Data Management

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 to Address Data Management Challenges in IoT Using Fabrics

How to Address Data Management Challenges in IoT Using Fabrics

Whenever we talk about data management, the whole conversation remains incomplete if we do not mention the most important aspect related to data management: the Internet of Things, IoT networks. Today, everything is connected, and all credits go to IoT networks. From smart towns to industrial sensors, our world is interconnected with smart devices, and the volume of data generated has reached unbelievable proportions. This is advantageous for our digital transformation initiatives but carries a parallel increase in vulnerability to data piracy, cyber attacks, and privacy infringements.

The amount of data generated is directly proportional to the higher stakes regarding safeguarding it. This raises the need for data protection measures in IoT ecosystems, which has now become a significant challenge for organizations. It has also necessitated robust data management strategies to guarantee IoT data’s integrity, security, and privacy.

However, enterprises are still making errors. They emphasize more on expanding IoT and are least interested in making the data streams safer and more authentic. More comprehensive IoT networks assure more users and faster streaming, yet they lack in terms of data protection.

Critical data management challenges in IoT

In the domain of IoT, significant data challenges emerge, including security risks, privacy concerns, data authenticity, and data proliferation. Security risks create a constant threat, as IoT devices are vulnerable to breaches, unauthorized access, and tampering, potentially resulting in data leaks and network attacks. 

Safeguarding privacy is crucial due to the collection and transmission of personal data by IoT devices containing sensitive information like location, health data, and behavioral patterns. 

Securing data integrity and authenticity is difficult in IoT environments, as changes often lead to erroneous decisions and compromise system reliability. 

Besides this, the sheer volume of data created by IoT devices can overcome traditional management systems, making it necessary to have sufficient storage, processing, and analysis strategies in a timely and cost-effective way. As per the ‘State of IoT Spring 2023’ report released by IoT Analytics, the worldwide count of operational IoT endpoints rose 18% in 2022, reaching 14.3 billion connections. 

How can data fabrics handle these challenges?

Data fabrics are essential in allowing scalable data management in IoT ecosystems. They provide valuable support in different aspects of IoT data management. They play a vital role in privacy protection by using data masking techniques that pseudonymize or anonymize sensitive information.

By substituting original values with masked or randomized data, the identity of individuals or devices remains safe, diminishing the threat of data breaches.

Data fabrics also allow access control, restricting access to authorized personnel or systems. Encryption also improves security by shielding transmitted or stored data from unauthorized access. Data fabrics offer an extra layer of security against attackers by integrating encryption with masking.

In addition, data fabrics support data minimization by reducing the amount of sensitive data stored or transmitted, using masked or aggregated data instead.

  • Data integration and aggregation: Data silos create a  significant challenge in IoT, as they can cause data duplication, loss, or inaccessibility by different systems. Data fabrics can support breaking down data silos by offering a unified view of data across the IoT ecosystem. Data is created from different sources and in diverse formats; data fabrics can enable the integration of this data into a suitable view. This allows organizations to comprehend their IoT data landscape and make informed decisions. Data fabrics can collect and merge this data in real-time, offering a compressed and contextualized view of the IoT environment. This collected data can be used for real-time analytics, irregularity detection, and predictive modeling, allowing organizations to derive valuable insights and make proactive decisions.
  • Data processing and analytics: Data fabrics offer processing power, permitting IoT data to be analyzed and changed into actionable intelligence. By using distributed computing and parallel processing, data fabrics can handle IoT data’s high volume as well as velocity. This empowers organizations to conduct complex analytics on the gathered IoT data, like machine learning algorithms, extracting valuable patterns, trends, and correlations. 
  • Data management and quality: Data fabrics offer a management layer guaranteeing data quality, consistency, and compliance. As we know, IoT data comes from different sources and devices, and it is necessary to ensure data integrity and reliability. Data fabrics can implement data management policies, perform data validation and assure data quality standards are fulfilled, thereby enhancing the reliability and trustworthiness of IoT data.
  • Scalability and flexibility: IoT establishment often includes multiple devices creating data at a high frequency. Data fabrics are designed to be scalable and flexible, enabling organizations to manage the high intensity of IoT data and acclimate future growth. They are seamlessly scaled horizontally, adding more resources as required and adapting to evolving IoT infrastructures and data requirements.

Not just this, data fabric tools also enable real-time data processing and help in decision-making. In IoT systems, real-time responsiveness is essential for upcoming maintenance, monitoring, and dynamic resource allocation applications. Data fabrics can process and analyze data in real-time, allowing organizations to take prompt actions based on IoT insights.

Some robust platforms for managing IoT data

For handling IoT data, many platforms offer robust capabilities. One such platform is K2View, a data integration and management solution that allows organizations to merge and manage their data from various sources. Their technique pivots around micro-data management, emphasizing granular test data management instead of replicating entire datasets. This strategy streamlines operations, decreases complexity, and minimizes the risk of data inconsistencies. Organizations can overcome data silos, improve data quality, and achieve valuable insights for informed decision-making using their scalable and flexible architecture. 

For companies planning their AI move, IBM Pak is an available option. It is a pre-integrated, enterprise-grade data and AI platform that assists businesses in accelerating their journey to AI. It offers a unified view of data, streamlines data preparation and control, and allows rapid growth and deployment of AI models. It is also available on-premises or in the cloud.

There are other platforms like Talend, known for its data integration and transformation capabilities. Talend is a data integration platform that gathers, cleans, and converts data from IoT devices. It also offers a combination of connectors to other data sources, making it uncomplicated to build a data fabric. It also offers a set of data integration, quality, administration, application, and API integration capabilities. Their Fabric also supports organizations in getting trusted data promptly, improving operational efficacy, and reducing threats.

The realm of IoT- connecting everything

The Internet of Things (IoT) will become the most powerful domain in the coming years, and data fabrics will be the best solution to encounter and subdue data challenges. They empower businesses to break free from silos and gain a holistic view of their digital landscape. With the help of data fabric, real-time insights become the standard, promoting intelligent decision-making and growing businesses into new frontiers. With the adoption of this paradigm, data fabrics come out as beacons driving organizations to the vast intricacies of IoT data and unlocking endless opportunities.

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.

Big Data Analytics in IoT

What are the challenges with Big Data Analytics in IoT?

A successfully running IoT environment or system embodies interoperability, versatility, dependability, and effectiveness of the operation at a global level. Sift advancement and development in IoT is directly affecting data growth. Multiple networking sensors are continually collecting and carrying data (say geographical data, environment data, logistic data, astronomical data, etc.) for storage and processing operations in the cloud.

The initial devices involved in acquiring data in IoT are mobile devices, public facilities, transportation facilities and home appliances. The flooding of data suppresses the capabilities of IT architectures and infrastructure of enterprises. Besides this, the real-time analysis character considerably affects computing capability.

The generation of Big data by IoT has disturbed the current data processing ability of IoT and demands to adopt big data analytics to boost solutions’ capabilities. We can interpret that today success of IoT also depends on the potent association with big data analytics.

Big data is recommended for a thick set of heterogeneous data present in the unstructured, semi-structured and structured forms. Statista shares that big data revenue generates from service spending, representing almost 39 per cent of the total market as of 2019. In 2019, the data volume generated by IoT connected devices was around 13.6 zettabytes, and it might extend to 79 zettabytes by the end 0f 2025.

Big Data and IoT

Big data and IoT are two mind-blowing concepts, and both need each other for attaining ultimate success. Both endeavors to transform data into actionable insights.

Let’s take an example of an automatic milking machine developed using advanced technology like IoT and Big data.

Source: Prompt Dairy Tech

Automatic milking machine software is designed by Prompt Softech. The Automatic Milk Collection Software (AMCS) is a comprehensive, multi-platform solution that digitizes the entire milk collection system. All the data is uploaded on the cloud, which provides real-time information on milk collection to the stakeholders.

AMCS enables transparency between dairy, milk collection centre and farmers. The shift from data filling on paper to digital data storage has reduced the chances of data loss along with human errors. A tremendous amount of data is processed and stored in the cloud daily. On the other hand, farmers get notified about the total amount of milk submitted and the other details. They can access the information about the payment and everything using the mobile app at any time.

This combination of real-time IoT insights and big-data analytics cuts off extra expenditure, improves efficacy and allows effective use of available resources.

Using Big Data:

Big data support IoT by providing easy functioning. Connected devices generate data, and it helps organizations in making business-oriented decisions.

Data processing includes the following steps:

  1. IoT connected devices generate a large amount of heterogeneous data stored in big data systems on a large scale. The data relies on the ‘Four “V” s of Big Data: Volume, Veracity, Variety & Velocity.
  2. A big data system is a shared and distributed system, which means that a considerable number of data records in big data files are present in the storage system.
  3. It uses an excellent analytic tool to analyze the data collected.
  4. It examines and produces a conclusion of the analyzed data for reliable and timely decision-making.

Challenges with Big Data Analytics

The key challenges associated with Big Data and IoT include the following:

Data Storage and Management:

The data generated from connected devices increases rapidly; however, most big data systems’ storage capacity is limited. Thus, it turns into a significant challenge to store and manage a large amount of data. Therefore, it has become necessary to develop frameworks or mechanisms to collect, save, and handle data.

Data Visualization:

Usually, data generated from connected devices are unstructured, semi-structured or structured in different formats. It becomes hard to visualize the data immediately. This implies preparing data for better visualization and understanding to get accurate decision-making in real-time while improving organizational efficiency.

Confidentiality and Privacy:

We all know that every IoT-enabled devices generate enormous data that requires complete data privacy and protection. The data collected and stored should stay confidential and have complete privacy as it contains users’ personal information.


Smart devices are specialists in sensing, communicating, information sharing, and carrying analysis for various applications. The device assures users of no data leakage and hijacking. Data assembly methods must use some measure and condition of integrity strongly with standard systems and commands.

Power Captivity:

Internet-enabled devices need a constant power supply for the endless and stable functioning of IoT operations. Many connected devices are lacking in terms of memory, processing power, and energy –– so they must adopt light-weighted mechanisms.

Device Security:

Analytics face device security challenges as big data are vulnerable to attacks. Data processing faces challenges due to short computational, networking, and storage at the IoT device.

Many Big Data tools provide valuable and real-time data to globally connected devices. Big data and IoT examine data precisely and efficiently using suitable techniques and mechanisms. Data analytics may differ with the types of data drawn from heterogeneous sources.

Source: IoTForAll – Challenges with Big Data Analytics in IoT

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.


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

Why does Your Organization Need to Shift to the Cloud?

Today organization, either small or medium or large, are looking for more business value from their data. Business data has become spinal of businesses because every decision and actions are dependent on it. Thus, the increased dependency has increased the pressure on data executives to access, manage and distribute and analyze the data coming out from different sources before it becomes worthless.

The task of processing the volume data are both challenging as well as expensive with legacy systems, architectures and storages plans while shifting organizations towards cloud migration. The shift to cloud migration can cut off the cost and increase access and viability.

Well, the reason for a shift can be different for different organizations and is based on the organization’s need.

Here the list of few common reasons for shifting to the cloud:

  • Consumption-based model: The consumption-based model is one of the beneficial advantage offered by the cloud. The high price of storage, servers and operations designed for on-premises implementations are the reason for the switch to clouds. Cloud provides a utility-based model which allows user to pay for ‘what and when’ it is used.
  • Value-added insights: Aquiring value-added insights from the data is a goal of organizations. The available ongoing costly solution requires high maintenance due to the complex and massive data flow. The traditional data warehouse solutions are not capable of meeting the needs of an organization and does not provide value-added insights for better results.
  • Reaching end-of-life/end-of-support: The traditional platforms and technologies are not capable of reaching end-of-life/end-of-support or scale-up. Cloud Migration that results from data warehouses is capable of reaching the end of life/support by the provider.
  • Leveraging analytics and AI/ML: Old platforms and warehouse solutions disappoint in leveraging analytics and AI/ML for extracting better business insights. They fail to support organizations in meeting the set objectives and goals. On the other hand, they require a high cost of maintenance too.

Well, if you have decided for cloud migration, then let’s get to ‘where’ and ‘how’ questions directly.

From where to start for migration?

Suppose an organization has made a mind for cloud migration, then the most important question which strikes in mind is ” where to start?”. Cloud migration is no different from any large transformational initiatives as it involves a logical starting point. The already existing data warehouse is actually a starting point because it stores a large amount of data.

Read More: Six Inevitable Steps to Bring Digital Transformation in Your Business

How to start?

The database migration or complete end-to-end cloud migration requires a whole focus on the source of the transformational initiative.

Some of the success factors which can help in cloud migration are as follows:

A) Developing a strong business use case resonates across the organization. It ensures clarity, provides vision, offers strategic guidance for initiative and provides a methodology to measure success. A well-designed business use case involves different technologies, platforms and IT. It provides a common framework to deliver optimal value.

B) Keeping knowledge of current state and future state while sharing a common perspective across the organization is essential. This involves cross-checking the technical architecture, understanding the cultural and political dynamics present around the initiative. When everything is clear that is a solid understanding of the current state and its requirement, future goals and objectives and existing gaps, then a precisely planned roadmap can be efficiently followed for near-term or long-term value.

C) While undertaking a data-driven program, one should follow the set standard and requirements for collection, identification, storage purposes. Data governance involves unstructured data, semi-structured data, structured data, registries, taxonomies and ontologies as it contributes to organizational success through regular and compliant practices. Guidelines from administration need to discuss all types of new data requirements that must be included as a part of any new program. Thus this must be addressed at the source to ensure that the resulting insight can be trusted to assist the organization in achieving value from the investment made.

D) Precise planning and keeping an eye on future guarantees success. Today, new tools and technologies are available in the market. Creating a well-planned data strategy allows organizations to scale-up and nurture their investment in a most-favourable manner. Data strategy recognizes the critical skills required and what is needed to achieve business objectives, plans and strategies.

A complete data strategy will also help in overviewing data management and assures that all essential steps involved in the modernization process are followed. The modernization process involves data migration, cleansing, standardization, and governance.

To confirm long-term, scalable and sustainable data program, an organization must go for single approach instead of being involved in different projects for the same purpose. Disparate projects do not lay the required right foundation for business transformation. An enterprise data warehouse implementation or modernization is not an easy task. Still, a precisely-designed, well-socialized, futuristic strategy supported by the right level of capability can assist organizations in achieving their objectives.

Prompt Softech is an IT company working with a motive to elevate businesses through innovative and smart technologies. The IT company assists businesses which require cloud migration. The Softech company gives priority to the current need of businesses and delivers the most fitting solutions. If you wish to benefit more from the data, then must switch to the cloud and enjoy the advantage by generating the business value data.