Demystifying Data Discovery and Enterprise Search in Product Lifecycle Management - Navigating the Product Data Jungle

In the 21st century, the focus of product development is increasingly around the business outcome delivered to the customer rather than the product itself. This means that product data needs necessarily to be associated more closely with consumer data, service data, and other sources which traditionally lay outside the domains of the engineering department and their PLM system. Data?discovery refers to the process of identifying, exploring, and understanding data assets within an organization to extract valuable information from raw data, transforming it into meaningful knowledge that can drive more innovative products and drastically increase profitability. Now, with the rapid maturation of artificial intelligence, edge computing, and SaaS applications, the acceleration of time to market becomes increasingly critical to business success.

This article will explore Data Discovery and Enterprise Search in the Product Lifecycle Management world seeking to find a way to harmonize these very worlds to the advantage of manufacturers.

Data Discovery

As mentioned before, Data discovery refers to the process of identifying, exploring, and understanding data assets within an organization. It involves the search, exploration, and analysis of data from various sources to uncover insights, patterns, relationships, and trends. Data discovery aims to extract valuable information from raw data, transforming it into meaningful knowledge that can drive decision-making, problem-solving, and strategic planning.

In the context of product lifecycle management (PLM), data discovery involves the exploration and analysis of product-related data throughout its entire lifecycle. This includes data from different stages, such as design, development, manufacturing, supply chain, sales, and customer feedback. By leveraging data discovery techniques and tools, organizations can gain a comprehensive understanding of their product data, enabling them to make informed decisions, identify opportunities for improvement, and drive innovation.

Data discovery often involves activities such as data profiling, data visualization, data exploration, data mining, and data analysis. It utilizes technologies like advanced analytics, machine learning, artificial intelligence, natural language processing, and data visualization tools to uncover hidden patterns, correlations, and insights within large and complex datasets.

Overall, data discovery is a vital process that helps organizations unlock the value of their data, enabling them to gain actionable insights and make data-driven decisions to optimize processes, enhance efficiency, and drive business success.?

Data Discovery and PLM

In the PLM world, data discovery is a powerful but poorly understood tool that can be used in a number of critical use cases:

1. Identify Relevant Data Sources: Determine the various data sources involved in the product lifecycle, such as design systems, manufacturing databases, customer feedback platforms, and supply chain systems. Understand the types of data generated at each stage to ensure comprehensive coverage.
2. Define Data Discovery Objectives: Clearly outline the specific goals and objectives of data discovery in product lifecycle management. Identify the insights and information you want to uncover, such as identifying patterns in customer feedback, optimizing manufacturing processes, or analyzing the impact of design changes on product performance.
3. Employ Advanced Analytics Techniques: Utilize advanced analytics techniques, including data mining, statistical analysis, and machine learning algorithms, to extract insights from the collected data. Apply these techniques to identify patterns, correlations, and trends that can inform decision-making and drive improvements in product development and management.
4. Leverage Data Visualization Tools: Utilize data visualization tools to present complex product data in a visual and intuitive manner. Visualizations such as charts, graphs, and dashboards enable stakeholders to gain a clear understanding of the data, identify trends, and communicate insights effectively. This promotes data-driven decision-making and enhances collaboration among teams.
5. Foster Continuous Improvement: Use data discovery as an iterative process in product lifecycle management. Continuously refine and enhance data discovery strategies based on the insights gained and feedback received. Regularly assess the effectiveness of data discovery techniques and adapt them to evolving business needs and technological advancements.

By following these steps, organizations can unlock the potential of their product data, drive innovation, and make informed decisions throughout the product lifecycle.?It is important to note that much of the data in the Data Discovery world is time-series data, in other words, intimately related to real-world behaviors and trends and of a totally orthogonal nature to the data that PLM typically deals with such as change,?requirements, BOMs, configuration, etc.?

Enterprise Search

Enterprise Search refers to the process of searching and retrieving information from various data sources and repositories within an organization. It involves using specialized search technologies and techniques to provide users with a unified and comprehensive search experience across multiple systems, databases, documents, and other sources of information - usually NOT time-based like IOT data or simulation data.

In an enterprise context, organizations generate and store vast amounts of data across different systems, such as document management systems, customer relationship management (CRM) platforms, content management systems, email servers, OneDrive/SharePoint shared drives, intranets, databases, and more. Enterprise search aims to overcome the challenges posed by the distributed nature of this data by providing a centralized search solution that enables users to find relevant information quickly and efficiently.

Enterprise search platforms typically offer advanced search functionalities, including keyword-based search, natural language processing, faceted search, filtering, relevancy ranking, and content analytics. These capabilities help users refine their search queries, navigate through search results, and discover relevant information even in large and complex datasets. Moreover, enterprise search often incorporates features such as security controls, access permissions, and user authentication to ensure that search results are aligned with an individual's role and privileges within the organization. It may also support additional functionalities like federated search, which allows users to search across external data sources or third-party systems.

By implementing an effective enterprise search solution, organizations can improve productivity, knowledge sharing, and decision-making by enabling quick access to relevant information. It promotes collaboration, reduces duplicated efforts, facilitates compliance and regulatory requirements, and enhances overall efficiency in information retrieval and discovery within the enterprise.?

Enterprise Search and PLM

In the context of product lifecycle management (PLM), enterprise search plays a crucial role in enabling efficient access to relevant product-related information throughout the various stages of a product's lifecycle. Here's how enterprise search applies to PLM:

1. Centralized Access to Product Data: Enterprise search provides a unified interface that allows users involved in PLM processes to search and retrieve information from disparate sources. This includes product specifications, design documents, engineering drawings, manufacturing instructions, quality data, supplier information, and customer feedback. By centralizing access to this data, enterprise search streamlines information retrieval, reduces time spent searching for information, and enhances collaboration among cross-functional teams.
2. Improved Visibility and Traceability: PLM involves managing and tracking product-related data and activities across multiple systems and departments. With enterprise search, users can quickly locate and trace critical information throughout the product lifecycle. This includes tracking design changes, identifying the status of manufacturing processes, monitoring quality metrics, and accessing historical data for regulatory compliance. The ability to easily navigate and search across these diverse data sources enhances visibility and facilitates effective decision-making.
3. Enhanced Decision-Making: Enterprise search empowers users involved in PLM to make informed decisions by providing quick access to the relevant information they need. For example, engineers can search for similar design components or materials used in previous products, enabling them to leverage existing knowledge and avoid reinventing the wheel. Supply chain managers can search for suppliers based on specific criteria, such as certifications or past performance. By facilitating access to comprehensive and up-to-date data, enterprise search supports data-driven decision-making in PLM.
4. Accelerated Problem-Solving and Issue Resolution: In the course of a product's lifecycle, issues and challenges inevitably arise. Enterprise search expedites problem-solving by enabling users to search for similar issues encountered in the past and access the corresponding solutions or resolutions. This helps teams avoid duplicating efforts and fosters knowledge sharing, ultimately leading to faster issue resolution and improved product quality.
5. Regulatory Compliance and Audit Support: PLM often involves adhering to various regulatory standards and undergoing audits. Enterprise search assists in meeting these requirements by providing a centralized platform for accessing and retrieving relevant data for compliance purposes. The ability to quickly search for and retrieve information pertaining to product specifications, certifications, testing records, and documentation simplifies the audit process and ensures regulatory compliance.

By leveraging enterprise search in PLM, organizations can streamline data discovery, enhance collaboration, improve decision-making, and facilitate regulatory compliance. The centralized and comprehensive search capabilities provided by enterprise search enable more efficient management of product data and contribute to overall productivity and effectiveness in product lifecycle management.

What Toolsets Are Available for this?

Hopefully, I have convinced you of both the critical nature of these solutions to product success and how all three of these technologies - PLM, Data Discovery, and Enterprise Search - are related. Unfortunately, there to my knowledge no holistic solutions that treat all of these simultaneously. PLM platforms contain enterprise search (based on EXALEAD for 3DEXPERIENCE and Apache SOLR nearly everywhere else), but the datasets are limited to those directly referred to by the PLM database with a few exceptions (like OnePart which is based on EXALEAD for spare parts management). ThingWorx is arguably part of a Data Discovery tool, but it is primarily aimed at IOT data. Mindsphere from Teamcenter has similar limitations.?

Products however have become viewed as holistic experiences where the customer rather than features and functions become central. This means that PLM has to adventure into new spaces with new kinds of data that they never dealt with before. However, we have also seen PLM vendors push their boundaries back towards customer relationship management (CRM) and forward to both enterprise resource planning (ERP) and manufacturing execution systems (MES) as well as both supply chain management (SCM) and advanced planning and scheduling (APS). It is probably a good first step to implement Enterprise Search to open up these data siloes leading to a second step of implementing data governance and implementing true digital threads and enabling digital twins.

What the market needs is a strong Enterprise Search tool that gathers all the product information to feed both the master data management (either in an MDM like Stibo or an ERP like SAP or Dynamics) and the PLM with clean, relevant data for decision-making and design innovation. The Data Discovery then can be tacked on to get behavioral information into the process to see how customers react, how the product behaves, what the what-if simulations say, etc. Today's Enterprise Search engines do not focus on technical data which represents a massive opportunity for vendors in the manufacturing space.

How are you integrating product-related data in your enterprise and filling in gaps that PLM cannot quite reach or wrap its head around? I look forward to your comments.

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