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?

Functionality

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.

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  • How does the solution support trending analysis of the data? How are trends visualized and reported?
  • Can the solution analyze trends in data quality and predict when metrics will exceed predefined thresholds?
  • How does the solution document completeness (integrity) of the ingested data and ensure data is not lost in transit?
  • How do you work with third-party vendors? Which have you worked with in the past?

Expert Tip: Look at examples of proven solutions with third-party vendors so you can have confidence in being able to connect your disparate data sources.

  • Is the front-end framework built on open standards? How do you support open front-end frameworks?
  • How does the solution ensure that data is processed quickly and readily made available e.g. time series data?

Expert Tip: Access to centralized, remote, relevant-time data creates opportunities for many new use cases at both the site and enterprise levels.

  • Does the solution require plugins such as Microsoft Office or Adobe Flash?
  • Is it able to ingest both tabular and graph-structured data without loss of information?
  • When receiving asynchronous time series data, how does the solution handle time-stamping?
  • Is the solution able to handle data inserts, updates, and deletes by itself?
  • Does it support multiple modes of operation, such as batch and stream-based ingestion and in-memory versus persistent data storage?
  • Does it follow agile development principles and how do you ensure it is up-to-date on market trends and technical standards?
  • How does the solution support compression of data and metadata?
  • Does it report the source for each data point, event, and time series, plus associated metadata for users to assess the data quality?
  • How are the metadata fields of existing data and metadata updated? How are updates executed and managed?
  • How is the connection between data and metadata made? Are they stored or linked? Can metadata be linked to several data entries?

Connectivity

  • How does the proposed solution support integration with external systems and what are the requirements of such integrations?
  • What integrations are prebuilt and readily available for data extraction, and for the application layer?

Expert Tip: Prebuilt data extractors should exist for many open protocols and advanced Industrial DataOps solutions will have existing extractors to individual industrial solution providers such as Siemens, ABB, and Emerson.

  • How easily can we (the client) develop our own applications on top of the product?

Expert Tip: Further assessment is needed when thinking about application development for data engineers and domain experts. Proposed solution providers should have pre-built connections to well adopted applications such as Microsoft Power BI or Grafana.

  • Does the proposed solution provide an associated SDK? What languages are supported?
  • What types of underlying data sources are supported? What connections are most common?
  • What is the solution’s capability of accessing real-time data? What are the scalability limitations to this capability?
  • Does the solution have connectivity and native access to relational databases?
  • Does it have connectivity and native access to non-relational structures?
  • How do you ensure interfaces for data exchange (such as REST APIs) are kept stable and robust to changes?
  • Does the solution support versioning for continuity so that both new and previous versions of data pipelines are supported? Can versions be rolled-back?
  • Does the solution support a layered and scalable REST API?
  • Is the REST API stateless, enabling easy caching with no need for server-side state synchronization logic?
  • Can underlying data be exported from the proposed solution as a CSV or XLSX file? Is data and metadata exported in standardized formats?
  • Are there any limitations to the ability to extract historical data?

Solution Architecture

Every organization will have unique architecture requirements that should be addressed from the beginning. The key here is to ensure that the proposed solution provider is set up to meet the requirements of your existing environment.

Questions to evaluate a potential solution provider:

  • What are the key components of the proposed solution and how do they operate/interconnect?

Expert Tip: Any architectural requirements can be included here. Many organizations have already made investments to integrate OT/IT data silos into data lakes or data warehouse solutions. Your Industrial DataOps solution should leverage the investment into the existing infrastructure.

  • Is your software cloud native? Which vendors (AWS, Azure, GCP) do you support?
  • Do you support hosted/private cloud or on-premise deployment?
  • What is the solution’s ability to support real-time deployment?
  • How does it support horizontal and vertical scaling?
  • How does it offer high availability and how are failover procedures handled?
  • How does it support backup and recovery procedures?
  • How does it handle archiving?
  • How do you support edge capabilities? Do you offer on-premises deployments?
  • Is the solution validated with the standards of W3C and HTML5 to enable browser independence?
  • Does it track the lineage of all data objects and code, showing upstream sources and downstream consumption?
  • How does development occur when introducing changes to core components, adding extensions?
  • Is it possible to test reconfigurations, upgrades, and extensions before they are put into production?
  • What are the software and hardware prerequisites?

Project Execution, Services, and Support

Questions to evaluate a potential solution provider:

  • Can you describe the go-live period between proposed solution validation/operational deployment, and final acceptance/beginning of any maintenance and support agreements?
  • What maintenance and support do you offer during and after implementation?

Expert Tip: The proposed solution provider should have a designated customer support representative to ensure project success.

  • What does a typical project implementation process look like? What support is available?
  • What level of services do you typically provide?
  • Please describe how your skilled experts will interact with our (the client’s) in-house experts to maximize the benefit from collaboration?
  • How do you enable/support search in the proposed solution? Can you provide documentation?

Expert Tip: Search functionality saves time for data engineers and makes data discoverablefor domain experts and other data consumers.

  • How does the solution support documentation and how is it made accessible?
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  • What training programs are included and offered? What is typical?
  • How do you ensure that competence is built within our (the client’s) organization?

Expert Tip: Building competence within your organization is an important step toward digital maturity. Your solution provider should be enabling these competencies. Otherwise your organization runs the risk of being in a service-based relationship with the solution provider.

  • What resources and support are provided during this period?
  • What standard of support do you provide in problem resolution? Do you offer varied support levels?

Security

With the importance of security always increasing, the potential solution provider must be ready to meet the needs of your organization. This is not intended to be a comprehensive security list, as your IT department has likely developed its own security requirements for new software products. However, here are some of the key issues to consider.

Questions to evaluate a potential solution provider:

  • What is your company’s strategy for penetration testing and third-party assessments?
  • How does the solution maintain an audit trail of all data manipulation?
  • How does it offer monitoring and statistics of backbone components?
  • How do you ensure that we (the client) have access to our own data?
  • How is high availability maintained for security, access, and governance?
  • How do you support revocation of access at both user and group level?
  • When and how is data encrypted in the proposed solution?
  • What is the solution’s capability with regard to access control? What is the granularity?
  • Does it support groups for access control?
  • Can authentication requirements be customized in the proposed solution?
  • How does a user report suspicious activity related to data points?
  • Can users be assigned special roles to fix or disapprove reported suspicious data points?
  • Does the solution support ISO standards (or other standards as required)?How does the solution track the chain of custody?
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Usability

The successful rollout of any software solution depends on user engagement. Poor usability is a leading cause of poor product adoption. In order to make data discoverable and usable for all data consumers, the proposed solution must be intuitive and have well designed user interfaces that do not require strong coding backgrounds to operate. In addition, one of the biggest complaints amongdata scientists is accessibility to data, even when centralized in a data lake. The potential solution provider needs to support both of these user groups to truly make data usable.

Questions to evaluate a potential solution provider:

  • Are users able to navigate through different parts of the proposed solution without help?

Expert Tip: Asking for a product demo is helpful when trying to assess this topic.

  • Do users see and feel the solution respondingin real time?
  • How many concurrent users does the solution support? And is the environment collaborative?

Expert Tip: As your Industrial DataOps solution gains users, your organization should be striving to increase adoption further, driving use case development across multiple departments.

  • Can users easily refine search results?
  • Can users create data pipelines without IT assistance and without deep training in data engineering, SQL, or production processes? Do you provide a graphical user interface for pipeline creation?
  • Can users execute other tasks during the execution of jobs? Are users alerted when jobs are complete?
  • How do you ensure search results are quickly returned to users?
  • How do users report errors, bugs, service failures, and requests for new services or extensions to existing services?

Software Maintenance

This section is designed to give you an understanding of the upkeep required after a solution has been implemented. Reliability is another important factor in product adoption. Improvements and enhancements to the proposed solution should not result in unexpected downtime, nor should the solution require a high level of manual support to ensure proper operation.

Questions to evaluate a potential solution provider:

  • How often do you release improvements to your products? Do you have major and minor release cycles?

Expert Tip: Depending on what your organization requires, be sure to understand the different management requirements between on-premise, private cloud, and public cloud offerings.

  • Are clients entitled to all product upgrades with the base software? When are upgrades required?
  • How are clients notified about both scheduled and unscheduled maintenance/downtime?
  • How are new versions/updates managed?
  • What level of availability and uptime do you guarantee? How do you track system uptime?

Future Development

Ensure that the potential solution provider’s roadmap is aligned with your organization’s goals. Seeing their top technology development priorities will provide you with clarity on the product direction and it can continue to support your organization’s growth.

Questions to evaluate a potential solution provider:

  • Can you provide a short-term (6–12 months) and long-term (2–5 year) product roadmap?
  • What is your approach to developing new products and the possibilities for developing customizations/extensions?

Pricing Model

To date, price convergence has not yet taken place in the industrial software industry. Asking the high-level questions to understand the initial price (including services) required to get started will be valuable when assessing potential solution providers. In addition, Industrial DataOps solutions are designed to scale, so it’s also important to understand the levers of pricing when data sources, users, and use cases start to increase.

Questions to evaluate a potential solution provider:

  • How do you price the product? How does your pricing model support increasing use case and product adoption?
  • What factors do you predict will be the main cost drivers for your product and services?
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As mentioned above, the purpose of this document is to provide a blueprint to building an RFP for your Industrial DataOps solution, so you can achieve both current and future project success.

Current technology and functionality is often given the utmost importance, but having a solution that is easily adopted across your organization is equally important.

Industrial DataOps software, like all industrial software, can turn into shelf-ware if the solution is unfriendly and users are unable to effectively apply the available technology. This will inevitably lead to underperformance against your expected ROI.

Lastly, Industrial DataOps solutions are designed to become an integral part of your daily operations, so it’s crucial to work with an experienced solution provider whose product roadmap shows it will grow with your future needs.

With all of this in mind, you now have the knowledge to build an RFP for an Industrial DataOps solution that will enable your organization to extract significantly greater value from your data.