What to Consider for your Insight Engine Rollout: Define the Success Criteria

In part one of "What to Consider for your Insight Engine Rollout," we covered the critical first step of identifying and determining the use case.

In this piece, we discuss step two – Define the Success Criteria.


Step Two: Define the Success Criteria

Once a use case is selected, determining the next steps is much simpler for management. Not all information insight solutions on the market are relevant or suitable for every use case. Defining success criteria can help you pinpoint potential vendors to avoid and which ones to explore further.

To define success criteria, one must determine a few key parameters.

  • Business Needs 

What are the business needs? What is the current situation versus the target situation after implementation?

Covering this parameter helps a company summarize its ideal business landscape for the previously defined use cases. Having a grasp on desired outcomes allows one to be productive in kick-off meetings and discussions throughout implementation. The solution provider will then have a clear vision on how to help the customer reach their information insight goals. In reverse, the customer will also be in a prime position to determine which AI providers can help them check all their boxes.

  • Data Sources and Quality

Once objectives are clear and the needs of the enterprise are established, businesses will need to know what data they need and in which data sources they are located.

Mindbreeze connecting data sources

We know by now that businesses possess and continue to accumulate large quantities of data, and this data is not easily findable in a single application. One would not need to follow these steps to implement an insight engine if it were. Is the information required hiding in historical company data, public datasets, documents, images, videos, sensor and machine data, data from SharePoint, or other business applications? The potential list goes on and on and is likely a combination of all the above.

  • Semantic Relationships and Extraction

To extract information from enterprise data and use it as a knowledge base, many different mechanisms like neural nets, deep learning, etc., interact with each other. Nevertheless, interlinking the variety of information scattered around in many relevant data sources makes genuine information insight and discovery possible. As part of the standard product, the right connectors are critical to getting started fast with a higher-level understanding and analysis.

  • ROI Calculation

A company must define Key Performance Indicators (KPIs) in advance to properly measure the system's success. All stakeholders within a company must understand the KPIs defined, so the evaluation of a product is based on hard facts rather than opinions.


In the next part of the series, we will discuss the importance of hands-on testing using actual company data. In the meantime, do not hesitate to contact us or revisit part one.

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