2021 is challenging yet another exciting year for human resource management. Like last year, we continue to see the critical nature of data and data-driven HR in maintaining day-to-day operations in organizations in the COVID-19 era. While last year might have been interesting, experts warn that you will see even exciting trends this year. The good news is that these trends create amazing technologies for small businesses, individuals, and startups, that were never there before. According to the Gartner report of October 2020, big data analytics will help organizations plan ahead and make critical decisions urgently to deal with the pandemic and its effects. Here are other trends that you need to pay attention to in 2021.

1. AI will become faster, smarter and more responsible

As artificial intelligence continues showing a lot of potential in data analytics in the HR arena, enterprises will move from piloting to operationalizing it. This will drive about a 5X increase in streaming of data and analytics. With the pandemic still with us, current techniques such as natural language processing (NLP), machine learning (ML) and optimization are critical in generating insights and predictions about the virus and the development of the vaccines. In HR, AI is helping process large amounts of data from employees such as resumes, gauge performance and make decisions on areas that need improvement. It brings transparency to hiring processes.

2. Data scientists will increase

Every serious organization can now bet on data science as the best career move for technology enthusiasts, especially when it has everything to do with machine learning engineering and research. From the trends, there is a strong growth for data science jobs and skills, that according to The U.S. Bureau of Labor Statistics, will be around 28% by 2026. With the sophistication of technology, companies will be seeking employees such as scientists and machine learning engineers to help them in their operations and analysis. These skills will be in high demand going forward.

3. Automated decision-making with AI

According to statistics, large organizations will have analysts that practice decision intelligence and decision modelling. This will bring together various disciplines that include decision support and decision management. With the automation of decision-making, adaptive systems will bring together different traditional and advanced disciplines. Automation allows leaders to design, compose, monitor, process and align their decisions and outcomes. With the improving AI and big data analytics, these two technologies will continue shaping how managers, human resource leaders and operators make their decisions. It will create tremendous opportunities for organizations regardless of the industry.

4. An opportunity for data marketplaces and exchanges is in the offing

Data is indeed the future, and big companies such as Apple, Facebook, and Google are setting the trend in ensuring that companies do not expose data without first-party permission, if their recent announcements are anything to go by. Although this can be a good thing in creating data marketplaces or exchanges, it may be detrimental to advertisers. With this new trend, we will likely see platforms that buy and sell data, including licensing, publishing and distributing it. Although this new industry is in its early stages, it can explode into something to admire if things go well. These exchanges could mean more job opportunities for people, although a challenge is presented to advertisers with regard to employment.

With the positive developments concerning COVID-19 and vaccinations worldwide, we will likely start seeing the workplaces that were closed over the past year returning to normal. With this, HR officers will need to develop a swift, flexible and easy transition post-COVID. This progress will be led by proper use of data that involves using modern technologies such as AI and big data analytics.

Businesses quickly discover the importance of big data analytics in their operations. They have embraced ideas behind data analytics and associated technologies and even technologies such as machine learning and artificial intelligence. With the focus now shifted to online after the effects of the coronavirus pandemic, enterprises are modernizing their analytics strategies, testing new technologies and tools and training staff, or acquiring new talent to meet the new realities. As the pandemic continues, many organizational leaders are thinking of how they should prepare their data analytics for 2021. Here are areas of data analytics that you need to concentrate on as we begin the new year.

• Self-service will be a catalyst for a digitized economy

Before the COVID-19 pandemic, there was an increasing adoption of IT in different processes in organizations. Enterprises were planning how to improve and ensure speedy extraction of insights by investing in advanced infrastructure while guaranteeing a minimum disruption of existing business processes. The speed of modernization was accelerated, resulting in increased budgets and resources due to the pandemic. With the rise in needs and fewer resources for IT and data teams, self-service and democratization will become a main area of focus. Organizations will provide insights and means for users to resolve business issues inside departments. This means strong data governance policies will be developed to allow data access by business users while ensuring top levels of security and compliance are met.

• Augmented data management will be a new normal

The use of active metadata, machine learning, and data fabrics will increase as organizations seek to connect and automate data management tasks dynamically. This will reduce data delivery time by more than 30 percent by 2021. Artificial intelligence techniques will be used to recommend the best course of action and data governance controls. According to Gartner, data fabric is something that uses continuous analytics over the existing metadata assets that are discoverable to support the design, deployment, and use of integrated and reusable data objects irrespective of the platform or architecture.

• Invest in data transformation to improve analytics

With the upsurge in data sources and the amount of data in organizations in general, data streams seem not to be keeping up fast enough. IDG Research survey reports that organizations spend almost half of their time preparing data. They take up to a week to prepare data for a normal project. With this inefficiency, enterprises are looking for better ways to enhance data transformation processes and ensure analytics-ready insight is obtained. Most of them have shifted to the cloud to reduce the time it takes to join siloed data together, denormalize and enrich it as well as apply business logic. As pressure increases on organizations to invest more resources and raise budgets, most of them will try cloud solutions to help their data transformation efforts due to the fewer resource requirements needed to do it.

• AI will become a smarter, faster, and most sought after analytics technology

According to Gartner, over 75 percent of enterprises will shift from piloting AI to fully operationalizing it by 2024. This means that AI adoption and operationalization will increase as soon as this year. By the end of 2024, streaming data and analytics infrastructures will have gone up five times. The current challenges will be reduced, and most historical data will likely be obsolete by then. The disruptions will oversee enterprises embracing learning algorithms, reinforcement learning, and edge computing to improve data analytics and get more insights. Data analytics will help organizations find solutions and direction through its ability to ease gathering, transforming, and analyzing data.

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.

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.

Big data is fast gaining momentum, and so are the big data projects. Companies are increasing in size and ambition. However, the rising number of big data projects does not mean that they all succeed. Gartner estimates that the number of big data projects that fail is about 60 percent in 2016. In 2017, Gartner revised the number of big data projects that fail to be about 85 percent, which was even higher than the rate stated before. Nothing has changed since then. Even in 2021, the rate of failure still ranges at around 80 percent. Here are some of the reasons why big data projects fail.

1. Poor integration

Siloed data is a leading technological problem that causes big data failures. Since data is stored in multiple sources, integrating it into one and using it to get insights that a company needs is a big challenge. This is even bigger problem if legacy systems are involved. It costs a lot of money and often does not result in the desired outcome. According to Alan Morrison of PwC, siloes create data lakes that are just data swamps. Organizations can only access a small percentage of data with little relationships that are inadequate to find patterns and get enough knowledge. Without a graph layer that interprets all instances of data mapped underneath, you have a data lake that is a data swamp.

2. Not defining goals

Like any other project, big data projects require a proper definition of goals and objectives. Sadly, most people who undertake big data projects do not set goals that they need to achieve. Most of them think they can simply connect the structured and unstructured data and get the insight they need. As a project manager, you need to define the problem and develop the goals you want to attain. Having a clear definition of the problem and defining it in time helps achieve the desired goals accurately. However, many big data project leaders lack vision. This ends up confusing the company on big data projects and its desired objectives.

3. Shortage of skills

There has been a widespread shortage of talent in the data science industry over the past few years.  A 2018 report by LinkedIn reported a shortage of more than 150,000 individuals with data science skills. These are people such as data engineers, mathematicians, data analysts, and others. Since the field is in its initial stages, it is often hard to get people with the required skills. This slows production and ends up stalling the well-intentioned big data initiatives. Additionally, many enterprises cannot run several projects simultaneously without the right skills because they lack enough personnel.

4. Lack of transparency

Lack of transparency in big data projects can result in a disconnect between technical and business teams. For instance, while the data science teams usually focus on the accuracy of models that is often simple to measure, business teams, on the other hand, are concerned mainly with metrics like business insights, profits/financial benefits, and interoperability of the final model produced. The lack of clarity and proper alignment between the teams leads to the failure of big data projects as the different teams try to measure different metrics. This is made worse by the traditional data science initiatives that use blackbox models that lack accountability and are hard to interpret, making it difficult to scale.

The above reasons for the failure of big data projects indicate the need for proper plans when implementing big data projects. The problems can be sorted by planning ahead, working together, and setting realistic goals.

According to reports, the global big data as a service (BDaaS) industry is expected to grow significantly in the coming years. The sector was valued at $4.99 billion in 2018 but will likely reach more than$61 billion by 2026. This growth is attributed to the fast adoption of big data as a service in different industries. Other factors that are expected to drive the BDaaS industry are the rising demand for actionable insights and the increasing organizational data across businesses due to the digitization and automation of most business processes. Here are trends that you should expect in the BDaaS industry:

• The increased adoption of BDaaS by social media platforms will lead to growth

The increase in digitization and automation of business processes is the leading factor in the adoption of BDaaS and its subsequent market growth. With the ongoing deployment of the 5G infrastructure, this demand will become rapid as social media platforms such as Snapchat, Instagram, Twitter, Facebook, and YouTube, among others, embrace data as the main approach to reaching customers for growth. Consequently, social media platforms will play a crucial role in the rising global BDaaS market.

• Big companies will hold the largest share

Large multinationals continue to lead in the adoption of BDaaS solutions. With competition heating up, they are likely to continue investing in these solutions as they seek to access customer data and gather the right insights for improved decision-making. They help collect data scattered in various locations or departments to gain valuable insights through big data analysis. Large corporations are spending large amounts of money on training their employees and leveraging the benefits of BDaaS solutions as they seek to edge their competitors and know exactly what their customers want.

• Hadoop will continue in its leadership in this area

In the last year, Hadoop was a significant player in big data as a service. The Hadoop-as-a-service segment held about 31.6%, with the rest sharing the remaining 68.4%. Moving forward, this Hadoop segment is expected to grow exponentially, gaining more CAGR in the future as the craze for BDaaS continues rising. The growth will result from the continued adoption of Hadoop-as-a-service solutions among the small and medium-sized companies (SMEs) worldwide who seek to take advantage of this technology in their service provision. 

• North America will continue dominating BDaaS investments

In 2020, North America was leading in BDaaS investments with $ 6.33 billion. This region is expected to continue holding the leadership spot between now and 2026 in terms of adopting big data as a service and the revenue coming from this industry. This is due to the number of significant players that will invest in it and others such as Intel Corporation that will go on manufacturing chips that will help in the expansion of the existing storage. However, the Asia Pacific region will register a significant increase as countries such as India, China, Japan, and South Korea raise their investments.

• Large companies will embrace joint ventures to strengthen their positions in the market

Large companies that have a global presence are looking for better alternatives to stay ahead in the competition. One of the strategies includes mergers, acquisitions, partnerships, and joint ventures. In most cases, smaller companies are acquired by bigger ones, while others may strike partnership deals to compete favorably in the market. IBM is one of the companies with large big data as a service market share and has been launching solutions and building partnerships that help companies gather data of customers for use in marketing and decision-making activities.

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.

Ever since it became available in 2011 as an open-source program, Hadoop has made a significant impact in solving big data issues. It has also undergone significant transformations that have not only made it more robust but effective as well. Since it was founded, Hadoop has gone through major changes in versions that have made it stand tall in the big data arena. The good thing about Hadoop is that it has an active community that releases updates that keep the platform new. The latest update on Hadoop is Apache Hadoop 3.x rolled out in 2017. This new version comes with bug fixes, features, and enhancements over Hadoop 2.x. Here are some of the new features that will boost Hadoop in version 3.x that you need to know.

• JDK is the Minimum runtime version for Hadoop 3.x

With the end of Oracle JDK 7 in 2015, Hadoop 3.0 JAR files are compiled to run on a new version-JDK 8. This enables Hadoop 3.x to have a new dependency upgrade to modern versions because most libraries support only Java 8 and above.  Hadoop users who still use lower versions need to upgrade to the higher JDK 8 version compatible with these files.

• Erasure coding in HDFS support

With the rapid growth in data and the data center hardware, erasure coding support in Hadoop 3.0 is a critical feature. This technique lets any random piece of data be recovered based on another piece of data.  This technique is like an advanced RAID technique that automatically recovers data when hard disks fail. The HDFS in Hadoop 2.0 inherits a 3-way replication from Google File System (GFS) to replicate each piece of data thrice for the purpose of reliability. Hadoop 3.0 will cut physical disk usage by more than half, and the fault tolerance also increases by more than 50%. This new feature in Hadoop 3.0 will save customers a lot of money on hardware infrastructure.

• Shell script rewrite

The previous versions of Hadoop had many bugs and compatibility issues. With this, the new version of Hadoop shell scripts have been rewritten to resolve bugs, compatibility issues, and installation problems. Some of the critical areas that have been updated include all the shell script subsystems, which now execute Hadoop-env.sh, allowing all environment variables to be in a single place. With this new version, daemonization has been moved from -daemon.sh to the bin command. The updated scripts test and report error messages better. These are just a few of the updates.

• Support for opportunistic container

The Execution Type notion has been introduced in the new version to allow applications to request containers that can be of an opportunistic type. The update allows containers to be dispatched for execution in the Node Manager even without any resource. Containers are queued at Node Manager, waiting for resources before it can start. The new opportunistic containers are of low priority and are preempted. As such, cluster utilization has been improved in the new version significantly.

• Port changes for various services and the addition of new of new default ports

There have been notable changes in default ports such as the movement of Secondary NameNode, NameNode, DataNode, and KMS to Linux ephemeral port range (32768-61000) to avoid any potential bind errors on startup due to conflict with other applications. With this feature, there is enhanced reliability with regard to rolling restarts on larger clusters of Hadoop.

Although these are just a few improvements that have been made on Hadoop 3.0, the updates are a major advancement in the big data space. With the above features and others that were not covered and others that are likely to be announced in the coming days, Hadoop will remain a competitive platform for the unforeseeable future.

Big data continues to be a major talking point for many organizations. It is now a leading concept that continues to capture the imaginations of brand marketers and business executives looking to capitalize on it to change the landscape to their advantage. Indeed, the ability of a business to capture vast sets of data, collect vast data ranges in real-time, and process data into valuable insights can change the tide and make a business an absolute force. However, running big data is not only complicated but expensive as well.

While it is fashionable and prestigious to talk about big data, it is often easier said than done. It requires massive investment into things such as databases, analytic tools, professionals, and other infrastructure.  This means that many organizations need to rethink their big data strategies and never overlook the small data at any given point. Businesses must maximize the exiting investments, such as structured data sources, to conserve cash while using the small data available properly. Small data is just data that is readily available in your website, ad servers, email platforms, social listening tools, content management systems (CMS), and CRM. This data is helpful because it offers direct business insights and is often under-utilized.

Small data describes data sets that have fewer than 1000 rows or columns. It is the opposite of big data, which describes datasets that are too small for traditional statistical methods. Unlike big data, the analysis of small data is often simple and can be done using estimation. Some of the examples of small datasets are social media posts and customer transaction data. The use of small datasets has been here for some time. While big data has been the key talking point over the past decade, small datasets prove to be crucial because of the rise of AI. With the developments in artificial intelligence, organizations will be able to do more with less. This is where small data will come in. Also, the rise of regulations such as the GDPR and California Consumer Privacy Act means that it will become challenging to come across large customer data, which is the case with big data.

The talk about small data seems to be new. However, it has been here for some time. In 2014, for example, David Lavenda wrote about exploiting small data due to the failure of big data in providing individual workers with insights needed for daily operations. Even today, although big data is good for business, it is not actionable for individual workers. What is needed for daily operations is not big data but rather relevant data provided in small and smart chunks.

According to Gartner, small data is one of the trends in the business landscape. While many people continue to talk about big data, small data proves to be one of the leading contributors in a post-pandemic business landscape. Although it is hard to pin the pandemic as the cause for the shifting focus towards small data, it is evident that it has contributed to this change. The disruption of the coronavirus pandemic on organizations has forced most of them to focus on digital tools and processes. Most businesses have been forced to identify key technology trends and prioritize appropriately to continue being relevant in the changing business landscape.

Investing in small data analysis, which yields insights that can be used in businesses' daily running, is a step in the right direction. This means that organizations invest in platforms such as automation, CRM, CMS, and social media listening tools that capture data that is directly actionable and useful in making quick and valuable decisions.

Data has become one of the most crucial resources in organizations today. Unlike in the past, no business can succeed without data which is vital in decision-making. With massive amounts of data being generated every second from business transactions, customer interactions, and sales figures emanating from various sales platforms, data has become the fuel that drives businesses. All this data is referred to as big data. Data from multiple sources coming to an organization needs to be gathered and analyzed to enhance decision-making. However, this is easier said than done. There are various challenges to big data that are encountered during this process. Here are some of the issues that you should manage in your big data initiative.

• Inadequate understanding of big data

Many organizations fail in their big data initiatives due to inadequate understanding of this concept and how it works. Most employees do not know what big data is, how it is stored, processed, and used in decision-making as well as its importance. On the other hand, professionals might be aware of it, but others may lack comprehensive knowledge that could have been helpful to their respective organizations.

• Too many big data technologies

Big data technologies are coming to the market thick and fast. Although this is a good thing for big data, it is easy for professionals and organizations’ leadership to get lost in the technologies that are now available in the market. For instance, choosing the right technology between Spark or Hadoop MapReduce will become a challenge. Similarly, it becomes difficult selecting one between Cassandra or HBase in the storage of data. Without the proper knowledge, the availability of these technological opportunities can hinder appropriate decision-making. This can only be sorted if those new to the world of big data seek professional help. Hire the right people for consultation.

• Data growth challenges

The rapid increase in the amount of data that requires storage is one of the most pressing challenges in the era of big data. The amount of data streaming into data centers and databases is rising rapidly. With this exponential growth, it becomes tough to handle data. Most of this data is in different formats, mostly unstructured, and comes in forms such as free-text, videos, audio, documents, and other sources. This data can be handled by adopting modern technologies such as tiering, compression, and deduplication. Doing so reduces the number of bits or size while deduplication removes duplicated data from a dataset. Tiering, on the other hand, enables companies to store data in different storage tiers.

• Inadequate data professionals

To effectively use big data technologies and tools, companies need skilled professionals. These professionals include data scientists, analysts, and data engineers, knowledgeable and experienced in working with tools and translating big data sets. As the adoption of big data increases, organizations continue facing a challenge in getting enough data professionals to help them in implementing their big data initiatives. This means that more actionable steps from different stakeholders are needed to sort out this issue and avail enough data scientists.

• Security

With data becoming a valuable resource for organizations, malicious actors look for ways to access it and use it for their personal gain. As such, securing data has become one of the biggest challenges of big data. Sadly, most companies concentrate on understanding, storing, and analyzing data while leaving security for the last. This is not a good move since unprotected datasets can become a target for malicious actors. This may lead to massive losses in case of a breach.

In the world today, we are facing three technologies that have the potential to take how we work or do things to a new level. These technologies are big data, the internet of things (IoT), and artificial intelligence (AI). These three technologies work together in many perspectives and can take different industries and businesses to the next level. Although they are closely related, they are so distinct in a variety of ways. The main question, however, is what will happen if two of these three works together? Here, we highlight the relationship between the internet of things and big data, two of the three technologies that are currently a buzzword in every tech discussion.

To understand the relationship between IoT and big data, we first have to look at the role of big data and its important characteristics.

What is big data?

Big data means massive amounts of information. It is data that comes from different sources in various forms. Until recently, organizations had the potential to collect massive amounts of data, but computers had no potential to process large amounts of data. However, this has been made possible by increasing computing power, allowing organizations to process and use data in their decision-making. Through advanced software and applications, businesses can now sift through large data for actionable insights, which are helpful in the decision-making

The primary characteristics of big data include what is known as the “four Vs.”. They include volume, variety, velocity, and veracity. The volume describes the massive amounts of data coming from different sources such as social media, sensors, emails, and online transactions. According to statistics, accumulated data is about 44 zettabytes. Data comes in various forms and types, such as social media posts, videos, plaintext, etc. This is where the attribute of variety comes from. Velocity describes the speed at which data is collected from various sources, while veracity refers to the truthfulness or accuracy of a specific data set.

Big data and IoT

IoT and big data are critical technologies in the world that is completely data-driven. The two helps businesses get actionable insights that can be used to make crucial decisions. Data gathered from different transactions can be leveraged by industry. IoT devices such as connected sensors and other “things” collect data and feed into an ocean of big data. These many sensors and “things” contribute to extremely large volumes of data gathered in different industries such as retail, supply chain, and smart homes and can be used to make certain decisions that can help in aspects such as asset management, tracking assets, or fleet, remote monitoring of patients and more.

There is no doubt that IoT makes it easy to gather data with the help of connection techniques in different appliances.  On the other hand, the big data and analytics tools are useful in putting together large data streaming from IoT devices to ease the management of data in organizations. Many IoT devices depend on cloud computing or communication with a remote server in the processing of data. However, there is now the idea of edge processing where the device can process data locally. Big data and IoT depend on each other for success. Both aim at converting data into actionable insight.  For example, shipping companies attach IoT devices to their trucks, planes, boats, or even trains to track aspects such as speed, the status of the engines, and the status of the items in transit. The sensors can also track things such as stops and routes to aid in fast decision-making concerning the need for maintenance and general performance.

The combination of IoT with big data analytics leads to cost savings, enhanced efficiency, and better use of the company’s resources.