The current challenge for companies is the ability to manage enormous volumes of data (big data), often raw, and quickly extract the most significant information that can positively impact the efficiency and productivity of the entire organization. Having access to large amounts of data is not enough; it is crucial that these data are analyzed and interpreted correctly. The true value of a Business Intelligence and Advanced Analytics solution lies in its ability to translate this mass of information into useful insights: the increasingly valuable Actionable Data.
What Are Actionable Data?
Actionable data are the final product of a BI tool’s processes. Unlike raw data, they can be directly used to make effective decisions or take actions.
Why Are Actionable Data Important?
Besides providing insights and knowledge, they are also sufficiently detailed and relevant to allow immediate action, which is particularly valuable in areas such as business, data science, and operations management, where making quick, informed decisions is crucial.
To be considered actionable, data must have certain characteristics:
- Accurate: Making decisions based on incorrect information leads to wrong decisions. A good BI tool includes features like data cleaning, validation, and formatting correction.
- Accessible: Always available and easy to use. Opting for cloud BI solutions with intuitive dashboards is beneficial.
- Timely: Obtaining data in real time has a significant impact on operational effectiveness.
Examples of Actionable Data
Any information that translates into informed actions is considered actionable data, such as sales data showing which products are
most popular in certain regions or periods. These data can guide marketing and procurement decisions.
Customer feedback, when properly translated into strengths or weaknesses of products or services, indicates the most effective product improvements.
Website or marketing campaign engagement metrics, such as conversion rates and ROI, can be used to enhance customer experience and the effectiveness of communication and sales strategies.
There are also actionable data obtained from predictive analysis on historical data to forecast future trends and guide strategic decisions, such as product demand forecasting, inventory management, procurement process optimization, or avoiding machine downtime through predictive maintenance.
Even employee data can be transformed into actionable data: performance metrics or the adoption of specific innovations, as well as satisfaction levels, can identify areas for improvement in human resources management or corporate culture, significantly impacting the efficiency and profitability of the entire organization.
Extracting Data for Actionable Analytics
Where to find actionable data? Every organization has a range of tools available to collect these types of data: CRM systems for sales and marketing actionable data, ERP systems for financial, production, and human resources data.
There are also actionable data related to individual applications or that can be obtained from analytical reports generated by Data Analytics software. In such a context, the situation can become fragmented and hard to use, making data integration crucial for having a single application from which to call up the most significant information regardless of the data source.
If transforming data into meaningful insights is the first step of Business Intelligence, making such information usable quickly (turning it into actionable data) is the goal.
From Reports to Natural Language
Actionable data can be presented in various ways, depending on the context and the end-user’s needs. Charts, tables, maps, interactive panels are just some of the integrated modes of interaction. A good BI solution integrates and combines data and tools into intuitive and intelligent dashboards, consolidating actionable data scattered across different software and spreadsheets.
Thanks to augmented reality and virtual reality viewers, it is possible to interact with actionable data in an immersive and interactive way. Mixed reality can combine the experience of Advanced Analytics with the physical world. Moreover, it is possible to call up and retrieve the most significant information using natural language processing (NLP). Chatbots and Virtual Assistants make interacting with Data Analytics systems even more intuitive and accessible, even for those without advanced technical skills.
An advanced Business Intelligence solution must be able to meet the needs of modern organizations (Data-Driven Companies) by providing the most useful actionable data at the right time, in a simple and timely manner, freeing organizations from the burden of managing the most technical aspects.
We realize Business Intelligence & Advanced Analytics solutions to transform simple data into information of freat strategic value.