Big Data Analytics

admin
February 3, 2022
Big Data, Data Analytics, IoT

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.

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Big Data Analytics in IoT
admin
May 26, 2021
Big Data, 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.

AMCS
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.

Integrity:

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

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admin
December 14, 2020
IoT

Adding Operations Performance Management in IoT

Internet of Things is the new sensation in the technology market. Business owners and technology lovers are embracing the presence of new technologies and endeavouring to get the best output from it. Today, most of businesses intend to make their business smart and more organized. We all know that the Internet of Things has limitless potential to revolutionize everyday life and environment we are living. Unknowingly, we are already utilizing technologies in our daily life, and no doubt, the number of connected devices and services is growing exponentially day by day.

In 2015, the total number of connected devices was 15.4 billion, and as per IHS, this number would extend up to 30.7 billion in 2020 and 75.4 billion by 2025. No matter how accurate these predictions are, here we are talking about billions of devices at any rate.

However, to acquire the true potential of IoT, one requires operationalizing of the data generated from devices in context with operational workflows. No, doubt operationalizing data from different types of connected devices, OT, and IT systems is not an easy task. Secondly, the accumulation of data and delivering it to users in real-time and in connection with their operational workflow is more challenging.

Operationalizing IoT Systems Is Challenging

Coming to a very important and one of the hard things in IoT, i.e. operationalization IoT system. Though big data and advanced analytics have assisted us a lot to learn about the dynamic nature of constructed environments; however it has remained a challenge to operationalize the information in the daily work that happens in smart environments (smart towns, smart home, smart hospitals, etc.)

The fragmentation of workflows across multiple systems and the challenges of understanding how people are acknowledging the built environments and massive data flow restricts the meaningful transformation of these aforementioned smart spaces.

Organizations aiming to intertwine people, process and things are required to create a smarter way of working within the built environment. Thus, this situation drags in need of having Operations Performance Management (OPM).

Any idea about Operations Performance Management (OPM)?

Operations Performance Management (OPM) is an essential complement to an IoT deployment. It allows operationalizing and draws continuous business value from IoT systems. We can simply say that it sets power in the hands of the people, enables workers to take action on the basis of context-rich and real-time information about their smart environment.

OPM allows the real-time organization of people, systems and things that explores and initiates the real growth potential of IoT.

Operations Performance Management for the Internet of Things systems facilitates focused view into distant built environments. Not just this, it also allows smart building and hospital operators to analyze the data insights with valuable operational intelligence deeply.

Using OPM, operators can perform predictive modelling and execute self-tuning operations for their real-estate assets and in-building processes. This helps in getting better results for businesses and provides helpful experience for the tenants living and working within buildings. It even assures that all the business units working within organizations are integrated and working together to accomplish core business objectives.

OPM allows IoT system managers to achieve clear links between operation key performance indicators and significant business metrics. Therefore, it is clear that OPM initiates 360-degree view into an organization to help decision-makers in evaluating, assessing, planning, preparing, predicting and eventually saving operational costs as well as maintenance costs.

It’s just not about the platform; applications are designed specifically to acknowledge the big issues faced by smart hospitals and smart building. It enables meaningful digital transformation in hospitals and buildings. For example, in a hospital, OPM has authorized one leading hospital network to decrease code blues by 61 per cent. This has helped another hospital to realize an annual saving up to $2.7 million and a yearly ROI of around 900%.

Operations Performance Management is transforming the way of interactions existing between people and the environment. It provides real-time and context-aware insights into the working of the built environment. Now, health professionals, admins and building operators can answer to patients or tenant’s experience and get suggestions for further improvement in real or near-real-time.

It is now clear that generating connections between people through smart devices can produce a more beneficial outcome for building owners and their operators.

OPM is also helpful for people living, working and receiving care withing built environment.

Thus, it is pretty easy to conclude that OPM help business in realizing their IoT deployment model’s real potentials and it charters a better path for connected smart cities. If you are looking for an IoT service or planning to operationalize the IoT system, then contact us. We’ll guide you and provide the best services to you for the elevation of your business. Together we can create smarter cities, automated and efficient buildings, safer hospitals and workplaces.

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admin
May 12, 2020
Big Data

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.

Read More: How can IoT be Used to Fight Against COVID-19?

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.

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