Big data refers to extremely large and complex datasets that traditional data processing tools cannot easily handle. However, with the right analytics techniques and technologies, meaningful insights can be uncovered from big data. The insights derived from big data analytics have the potential to improve decision-making, enhance operational efficiency, drive innovation, and deliver competitive advantages.

The Value and Benefits of Big Data Analytics

Valuable insights are often hidden in the huge volumes of data being generated today. Some benefits that big data analytics can provide include:

- Identifying trends and patterns: By analyzing large historical datasets, organizations can predict future trends. This supports demand forecasting, targeted marketing campaigns, and data-driven decision making.

- Providing operational intelligence: Detailed analytics on key metrics helps to enhance efficiency, productivity and performance. Resources can be optimized and issues can be proactively addressed.

- Driving new products and services: Customer preferences and needs can be better understood through analytics, inspiring the development of customized or entirely new offerings. Firms can monetize data itself as an asset.

- Detecting and preventing threats: Suspicious patterns of behavior by individuals or groups can be indicative of fraud or security issues. Analytics helps to rapidly uncover anomalies and mitigate risks.

The Data Analytics Process

While each analytical workflow differs, there is generally a standard process:

1. Identifying the business challenge or goal

2. Gathering relevant big data inputs from one or more sources such as sensors, apps, websites, social media etc.

3. Pre-processing data for cleansing, standardization and enrichment

4. Analyzing data using descriptive, diagnostic, predictive and prescriptive methods

5. Interpreting data and developing actionable insights

6. Communicating insights to stakeholders for informed decision making 

Powerful Analytics Techniques

Converting raw data into real value requires the application of powerful analytics techniques and algorithms, including:

- Data mining extracts hidden predictive information from large historical datasets. Associations, sequences, classifications and clusters can be detected.

- Machine learning uses statistical models that iteratively learn from data to make accurate predictions or optimize decisions without explicit programming. Deep learning is an advanced subfield.

- Predictive analytics makes reasoned assumptions about future outcomes based on current and historical data. What-if scenario modeling helps to weigh options.

- Sentiment analysis assesses the overall opinion and emotional tone within textual data to understand perceptions. Natural language processing and computational linguistics techniques are leveraged.

- Spatial analysis evaluates proximity, relationships and patterns involving geographic space and location data using specialized geoanalytic tools and statistical techniques.

Big Data Analytics in Action

The insights uncovered through advanced analytics techniques have profoundly changed how decisions are made across nearly every industry and sector. A few examples include:

- E-commerce sites optimizing product recommendations and ad targeting to boost sales

- Healthcare providers predicting the spread of illnesses and preemptively allocating resources

- Smart city planners analyzing traffic patterns to reduce congestion and commute times

- Financial institutions monitoring transactions to detect payment fraud in real time

- Energy firms forecasting electricity demand peaks and adjusting production accordingly 

- Manufacturers improving product quality by identifying defects and anomalies

The possibilities to exploit big data analytics are vast. As analytics capabilities continue advancing, more value will be uncovered from data across the board.

Big data analytics has continued to be the change that many businesses need to revolutionize various aspects of their operations, including maintenance management. Thanks to the evolution of use of data, predictive maintenance strategies have become increasingly popular in recent years, with the main aim being to reduce and eliminate failures during production. With the help of the power of big data and related techniques, companies can now improve the transparency of system health conditions, boost the speed and accuracy of maintenance decision-making, and minimize costly downtime.

An example of the influence of big data analytics in different areas can be seen in a book chapter titled "Big Data Analytics for Predictive Maintenance Strategies" published by IGI Global, the authors Lee C.K., Cao Y., and Ng K.H. The book explores various ways in which predictive maintenance can boost operations. The authors highlight the significance of a Maintenance Policies Management framework under the Big Data Platform. This framework allows companies to leverage sensor monitoring and simulation to make informed decisions in a sensor-monitored semiconductor manufacturing plant.

Further, how big data analytics transforms the maintenance decision-making process is discussed in the book. As evident in the chapter, analyzing vast amounts of data collected by sensors and other monitoring devices allows companies to detect patterns and anomalies that may indicate future maintenance needs. This information enables proactive maintenance planning, increasing the efficiency of equipment maintenance and reducing costly breakdowns.

A leading benefit of using big data analytics in predictive maintenance is cost reduction. Analysis of data collected by sensors allows them to predict when equipment is likely to fail. This helps organizations plan maintenance activities more efficiently, which reduces the need for emergency repairs and minimizes downtime, leading to significant cost savings in the long run.

Predictive maintenance also increases equipment reliability. This is achieved by addressing potential issues before they escalate. Doing so not only extends the lifespan of assets but also ensures that they operate at peak efficiency, contributing to overall operational excellence. Similarly, identifying and addressing potential issues before they result in equipment failure contributes to a safer working environment. Predictive Maintenance helps prevent accidents caused by equipment malfunctions, safeguarding both personnel and assets.

It has also been proven that predictive maintenance enhances operational eefficiency. It predicts problems before they occur, therefore minimizing unplanned downtime and optimizing maintenance schedules. Therefore, using Big Data Analytics for Predictive Maintenance improves operational efficiency, which eventually allows organizations to maximize the utilization of their assets and resources, ultimately leading to increased productivity.

The process of implementing big data analytics has many steps. These includes gathering and integrating data, data cleaning and preprocessing, model development and training and real time monitoring and feedback. During the data collecting and integration phase, a robust data collection infrastructure is established. This involves deploying sensors, IoT devices and other data sources to capture relevant information. On the other hand, once the data has been gathered, it must undergo a thorough cleaning and preprocessing phase. This ensures that the data is accurate, consistent, and free from anomalies. The quality of the input data directly impacts the effectiveness of the predictive models.

Machine learning models are then developed and trained using historical data after preprocessing. These models learn to identify patterns and correlations between various data points, enabling them to make accurate predictions about equipment health and potential failures. Lastly, the developed models are deployed for real-time monitoring of equipment. As new data is generated, the models continuously adapt and refine their predictions. This creates a feedback loop that improves the accuracy and reliability of the predictive maintenance system over time.

Some of the challenges that organizations might encounter in implementing big data analytics include data security and privacy issues, an inadequate skilled workforce and problems integrating Big Data Analytics solutions with existing systems and workflows can be challenging. It is crucial to ensure compatibility and seamless interaction to avoid disruptions to ongoing operations.

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.

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.

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.

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.

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.