Big Data

Big Data be Integrated into Your Business to Improve Output

How can Big Data be Integrated into Your Business to Improve Output?

Nowadays, information usage is soaring. This information, dubbed Big data, has expanded too large and complicated for typical data processing methods.

Companies are potentially utilizing Big data to enhance customer service, boost profit, cut expenditures, and update existing operations. This shows that the impact of Big Data on businesses is enormous and will remain impactful in the coming years.

But do you know from where these affecting Big Data come?

Big data is generated mainly by three sources:


Companies produce massive amounts of data on a daily basis. Some examples include financial data like invoices, billing and transaction data, and internal and external documents like business letters, reports, production plans, and so on. Big data generation is vital for enterprises transitioning from analog to digital workflows.


Communication is the data that one generates as an individual. Social media blogging and microblogging are all vital communication data sources. A new photo, a search query, and a text message contribute to the growing volume of big data.


Sensors integrated with IoT system produces IoT data. Smart devices use sensors to gather data and upload it to the Internet—for example, CCTV records, automated vacuum cleaners, weather station data, and other sensor-generated data. Overall, big data can be called massive data collections obtained from different sources. It can be utilized to find patterns, links, or trends to analyze and anticipate them.

Big data can be used to enhance security measures. Businesses and individuals use free VPNs and proxies to protect their data. They both depend on big data because it supports strengthening the technology.

Now, let’s get into the details of how businesses can potentially use big data to improve their operations and boost productivity.

How do businesses use big data?

Big data applications have multiple uses. Also, we can easily see various businesses employ the technology for different objectives. Insights collected are often used to make products and services more efficient, relevant, and adaptive for individuals who use them.

The applications of big data are:

Catching security defects:

With things getting online, data breaches and theft are among the most common problems as digital systems are getting complicated. Big data can be used to find out potential security troubles and analyze trends—for instance, predictive analytics catch illegal trading and deceitful transactions in the banking industry. Comprehending the “normal” trends permits banks to discover uncommon behavior quickly.

Comprehending more about customers:

This is one of the most critical and typical big data applications. Companies extract vast amounts of data to analyze how their customers behave and their choices. This enables them to predict the goods that customers desire and target customers with more relevant and personalized marketing.

One of the best examples is Spotify. The company also utilizes artificial intelligence and machine learning algorithms to motivate customers to continue connecting with the service. Spotify finds related music to design a “taste profile” as you listen and save your favorite tracks. Using this information, Spotify can suggest customers new songs based on their earlier choices.

Product invention:

Comprehensive data collection and client demand analysis can also be used to forecast future trends. Companies can utilize big data analytics to transform collected insights into new goods and services. It allows them to predict what their clients need. The corporation can offer data-driven proof for production based on customer demand, popularity, and interest. Instead of waiting for clients to tell their needs, you can fulfill their demands beforehand. Besides this, being more innovative than competitors is also a plus point for businesses.

Create marketing strategies:

Well, we are pretty familiar with the fact that a small marketing blunder can cost a lot to a company. A marketing that does not resonate with the target demographic might end up creating disaster. However, the availability of more specific data makes marketing more secure but complex.

This lets you gather information on how people respond to your advertising and allows you to create more personalized campaigns. This increased focus allows the marketing team to make a more precise approach, turn more effective, and reduce cost load.

Do you think big data is a big risk game in a business?

Till now, it’s very clear that big data provides enormous opportunities. Businesses flourishing in different sectors can take advantage of the available data. However, it could not be a smooth journey as various challenges are involved with this analytics method.

The accuracy concern:

This will also allow you to start combining data streamlining from a vast range of sources and formats. The challenge then comes to knowing which information is valuable and reliable and how to crack that information meaningfully. However, “cleaning” of data is a part of the big data sector; it is not without complication.

The price barrier:

Welcoming and adopting the world of big data carries several drawbacks. There are many aspects to be considered here- the hardware and the software. One must consider data storage and systems for managing enormous amounts of data. Furthermore, data science is increasing rapidly, and those who understand it are in high demand. The fee for recruits or freelancers can be high. Lastly, developing a big data solution that meets your company’s needs demands significant time and money.

The security challenge:

The challenge of safely storing such a large amount of data generated from collecting such a large amount. Therefore, Cybersecurity is another essential concern as data privacy and GDPR grow more vital.

The bottom line

We can easily conclude that Big data is fetching enormous benefits to many companies belonging to different sectors. Therefore, companies may thrive in the digital economy by effectively analyzing and managing flooding data. There may be many hindrances in integrating big data into business infrastructure. Still, the initial investment overcomes the rewards and advantages offered by big data and its potential application in the business. Therefore, spending time deciding whether to go for big data or not will surely land you at a loss.

Big Data is Changing the Outlook of the Renewable Energy Sector

How Big Data is Changing the Outlook of the Renewable Energy Sector?

The renewable energy sector is facing a significant transformation, and all credit goes to the power of big data. With its capability to gather, store, and analyze an immense amount of data in real-time, big data provides unprecedented insights into how we generate and use energy.

This has allowed companies in the renewable energy sector to create innovative solutions supporting us to adopt and create a more sustainable future.

So, let’s check how big data transforms the renewable energy sector and how the sustainable future will look!

Big data is a term used to describe the immense amount of data that organizations accumulate and analyze to achieve better insights into their operations. It can be sourced from various sections like:

  • Customer feedback
  • Transactional records
  • Sensor readings
  • Social media posts
  • Search queries, etc

All these together form a data set that can be utilized to make the most suitable decisions on the basis of analysis of correlations, ongoing patterns, and trends.

We can simply say that big data is a way to convert raw data into actionable insights, and this is what makes it so powerful.

Let’s know how big data functions

As we have already discussed, big data is utilized to collect and analyze vast amounts of data in real-time. This enables companies to understand consumer activities and behavior while optimizing their processes as well as operations.

Also, the analysis of big data can assist in identifying patterns that are unintentionally ignored. This is how companies are able to discover new opportunities and develop strategies accordingly. Not just this, big data also empowers organizations to get a better insight into their operations. 

For instance, energy companies can keep track of energy usage and identify areas where improvement is needed and efficiency can be enhanced.

Here, we can place the example of Tesla powerwall. It collects data from its solar panels to observe the production and consumption of electricity in real-time. Tesla’s power wall can be utilized to optimize energy usage by offering customers with customized options.

Three ways through which big data is transforming the renewable energy sector

So, at least, we have some knowledge of big data. Now, let’s find out how it is changing the renewable energy sector.

1. Improved Efficiency:

Big data analysis can support companies by identifying areas where efficiency can be enhanced in energy systems. For example, it helps in reducing wastage and optimizing output. This will ultimately improve the entire profitability produced by renewable energy businesses. This supports both seller and buyer as they can save energy costs and use the same to invest in other green-based projects or initiatives.

The skyrocketing cost of traditional energy sources has unveiled the importance of renewable energy. It has made it more attractive, and the involvement of big data can aid in making it more efficient. Big data will not only make renewable energy more feasible but will also make it a more attractive alternative for buyers.

2. Presaging Demand and Supply:

Big data can also be utilized to foretell the demand and supply of renewable energy.

By analyzing historical data, businesses can understand the market pattern and behavior and can easily calculate the present demand for distinct types of renewable energy resources. Therefore, they can change, shift, or adjust their production as per the need. In this way, companies can target a specific customer base, leading to more conversions and ultimately adding more profits. On the other hand, customers will also get whatever they want, so it turns out to be a win-win situation for everyone involved.

Other than predicting demand and supply, big data are also used to forecast weather conditions, which will allow businesses to plan their production of renewable energy resources.

For instance, the Tesla power wall can forewarn the weather conditions and shift energy production consequently.

3. Automation of a few processes:

In the end, we can say that the most significant advantage of having big data in the renewable energy sector is automation. By automating specific processes, organizations can save time as well as resources while making their operations more efficient.

For instance, solar panel systems can be linked to the internet and designed to adjust their output depending on weather conditions on a real-time basis. In this way, consumers can cut down their electricity bills by generating more energy when the sun is shining bright in the sky.

Besides this, companies can also utilize big data to automate the maintenance of their assets involved in renewable energy systems. By tracking and analyzing real-time data, they can interpret any issues and take action before they turn out to be a significant problem in the process.


With rising global temperatures and increasing greenhouse gases in the environment, it has become necessary to bend toward renewable resources for energy generation. The shortage of non-renewable resources and by-products it offers, like pollution, greenhouse gases, etc, is another reason to shift toward renewable energy resources.

In this initiation, the addition of big data is causing an immense impact on the renewable energy sector. It is making renewable energy more efficient by predicting demand and supply and automating a few processes to reduce time and cost. With the advancement of technology, in the coming years, big data will become an integral part of the renewable energy sector and churn the best result while promising a green and sustainable future.

Connecting Industrial Protocols and the Cloud

Why Connect Industrial Protocols with Cloud

Industrial protocols are conversations between industrial automation products for data collection or control. At the beginning of industrial automation, communications were a competitive differentiator, and automation vendors developed communication protocols to leverage technical advantage and lock in their customer base. It has changed with time; today, vendors have extended their protocols and even designated them industry standards to boost adoption. Vendors acknowledged that suppliers with the largest ecosystem of products to choose from, would have a better livelihood of winning parts of a project, if not the complete project. Vendors also learned that it is challenging to be a specialist in all areas of automation. Let’s find out different industrial protocols and those that can be compatible with cloud applications.

Different Types of Industrial Protocols

With time, the manufacturing marketplace has become prevailing by a set of protocols, possibly from the leading suppliers of automation products. Before examining the best-suited for the cloud, let’s know some of the most common industrial protocols. These include protocols such as Modbus, Profinet, CC-Link, Ethernet IP, etc. Many of these are present in different forms to acknowledge varying topologies and purposes eg-dedicated wires vs. Ethernet.

Attempt to bring standardization over the years fetched technology from the OPC Foundation, which was originally Microsoft technology-based, using COM and DCOM Windows technologies for communications between applications. Hence, OPC (OLE for Process Control – OLE that is, Object Linking and Embedding – the technology after COM) is delivered.

1: OPC

OPC obtained standards for accessing data, either subscribing or polling, and the purpose of different data types and how to manage them (Analog and Discrete variables, History Data, Alarms, and Events, among others).

In time, this standardization endeavor shifted from windows technology-centric to operating system-agnostic to aiding Linux and delivering functionality that would be useful to Internet-based communications.


The new standard was recognized as OPC UA- with OPC now representing Open Process Communications and UA representing Unified Architecture, one standard to supersede the previous standards that had developed.


Another technology that is more concentrated on the transfer of messages and less on the content of messages generated out of the need for a very distributed infrastructure with limited bandwidth, as found in the upstream oil and gas market. This protocol is known as MQTT. It is used in the industrial automation marketplace, specifically for cloud communications, and has become very popular in recent years.

4: BACnet

The vertical market shows unique requirements and has supported the requirement for unique developments. BACnet is the leading protocol in the Building Automation Systems (BAS) space. In the Power Generation and Distribution Space, several protocols like IEC-61850, 60870, and DNP-3.

Over time, these protocols have survived on various topologies, and today most of them offer Ethernet compatibility.

Why is the Cloud So Important?

The advantages of cloud computing are numerous and stimulating. They possess:

  • Transformation of capital expenditures to operational expenditures
  • No need to concentrate on infrastructure management
  • Benefiting a constantly scalable architecture
  • Furnishing accessibility to your absolute organization, anywhere and anytime
  • Benefiting services from domain experts (security, upgrades, solution development)

The cloud can endure different forms, from a solution delivery by industry leaders like Microsoft and Amazon to more scaled offerings for targeted markets. Ultimately, there are hosted solutions, pushing on-premise servers to virtual servers in the cloud, but fully controlled by the IT staff of the organization.

The objective of cloud computing is to provide a lower total cost of ownership by reducing expenses in system management and hardware ownership and the capability to take advantage of solutions offered by others. These third-party solutions are usually built for market purpose and provides multi-tenant capability, letting the service provider handle many customers whilst offering data and user isolation. The concept of cloud computing, specifically for the industrial marketplace, is still in its initial stage, and businesses are fighting with cloud connectivity and the idea of hosting their data to the outside world.

However, the benefits are convincing as it reduces operating costs, and domain experts have developed vertical market applications that require connectivity to the correct data. Additionally, service providers can utilize knowledge gained over their extensive array of customers and offer great value to an individual customer. So, the failure mode of a product in an environment can be predicted by learning about the failure mode in other environments. It helps in potential predictive analytics tuned by the results and anonymization of data from a similar ecosystem of users. While connecting to the cloud, evaluating which industrial protocols best suit the application is necessary.

Things to Consider When Connecting to the Cloud

The best attributes offered by cloud-based solutions fall into two main categories:

  1. Security ( including access security and cybersecurity)
  2. Transmission (the quality and reliability of data) 

Security is mainly managed using VPNs (Virtual Private Networks). It is an excellent way for bi-directional and ad-hoc communications as it is designed for remote troubleshooting. Using VPNs for ad hoc access, customers can use solutions to secure and broker access to endpoints in a very organized and controlled way. It includes approval processes, windows of access and time limitations, and extra levels of authentication. 

For information communication to the cloud, it is becoming more prevalent to utilize public-subscribe models and connection brokers to enhance security. Remote sites will share data to a tight and secure connection. The users of data and cloud applications will subscribe to the data through a broker, eradicating application knowledge of remote communication details that illustrate an exposure. Microsoft IoT Hub is the best example of this technology. 

Industrial Protocols for Cloud Connectivity

It is optional that all industrial protocols are compatible. Without knowing each protocol and determining if it can be integrated into a cloud, a complete solution to the connectivity issue is to add edge device technology. It will manage the communications to the IT and OT environment and the need for cloud data transfer. Their devices are now covering the market with specific cloud connectivity or a toolkit approach that can be eased their configuration. Most of them are designed with data transfer as their primary function, whereas others support data modeling, visualization, and analytics, in addition to data transfer.

Ethernet is also improving with time in both topology and performance. A more visible improvement is device synchronization and the power to shape traffic. These attributes and other things are Ethernet enhancement called TSN (Time Sensitive Networking). TSN promises the skills to prioritize communications on Ethernet and control traffic bandwidth.

Connecting Safely and Securely

With the expansion of industrial protocols in the market, it is now feasible and easy to connect virtually any automation solution to the cloud with complete privacy, directly or using edge gateways.

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.

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

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.

How China, US and Taiwan used Big Data In The Fight Against Coronavirus?

Big data is no new word in today’s tech wrapped world. Today in this corona crisis situation, Big data has emerged as an incomparable tech invention for different purposes.

It is used in the fight against CoronaVirus, thus suggesting the need for the further development of big data and requirement of Big Data analysis for different purposes.

There are many Big data specialist companies delivering Big Data solutions for better approaches to the result.

Countries are tapping into Big data, Internet Of Things and Machine learning to track and identify the outbreak. They are using digital technology to get real-time forecasts and help healthcare professionals and government for predicting the impact of the COVID-19.

Let’s switch back to the real topic we were talking about. Let’s know about the vital role played by Big Data in this COVID-19 fight.

Surveillance Infrastructure In China:

The first in the list is China as it is the place where COVID-19 first case was reported. China’s monitoring culture emerged as a powerful weapon in the fight against COVID-19. China installed thermal scanners in train stations to detect the body temperature and separate the probably infected one. As we know, high fever is the symptom of COVID-19; the passengers showing the symptom were stopped by health officials to undergo coronavirus testing. If the test comes positive, then the administration would alert all other passengers who might have exposure to the virus so that they could follow self-quarantine.

China has installed millions of security camera to keep a track over the citizens’ activities and curb the crime rates. These cameras were used to discover people who were not following the proposed quarantine to stop the spread of the virus.

If a person who was supposed to be in quarantine, but cameras tracked them outside their homes, authorities were called to take appropriate actions.

In fact, the Chinese government also used an app named “Close Contact Detector” that notified users if they had contact with someone who was corona virus-positive.

Travel verification reports/data shared by telecom providers were used to list all the cities visited by the user in the last 14 days to check whether quarantine was recommended based on their location or not.

The integration of data collected by using the surveillance system helped the country in exploring the ways to curb the spread of the coronavirus.

Read More: Will 2020 Be The Transition Phase of Internet Of Things?

Big Data Analytics and Taiwan’s successful pandemic strategy:

After the observation of painful stage in China because of Corona spread, it was expected that Taiwan would be hit harder than China.

But, surprisingly, Taiwan faced the virus havoc very smartly. It used advanced technology and strong pandemic plan, which they prepared after the 2003 SARS outbreak to control the virus’s impact there.

Taiwan has integrated its national health insurance database with migration and custom database. Through this centralisation of data, the country faced the coronavirus strongly. They got real-time alerts regarding the probably infected one on the basis of symptoms & their travel history.

The country even had a QR code scanning and online reporting of travel and health symptoms that helped the medical officials to categorise the travellers’ infection risk. They even provided a toll-free hotline for citizens to report symptoms.

When the first corona case was reported, and WHO informed about the pneumonia of unknown cause in China, Taiwan activated all its warriors, including technology. This quick and active response taken by the country saved it from the severe effect of this fatal disease.

Use of Mobile Apps in Pandemic:

In America and Europe, people’s privacy is the priority still medical researchers and bioethics focused on the power of technology and supported its use for contact tracing in a pandemic.

Oxford University’s Big Data Institute co-operated with government officials to explain the advantages of a mobile app that provides valuable data for controlling coronavirus spread.

As we know, mostly coronavirus transmissions occur before the symptoms are visible thus speed and effectiveness to alarm people had been deemed as supreme during a pandemic like a coronavirus.

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A mobile app that holds the advanced 21st-century technology can help in the notification process while maintaining principles to decelerate the infection spread rate.

In 2011, Tech experts had developed a solution to monitor and track the spread of flu efficiently, but the app wasn’t adopted, thus limited its usage.

Now organisations are working to develop app solutions that can provide a platform where people can self-identify their health status and symptoms.

For the development of such apps, there are many app development companies that offer the most advanced and reliable services.

Corona has not just given us health challenges but also providing necessary learning experiences for data science in healthcare.

In US, the government is conversing with tech hulks like google, Facebook, and many others to know the possibilities for using location data from smartphones to track the movements of its citizens and analyse the patterns.

Dashboard to track the virus spread:

Dashboard is another tool that has been proved helpful for citizens, healthcare workers, and government policymakers to see the progression of contagion and how invasive this virus would be.

Dashboard collects the data from around the world to display the no. of confirmed cases and deaths caused by coronavirus with locations.

This data can be analysed and used to create models and find the existing hotspot of the disease, which could help in proper decision making for the home-quarantine period and help healthcare systems to prepare for the coming challenges.

Outbreak analytics use all the available data like confirmed cases, infected people, deaths, map, population densities, traveller flow etc. and then process it using machine learning for the development of possible patterns of the disease. These models are further used to get the best predictions of the infection rates and results.

Thus, it is obvious that proper use of big data solutions and big data analysis can help countries in this pandemic. Big data, machine learning and other technologies can provide a model and predict the flow of a pandemic. It can analyse data to assist the health officials in preparation for the fight against Corona or any other future pandemics.