Guide: Improving Operations with Self-Service Analytics

There are lots of buzzwords that are used to talk about analytics and digital tools, such as Digital Twin, AI, and Machine Learning.  These are critical technologies that are affecting every industry, but how practically can these be used to improve energy operations?
Read on to explore our guide, "Beyond the Hype: Self-Service Analytics to Really Get Things Done," and start improving your operations with self-service analytics. 


First of all, let’s look at how data flows between people and processes for a typical energy  operator (See figure A, below):  

This figure shows information flowing between the multiple parties responsible for turbine performance, from a data, role, and communication perspective. 

On the right are asset managers, performance engineers, and data scientists. They review data, run analyses, and create Insights from the data and their knowledge.  They communicate with site teams in order that actions can be taken on turbines.   Site personnel need insights into the turbine’s status and operation in order to schedule maintenance, have the right parts available and know which corrective actions to undertake.    

The key to making this communication work is that we need clear, actionable communication from right to left.


What we’re really trying to do is take everyone's expertise that is involved in this process and use that knowledge to improve operations.   In order to accomplish this, there are three main needs of the Analytic Solution:

           Actionable Insights

The main goal of analytics is for them to generate insights that lead to valuable actions.  To do this, analytics need to generate insights (not just simple alerts), that incorporate an organizations’ knowledge alongside analytics to create outcomes that people can take action on.  This can include recommended corrective actions, based on triggers in the data, as well as critical findings that require action. 

         Improve Communications

Analytics must be easy to understand for everyone involved - performance engineers, asset managers, and site personnel.   Therefore analytics must use plain language to describe what caused them to trigger in the data, and the recommended next steps.  Analytics that cannot be understood will not be used, the result of which is poor adoption and results.  



Clearly,  issues re-occur all the time. And this gives us an opportunity to continually improve the process by utilizing feedback from different stakeholders that are involved.  These can’t just be free form comments, however, because unstructured data is difficult to utilize.  Therefore the solution needs to provide a way of easily recording structured feedback and a way of using this feedback the next time the issue occurs as efficiently as possible.


What does a successful transformation look like when it’s effectively implemented?  There are four areas where you see benefits:

Successful Transformation

(Process, People, and Tools)

First, you will see High Adoption of the solution;  Users are engaged, they are creating inputs, using the outputs, and giving feedback into the system.

Second, there is Continual Improvement.  Users can now focus, for the first time, on improving responses to triggers, improving the accuracy of detection, updating corrective actions.  Because the organization is using and capturing its knowledge, it enters into a virtuous cycle of improvement.  

Third, there is an Integration of Systems.  Data is flowing in from monitoring systems, new features are being created, work orders are being created automatically - all resulting from a seamless integration of systems.  Modern solutions involve multiple systems, each providing its own specialized area of application, and which need to talk to one another. This doesn’t all happen on day one, but it is part of a complete transformation. 

Fourth, you will see System Improvements.  This includes improvements in asset performance and availability, but will also impact other areas of the organization including the productivity of personnel, improvements in supply chain/parts ordering, and even in safety.


When deployed correctly, self-service analytics have a dramatic impact on organizations.  

At NarrativeWave, we have seen significant improvements in our client’s downtime, asset performance, supply chain operations, and productivity, when adopting analytic-driven transformations. This sets the foundation that results in significant and lasting organizational gains.


github narrativewaveReady to try it yourself?

Visit Github by clicking here, and access your analytic code.



Questions? Let us know. We’re always here to help.

Contact us today to learn more: