Over the past few years, big data has evolved from a boardroom buzzword to a force that is now taking the world by storm. A few years ago, business leaders lacked ways to harness vast amounts of data and analyze it to get information. However, big data has become a game-changer, with administrators and business leaders now analyzing and getting knowledge about customers, business processes and daily operations. Although a point of success within a business, big data presents many challenges that must be addressed for success to be realized in big data initiatives.

What makes big data a challenge is the massive amount of data from different sources and in different formats that require large storage and good processing and analysis. With unstructured information flowing fast from different sources, leveraging it and getting the right insight for decision-making is never easy. This is where big data consolidation comes in. Data consolidation aims to make collected data manageable and usable.

Here are the four tips for success in big data consolidation:

• Migration of data away from legacy applications

Migration is always the first step in data consolidation. It entails moving information away from legacy applications or programs and ensuring that it can be leveraged successfully in the application that uses this data frequently. It is recommended that legacy applications be retired altogether to achieve better consolidation of big data. It is important to collect critical information in legacy systems then migrate it into new applications or systems before retiring the older applications. Doing so makes data actionable, and systems are faster.

• Understand the real cost of data consolidation

First, you must understand the implementation cost of not consolidating data. Once you know the cost of not doing so, find out the cost of consolidation and alternatives, if any. Check if consolidation is an economically viable option. As a business, this action is critical because you would not want to operate at a loss. In reality, the cost of consolidation is often a one-time thing. Once you are done with consolidation, you will enjoy the potential of big data within your company. Consider aspects that may lead to loss, such as security, personnel and natural disasters. These three can affect your applications and data centers that store your data.

• Be selective

Although all data is indeed important for an organization, not all the data should be consolidated. Instead, stakeholders should carefully choose what should be consolidated and what should not. Select information that, if consolidated, can ensure productivity and does not hamper performance. Administrators should consider instances where data does not need consolidation, such as when security dictates that some data should be kept in separate servers or when data is outdated and will only serve to fill the database. Some data should not be consolidated because they will not add any value and might slow down hardware resources.

• Make use of professional services

Although most stakeholders and decision-makers love doing their big data consolidation internally, seeking professional help to streamline your operations will be helpful. You must always know that big data consolidation can be complicated, and having an extra professional hand can be advantageous to the organization and data. Consider stakeholders' opinions before hiring professionals to help your organization in the management and consolidation of your data. A unified approach from skilled professionals can deliver the necessary consultative support, expertise, and proper planning and execution solution. By following the best approaches and with support from IT service providers, you will realize efficiency, flexibility, and cost-saving due to the right big data consolidation.

After almost a year of coronavirus pandemic, it is apparent that the pandemic has significantly strained the industry in a manner that no one has ever seen before. With the pandemic, it has emerged that the supply chain is the backbone of modern businesses. Although these challenges might have impacted the supply chain and logistics industries now, it will ultimately result in more resilience in the future. Here are some changes that the coronavirus pandemic has introduced to the global supply chain:

• Increased expectation for more visibility and resilience

The coronavirus pandemic has tested the resilience of the current supply chain, and it has been found wanting to a larger extent. After the pandemic is put under control, companies will require more flexibility in the industry. The pandemic has shown the need for more visibility and resilience, that can only be known after performing many stress tests. Supply chain managers should expect higher standards from the industry players and high transparency levels, including regular stress tests.

• Increased regionalization

COVID-19 has shown the importance of domestic sources in their manufacturing services and most operations. With the disruption that occurred on the traditional routes, supply chain companies and even governments will move their services towards regionalization. Alternatively, companies will create supply chain hubs to move closer to the customer. This is not to say that global supply chains will be no more. Instead, companies will prefer their partners to be closer. For example, for companies in the US, most operations will be near-shored to places like Mexico, from Asia, which has long been a favorite to many.

• Automation will continue

Automation has been proven to be the leading driver of efficiency in the supply chain industry. With the pandemic, robotics and its proven efficiency will continue being implemented at a much faster rate than earlier expected. The pandemic has brought into light the weaknesses of the human-only working environment. After COVID-19, supply chains are likely to implement a more automated working environment. An example is Amazon, that has already implemented automation but is advertising other positions to ensure balance. This means that although automation will continue being embraced, humans are expected to be hired to maintain balance in the business.

• Artificial Intelligence will be increasingly adopted

Post-COVID-19, supply chain companies will seek ways of enhancing decision-making and speeding up various processes. This will see the rise in AI adoption, that promises to enhance efficiency, flexibility, and decision-making. AI will allow supply chain companies to deriver patterns from data, and get alert on potential disruptions, highlight bottlenecks in operations, and improve service provision in general. Supply chains may use AI-based bots to offer customer support or use machine learning, a subset of AI, to customize services.

• Need for new tools and cost models

With the coronavirus pandemic, it has emerged that there is a need for new cost models and tools to enhance the supply chain firms. Organizations will seek new ways, tools, and technologies that will offer greater intelligence. Supply chain companies are now looking for risk evaluation tools that can allow them to find patterns regarding risks or opportunities in macroeconomic, exchange rate, and other critical data. Company executives are also forced to consider tools that are alternatives to the existing transportation and logistical approaches because the pandemic proved that the existing methods are not effective as it was earlier thought.

In a nutshell, the COVID-19 pandemic has taught organizations and supply chain companies to move towards resilience. The pandemic experiences will lead to a more resilient supply chain with the ability to withstand disruptions.

“It's time to speak up before another 17,000 people are dead. You know this is wrong. You don't have to be a part of this. Be a hero. Speak out before it's too late” read the text message that was sent to 1,750 state workers through the state’s emergency management system last month.

The state claimed that the IP address used to hack their system could be traced back to Rebekah Jones, a data analyst who was fired earlier this year from the Florida Department of Health for refusing to manipulate Covid data statistics.

Jones has been at odds with the Florida government and Govenor Ron De Santis after her superiors allegedly asked her to falsify data that would indicate decreasing Covid infections in the state. When she refused, Jones claimed she was relieved of her job. Helen Aguirre Ferré, a spokesperson for De Santis, said Jones was “exhibited a repeated course of insubordination during her time with the department” and no one had asked her to manipulate the data.

Jones went on to create her own Covid Dashboard which includes key Covid metrics in the state of Florida such as new cases and available ICU beds. According to her dashboard, at the time of writing this – there are currently 4.6K hospitalizations due to Covid-19 in the state with 12.4% of ICU beds available.

As a result of the message, Jones’ home was raided on December 7th with weapons drawn. Jones claimed the police seized her phone, computers and other hardware while pointing guns at her and her family.  The police disputed her claim despite video Jones’ released that showed a gun pointed in her direction.

Commissioner Rick Swearingen justified the move explaining in a statement, "Agents afforded Ms. Jones ample time to come to the door and resolve this matter in a civil and professional manner.” He went on to say, “…any risk or danger to Ms. Jones or her family was the result of her actions.”

Jones denies the allegations stating she had nothing to do with the message. She claims the raid was in retaliation to her “persistent criticisms of how Florida has handled the pandemic.” In a tweet on Monday she claimed, “This is what happens to scientists who do their job honestly. This is what happens to people who speak truth to power.”

Jones also claims that she is not well versed in computer programing a trait that hackers need when infiltrating private networks. She explained in an interview with WPTV, “Being a statistician doesn’t mean you know how to program computers. It means you know how to analyze information.”

At the time of writing this, Jones has not been arrested, but as already raised $200K on a GoFundMe to be used for a legal defense as well as moving expenses to “get out of the Governor's reach."

When asked by a reporter if he was aware of the raid at a mental health roundtable, Governor DeSantis insisted it wasn’t a raid and insisted claims that it was, was disinformation.

The most successful data analysts are able to take key data and weave it into a story to convey their point to stakeholders. This is often called a “data story” and is used by analysts who are presenting to an audience who may not understand the intricacies of data. They do this by presenting their findings visually – which is easier for most people to comprehend.  Often time this is through graphs, charts, or other visualization tools like infographics. But is a data story really that important in the world of statistics?

The short answer is “yes.” “Data storytelling gives anyone, regardless of level or skill set, the ability to understand and use data in their jobs every single day,” explains Anna Walsh in a blog post by Narrative Science. A data analyst is able to glance at data and understand critical findings. A stakeholder on the other hand may struggle to find trends in a series of numbers and statistics. The data story bridges the gap between an analyst and to ensure that everyone is on the same page when it comes to predicting trends and correlating facts and findings.

What’s even more important is the way a data story connects different team members allowing them to understand the same information quickly in order to make important decisions. “By providing insights in a way that anyone can understand, in language, data storytelling gives your team what they want—the ability to get the story about what matters to them in seconds,” Walsh notes. It’s no secret that everyone learns differently – by telling a story more people are able to comprehend the message in an easy digestible way.

One method to tell a data story is through infographics that incorporate key metrics in an aesthetically pleasing document that anyone from a vice president to a office manager can look at and comprehend key findings. The best infographics have a wide array of graphs that depict trends. The most popular charts include pie charts and bar graphs that show percentages, spend, and other important data. According to Analytiks, infographics are a critical marketing tool because they are “excellent for exploring complex and highly-subjective topics.”

According to Lucidchart – it’s important that a good data story has three components. Data, visuals, and a narrative. Without these three components – the data story falls flat. The article explains, “ Together, these elements put your data into context and pull the most important information into focus for key decision-makers.” Without visualization – a decision maker might be confused as to what they are looking at. Without a narrative – a decision maker may draw the wrong conclusion than an analyst intended. Together – the decision maker will understand what they’re looking at to make intelligent decisions.

Visualizing data has helped companies make smart and calculating decisions that help their businesses succeed. It’s important that data scientists understand that not everyone is a “data person”. Using their key findings to develop a story will help decision makers and key stakeholders comprehend the results and feel confident in their decisions on how to progress the organization forward.

Ever since the invention of computers many developments have shaped human lives. The invention of the internet was a landmark achievement which set up the stage for more things that followed. Many would have thought that the internet was the biggest thing ever but it was only a lead-in to developments in the world of big data, AI and IoT.  Big data, AI and IoT have revolutionized the world we live in but what exactly are these terms?

AI, IoT, and big data are among the most talked about topics but still highly misunderstood. The tech jargons has been difficult to grasp for non-tech people but this article sheds a little light on the difference between the three terms, how they are related and how they differ.

The advent of social media and e-commerce led by Facebook and Amazon respectively shook the existing infrastructure. It also altered the general view of data. Businesses took advantage of this phenomenon by analyzing social media behavior through the available data and using it to sell products. Companies began collecting large volumes of data, systematically extracting information and analyzing it to discover customer trends. The word big data then became appropriate because the amount of data was orders of magnitude more than what had previously been saved. Basically, big data are extremely large sets of data which can be analyzed to reveal patterns, associations, and trends by using specialized programs. The main aim of doing so is to reveal people’s behavior and interactions, generally for commercial purposes.

Once the concept of big data had settled in and the cloud became a convenient and economical   solution for storage of huge volumes of data companies wanted to analyze it more quickly and extract value. They needed to have an automated approach for analyzing and sorting data and making decisions based on accurate information by businesses.

To achieve this, algorithms were developed to analyze data which can then be used to make more accurate predictions on which to base decisions.

Cloud’s ability to enable storage coupled with the development of AI algorithms that could predict patterns of data, meant that more data became a necessity and so was the need for systems to communicate with each other. Data became more useful as AI systems began to learn and make predictions.

The internet of things (IoT) is a collection of devices fitted with sensors that collect data and send it to storage facilities. That data is then leveraged to teach AI systems to make predictions These concepts are now making way into our homes as smart homes, smart cars, and smartwatches which are in common use..

In short, big data, AI and IoT are interrelated and feed off each other. They depend on each other for operations as AI uses the data generated by IoT. On the other hand, huge datasets would be meaningless without proper methods of collection and analysis. So yes, big data, IoT and AI are related.

The concept of big data has been around for a number of years. However, businesses now make use of big data analytics to uncover trends and gain insights for immediate actions. Big Data Analytics are complex processes involved in examining large and varied data set to uncover information such as unknown correlations, market trends, hidden patterns, and customer’s preferences in order to make informed business decisions.

It is a form of advanced analytics that involves applications with elements such as statistical algorithms powered by high-performance analytics systems.

Why Companies Use Big Data Analytics

From new revenue opportunities, effective marketing, better customer services, improved operational experience, and competitive advantages over rivals, big data analytics which is driven by analytical software and systems offers benefits to many organizations.

• Analyze Structured Transaction data: Big data allows data scientists, statisticians, and other analytics professionals to analyze the growing volume of structured transaction data such as social media contents, text from customer email, survey responses, web server logs, mobile phone records and machine data captured by sensors connected to the internet of things. Examining these types of data help to uncover hidden patterns and give insight to make better business decisions.
• Boost Customer Acquisition and Retention: In every organization customers are the most important assets; no business can be successful without establishing a solid customer base. The use of big data analytics helps businesses discover customers’ related patterns and trends; this is important because customers’ behaviors can indicate loyalty. With big data analytics in place, a business has the ability to derive critical behavioral insights it needs to retain uts customer base. A typical example of a company that makes use of big data analytics in driving client retention is Coca-Cola which strengthened its data strategy in 2015 by building a digital-led loyalty program.
• Big Data Analytics offers Marketing Insights: In addition, big data analytics helps to change how business operates by matching customer expectation, ensuring that marketing campaigns are powerful, and changing the company's product line. It also provides insight to help organizations create a more targeted and personalized campaign which implies that businesses can save money and enhance efficiency. A typical example of a brand making use of big data analytics for marketing insight is Netflix. With over 100 million subscribers; the company collects data which is the key to achieving the industry status Netflix boasts.
• Ensures Efficient Risk Management: Any business that wants to survive in the present business environment and remain profitable must be able to foresee potential risks and mitigate them before they become critical. Big data analytics helps organizations develop risk management solutions that allow businesses to quantify and model risks they face daily. It also provides the ability to help a business achieve smarter risk mitigation strategies and make better decisions.
• Get a better understanding of their competitors: For every business knowing your competitors is vital to succeeding and growing. Big data algorithms help organizations get a better understanding of their competitors, know recent price changes, make new product changes, and discover the right time to adjust their product prices.

Finally, enterprises are understanding the benefits of making use of big data analytics in simplifying processes. From new revenue opportunities, effective marketing, better customer services, improved operational experience, and competitive advantages over rivals, the implementation of big data analytics can help businesses gain competitive advantages while driving customer retention.

While the coronavirus pandemic has left the world bleeding, it has also highlighted weaknesses in the global healthcare systems that were hidden before. It is evident from the response to the pandemic that there was no plan in place on how to treat an unknown infectious disease like Covid_19. Despite the challenges that the world is facing, there is hope in big data and big data analytics. Big data has changed how data management and analysis is carried out in healthcare. Healthcare data analytics is capable of reducing the costs of treatment and can also help in the prediction of epidemics’ outbreak, prevent diseases, and enhance the quality of life.

Just like businesses, healthcare facilities collect massive amounts of data from patients during their hospital visits. As such, health professionals are looking for ways in which data collected can be analyzed and used to make informed decisions about specific aspects. According to the International Data Corporation report, big data is expected to grow faster in healthcare compared to other industries such as manufacturing, media, and financial services. The report estimates that healthcare data will experience a compound annual growth of 36% by 2025.

Here are some ways in that big data will make a difference in hospitals.

1. Healthcare tracking

Along with the internet of things, big data and analytics are changing how hospitals and healthcare providers can track different user statistics and vitals. Apart from using data from wearables, that can detect the vitals of the patients, such as sleep patterns, heart rate, and exercise, there are new applications that monitor and collect data on blood pressure, glucose, and pulse, among others. The collection of such data will allow hospitals to keep people out of wards as they can manage their ailments by checking their vitals remotely.

2. Reduce the cost of healthcare

Big data has come just at the right time when the cost of healthcare appears to be out of reach of many people. It is promising to save costs for hospitals and patients who fund most of these operations. With predictive analytics, hospitals can predict admission rates and help staff in ward allocation. This reduces the cost of investment incurred by healthcare facilities and enables maximum utilization of the investment. With wearables and health trackers, patients will be saved from unnecessary hospital visits, and admissions, since doctors can easily track their progress from their homes and data collected, can be used to make decisions and prescriptions.

3. Preventing human errors

It is in records that medical professionals often prescribe the wrong medication to patients by mistake. These errors have, in some instances, led to deaths that would have been prevented if there were proper data. These errors can be reduced or prevented by big data, that can be leveraged in the analysis of patient data and prescription of medication. Big data can be used to corroborate and flag a specific medication that has adverse side effects or flag prescription mistake and save a life.

4. Assisting in high-risk patients

Digitization of hospital records creates comprehensive data that can be accessed to understand the patterns of a particular group of patients. These patterns can help in the identification of patients that visit a hospital repeatedly and understand their health issues. This will help doctors identify methods of helping such patients accurately and gain insight for corrective measures, that will reduce their regular visits.

Big data offers obvious advantages to global healthcare. Although many hospitals have not fully capitalized on the advantages brought about by this technology, the truth is that using it will increase efficiency in the provision of healthcare services.

Datanomix Inc. today announced the availability of its Fusion platform in the Microsoft Azure Marketplace, an online store providing applications and services for use on Microsoft Azure. CNC manufacturing companies can now take advantage of the scalability, high availability, and security of Azure, with streamlined deployment and management. Datanomix Fusion is the pulse of production for modern machine shops. By harnessing the power of machine data and secure cloud access, Datanomix has created a rich visual overlay of factory floor production intelligence to increase the speed and effectiveness of employees in the global Industry 4.0 workplace.

Datanomix provides cloud-based, production intelligence software to manufacturers using CNC tools to produce discrete components for the medical equipment, aerospace, defense and automotive industries with its Fusion platform. Fusion is accessible from any device, giving access to critical insights in a few clicks, anytime and anywhere. Fusion is a hands-free, plug-and-play solution for shop floor productivity. 

By establishing a data connection to machines communicating via industry-standard protocols like MTConnect or IO-Link, Fusion automatically tracks what actual production is by part and machine and sets a benchmark for expected performance. To measure performance against expected benchmarks, a simple letter grade scoring system is shown across all machines. In cases where output has not kept pace with the benchmark, the Fusion Factor would decline, informing workers that expected results could be in jeopardy. 

“Our Fusion platform delivers productivity wins for our customers using a real-time production scoring technology we call Fusion Factor,” said John Joseph, CEO of Datanomix. “By seeing exactly what is happening on the factory floor, our customers experience 20-30% increases in output by job, shorter time to problem resolution and a direct correlation between part performance and business impact. We give the answers that matter, when they matter and are excited to now give access to the Azure community.” 

By seeing the entire factory floor and providing job-specific production intelligence in real-time, there is no more waiting until the end of the day to see where opportunities for improvement exist. In TV Mode, displays mounted on the shop floor rotate through the performance metrics of every connected machine, identifying which machines need assistance and why.

“TV Mode has created a rallying point that didn’t exist on the shop floor previously. Fusion brings people together to troubleshoot today’s production challenges as they are happening. The collaboration and camaraderie is a great boost not only to productivity, but also morale,” says Joseph.  

Continuous improvement leaders can review instant reports offered by Fusion that answer common process improvement questions ranging from overall capacity utilization and job performance trends to Pareto charts and cell/shift breakdowns. A powerful costing tool called Quote Calibration uses all of the job intelligence Fusion collects to help business leaders determine the actual profit and loss of each part, turning job costing from a blind spot to a competitive advantage.

Sajan Parihar, Senior Director, Microsoft Azure Platform at Microsoft Corp. said, “We’re pleased to welcome Datanomix to the Microsoft Azure Marketplace, which gives our partners great exposure to cloud customers around the globe. Azure Marketplace offers world-class quality experiences from global trusted partners with solutions tested to work seamlessly with Azure.”

The Azure Marketplace is an online market for buying and selling cloud solutions certified to run on Azure. The Azure Marketplace helps connect companies seeking innovative, cloud-based solutions with partners who have developed solutions that are ready to use.

Learn more about Fusion at its page in the Azure Marketplace.

2020 was a year with so many domestic and global challenges. But the big data industry seems to have grown, even more, gaining more force moving into 2021. The growth in this area was occasioned by the rise in online activities due to the pandemic. As we start a new year, big data is expected to grow even more to heights never experienced before. Despite the growth, many challenges should be expected in 2021. Here are some of the 2020 big data challenges that are not likely to go away in 2021:

• Growth of data

One of the biggest challenges for any big data initiative is the storage of data. This has been made worse by the exponential growth of data with time. With this, enterprises are now struggling to find ways of storing data that come from diverse sources and in different formats. The challenge is accommodating either structured or unstructured data in formats such as audio, video, or text. To make it worse, such formats, mainly unstructured, are hard to extract and analyze. These are the issues that impact the choice of infrastructure. Solving the challenge of data growth demands facilitation through software-defined storage, data compression, tiering, and duplication to reduce space consumption and minimize costs. This can be achieved through tools such as Big Data Analytics software, NoSQL databases, Spark, and Hadoop.

• Unavailability of data

One reason why big data analytics and big data projects fail is because of a lack of data. This can be caused by failure to integrate data or poor organization. New data sources must be integrated with the existing ones to ensure enough data from diverse sources is useful in analytics and decision-making.

• Data validation

As highlighted before, the increasing number of devices means more data from diverse sources. This makes it difficult for organizations to validate the source or data. Also, matching data from these sources and separating the accurate, usable, and secure data (data government) is a challenge that will linger for some time. It will require not only the hardware and software solutions but also teams and policies that will ensure this is achieved. Further, data management and governance solutions that will ensure accuracy will be needed, therefore increasing the cost of operations.

• Data security

Security continues to be one of the biggest challenges in big data initiatives, especially for organizations that store or process sensitive data. Such data is a target for hackers who want to access sensitive information and use it for malicious purposes. As big data initiatives increase, the number of hacking cases is expected to rise. The cases of theft of information are expected to rise. The loss of information can cost billions of dollars for a company due to lawsuits and compensation to the affected parties. The data security challenge will increase operational cost since cybersecurity professionals, real-time monitoring, and data security tools will be required to secure data and information systems.  

• Real-time insights

Datasets are a great source of insights. However, they are of little or no value at all if they are not insightful in real-time. Big data should generate fast and actionable data that brings about efficiency in result-oriented tasks such as new product or service launch.  It must offer information that will help create new avenues for innovation, speeding up service delivery, and reducing costs by eradicating service and operational bottlenecks. The biggest challenge going forward is generating timely reports and insights that will help satisfy customers who are becoming so demanding. This requires organizations to invest in more sophisticated analytics tools that will enable them to compete in the market.

Disasters are becoming too complicated and common in the world. Increasingly, rescue and humanitarian organizations face many challenges as they try to avert catastrophes and reduce deaths resulting from them. In 2017 alone, it was reported that more than ten thousand people were killed, and more than 90 million were affected by natural disasters worldwide. These disasters range from hurricanes and landslides to earthquakes and floods. The years that followed turned out to be equally calamitous, with things such as locust invasions, wildfires, and floods causing havoc across the planet.

Aggravated by climate change, the coming years may see such catastrophes coming more frequently and with a higher impact than ever before. But, there is hope even at such a time where all hope seems to be fading away. The advancement of big data platforms gives hope for a new way of averting catastrophes. The proliferation of big data analytics technology promises to help scientists, humanitarians, and government officials to save lives in the face of a disaster.

Technology promises to help humanitarians and scientists to analyze information at their disposal that was once untapped and make life-saving decisions. This data allows prediction of disasters and their possible paths and enables the relevant authorities to prepare through mapping of routes and coming up with rescue strategies. By embracing new data analytics approaches, government agencies, private entities, and nonprofits can respond to catastrophes not only faster but effectively.

With every disaster, there are massive amounts of data. Therefore, mining data from past catastrophes can help the authorities gather knowledge that helps predict future incidences. Together with data collected by sensors, satellites, and surveillance technologies, big data analytics allows different areas to be assessed and understood.  An example is the Predictive Risk Investigation System for Multilayer Dynamic Interconnection Analysis (PRISM) by the National Science Foundation, which aims to use big data to identify catastrophic events by assessing risk factors. The PRISM team consists of experts in data science, computer science, energy, Agriculture, statistics, hydrology, finance, climate, and space weather. This team will be responsible for enhancing risk prediction by computing, curating, and interpreting data used to make decisions.

A project such as PRISM collects data from diverse sources and in different formats. However, with interoperable frameworks enabled by the modern big data platforms, complexities are removed, and useful information is generated. Once data has been collected, cutting-edge analysis methods are used to draw patterns and potential risk exposure for a particular catastrophe. Machine learning is used to look at anomalies in data, giving new insights.

Knowing a history of a particular area, such as an area that has been receiving floods and by how much, provides useful information for mapping out the flood-prone areas and developing strategies and plans for where to store essential rescue resources beyond the affected areas. Google, for example, is using artificial intelligence to predict flood patterns in areas such as India. This has enhanced the accuracy of response efforts. In other countries, drones are now used to gather data about wildfires.

Responders can handle emergencies by using data generated by sensors and wearables, and other personal technologies. Devices such as mobile phones, smartwatches, or connected medical devices can be analyzed to help in setting up priority response and rescue efforts. Also, by assessing social media timestamps or geotagging locations, a real-time picture of what is happening can be drawn. Data from social media is direct and offers valuable insight from users. Lately, social media giants such as Facebook allows individuals to mark themselves are safe during a disaster. This is helpful for responders and friends and family who want to know the whereabouts of their members.

Hadoop is the leading open source technology for the management of data. In any discussion about big data and its distributions, you will never fail to come across Hadoop in the middle of the talks. As outlined in a technical paper by Google in 2006, it was designed as a distributed processing framework back in 2006. It was first adopted by Yahoo in the same year, followed by other tech giants such as Facebook, Twitter, and LinkedIn.  During this time, Hadoop evolved significantly into one of the most complex big data infrastructure known today.

Over the years, the platform has evolved significantly to encompass various open-source components and modules. These modules help in the capture, processing, managing, and analyzing large data volumes, that are supported by many technologies. The main components of Hadoop include the Hadoop Distributed File System (HDFS), YARN (Yet Another Resource Negotiator), MapReduce, Hadoop Common, The Hadoop Ozone, and Hadoop Submarine.

With the usefulness of this platform, big data management of Hadoop is becoming a critical aspect. It is important to understand that the best performance of Hadoop depends on the proper coordination of IT professionals who will collaborate in various parts of management. The areas that must be managed include planning of architecture, development, design, and testing.  Other areas include the ongoing operations and maintenance, that are meant to ensure good performance.

The IT team that will manage Hadoop will include the requirements analysts whose role will be to assess the system performance requirements based on the applications that will operate on the Hadoop environment. The system architects will evaluate performance requirements and hardware design and configurations while the system engineers will manage the installations, configurations, and tuning of the Hadoop software stack. The work of application developers will be to design and implement apps. On the other hand, the data managers prepare and run the integration of data, create layouts, and carry out other data management duties. System managers are also a critical part in the management of Hadoop. They ensure that the system is operational and manages maintenance. Similarly, the project managers are responsible for overseeing the implementation of the Hadoop environment and prioritization and development and deployment of apps.

Once Hadoop has been deployed, those in charge within the organization must always ensure that it runs with low latency and processes data in real-time. It must also be able to support data parallelism and ensure high faster computation. Doing so ensures that the platform handles analytics tasks that are needed without failing or without requiring further server customization, more space, and financial resources. Furthermore, the Hadoop framework should be used to improve server utilization and load balancing by the IT managers. They should also ensure data ingestion is optimized for the integrity of data. Furthermore, they must also carry out regular maintenance on different nodes in each cluster, replace and upgrade the nodes, and replace and update operating systems whenever possible.

Hadoop is an open-source platform; this means that it is free. While this is the case, it is important to note that deployment, customization, and optimization can raise the costs of using it. While this is true, any company can offer Hadoop-based products and services. Some of the companies that provide robust Hadoop-based services include Amazon Web Services (AWS), Cloudera, and Hortonworks, among others. The evolution that has been realized by Hadoop has transformed the business intelligence and analytics industry. As such, analytics that the user organizations can run as well as the types of data that can be gathered by and analyzed by applications have been expanded.

Data has become one of the critical components of any business in the modern era. It is for this reason that you keep hearing some conversations around big data and big data analytics. With this, data transformation has also gained fame. It is the process where data is analyzed, reviewed and converted by data scientists from a given format to another. This process is essential for organizations, especially at a time when data integration is required for an effective running of operations and security. It might involve the conversion of large amounts of data and data types, elimination of duplicate data, enriching data and aggregating it. Here is how data is transformed:

• Extraction and parsing

In the modern data-driven world, the process of extraction starts with gathering information from the data source. This is followed by copying data to a desired destination. The transformation process is aimed at shaping the data format and structure to ensure that it is compatible with the source and destination. At this stage, different sources of data will vary depending on the structure, the streaming service or the database that the data originates from. After data has been gathered, it is transformed and changed from the original form to another; for example, aggregate sales data or customer service data is changed into text strings. The data is then sent to a target place, such as a data warehouse that can handle different varieties of data, such as structured and unstructured.

• Translation and mapping

For data to be compatible with other sources, be moved easily to the other location, joined with data other data and added to additional data parts, it must be transformed accordingly. This is the second crucial part of data transformation. This step is important because it allows data from different departments in an organization to be made compatible and joined with other data. Some of the reasons for the transformation of data include allowing data to be moved to a new store or cloud warehouse, adding more fields and information to improve information, joining structured and unstructured data, and perform aggregations to enable comparisons.

• Filtering, aggregating and summarizing

Transformation of data is the stage where data is made manageable through proper listing. At this stage, data is consolidated through filtering the unnecessary fields, records and columns. On the other hand, data such as numerical indexes, in data that is needed for graphs or records from business regions that are not of interest and are omitted. Data is also summarized and aggregated by transforming those regarding customer transactions to either hourly or daily sales counts. With the business intelligence tools, filtering and aggregation can be done efficiently before data is accessed using reporting tools.

• Data enrichment and imputation

Data enrichment and imputation entails merging data from different sources to form denormalized and enriched information. With this stage, transaction data can be added into the table that has information about the customer to allow quicker reference. Enrichment entails splitting fields into many columns and the missing or corrupted values can replaced due to such transformations.

• Data indexing and ordering

Indexing data is the first step before other operations are undertaken. Indexing entails creating an index file that references records. During indexing, data is transformed so that it can be ordered logically. Doing so also suits a data storage scheme. Indexing improves performance and management of relationships.

• Anonymizing and encrypting

The data that has personally identifiable information (PII) or other critical information which if exposed can compromise privacy or security of individuals must be anonymized before sharing. This can be achieved through encryption in multiple levels ranging from individual databases cells to the entire database records or even fields.

• Modelling

This stage is crucial because it entails casting and converting data types to enhance compatibility, adjusting dates and times and formatting. It also involves renaming database schemas, tables and columns to enhance clarity.