Representative End User Clients
Representative Automation Clients
Representative Software Clients
Given the value modern analytics can provide, there are going to be many within an industrial organization who will want to create models and consume their output. This includes high-level math experts, such as data scientists, citizen data scientists (such as engineers), as well as field- and back-office workers. Broadening the adoption of analytics across this complex set of users is by no means a simple task.
These users often have specific, different, and sometimes competing perspectives on what data is valuable and how it is best used. Adding to this is the vast scale and scope of many industrial value chains, such as oil and gas, where like terms often have different meanings based on process application, multiple languages are used, and many individuals and roles are involved. This complexity can impact the adoption of analytics across the organization and must be accounted for within any solution.
AI Can Help Accelerate Adoption of Analytics
Ensuring a high-level of adoption begins with designing analytics to account for different roles and the transfer of knowledge to them. Data and insights must be delivered in proper context for different users, ensuring the analytics can easily be acted upon.
Many well-understood artificial intelligence (AI) technologies assist in this role-based transfer of knowledge. Natural language processing (NLP) ensures an analytics application can interact with humans using common language. For example, a technician working in a difficult environment, elevated on an oil platform, can interact with an analytics application to discuss, identify problems, query knowledge sources, and receive feedback as the work is being undertaken. Semantic search can be used by SMEs that know what value they want from the data but have lacked easy means to access, combine, and explore it. This latter example is particularly true of historian data.
Other methods, such as auto-suggestion and grammar correction, can help manage language barriers, education variances, and domain specificity. Collectively, these AI tools can be combined to speed the adoption of analytics and productivity of users by unlocking the specific role-based context that turns data into value.
AI technologies can also ensure dynamic knowledge transfer as data and operations change. As more data is fed into the engine, the analytics applied ensure the output becomes more refined and accurate. This process can be made continuous, so decisions are always enacted with the best knowledge available. And as more processes are digitalized, this knowledge base can scale to connect and serve more users and the roles they fill. This provides a visible trail into why decisions were made, which is invaluable when tied to tremendous cost implications.