Actionable Intelligence: Getting Quantifiable Benefits Out of Data

Lyndsay Wise's picture
 By | november 27, 2017
november 27, 2017

Many organizations get stuck looking at data visualizations and focusing on developing nice dashboards and measuring KPIs (Key Performance Indicators), without really gaining value from their creations. Today’s analytics’ marketing messages abound, and provide great examples of geo-spatial maps, gauges and dials, and charts.

All of these look great and might tell a story, but often fail to support the operational needs of the organization unless they are action-oriented. Unfortunately, many are not and are limited to a point-in-time view of performance. The challenge for many organizations is that they want to gain quantitative business value from their analytics investments, but struggle with marrying design and actionable functionality.

Designing successful analytics that are directly tied to business value is a challenge. Organizations need to integrate the right data, develop rules, understand the needs of business, and create an environment that supports self-service and collaboration. Being effective also requires strong data management principles and a way to tie the integrity of continual data access to proactive decision-making. Although complex to create, here are some general considerations that can make actionable intelligence easier to achieve.

Being action-oriented and leveraging alerts and other tools

One of the goals of analytics access is to be able to take disparate data sources, consolidate them, and gain insights that would otherwise not be available. Taking that one step further requires an understanding of how these insights can propel decision-makers to take action and provide support for next steps. For instance, unless sales management can understand through deeper analysis why their region is not meeting targets and understand what can be done to improve performance to meet targets, simply seeing the data will cause frustration and not bring added value.

Real-time information access

Point-in-time metrics serve their purpose, but when action is required – for instance getting a product to a customer on time or ensuring that there is a high level of quality control within a supply chain – it is not good enough. If a machine needs maintenance, getting that data after the fact can affect the whole supply chain and bottom-line revenues due to inefficiencies. If processes are monitored in real time, organizations can identify challenges before they occur and implement preventative maintenance to ensure better process flow and maintain high levels of production. The same applies in any industry. A real-time viewpoint for operational effectiveness supports better business.

Tying KPIs and analytics to business processes

Making KPIs actionable requires linking what is being monitored with what needs to be acted upon. This means understanding how the business works and making sure that actionable information is accurate. Not all KPIs or analytics access requires proactive outlooks. In some cases, trend-based data will be required to understand longer-term performance or to identify patterns in data. As an organization becomes more mature in its analytics use, it can adopt predictive models and begin exploring artificial intelligence to identify broader opportunities.

Empowering people with data

The most valuable aspect of analytics access is the ability to empower business users and analysts with information. Understanding customers better and ensuring that products are delivered on time support better business practices. Employees need self-service access to analytics to provide better customer support and ensure they can act upon opportunities and challenges as they arise – and not spend their time putting out fires.

Understanding the difference between data consumption and enabling the workforce to leverage data to support better business practices is the first step to linking analytics and strong dashboard design to action-oriented analytics delivery. This is required to ensure quantifiable outcomes from analytics delivery.