Data Analytics

Industrial Data Help Overcome All Business Challenges

How Can Industrial Data Help Overcome All Business Challenges

Today, if we see the ongoing competition between industrial companies, we can easily underline the challenging hurdles they face to become the best, primarily in operational objectives and in understanding the immense amount of data available to them to decide how best they are achieving those goals.

To meet this objective, industrial data management strategies must be adopted to leverage existing assets and systems to unlock the full potential of their plants and drive their businesses forward.

Currently, the flooding industrial data is mostly wasted. In fact, as per the European Commission, 80% of industrial data gathered is never utilized. Asset-intensive organizations need a holistic and integrated solution that offers seamless connectivity across all data sources while providing real-time monitoring capacity to ensure no data is wasted.

With such a broad framework, these companies can maintain asset reliability through predictive equipment failure analysis, reducing maintenance costs and improving overall plant efficiency. Yielding on this vision is a big task today as a flooding amount of data is present. Companies across these sectors have recorded and captured large amounts of data for decades. These data have incredible potential, and using them to good use is far easier than expected.

Unclosing high-potential value use cases that utilize this data in production optimization, machine learning, or emissions tracking needs potent data management strategies. After all, industrial data and systems have traditionally been located in organizational silos, having different pockets of functionality developed by various dealers at different times. This has made data management more difficult and rendered most data unusable at scale.

Going through the Data Lake confusion

To counter the challenges highlighted above, businesses often choose to construct data lakes in which data from different sources is collected.

These data lakes work as potential reservoirs that swiftly accumulate vast amounts of information.

Nonetheless, it is not easy to potentially utilize these data lakes as it requires a workforce skilled in data handling and analysis, ultimately creating a considerable challenge to industrial business. Hiring such highly skilled personnel becomes even more intimidating due to the promptly evolving workforce, where specialized expertise is at a compensation.

Going through this complex system requires a strategic approach, allowing businesses to unveil the full potential of their data lakes and secure a competitive benefit.

The need for real-time data platforms suitable for commercial use

An asset-intensive business offers potential solutions; however, traditional data historians remain key, allowing industrial organizations to access data, know what is relevant, place it into workflows, and make it usable. The market for these assets remains on an evolutionary path globally. As per Mordor Intelligence, it will grow from US$1.15 billion (€1.05 billion) in 2023 to US$1.64 billion (€1.49 billion) by the end of 2028, at a compound annual growth rate of 7.32% during the projection period. 

Today, plant operators and engineers use historians to monitor operations, analyze process efficiency, and look for new opportunities. These are target-oriented systems customized for the operation teams’ benefit. 

With time, there has been an increasing demand for cloud-based applications to aid advanced analytics and quickly scale up. Meanwhile, on the IT side, digitalization teams and products need to be structured, clean, and contextualized data to produce usable insights and expand use case volumes. 

However, different data sources, including historians, offer at-a-glance analyses; their customized nature makes it hard to automate consistency in contextualizing and structuring data.

Enforcing a new solution

The collaboration of plant-level historian solutions and enterprise data integration and management technology allows a uniform confluence of IT, that is, Information Technology, and OT, which is Operational Technology functions. Along with this, we are also noticing the rise of next-generation real-time data platforms, supporting industrial organizations in collecting, consolidating, cleansing, contextualizing, and analyzing data from their operations.

This data foundation shows the beginning point for the industrial organization to optimize processes using machine learning and AI and develop new working methods based on data-derived insights.

Such organizations will be competent in developing current data systems to gather, merge, store, and retrieve data to boost production operations with data-driven decisions or backing performance management and analytics across the business.

This new data consolidation strategy prints a key moment in the evolution of data management. An organization can unveil unimaginable efficiency, innovation, and visibility by centralizing information from different sources into a unified, cloud-based, or on-premises database. The collaboration of batch and event processing delivers track and trace capabilities and authorizes organizations to search into batch-to-batch analysis quickly.

Driving ahead positively

Today, industrial companies face umptieth challenges, including meeting operational objectives, comprehending large amounts of data, and improving asset reliability.

They need a data management approach that uses legacy assets and systems to manage these issues. This approach should have an integrated solution that enables organizations to connect all data sources, access real-time monitoring, boost asset dependability, and increase overall plant efficacy.

Conventional data historians are still crucial to this strategy but must be integrated with cloud-based applications, enterprise data integration, and management technology. This will help companies gather, consolidate, cleanse, contextualize, and analyze data from their operations. This real-time data platform has grabbed a competent place worldwide as companies seek solutions to enhance their operational efficiency and decision-making capacity. Not just this, companies will also be able to update current data systems to gather, store, merge, and get back the lost data. This will ultimately improve production operations with data-based decisions and help in performance management and analytics across the system.

Along with this, companies will also get access to real-time asset performance, track material progress through complicated processes, and interlink people, data, and workflows to support compliance.

How IoT Data Analytics Impact your business

What is the Impact of IoT Data Analytics on your Business?

Today, if we observe the trend and business processes, we can express that IoT solutions are changing the way of doing business globally. However, saying that all solutions provide equal benefits would be wrong. We can say that an IoT solution that shares data without analytics is like a symphony playing Mozart without a conductor. This means music is there but with no structure and loses its purpose, beauty, and meaning. We all know that there will be immense flooding of data by IoT, but the absence of a process to properly analyze the data would just cause complexity and noise without proper output.

The Impact of Data Analytics on businesses

Data is compelling and empowers by giving insights into all aspects of the business. It can assist organizations in refining processes, locating missing physical assets for cost saving, or even helping in defining new use cases for already available products. 

In the absence of data, a company can be just reactive or can assume future challenges and results. 

With the data offered by an IoT solution, a company can anticipate the emerging problem before it becomes complicated and resolve it as soon as possible. However, there are IoT data solutions that only offer the data and no other context to make it meaningful. In such cases, IoT analytics comes in as a savior. 

The capability to interpret the data before it comes in front of the user is compelling. For instance, data analytics can help alert a factory manager about the floor problem in real-time instead of waiting and then reading through reports on issues that have already happened. This can reduce time consumption and the possibility of errors. 

Analysis software is available in many forms, from one-size-fits-all products to low-code/no-code solutions to solutions that demand an experienced engineering team to execute and maintain.

Each type of solution has benefits and expenses, and your enterprise must determine the best-fitting solution to get the maximum benefit.

Well-known IoT Data Analytics Solutions

We all are aware that technologies like AWS IoT Analytics, on the one hand, are sophisticated and powerful but, on the other hand, very complicated to execute and demand a highly skilled engineering team having domain expertise. The advantages of the analytics solutions are- it offers customization. Everything needed in your business and unnecessary things to be left out. You can consider AWS IoT products like building blocks: you can get maximum from them, but they demand a lot of planning along with maintenance and oversight.

All businesses cannot afford or consider hiring an expert engineering team to execute these solutions. These businesses are inclined toward adopting a one-size-fits-all solution like Azure provides IoT Central

Azure even provides a solution analogous to AWS, but they are more successful in an out-of-the-box strategy. The straightforward analytics provided by this solution or any other one-sized solution can fulfill the requirements of many businesses. They enable businesses to connect promptly and design their dashboards and alerts within a few days or hours. If your business just needs simple alerting or has a limited number of devices to connect, then opting for this solution would be a great idea and cost-saving as well.

Customizable Solutions

The main challenge with the IoT data analytics solutions mentioned above is that they don’t provide customization options, are costly to scale, and might compel your team to do analytics using a third-party tool (which is no doubt another pricey option). Suppose you own a business having specific analytic requirements and many devices to be connected. In that case, a low-code/no-code solution, like the one proposed by Leverege (running on Google Cloud), could be a terrific middle-ground solution. This type of solution is customizable per the business’s requirement and, in parallel, does not need any technical expertise if it offers an end-to-end alternative and has analytics and an excellent alerting system, even without needing a dedicated and proficient engineering team. 

Irrespective of whatever solution you choose for a business to implement, ensure that a third-party tool to be integrated gives you maximum flexibility and value from the data. Tools like Power BI, Tableau, and Looker can be the best option to support your company in familiarly visualizing your data. If your company has already made a preferred analytics tool list, then it will enable your users to harness their expertise of that tool with new data sources.

Valuable Insights

Till now, hope that you have understood the importance and contribution of Analytics tools. It is essential to obtain the optimum value from IoT solutions irrespective of the products the business chooses. Neglecting these core capabilities may take your business to the loss side as it may miss valuable insights and maximize value. We can simply infer that IoT solutions, no doubt, enhance business operations but remain incomplete.

Data analytics gives direction and beauty to the solutions as it analyzes the data and offers favorable data to businesses to boost operations and amplify outcomes. Today, most companies are embracing the Internet of Things but are unaware of the importance of data analytics and ignore it. They face losses and then switch back to their old processes and operations. Therefore using IoT and offered IoT solutions must be opted for after attaining full knowledge.

Today, IoT is making its space in almost every sector, from smart homes to smart buildings, from smart towns to smart cities, and from smart farming to smart logistics; one can see the influence of IoT in every sector.

Similarly, data analytics is also contributing from its end to add more value to every solution offered by the Internet of Things. For instance, in the baking process, the availability of raw ingredients is insufficient, and it does not come together without a recipe. The recipe brings ingredients together in a beautiful way and offers the best. So, if you are still untouched by the magic of data analytics, then you might be losing a lot of benefits and leverages offered by it.

How is Data Science for IoT changing business outlook

How is Data Science for IoT Changing Business Outlook?

The Internet of Things has been noticed as a shape-changing technology that has changed the shape and working process of everything it has touched, either businesses or our daily lives. It has changed the outlook of every individual living a mediocre life into a smart device-connected life.

IoT connected devices produce tremendous amounts of data wirelessly over the network without any human interference, which is proved to be best for organizations trying to offer the best services to their clients. The only challenge is that IoT generates immense data for traditional data science.

Data Science and How It Applies to IoT

We can simply define data science as a study of processes that assists in extracting value from data. In the IoT system, data is referred to information produced by sensors, devices, applications, and other smart gadgets. Meanwhile, value means predicting future trends and outcomes based on the data.

For instance, suppose you are using a fitness tracker that calculates the number of daily steps. Using this information, data science can predict that:

  • Amount of calories burnt by you
  • How many kgs do you lose
  • When is the possible best time for your workout

This is a simple example of how data science works. Internet of Things is different as it produces high-volume data.

As per the reports, the amount of data to be produced by IoT by 2025 is around 73.1 zettabytes. This will cause trouble for standard data science as it cannot handle it, so it will have to update. Thus, IoT will help data science to go to the next level.

What are the Differences Between Traditional and Data Science for IoT?

There are only a few differences between traditional and IoT-based data science, so here we will check a few critical distinctions.

Data Science for IoT Is Dynamic:

The traditional version of data science is static as it is primarily based on historical information. For example, a company collects data from its clients about their choices and needs. The historical data becomes a base for predictive models that assist the company in understanding its future customers.

On the other hand, IoT changes the dynamic of data analysis as it is all about real-time sensor readings from smart devices. The gathered information permits data science consultants to create highly precise evaluations instantly.

In this case, customer data changes and updates- a feature that is not available in traditional data science. Data science for IoT allows continuous learning, changes with time, and improves operational processes simultaneously.

IoT Drives Larger Data Volumes:

Data science is developing with IoT because of its immense data processing. Here we are not discussing megabytes or gigabytes of data but data science for IoT deals with a massive amount of data that often reach zettabytes.

Better Predictive Analytics Method:

Data science for the Internet of Things is dynamic and wider than the traditional one. Additionally, it also makes a better predictive analytics method.

Thus, data science assists businesses in a great way; using it, businesses can develop better solutions that can diminish operational costs and acquire business growth.

IoT can improve this further through its real-time capabilities. IoT helps make decisions more accurate, assisting companies in identifying new opportunities and improving sales and customer experience while optimizing performance.

The Challenges faced by IoT Data Science:

We all know that data science for IoT holds vast potential, but it comes with challenges. Four major risks have to be overcome before it becomes mainstream.

Data Management and Security:

IoT produces a tremendous amount of data, which also implies that there are high chances of hacking or leaking private information. For example, Suppose hackers hijack the connection between the fitness tracker and doctor’s office app; they can easily access sensitive health records. Thus, it is pretty clear that privacy problems are the major issues with IoT data science.

For instance, many companies often face backlashes for releasing customers’ sensitive information without their consent.

Scaling Problems:

IoT data science is also important, but users often struggle to scale it up to fulfill their demands. When an organization plans to integrate an IoT system or add new sensors to its existing software solutions, it faces some issues and challenges.

Therefore, it is important to prepare for scaling projects in advance. Businesses must set up everything from software to personnel to scale data science processes successfully.

Data Analytics Skills:

Data science for IoT is extensively helpful, but classical data science consultant holds good dominance in the market as IoT analytics is still not very much embraced.

However, this could change soon as more companies adopt IoT technology. IoT scientists will have to add new skills and understand the oddities of the deployment process. For this purpose, they’ll have to learn about the following:

  • Edge Computing: It is defined as the practice of processing data close to the source to improve performance and reduce network congestion.
  • Computer-Aided Design: It is essential to know the logic behind the physical design of a smart device.
  • IoT Computing Frameworks: Data scientists must also employ open-source learning tools to grasp IoT hardware.
Operating Costs:

Another major problem with data science for IoT is the huge cost required to introduce new technology. This is the case for most companies willing to join this latest technology on a larger scale but is restricted by budget.

The Bottom Line:

We can conclude that data science for IoT brings a major upgrade to traditional data analytics. It requires efforts and dedication to make data science more robust, powerful, and accurate. IoT can make it possible through data generation abilities. The interconnected devices over the internet constantly communicate to offer businesses a huge amount of user-related data. This allows data scientists to draw relevant conclusions from their databases.

However, the process of deploying data science for IoT is not an easy task, but the benefits it provides negate every challenge. So, we can expect data science for IoT to be a part of the future at a great scale.

How to Prevent Data Lake from Turning into a Data Swamp?

IoT devices drive in many opportunities to gather more data than ever before. However, the challenge has changed; it is not about ways to get data but how to store an immense amount of data once it’s gathered. This is where data lakes come in the role. To clarify, a data lake is not just about a cheaper way to store data, but when it is appropriately crafted, data lakes act as a centralized source of truth that offers team members valuable flexibility to examine information that influences business decisions. This is only possible when we potentially utilize data lake practices. Raw data is like crude oil, requiring a thorough refinement process to distil more valuable products like gasoline. In the same way, raw data requires complex processing to get the most beneficial and business-rich insights to take action and measure outcomes.

With the increase in the volume of available data and the variety of its sources continuing to grow, many companies find themselves sitting on the data equivalent of a crude oil reservoir with no feasible way to extract the actual market worth. Traditional data warehouses are like gas stations; data lakes are oil refineries.

Data warehouses are becoming insufficient for managing the flooding business’s raw data. They need the information to be pre-processed like gasoline. Data lakes are the one that allows for the storage of both structured or unstructured data coming from different sources, such as business and mobile applications, IoT devices, social media etc.

Any idea? What does a well-maintained data lake look like? What is the best possible way to lead to implementation, and how do they impact the bottom line?

Explaining Data Lakes: How they Transform business

Data lakes are centralized storage entities to store any information mined to get actionable insights. These contain structured, unstructured, and other information from relational databases like text files, reports, videos, etc. A well-maintained data lake has real prospects to change the outlook of the business by offering a singular source for the company’s data regardless of its form and allowing business analysts and data science teams to extract information in a scalable and sustainable way. 

Data lakes are generally designed in a cloud-hosted environment like Microsoft Azure, Amazon Web Services or Google Cloud Platform. The vision offers compelling data practices that offer noticeable financial edges. These practices are approximately twenty times cheaper to access, store and analyze in a data lake rather than employing a traditional data warehouse. 

One of the reasons behind the domination of data lakes is the design structure or schema, which does not require to be written until after the data has been loaded. Regardless of the data’s format, the data remains as it is entered and does not separate into silos for different data sources. This automatically decreases the overall time for insight into an organization’s analytics. It also offers enhanced speed while accessing quality data that helps to inform business-critical activities. Advantages provided by data lakes like scalable architecture, cheaper storage and high-performance computing power allows companies to divert their shift from data collection to data processing in real-time. 

Rather than investing hours excavating scattered deposits, it provides one source to extract from that ultimately decreases dependency on human resources, which could be utilized to create stronger partnerships across teams. A data lakes give time to your data scientists to explore potential business-critical insights that could advise new business models in the future. 

Best Practices from the Experts

There are challenges in the data lakes process; it acts like a stagnant pool of water-polluting over time if it is not held to the correct standards. It becomes challenging to maintain and susceptible to flooding from insufficient data and poor design.

What to do to set up a supreme system for business transformation and growth?

Here we recommend the following actions to prevent your data lake from turning into a swamp.

Set Standards From the Start

A dynamic structure is the backbone of a healthy data lake. This means creating scalable and automated pipelines, using cloud resources for optimization, and monitoring connections and system performance. Initiate by making intentional data-design decisions during project planning. Mention standards and practices and ensure they are followed at each step in the implementation process. Meanwhile, allow your ecosystem to manage edge cases and the possibility for new data sources. Don’t forget; it is all about freeing up your data scientists from tending to an overtaxed data system so that they can shift their focus on other priority things.

Sustain Flexibility for Transformative Benefits

A healthy data lake exists in an environment that can manage dynamic inputs. This isn’t just about varying sources, sizes and types of data and how it is downed into storage.

For instance, creating an event-driven pipeline facilitates automation that offers source flexibility in file delivery schedules. Setting up a channel with trigger events for automation, based on when a file hits a storage location, eases concerns whenever the files come in. It is necessary to support the data science team’s fluidity around rapid testing, failing and learning to refine the analytics that empowers the company’s vital strategic endeavours, eventually driving unique, innovative opportunities.

Develop the System, Not the Processes

Most people have a misconception that problem-specific solutions may seem faster initially. One of the best things about data lakes is that they’re not connected or centralized around any one source. Hyper-specialized solutions for individual data sources restrict themselves to implementing change and need error management. Besides this, when a particular process is introduced, it doesn’t add value to the system as a whole as it cannot be utilized anywhere else.

Designing a data lake with modular processes and source-independent channels saves time in the long run by facilitating faster development time and streamlining the latest feature implementations.

Handle Standard Inventory to Find Opportunities

Event-driven pipelines are the best option for cloud automation, but the tradeoff demands post-event monitoring to comprehend what files are received and by whom and on which dates, etc.

One best way to monitor as well as share this information is to establish a summary dashboard of data reports from different sources. Adding alerting mechanisms for processing errors produces a notification when part of the data lake is not correctly functioning as expected. It even ensures that errors and exceptions are detected on time. When an immense amount of data is flooding, it becomes essential to track and handle it in the best possible way.

Right inventory initiatives create stable environments where data scientists feel supported in discovering additional metrics opportunities that can help make more robust business decisions in the future.

Revolutionize Business Intelligence

Data lake revolutionizes business intelligence by chartering a path for team members to peer clean data sources promptly and in the most effective way. A pristine data lake accelerates decision-making, removes struggle, and enhances business model ingenuity. So, we can conclude that prohibiting data lake getting muddied is necessary to get the optimal outcome. One must follow a few data lake practices that can reduce future headaches and keep your data streamlined and humming.

How does data preparation automation improve time to insights?

How does Data Preparation Automation Improve Time to Insights?

Today, most businesses depend on the data, and the data generated and consumed for the purpose are massive. It is an undeniable fact that with growing technology, the amount of data will keep on increasing in the coming years. It is assumed that by the end of this decade, the total amount of data will cross approximately 572 Zettabytes, which is almost ten times more than the amount of data present. Ultimately this will be a challenging task for an organization, as it will become hard to manage and organize data. Besides, this process of collecting meaningful data from the accumulated one becomes a time-consuming process.

One of the top challenges organizations face is obtaining real-time insights and staying ahead in the market from competitors and the resultant pressure to work faster together. 

We all know that doing everything manually has become an impossible task as it brings in many challenges. Therefore automation is the best option for organizations to earn valuable information and streamline the data transformation process. As per the data fabric trends report, it has been estimated that the data automation market size will extend up to $4.2 billion (€3.56 billion) in 2026.

Strategic data automation:

When people come across the concept of automation, there is a common misconception that automating business processes means replacing human resources with technology. It is essential to understand that automation does not take the place of humans in the workspace; instead, they ease their work by helping them complete tasks seamlessly and efficiently. No technology can replace human brains for doing any jobs. Though most of the repetitive and monotonous business processes can be automated, the basic need to implement business logic and rules to be used within the code is done manually.

Interpreting and making the right decision needs human intelligence for conducting various complex data analyses, and it can never be replaced.

Even after the availability of developers, the growing need for automation will fail to keep with the increasing amounts of data and gather expedient insights from it. Manual coding to execute the necessary logic into automation will be an arduous task when it has to be performed with a considerable amount of data in a given time. 

Exploring new ways for data preparation and business automation will help in obtaining insights promptly. Today, many data preparation tools are available in the market that provides trusted, current and time-based insights. These tools encrypt the available data and make it safe and secure.

Why do we need an automatic data transformation process?

Besides the need to automate repetitive and monotonous tasks and offer the organizations more time to work on the other complex data processing and analyzing, automation provides various other benefits. The list is as follows:

  • Manage data records – Automating data transformation methods empowers firms to organize new data set effectively. This will result in maintaining the comprehensive data sets and making them available whenever needed.
  • Concentrate on main priorities –  Business intelligence(BI) is just not meant to deliver timely and meaningful insights. They are assigned to work on innovative initiatives. Automation tasks provide them much time to work on business’ vital aspects.
  • Better decision making – Automation permits fast access to more comprehensive and detailed information. This enables management teams to create vital and speedy business decisions.
  • Cost-effective business processes –  Time management is an essential factor for any business.  Time is a critical factor in any industry. Automating the processes like data transformation and other data related tasks reduces the cost and resources consumption and ensures better results.

Ways to automate workflow

Employing of a built-in scheduler and third-party scheduler:

ELT (“extract, load, and transform”) products have a built-in scheduler. This ends the dependence on the third-party application or any other platform to launch the product. ELT tools also allow managing tasks centrally that making it easier to control and manage the tasks. 

Additionally, another benefit of using ELT tools is dependency management. Here a primary job can be used to start a second job. Dependency management allows an organization to categorize tasks and make management seamless. Many platforms enable performing APIs, and API calls can be scheduled in a specified way adopting the operating system’s built-in scheduler. Many third-party tools can perform ELT tasks. Employing these tools will offer functionalities to integrate with existing systems within the development environment. But, if one has to use third-party ELT tools, then additional charges have to be paid for services and resources used to execute a product.

Cloud service provider services:

Today, companies are switching to cloud technologies. It has been observed that  94% of enterprises have already adopted the cloud. In addition to storing and managing data, CSPs provides many other services that support automation. Like using messaging services to start a task. Any production tasks or custom tasks that hold messaging can listen to the upcoming messages in a job queue and start a job based on the content of the message. However, the general working concept remains the same. Some examples of messaging services are AWS SQS, Microsoft Azure Queue Storage. 

Furthermore, CSPs also offer serverless functions to aid with automation, and this serverless functionality can automatically activate the jobs. The benefit of using serverless functions is that the company only has to pay for service when it is in use. Google Cloud and AWS Lambda functions are some of the known examples of serverless cloud services.

Conclusion:

In the upcoming years, integrating processes with Artificial Intelligence and Machine Learning, automation will become easy and efficient. This will help organizations prepare data and acquire more meaningful insights. But to embrace these technologies, organizations should be ready to accept and welcome the changes that accompany them while adopting these technologies.

Why Your Organization Needs IoT Data-Based Maintenance Management

Why Your Organization Needs IoT Data-Based Maintenance Management?

IoT is nothing new, but it is not old even. It always comes up in the news with the new feature and allures everyone. Today, it is rare to find out someone who is not aware of the Internet of Things or its benefits. Small, medium or large, all size companies are thriving to become part of this beautiful and limitless technology. One who has already adopted it are enjoying immense success and benefits in their business.

How is significant IoT Data?

IoT data holds tremendous value to maintenance management functions, but no doubt, the quality of value is directly dependent on the quality of the data you receive. This implies that source, timeliness and accuracy greatly influence the overall value data can offer. If you are planning to create IoT data that can aid to materialize your business objective, then, you must find out the following aspects.

  • The first aspect is to find out the data type required to meet your objectives and the data you can quickly gather from machines or in the field. You might find the gap between the two data points and no doubt, overcoming this gap is a long-term goal that could be accomplished as the sensor and network technology modernizes in the future.
  • Now, you have to validate the available data on the aspect of reliability, accuracy and timeliness to find out the relevant data.
  • You have to build a CMMS software architecture that can interpret the appropriate data into information.

Let’s check how companies in asset-intensive industries are utilizing IoT to change their existing maintenance management functions.

Predictive Maintenance:

The best feature that IoT data can offer is predictive maintenance, and we can say this on the basis of two key reasons. The progress in sensor and network technologies allows IoT data to help asset-intensive industries to optimize their maintenance management functions.

The first key reason: IoT data permits you to predict maintenance requirements and asset failures. It provides you with enough time to schedule the most favourable field service technicians based on their availability and skill set. Thus the process is streamlined successfully.

The Second Key Reason: The data-driven ability to conduct maintenance scheduling on an ad-hoc basis saves time and reduces the cost and improves first-time effectiveness.

For instance, HVAC equipment has temperature sensors to monitor the airflow efficiency and sends alert for filter replacement or maintenance when the airflow changes. In the same way, sensors embedded in solar panels which are connected through IoT can generate work orders whenever required and as per the need.

Data-Driven Inventory Management:

Inventory is an essential part of the maintenance function. There are many organizations that are dependent on a spreadsheet or other paper-based methods for inventory control and management. These processes, either a spreadsheet or a manual one, both can cause common inventory management mistakes like:

  • Data entry error: Manual data entry invites lots of errors and results in misleading information.
  • Mismanagement in the warehouse: Well, we can say that data entry method is not the sole reason behind the error, but it is the type of data being recorded which disturbs the whole process. Since the entire process is manual, there is no mechanism available to check the data quality.
  • Poor Communication: Poor communication is the third setback within the organization, particularly between office administrative/executives and warehouse staff. This miscommunication often leads to error in data entry.

To avoid these mistakes, companies have started using computerized maintenance management software. The software can collect and process the IoT data to facilitate companies with perceptibility into inventory levels. Use of IoT data to foretell the inventory levels say stock-in and stock-out of spare parts in different locations, organizations can optimize the spare parts stock and control the expenditure on new expenses. For instance, you can schedule a visit whenever required and order the new stock as per the need.

Performance Measurement:

IoT data helps in making decisions related to asset and team performance. It allows the management team to monitor and track teams and assets to set Key Performance Indicators and track process (KPIs).

For example, you can find out the best performer in the team; in fact, calculate the team members’ regular average performance. Using the data, organizations can plan training and skill development programs for field service technician staggering. An organization can even organize reward, recognition and compensation program for star performers. In the same way, organizations can use data to replace the asset that is regularly causing threat and reducing downtime.

End words:

As we already know, IoT provides a lot more than we know, so using it for maintenance management functions can be a bliss for organizations. An organization should opt for better planning at the initial stage to ensure better data. Using relevant data, you can achieve reliable information and get enhanced decision-making capabilities. It is noted that early implementers of IoT in maintenance enjoy extraordinary benefits of transparency, visibility and efficiency in the operations. You should also review your on-going process and check how IoT can enhance your present maintenance management function.

How Operational Analytics Helps Businesses in Making Data-Driven Decisions

How Operational Analytics Helps Businesses in Making Data-Driven Decisions?

With the adoption of the latest technologies in businesses and growth in disruptive technologies, cloud computing and IoT devices are causing immense data generation than ever before. However, the challenge is not collecting data but using it in the right way. Thus, businesses have found an option to analyze the data most potentially. Organizations are using futuristic analytics features to understand the data. Operational analytics is one of the popular solutions to upheave business.

Nowadays, data is increasing tremendously. Every time a user interacts with the device or website, an immense amount of data is produced. At the workplace, when employees use company’s device like computer, laptop or tablet, then the data produced by them is also added in the company’s data house. The generated data turns useless if not used appropriately.

Operational analytics is at the initial stage of getting the place in the business industry. A survey by Capgemini Consulting states that 70% of organizations prioritize operations than customer-focused operations for their analytics initiatives. Nevertheless, 39% of organizations have widely combined their operational analytics initiatives with their processes, and around 29% has achieved the target from their endeavours.

Any idea about operational analytics and how it works?

Operational analytics can be defined as a type of business analytics which aims to improve existing operations in real-time. The operational analytics process involves data mining, data analysis, business intelligence and data aggregation tools to achieve more accurate information for business planning. We can say that operational analytics is best among other analytic methods for its ability to collect information from different parts of the business system and processes it in real-time, enabling organizations to take a prompt decision for the progress of their business.

How Operational analytics helps in business?

Operational analytics allows processing information from various sources and answers different questions like what appropriate action a business should take, whom to communicate and what should be the immediate plan etc. Obviously, actions taken after considering operational analytics are highly favourable as they are fact-based. Thus, this analytics approach fully automated decision or can be used as input for management decisions. Operational analytics is used in almost all industries.

We can have a look at some of them:

  1. Today, banks use operational analytics to segregate customers based on aspects like credit risk and card usage. The data provided helps the bank to provide customers with the most relevant products that fall under the customers’ personalized category.
  2. Manufacturing companies are also taking advantage of this beautiful technology. Operational analytics can easily recognize the machine with issues and alerts the company on machinery failures.
  3. Adding operational analytics in the supply chain enables an organization to get a well-designed dashboard that provides a clear picture on consumption, stock and supply situation. The dashboard displays critical information that can examine and promptly coordinate with the supplier on a supplemental delivery.
  4. Operation analytics is also active in the marketing sector as it helps marketers segregate customers based on shopping patterns. They can use the data to sell related products to target customers.

What are the benefits of operational analytics?

Adoption of operational analytics brings many benefits for businesses. It imprints a positive impact on the entire enterprise.

Speedy decision-making:

Businesses that have already adopted operational analytics enjoy the privilege of making decisions in real-time based on available customer data. Previously, companies were restricted to decide on annual or half-yearly or quarterly data. Adopting operational analytics has empowered companies by providing the data in real-time, which ultimately helps in changing the processes and workflow. A recent study has proven that improving operations can make a US$117 billion increase in profits for global organizations.

Improved customer experience:

Operational analytics works as a real-time troubleshooter for companies. For instance, if a shopping site or an air travel company encounters money transaction problems, then operations analytics immediately finds the issue and informs that the payment portal of the app is corrupt. It notifies the employees for the same and clears it quickly.

Enhanced productivity:

Operational analytics has allowed organizations to see the drawbacks that hinder the growth and disrupts the workflow. Businesses can streamline the operations and process, depending on the data.

For example, suppose an organization follows a very lengthy process to authorize something. In that case, the company can detect the issue, remove it, or change it to online modes to simplify the process.

Operational analytics software:

Operational analytics software supports organizations to achieve visibility and insight into data, business operations and streamlining events. It empowers an organization to make decisions and promptly act on the insights.

Some of the famous operational analytics software are:

  • Panorama NectoPanorama Necto is renowned as a business intelligence solution that caters enterprises with the latest ways to cooperate and produce unparalleled contextual links.
  • Alteryx– This software helps operations leaders and analysts in answering strategic investment questions or critical process in a repeatable way.
  • Siemens OpcenterSiemens Opcenter is considered as holistic Manufacturing Operations Management (MOM) solution that allows users to execute a plan for the whole digitization of manufacturing processes.

Conclusion

We can now conclude that businesses are welcoming operational analytics to improve workplace efficiency, drive competitive advantages, and provide the best customer experience.