Industrial operational intelligence is a relatively new field of data science and business analytics. Today, it is quickly gaining adoption across different industries, helping organizations and industrial automation teams to boost the effectiveness of their operations, increase business value and optimize virtually all operations and work processes.
In this article we are going to talk about industrial operational intelligence in detail, providing its definition, describing the technologies that are typically used as part of OI solutions, and providing tips and recommendations on how to implement an operational intelligence platform within your organization.
Operational intelligence (OI) is an umbrella term that describes various types of techniques and approaches to data analysis based on data regenerated and processed in real time. Businesses, organizations and industrial facilities use information generated by OI solutions to enhance visibility, optimize workflows and improve overall efficiency of business operations.
Modern-day operational intelligence is based on the latest automation technologies, ML (machine learning) and AI (artificial intelligence) algorithms that enable dynamic real-time business analysis and deliver relevant and timely actionable information to workers and business managers.
OI solutions are typically integrated into existing enterprise IT infrastructure, running queries against a stream of real-time data and delivering analytics results.
Most often, the results of OI data processing are provided in the form of operational instructions aimed to be immediately used by front-line workers and managers.
Modern-day operational intelligence solutions are considered to be the next step of evolution after real-time business intelligence (RTBI) tools that predate OI.
One auto manufacturer had difficulty managing growing complexity in its product variants, and sought to improve and automate its decision making. To do so, it installed an enterprise manufacturing intelligence (EMI) system that ingested data from more than 400 IoT sensors, enabling predictive intelligence to be applied to maintenance, quality, and parts supply. Introducing the new system improved overall equipment effectiveness by 10 percent and first-time-right delivery by 15 percentage points.
McKinsey
Real-time business intelligence (RTBI) utilizes complex business intelligence (BI) solutions, smart data warehousing, data visualization and virtualization capabilities, as well as service-oriented architectures (SOA) and enterprise app integration (EAI), to process data and deliver quick results.
RTBI requires organizations to implement rather complex infrastructure to store and process data in order to leverage all the benefits of such a solution. The core elements of RTBI infrastructure include data warehouses, data lakes, and other smart data storage and processing systems which are typically expensive to implement and maintain. The main difference between OI and RTBI is that RTBI tools are mostly designed to work with financial and business information such as revenue streams, expenses, costs and other financial transactions, while OI solutions analyze data generated by internal computer systems and industrial machinery in form of logs, configurations, system reports, alerts, etc.
There are other core differences between OI and BI/RTBI which we will address in more detail further in this article.
Proper application of OI tools and technologies allows organizations and companies across various industries and market fields to monitor all key business activities in real time, detecting threats and inefficiencies, identifying new business opportunities and providing front-line teams and workers with useful information and operational solutions to immediately make them a part of the work process.
A core business advantage of operational intelligence is an event-centric approach to the analysis of data, with a constant stream of new information that enables employees to be more efficient and make better decisions in real time.
In organizations that don’t utilize the power of modern operational intelligence tools, in order to adjust a workflow or make an improvement of an operational process, managers and employees need to monitor business activities over a certain period of time. Only after a sufficient amount of information is accumulated, an organization can proceed to creating charts, graphs and other visualizations that allow it to identify the areas that require improvement.
With such an approach, collection, organization and visualization of data often takes weeks, months or even years in some cases. Needless to say, the information obtained in such a way typically is far from being timely and relevant.
OI solutions aim to fix this problem, providing organizations with quick processing of data that is collected and analyzed in real time. OI typically provides front-line workers with activity-centric solutions that can be implemented immediately.
Some examples of front-line business workers that could benefit from real-time information provided by an operational intelligence solution the most are sales representatives, industrial and manufacturing process managers, customer support and call center agents, marketing specialists, airline flight coordinators, logistics managers and people performing other jobs where real-time information is vital.
Let’s review the most important business benefits of implementing OI solutions.
Here is what OI implementation can help an organization to achieve:
Modern-day OI platforms that are used in industrial environments typically include a number of components that operate in combination with each other, collecting, processing and analyzing data, and delivering the analysis outcomes to the front-line teams and individual workers.
High-functional industrial OI platforms typically include but are not limited to the following components:
The ability to properly visualize and present the results of operational intelligence to workers and business managers also plays an important role. That's why a powerful industrial OI platform should support a variety of customizable interactive dashboards with different metrics, charts and KPIs, as well as being able to automatically deliver latest discoveries to end-users by sending them alerts and notifications.
The need for high-quality analysis of business data with new insights generated and delivered in real time results in a rapid increase of demand for operational intelligence solutions from organizations and businesses in various industries.
According to one of the latest reports, the operational intelligence market is expected to grow at a rate of 12.08% CAGR from 2023 to 2029, reaching almost $5.16 bln by 2029.
When it comes to specific industries, the retail industry will be the most active in adopting OI tools and solutions, growing at the highest CAGR of 13.2% during the forecast period. Which is well-understandable as the retail sector usually generates huge amounts of data related to goods delivery, sales and financial transactions. This data is also quite easy to collect into a centralized storage from software solutions where it is generated originally. Such as ERP systems, e-Commerce, retailing and point-of-sale tools, as well as various accounting and financial management solutions.
All this information can be analyzed in real-time or near real-time mode to gain insights into customer behavior and buying habits, identify patterns in customer demand for goods and services, find new opportunities for promotions, discounts and loyalty programs, come up with ways to increase sales of specific products, etc. All this can be achieved with a smart application of operational intelligence solutions.
Quickly growing adoption of IoT (Internet of Things) and IIoT (Industrial Internet of Things) solutions, cloud computing, Big Data analytics, data lakes and unified namespaces (UNS) in industrial and business environments is one of the main factors driving increasing demand for OI platforms.
The adoption of IIoT plays an especially crucial role in enabling a new generation of industrial operational intelligence platforms. With omnipresent implementation of IIoT, virtually any machine, device, system, robotic solution and piece of equipment can generate a stream of real-time data about its operations and transmit it to a centralized storage for subsequent analysis and generation of new insights.
Operational intelligence solutions are widely used across all major industries and market fields. Let’s look at the industries that are adopting OI technologies most actively and how exactly they typically utilize them.
As we already mentioned, today companies in retail are probably the most active users of OI solutions. Retail leverages operations intelligence to gain valuable insights into customer behavior, supply chain issues, logistics, and merchandising. Another use of OI in retail is real-time monitoring of e-Commerce websites and analysis of user behavior.
When it comes to the use of industrial operational intelligence, the manufacturing sector is on top of the game here. Industrial OI allows manufacturing facilities to implement a continuous monitoring of industrial machinery, systems and processes by integrating smart sensors in them and collecting data generated on the factory floor. Proper application of OI enables industrial facilities to achieve high-quality monitoring and supervision of all major processes, product creation and delivery.
Transportation industry also generates vast amounts of various operational data that can be collected and analyzed by OI platforms. These data flows include information related to the management of airplanes and other vehicles, operations of airports, train stations and various other transport hubs and junctions, passenger flows, routes and logistics issues, incidents, transport industry employees, passengers’ travel experiences, etc.
Financial institutions use operational intelligence technologies to monitor the performance of financial systems, detect potential security issues, identify attempts of fraud, track stock markets and money flows, and so on.
In telecommunications, OI solutions are most frequently used for monitoring the performance of network equipment, detecting malfunctions, network failures and security breaches, identification and prevention of errors, etc.
Healthcare institutions are also leveraging modern-day OI solutions to achieve a variety of goals and benefits. Specifically, OI can be used for pharmaceutical inventory management, monitoring of healthcare facility operations (including flows of patients in hospitals, working shifts of doctors, nurses and other hospital staff), tracking of medical records, forecasting hospital needs for drugs and medical equipment, etc.
As we said earlier, industrial OI platforms are complex solutions that include multiple tools and components working in combination with each other. Naturally, OI solutions can be applied in different industries and for different purposes, serving multiple goals.
A typical OI platform consumes operational data collected from various nodes in the enterprise network in the form of events and time series data. This data is processed and analyzed by the system that is generating insights which are then visualized and presented in dashboards or other forms that can be easily consumed by end users — front-line workers and business managers.
Let’s look at the most common and frequently used features of modern-day OI platforms.
Real-time monitoring of industrial processes and operations is one of the most common applications for operational intelligence. This can include monitoring of various machinery, pieces of mechanical equipment and infrastructure through sensors integrated into them, as well as monitoring of IT networks and software systems, server event logs and other elements of industrial infrastructure that need to be up and running 24/7 and can generate real-time updates, quickly transmitting them to a central location for subsequent processing and analysis.
Another key feature of operational intelligence solutions is the ability to perform correlation, which is a crucial capability that allows organizations with complex IT infrastructure to quickly identify and fix errors and issues in their networks. Typically, in case when an incident occurs within an enterprise IT network, the investigation and identification of its causes can take a significant amount of time as this process would require the analysis of multiple data silos coming from various parts of IT infrastructure. Utilization of OI platforms allows organizations to significantly speed up this process thanks to the event correlation feature that pulls the data from different sources together and conducts an investigation based on all these silos at the same time.
If an organization has a modern data storage infrastructure in place, such as data lake and unified namespace (UNS), it can be leveraged to enable an OI platform to utilize large amounts of operational data kept in this storage. Using this data, the platform can generate market predictions, business trends and forecasts.
Another valuable application for OI solutions in industrial environments, as well as in business operations, is root cause analysis and multidimensional analysis. Root cause analysis performed with advanced OI technologies allows organizations to quickly identify causes of various kinds of problems and issues in business operations, providing potential solutions and recommendations on how to improve the efficiency of related processes to minimize chances of similar problems emerging in the future. Multidimensional analysis allows organizations to observe and analyze data from various viewpoints at the same time, coming up with trends and predictions they may not be able to spot otherwise.
Naturally, at the core of most OI solutions is the ability to model business metrics and generate KPIs (key performance indicators) that are calculated using the data collected from sources throughout the organization's IT network.
Finally, all the business metrics, trends, predictions, KPIs and other calculations are visualized by OI platforms in attractive visual forms, most typically in the form of dashboards. Dashboards and other visualizations are then delivered to end-users. Advanced OI solutions often support automatic user alerts and notifications in cases when the information generated by an OI tool should be delivered fast and acted upon immediately.
Now, as we covered the main features of industrial OI solutions, let’s also take a look at the technologies that are used to power the above-described capabilities.
Complex event processing component, utilized as part of an industrial OI platform, allows the OI solution to perform complex and advanced analysis of multiple streams of real-time data, correlation of events, root cause analysis and other key functionalities of OI.
AIDC is a component responsible for detecting and recording information typically provided to the system via various wireless tagging solutions such as RFID (radio frequency identification) and barcodes, as well as in text and voice (voice recognition component would be required in such a case).
Metadata management frameworks are centralized repositories of all documents and files with data produced and stored within an organization. The access to a metadata framework allows an industrial OI platform to quickly generate business metrics and KPIs, connect events that occurred throughout organization’s IT infrastructure to documents and textual resources, and conduct multidimensional analysis based on a full library of data used by the organization.
The availability of a BPM component allows an OI platform to analyze and model business processes within an organization, suggesting ways to optimize workflows, improve efficiency and remove bottlenecks. They are also able to perform simulative execution of new policies and business processes based on newly created models.
Dimensional databases are typically used for the warehousing of large data volumes and quick processing of new data. Industrial operational intelligence solutions rely on dimensional databases to be able to process large numbers of queries in real time, access data received from a multitude of sources, and summarize it in different ways.
Real-time monitoring module allows industrial and business-focused operational intelligence platforms to monitor the activity of various processes within an industrial network, track operations of machinery and different manufacturing equipment, etc. When applied in non-industrial business environments, such a solution is able to monitor business activities that are represented in enterprise software systems such as ERPs, CRMs, point-of-sale (POS) solutions, etc.
In cases when industrial operational intelligence is properly implemented and operated, OI solutions are able to significantly accelerate and increase effectiveness of fault detection. Real-time monitoring of industrial machinery allows OI platforms to quickly identify malfunctions, faults, defects and other problems with the equipment. Industrial OI tools are also able to predict potential failures and generate maintenance recommendations to extend machinery equipment life cycles and wear of equipment parts and components.
The majority of modern-day industrial OI systems use dashboards as a way to generate interactive and well-presented presentations of insights, trends, predictions and other products of the analysis of data collected from various parts of organizational infrastructure, both hardware and software. Dashboards typically provide users with multiple customization and personalization options, supporting different types and modes of data visualization depending on the data presented and needs of the end-users receiving this information.
Finally, industrial operational intelligence solutions typically support a variety of user notifications and publishing methods, delivering the information generated by the analysis of data to front-line workers and business managers.
Industrial OI solutions are able to consume data from various sources within the enterprise network including IoT sensors embedded into machinery, software systems and telecommunication equipment.
Here are the types of data that are most commonly sent to industrial OI systems for processing and analysis.
By analyzing these and other types of data consumed from various sources, industrial OI solutions can generate insights and recommendations on the best usage cycles for industrial equipment, machinery maintenance requirements, the need to deploy technical specialists to manage systems, etc.
Certainly, industrial OI is still a very young market and data science field. Even though it is growing and evolving at an accelerating pace, there is no shortage of issues and challenges that are holding this growth back. Let’s go through the most noticeable problems the industrial operational intelligence market is facing today.
As you probably know, lack of talent today is a universal problem across the tech market as a whole. The so-called IT talent gap keeps growing. According to a new report by the IMF, the tech talent shortage will surpass 85 million tech workers mark by 2030, which can lead to lost revenue of over $8 trillion annually. In 2020, the United States failed to fill more than 250,000 positions.
Naturally, this problem is particularly acute when it comes to data talent. In order to make effective use of industrial operational intelligence technologies, organizations need professionals with knowledge and experience of setting up data warehouses and data lakes, integrating data silos from various network components into a single storage, establishing an effective data analytics process and so on.
Another major challenge is implementing the connection of multiple data sources to a single repository to allow the industrial OI solution to collect this data for subsequent analysis. Since different systems and machinery components used in industrial environments typically work with raw and structured data, this makes it difficult to establish a universal connection of an OI platform to all network nodes that need to transmit the information to it. When an OI system receives data in different formats and structures, it cannot apply the same logic to process and analyze all of it.
This is the reason why many modern-day industrial automation professionals increasingly lean towards implementing a UNS and a data lake, which would serve as a single source of truth of all data collected from various software systems and hardware components across the enterprise network.
Low data quality is another issue that can significantly tamper with the effectiveness of analysis performed by an industrial operational intelligence platform. Uncommon data formats are just one of the problems. Other issues typically affecting the quality of data and making it not suitable for processing and effective analysis by industrial OI systems are human errors of different kinds, inconsistency, duplication of data, and other similar problems.
In order to resolve these issues or avoid having to deal with it in the first place, organizations need to invest time and effort in adopting a data management strategy that would support easy integration of network components with a data warehouse and industrial OI system’s analytics components.
The most important parts of a data management strategy, in the context of operational intelligence and analysis of data, is data architecture and data modeling. Data architecture typically is specified at the early stages of implementing an organization's IT infrastructure. It describes all the technical aspects of data management and utilization, from data retrieval and transmission to its transformation into applicable formats and consumption by appropriate tools and systems.
Data modeling is also a vital part of a successful data management strategy. It describes the process of organizing and visually representing various kinds of information within the system. When it comes to industrial operational intelligence solutions, data modeling helps to create functional connections between different types of data, software systems and user roles within this system. A single user can play different roles within IT infrastructure as a whole, so, in order to avoid data redundancy, the data generated by this user should be adequately categorized via data modeling.
Even though the main challenges related to utilization of industrial operational intelligence solutions consist of acquiring the right data and performing its analysis effectively and quickly, properly presenting the insights and other outcomes of data processing by OI platforms can also be a source of problems and challenges.
In most cases when OI solutions are utilized, the data they generate is quite complex. It is not so easy to adequately present it to end-consumers (such as front-line workers and business managers) in a visual form. If the outcome of data analysis by an OI solution is not presented in a proper way, it can easily end up being overlooked or failing to produce any real impact.
This is why the majority of industrial OI platforms choose to utilize interactive dashboards as a way to visualize data in a compelling form. Nevertheless, just using a dashboard can’t solve this problem completely. There are still many issues related to proper usage of dashboards such as choice of dashboard templates, customization of a chosen dashboard template to fit the requirements the best way possible, and even the choice of colors, charts and diagram types.
Implementing an industrial operational intelligence solution from scratch (including a data warehousing infrastructure, analytics modules, data collection across network nodes and its integration into a centralized repository) typically requires significant initial investments. And these costs are rising as the demand for OI solutions grows. The initial investments in implementing an OI platform include the costs of hiring and retaining highly qualified data talent, implementation of hardware infrastructure, licensing or development of the software, etc. After an industrial Oi solution has been implemented, maintaining it also can be expensive, including costs of software updates, upgrades of data storage and security infrastructure, salaries of analytics experts and consultants required to operate and manage the system, and so on.
Another major challenge companies that try to implement industrial OI projects are regularly facing is choosing the right tools and technologies for this purpose. As the industrial operational intelligence market grows, the selection of OI solutions is also increasing. In order to implement a truly powerful and highly functional OI platform that would fit all the requirements, an organization needs to select the most appropriate components not just for data storage and real-time analysis but also for data visualization and its delivery to end-users.
The data analysis platform by Clarify incorporates a number of next-gen OI features, allowing users to integrate, organize, collaborate and visualize industrial data. It supports a streaming data timeline technology that enables users to quickly navigate and visualize hundreds of data signals at the same time without losing overview or performance. Clarify can be easily integrated with the majority of time series databases, data historians, SCADAs and ERPs from all vendors, including the ones that only support on-premise deployments. Want to see all the capabilities of Clarify with your own eyes? Schedule a live demo call with one of Clarify’s experts right now.
There are multiple other methods of advanced analytics, data processing and intelligence that are commonly used both in industrial and business environments today. With such a variety, it is really easy to confuse industrial OI with one of the other tools and techniques.
Let’s go through the most common alternative methods of data analysis one by one, looking at their differences and similarities when compared to operational intelligence.
Industrial operational intelligence is most frequently confused with business intelligence (BI). Which is unsurprising as both are advanced methods of real-time analysis of data related to business operations and processes. They are, however, quite different from each other.
Business intelligence is focused on processing historic time series data that has been collected and organized in a centralized repository before being subjected to the analysis. Industrial OI, by comparison, usually deals with real-time data that is related to current business activities and industrial operations.
Another major difference is that BI solutions typically work with business information that is related to financial management and accounting, such as budgeting, business expenses, revenues, profits, losses, and other similar KPIs. Most of BI data is reactive, being based on historical information and delivered in the form of static reports.
Operational intelligence solutions, on the other hand, are designed to be proactive, focusing on activities and processes that occur in real time and aiming to inform front-line workers and business managers about the current state of operations, helping them to make immediate decisions based on the information provided.
Considering the above-described distinctions, BI solutions are a lot more dependent on databases and the information stored in them. For OI platforms, real-time data stream integrations play the primary role as they allow these tools to analyze data on the fly.
Additionally, it is more common for BI tools to let users run queries into the results of data analysis and create their own visualizations while OI solutions typically automate this process.
There is a connection between industrial operational intelligence and complex event processing (CEP) but they are not the same. CEP modulus typically play one of the main roles in a functional OI solution.
Event processing is a technique that allows software solutions to track and analyze multiple streams of events-related data at the same time. Complex event processing utilizes multiple tools and methods to organize and process events in real time or soon after they arrive, generating insights and other analysis outcomes as soon as it is possible.
As you can see, the purpose of CEPs is very similar to the most typical function of industrial OI solutions. However, companies that provide CEP modules are normally just focused on developing specialized CEP frameworks that are later supplied to other organizations to be integrated into more complex OI systems.
Business activity monitoring (BAM) is another technology that serves as one of the components used in industrial operational intelligence solutions. But it is not to be confused with OI as a whole.
BAM are specialized software solutions used to monitor business activities that are part of computer systems. BAM modules are responsible for real-time retrieval, analysis and presentation of information about activities within an organization to workers and managers, as well as business partners and customers.
Very often, BAM and CEP are implemented alongside each other in industrial OI systems. The main difference between OI and standalone BAM systems is that BAMs are typically used to monitor the most crucial and high-level business processes while OI focuses on combining as many process data streams as possible, analyzing them all at the same time to find correlations and identify malfunctions.
Systems management also has similar features to industrial OI uses. Systems management describes software solutions that are used for enterprise-wide monitoring and administration of IT infrastructure and networks. The IT infrastructure managed by such solutions usually include industrial equipment, routers, servers and other parts of enterprise network hardware.
Another part of systems management modules is application performance management (APM) techniques that are used to monitor and administer crucial enterprise software solutions.
Business process management (BPM) is a major field of technologies and methods used for identification, modeling, optimizing and automating business processes. BPM modules are another frequent component of advanced industrial OI platforms.
BPM modules that are utilized as part of OI solutions are typically used to organize and structure business processes within an organization for subsequent integration of data streams generated by these processes into the OI platform. BPM modules are also used to define and manage enterprise-wide business policies, tie them to events and initiate actions according to the predefined rules.
Finally, there’s value stream management (VSM), which is a set of methods and management techniques used to identify, organize, manage, optimize and improve the flow of measurable business value outcomes in a centralized way through the whole product delivery life cycle. VSM components are utilized within an organization to visualize the whole business value cycle, making all the issues, delays, wastes, and other areas of potential improvement obvious. Ideally, the proper implementation of VSM leads to overall improvement of customer experience, product delivery efficiency and other value streams within an organization.
Even though VSM modules are typically not included into industrial OI systems, parts of them or components with similar functionality can be used as part of an OI platform. Additionally, Oi can be used as a replacement for VSM, helping your organization to boost the value of products, reduce waste and optimize all operational processes.
As we already learnt, industrial operational intelligence platforms are rather complex multicomponent solutions, and implementing them takes considerable resources, time and effort.
Here are the steps we recommend you to follow prior to and during the implementation of an industrial OI project.
OI solutions have a very broad range of applications within an organization. This is why, before you initiate an implementation of an OI project you should clearly formulate its goals and objectives including specific features you are planning to use, processes and business operations that will be impacted by the OI systems, and key problematic areas that should be improved as a result of OI implementation.
When the goals and objectives are clearly defined, the next logical step would be the development of an industrial OI solution implementation strategy or roadmap. Such a strategy should include all information related to the implementation of such a project including but not limited to the following:
When the project roadmap is completed, you need to build a team that will be responsible for actually implementing it. Naturally, such a project would require data specialists and engineers with prior experience in industrial operational intelligence of similar analytics solutions. Such projects are typically supervised by one of the organization’s top managers. Most often it’s the company CTO but CIOs, CFOs and other company stakeholders of the same corporate level can also take this role.
One of the first active steps towards implementing an industrial OI solution would be to perform a thorough audit of all operational data that is already available at your organization. It is crucially important to understand what data you already have, in which formats it is generated, where it is stored, what is its quality, how it is currently analyzed and so on. Without having an understanding of the quality of your existing data, you won’t be able to stream it into the OI solution and achieve desired results, no matter how sophisticated the actual OI platform may be.
After the existing operational data has been audited and structured, most organizations would find out that it has to be of a better quality in order to be effectively analyzed by an OI platform. If the data you have is outdated, of a low quality, incomplete, or has other problems, an OI platform would not be able to perform a proper analysis of it, coming up with unsatisfactory results. This is why the project team needs to invest time into cleaning the data and improving its quality.
In most cases, the existing operational data silos within the organization need to be augmented with new streams of data, integrated and synchronized across different systems, tools and company departments. Or, in other words, your teams need to achieve data integrity before it can be effectively processed by an industrial OI platform.
The following are the main qualities of high-quality data that can be used and consumed by an industrial OI platform. The data needs to be:
Another central aspect of a successful OI solution implementation is the selection of software tools and technologies to use as part of this project.
Some organizations prefer to use out-of-the-box industrial OI solutions, customizing them for their needs and purposes. But given the lack of such products designed to be used in industrial environments, most companies still have to assemble their own OI platforms from the selection of available components.
Have in mind that depending on your needs and objectives, in order to implement a high-functional Oi platform you would require the following modules and technological components:
With such a huge variety of analytics tools, software components, databases, and data integration instruments, the effective implementation and maintenance of a functional industrial operational intelligence platform can be a problem. Clarify is a time series data solution that reduces the complexity of turning your enterprise data sources into value. It is easy to integrate with automation systems and databases regardless of what data formats they are using. Clarify provides industrial teams with a next-gen level of time series data intelligence, helping to make data points from historians, SCADAs and IoT devices useful for the whole workforce, from field workers to data scientists.
Want to learn how to use Clarify as part of your industrial OI solution to save on implementation costs and achieve a new level of business intelligence? Get in touch.
Along with the software components of your industrial OI solution, you also need to take care of a data storage infrastructure to efficiently and securely collect and store the data required for the analysis. Some industrial enterprises are still using on-premise data warehouses while the majority have moved or are in the process of migrating to cloud-based data storages.
Today, industrial automation experts recommend organizations to modernize their data storage infrastructure by adopting a unified namespace (UNS), Unified namespace is a software layer in the industrial automation system of the future, which acts as a centralized repository of all data collected from sensors, IIoT devices, machines, robotic solutions and other system components, as well as all its context.
A typical unified namespace consists of a physical storage facility where all the data is stored, an API that allows all software applications and systems within an organization to access the data stored in UNS, and a combination of software and hardware systems used to collect the data from the network components that can’t be connected to the UNS directly.
UNS records and presents only the current, real-time, state of every process, application or data stream. In order to access the historical time series data, another system component is required: the data lake.
When the industrial OI platform is completely or partially implemented, introduce front-line workers and managers across various teams and departments to its capabilities. Teach employees how to leverage the features of OI platform in their day-to-day operations. Schedule regular meetings with OI stakeholders and employees to teach them about the nuances of using the OI technology, collect feedback, review the progress and identify the need to introduce potential changes and corrections into the system.
It is universally recommended to start launching your industrial OI solution with a trial version before expanding it to the whole required scope of operations. Limit the initial trial to several crucial and/or easily trackable KPIs and follow the progress. This will allow the implementation team to test the capabilities of the platform, identify potential weaknesses and bottlenecks and fix them in a timely manner.
Finally, after the testing is done, you can expand the industrial OI platform’s reach to all required business operations and KPIs.
When your industrial OI platform is fully launched, regular reviews and team meetings need to be scheduled in order to gather information about its functionality and state. Also don’t forget that it would require regular updates as the time goes by. Potential upgrades of data storage infrastructure is also a possibility.
Selecting the right tools and components to comprise your industrial operational intelligence platform is one of the most difficult parts of such a project. When choosing components, you need to consider a number of things at the same time: the specific OI needs and requirements of your organization, your existing IT infrastructure, if new components are compatible with it and especially your data infrastructure, your industry, and some other things.
Here are a few key questions that should help you to select industrial OI tools and technologies that would be the best fit.
Is it a cloud-based tool or it needs to be installed on-premise?
The majority of modern-day tools are cloud-based but some still require an on-premise installation. Naturally, cloud-based tools are much better as they are easier to manage and more scalable.
Does it have a user-friendly interface and low learning curve?
This is an important factor to consider as some data analytics solutions are pretty complex and not easy to grasp by regular users without prior data science experience. Since your OI platform would need to be used by regular front-line employees and business managers, it needs to provide a user-friendly interface and be easy to learn and apply.
Is it compatible with your existing data analysis and data storage tools and solutions?
This is another key consideration criteria. You need to select tools and technologies that are most easy to integrate with your existing IT infrastructure as it would reduce implementation costs and potentially minimize incompatibility problems.
Can it truly conduct data analysis in real time?
Real-time data analysis is one of the most crucial features separating industrial OI solutions from other analytical technologies. However, not all OI tools truly support the real-time analysis feature. When selecting one, you need to make sure the solution actually supports all the required features in full.
Does it support customizable dashboards and other data visualization tools?
Even though the majority of modern-day advanced data analytics solutions support dashboards and other modes of data visualization, not all of them do. Additionally, not all data visualization tools would be a good fit for your industry and requirements of your organization. So if you are building an industrial OI platform, you need to look for tools that are tailored to the industrial needs and applications.
Is it ready to be connected to your data silos?
Different OI tools and platforms typically support a range of various data connections via APIs, per-built software connectors, drivers, and other methods of data integration. Make sure the OI solution of your choice can be easily integrated with the existing data silos and the ones you are still planning to establish as part of your industrial OI infrastructure.
Industrial operational intelligence is a powerful technology able to empower your business analytics and monitoring of industrial and manufacturing systems, providing you with new insights extracted in real time and turing the data generated by the machinery and software systems into valuable information that helps to boost productivity, increase efficiency, improve security, minimize waste and generally optimize all business operations across the whole business cycle.
In order to fully leverage the power of modern-day operational intelligence, advanced data analysis tools are required that would be easy to integrate into the existing industrial automation stack and won’t take too much time for your employees to get familiar with. Clarify is an operational intelligence platform that can be integrated into your IT/OT environment, helping your organization to analyze process manufacturing data collected over the years and visualize the acquired insights in an attractive manner that is easy to understand by managers, front-line workers and virtually all company stakeholders.
Clarify can be easily integrated with the majority of data historians and industrial automation systems from all vendors, including the ones that only support on-premise deployments, allowing you to combine data from multiple time series databases, visualizing and accessing it in real time. Clarify also simplifies the process of connecting third-party data science tools and applications to your data for advanced analysis.
Regardless of your industrial OI requirements, Clarify platform is a versatile solution that can be used to solve challenges with processing, integrating and visualizing time series data across industrial automation systems and software components.
Want to see the capabilities of the Clarify platform with your own eyes? Take a tour.
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