and accuracy of data. Additionally, instant updates to data must be available in real-time to get valuable diagnostic analytics for course corrections or prescriptive analytics to understand what actions should be taken next.
The more accurate your data the better diagnostic and prescriptive analytics will be available, and the faster these accurate updates are available, the more impact they have on data-based decision making and action.
3. Compatible for exploration
A large pain point of data management is formatting data transforming the data in a format that enables comparison between data sources.
Data must be prepared or easily prepared to compare points in different contexts. Instant data enrichment and data blending is possible and must be done to make data available for decision making. After all, data is not nearly as actionable or useful if it is unable to build connections. Sometimes this process is done manually, through custom code, but new tools are available to ensure this process runs more smoothly.
Get Actionable Data Insights with Toric.
To get actionable insights you need a cohesive data strategy. The easiest way to get to a data strategy is to use a program that can facilitate all data pipeline components in one program.
Toric is an all in one data workspace. One allows users to build reusable data apps in which data sources are easy to swap out and integrate with, ensuring that data insights within reports are accessible, accurate, and smart.
1. Access all of your data, wherever it is.
Connect to your data in real-time
Swap any data into Toric, including local files and integrations. Instantly update reports with the most recent data by swapping in data sources.
Do this with one click using plugins and integrations with software like Microsoft Excel, Salesforce, Quickbooks, Revit, Procore, etc. These connections ensure a live relay of data from that software into Toric to maintain real-time accessibility.
Collaborate with your team
In addition to this, Toric has the Node Inspector as part of the user’s dataflow. This feature allows the user to access and validate their data at every step of the transformation process. These features combined enable the user to gather descriptive and prescriptive analytics because of the availability of accessible, real-time data.
2. Get accurate data across all reports instantly.
Users can achieve this by using the logic-based nodes in Toric's dataflow builder. These nodes provide mathematically accurate outputs for data transformations in the user’s dataflow.
In addition to this, Toric has the Node Inspector as part of the user’s dataflow. This feature allows the user to access and validate their data at every step of the transformation process.
3. Leverage smart data apps and reusable dataflows.
Our unique Data Apps allows the user to create smart visualizations of their data as they transform it in their dataflow. This enables the user to create a smart document / smart data app which can be shared with the user's team and organization. The smart data app can be shared by a public URL or an embedded link which allows the user to embed the data app in SharePoint, Notion, and other platforms. This improves prescriptive analytics as different users have access to the same interactive data app.
Ensure that you have the missing link to truly actionable insights by leveraging repeatable data apps that provide accessible, accurate, and smart data reports. Maximizing your insights to maximize your data-driven success by leveraging a single data pipeline for your data strategy.