So you have established that Industrial DataOps can play a vital role in your effort to truly transform your business. The challenge now is to define what capabilities your Industrial DataOps solution needs to support your business. This section provides a guideline to build out your request for proposal (RFP) and ensure you account for all critical capabilities and functionalities required for success.
This guideline will present the key areas to consider and should be used as a starting point to build a framework tailored to the needs of your organization. Issues to consider are presented in the form of questions. You may choose to put some or all of these directly to potential solution providers, as well as using them as an internal assessment tool.
As no single solution will solve all your data challenges, you will need to align your organization around the right capabilities critical to unlocking the potential of your industrial data.
What to Consider When Creating a Request for Proposal (RFP) for an Industrial DataOps Solution
Use Cases and Past Successes
First and foremost, Industrial DataOps must be able to deliver long-term value to your organization. Making this happen requires alignment between your organizational goals and the potential solution provider’s capabilities. Knowing that your solution provider has competency within your domain will increase the probability of delivering on your expected ROI.
Questions to evaluate a potential solution provider:
- Can you provide a brief description of your company, industrial business areas, main products/services, relevant expertise and business strategy?
- Are your products/services general or specific to the relevant industry? Can you describe your domain expertise?
- How would you describe your key product differentiation?
- What is your experience with helping clients build business cases and developing a target ROI? Can you provide examples of successful business cases delivered?
Expert Tip: Successful Industrial DataOps solutions should start with 1–2 use cases defined before any work begins. Have a backlog of 2–5 more to move on to once success is achieved with initial use cases.
- Does the proposed solution enable more effective asset management? Can you provide examples?
- How have you applied machine learning solutions to solve client use cases? Can you share any use cases using hybrid AI solutions (combination of physics and ML capabilities)?
- What use cases have you delivered regarding unstructured data (e.g. video, 3D)?
- What are the most common types of use cases you have delivered?
- Do you have reference customers we can talk with?
- Can you provide a product demo?
Properly assessing Industrial DataOps software requires an understanding of two components: the foundation and the connectivity. Assessing the foundation is critical to ensure that the proposed solution will support your industrial data use cases and provide the tools needed to minimize time to value, and maximize scalability and repeatability.
Connectivity has two components: data extraction and application layer. Data extraction capabilities must allow you to connect to both existing and future data sources. The application layer focuses on how the solution provider will support applications on top of the foundation to deliver use cases.
Questions to evaluate a potential solution: Foundation
- How does the solution perform data contextualization (data mapping)? Is it automatic or semi-automatic? Does the solution suggest relationships to make identification and construction easy?
Expert Tip: The ideal solution should automate this process as much as possible, otherwise manually expanding the system to include new data sources will be extremely time-consuming and hard to manage.
- How is the contextualization (data mapping) process managed? Is it easily accessible?
- How do users make edits?
- How is the data model created in the proposed solution? How are relationships between data sources managed?
- What types of data formats are supported in the proposed solution?
- How does the proposed solution support data visualization?
- How does the proposed solution manage data quality? Are rules pre-built? Can rules be modified? Are rules applied universally or per use case?
Expert Tip: Data models are designed to be reused. Data quality should have the flexibility to be applied per use case. For example, different use cases may require the same data, but using this data for remote monitoring of an asset will not require the same update rate as using the same data to run an analytics model measuring performance.
- Does the proposed solution support templatization? How can applied work be reused?
Expert Tip: Templatization is a key component to scale solutions and ensures your organization will avoid getting trapped in proof-of-concept purgatory.
- How are notifications/messages supported in the proposed solution with regards to users associated with data and administrators?How does the solution score on scalability?
Expert Tip: As you expand beyond initial use cases, you will need a solution that is scalable. Industrial DataOps should be able to address scale at both site and enterprise levels.