Explore how predictive analytics is transforming business decision-making across various industries by leveraging historical data and advanced algorithms.

Predictive analytics is revolutionizing the way businesses approach decision-making. By leveraging historical data and sophisticated algorithms, companies can now forecast future trends with unprecedented accuracy. This advancement empowers organizations to make data-driven decisions that are not only proactive but also strategically aligned with their long-term objectives.

Predictive analytics uses a combination of statistical methods, machine learning algorithms, and data mining techniques to analyze current and historical facts, enabling the prediction of future events. Businesses that have successfully integrated predictive analytics into their decision-making processes often witness enhanced performance in various domains such as marketing, operations, and risk management.

A recent example is a major retail chain in the United States that used predictive analytics to optimize inventory management. By analyzing consumer purchasing patterns and seasonal trends, the company was able to reduce overstock and stockouts, resulting in cost savings and improved customer satisfaction.

In the finance sector, predictive analytics is used for credit scoring and risk assessment. Financial institutions analyze historical transaction data, customer profiles, and market trends to predict defaults and assess the creditworthiness of potential borrowers. This practice has not only streamlined the approval process but also minimized the risk of bad debts.

The healthcare industry has also embraced predictive analytics to enhance patient care and operational efficiency. By analyzing patient data, healthcare providers can predict disease outbreaks, optimize resource allocation, and personalize patient treatment plans.

Businesses adopting predictive analytics need to ensure that their data is of high quality and that their analytical models are continually updated to reflect the latest trends and patterns. Organizations should also be aware of ethical considerations and data privacy issues, ensuring that their predictive analytics initiatives comply with relevant regulations.

As technology continues to advance, the use of predictive analytics in business decision-making will only grow. By harnessing the power of data, companies can position themselves as leaders in their respective industries, ready to seize opportunities and mitigate risks in real-time.

This article explores the transformative role of predictive analytics in supply chain management, highlighting real-life business cases and the potential benefits this technology brings to improve efficiency and decision-making.

Predictive analytics is rapidly reshaping the landscape of supply chain management, offering companies a powerful tool to anticipate and respond to potential disruptions and inefficiencies.
By leveraging historical data, predictive analytics can forecast trends, demand fluctuations, and possible bottlenecks. This foresight enables companies to make strategic decisions ahead of time, minimizing risks and optimizing operations.
Advanced analytical tools are at the core of this transformation. They facilitate real-time data processing and provide insights that were previously unattainable, enhancing a company's ability to adapt to market changes quickly.
One real-life example that showcases the impact of these tools is how a leading U.S. retail chain streamlined its inventory management. By applying predictive analytics, the company accurately forecasted demand during peak seasons, minimizing overstock scenarios and reducing stockouts, ultimately saving millions in potential lost revenue.
Moreover, the integration of machine learning algorithms into predictive analytics allows for continuous improvement as these models learn and adapt to new data. This capability is crucial for supply chain management, where variables are in constant flux.
Despite the clear advantages, a significant challenge remains: the need for skilled data analysts who can interpret complex datasets and translate them into actionable insights. As more businesses adopt predictive analytics, the demand for talent in this field is expected to rise.
For companies looking to adopt predictive analytics, the time is ripe. Industry experts suggest that as the technology becomes more accessible and cost-effective, even small to mid-sized enterprises stand to benefit significantly.
In the age of rapidly advancing technology, predictive analytics stands out as a game-changer. Businesses that embrace it are better positioned to navigate the uncertainties of supply chain management, ensuring resilience and sustainability in their operations.

Explore the latest trends in Big Data Analytics, focusing on predictive analytics' roles in various industries. Discover real-life usage in retail, financial services, and healthcare.

The realm of Big Data Analytics has rapidly evolved, with trends echoing a seismic shift in how businesses leverage data for critical decision-making. Over the past few days, industry updates indicate a significant leaning towards predictive analytics, which has emerged as a game-changer for various sectors. Businesses are now transcending the traditional BI dashboards, moving to a predictive model that forecasts future trends with accuracy.

Predictive analytics combines historical data with machine learning algorithms to predict future outcomes. This trend is gaining traction particularly in the retail industry, where companies like Amazon have pioneered using analytics to drive sales strategies and inventory management. The shift towards a data-driven culture in retail has proven to enhance customer satisfaction and forecast demands efficiently.

In financial services, predictive analytics plays a pivotal role in fraud detection and credit scoring. Financial institutions are leveraging advanced algorithms to detect fraudulent activities in real-time, saving millions in potential losses. A key example is J.P. Morgan, which uses AI-driven analytics to enhance their fraud detection mechanisms, significantly improving their security measures.

Moreover, the healthcare industry is witnessing a revolution in patient care and management through predictive analytics. By analyzing patient data, healthcare providers can predict potential health risks, allowing for proactive measures rather than reactive treatments. This not only improves patient outcomes but also optimizes resource utilization within medical facilities.

As businesses continue to embrace this trend, the importance of clear, actionable insights cannot be overstated. Companies must strive to build robust data infrastructures that support advanced analytics. The integration of cloud technologies and IoT has further bolstered this shift, providing scalable solutions for handling vast amounts of data and complex analytics processes.

While privacy concerns and data security remain critical challenges, the benefits of implementing predictive analytics are undeniable. Managing these concerns requires a balanced approach with stringent data governance frameworks and compliance with international data protection laws.

In conclusion, the rise of predictive analytics demonstrates a tectonic shift in business strategies across industries. By harnessing the power of data, organizations are not only gaining competitive advantages but also fostering innovations that redefine market standards.

Explore how advanced analytics is revolutionizing business decision-making with predictive insights and big data integration.

In today's fast-paced business landscape, the ability to make informed decisions based on data-driven insights is more critical than ever.
Advanced analytics tools have emerged as game-changers, enabling businesses to sift through vast amounts of data to identify trends, predict outcomes, and optimize operations.
One notable trend in the realm of analytics is the increasing adoption of predictive analytics. This subset of advanced analytics uses historical data to forecast future events, allowing companies to anticipate market changes and adjust their strategies accordingly.
For instance, a leading retailer in the United States harnessed predictive analytics to streamline their supply chain operations. By analyzing purchasing patterns, weather forecasts, and social media trends, the retailer optimized their inventory management, reducing costs and increasing customer satisfaction.
The integration of artificial intelligence with advanced analytics tools enhances the capability to provide real-time insights. Businesses can receive up-to-the-minute information, allowing them to react swiftly to market dynamics.
Moreover, the synergy between advanced analytics and big data platforms allows companies to handle and analyze massive datasets efficiently. This capability was exemplified when a major healthcare provider utilized big data analytics to improve patient outcomes by tailoring personalized treatment plans based on individual data.
Despite the vast potential of advanced analytics, challenges remain. Data privacy concerns, the need for skilled personnel, and the integration of disparate data sources are significant hurdles that businesses must overcome.
Yet, as technology continues to evolve, the barriers to entry for implementing advanced analytics tools are diminishing, making it accessible to businesses of all sizes. Companies investing in these technologies are poised to enhance their competitive edge, achieve operational excellence, and drive sustainable growth.

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.

Explore the rise of innovative data providers transforming data management with advanced analytics and enhanced security solutions. Discover how businesses are leveraging these providers to drive success and boost decision-making.

As the digital landscape continues to evolve, the demand for innovative data providers is more pressing than ever. Data management has become a critical aspect of every organization, from small startups to industry giants. The ability to collect, process, and leverage data effectively can drive a company towards unprecedented success.

Recent trends indicate a significant surge in the importance of robust data solutions. Organizations are increasingly leaning on data providers that offer more than just basic services. Today’s top data providers are focusing on advanced analytics, AI integration, and offering custom-tailored solutions to meet unique business needs.

A notable example encapsulating this trend is a collaboration between a leading e-commerce platform and an innovative data provider. The e-commerce platform was struggling with fragmented data systems and sought to unify its data to gain deep business insights. The partnership allowed them to create a streamlined data architecture, enabling real-time insights across multiple levels of their operations. This transformation helped boost their decision-making processes, ultimately leading to increased revenue and customer satisfaction.

Security remains a top priority in data management. Innovative data providers are recognizing this and are investing heavily in enhancing their security protocols. With data breaches being an ever-present threat, ensuring data security measures align with global best practices has become non-negotiable. This includes implementing multi-factor authentication, encryption, and regular security audits.

Looking ahead, the role of data providers is expected to expand further. As businesses continue to deal with vast amounts of data, the need for providers that can efficiently manage and extract value from this data will grow. Providers need to stay ahead of emerging trends, adapt rapidly, and offer flexible solutions capable of scaling with the business.

Explore how big data providers like Google Cloud and AWS are transforming the market with innovative solutions, enhancing analytics, and data management.

The surge in big data continues to transform how businesses operate, driving a profound shift in decision-making and strategy formulation. This evolution has necessitated a range of innovative data provisioning solutions, cementing the significance of data providers in today's digital economy.

In recent days, the spotlight has been on key players in the big data provider segment who are revolutionizing how analytics and data management are approached. These providers are not only delivering vast amounts of data but are also ensuring that this data is actionable, real-time, and seamlessly integrated across platforms.

A prominent example is a collaborative initiative by providers such as Google Cloud and AWS, who are enhancing their offerings to include advanced machine learning and AI capabilities. This development allows enterprises to harness data with unparalleled precision, ensuring predictive analytics are more insightful and actionable.

With a growing emphasis on data privacy and security, providers like IBM are leading initiatives to ensure data protection without compromising on accessibility. They are implementing robust encryption techniques and comprehensive compliance frameworks, helping businesses adhere to stringent data regulations globally.

Exploring real-world applications, a notable success story involves a Fortune 500 retailer who partnered with a leading data provider, resulting in a 25% increase in operational efficiency. By leveraging real-time data analytics and consumer behavior insights, the retailer optimized its supply chain and marketing strategies, realizing significant cost savings and improved customer engagement.

Furthermore, the introduction of hybrid cloud solutions by data providers is a noteworthy trend, offering enhanced flexibility and scalability for businesses of varying sizes. These offerings are particularly useful for startups and SMEs aiming to leverage big data without the associated overhead costs of traditional infrastructure.

In the fast-evolving world of data, providers continue to push boundaries, delivering innovative solutions that empower businesses to pivot and thrive in competitive markets. The future promises more integrations, faster processing capabilities, and superior analytics tools tailored to specific industries and needs.

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.

Explore how modern data analytics tools are driving business growth by transforming raw data into actionable insights. Learn about the latest trends in AI-driven analytics, user-friendly interfaces, predictive analysis, and real-time data processing.

In today's digital age, businesses are inundated with vast quantities of data. The ability to harness and analyze this data has become pivotal for organizations that aspire to remain competitive. Fortunately, advancements in data analytics tools are empowering firms to transform raw data into insightful and actionable business intelligence.

Recent trends suggest a significant shift towards integrating artificial intelligence (AI) in data analytics tools. AI algorithms are being leveraged to automate the analysis process, significantly reducing the time needed for data processing and interpretation. For instance, major corporations like Netflix have successfully utilized AI-driven tools to predict viewer preferences and, in turn, tailor their content offerings.

Another fascinating development is the move towards more user-friendly interfaces in analytics platforms. Tools like Tableau and Power BI are leading the charge by providing intuitive drag-and-drop features, enabling even non-technical users to create complex data visualizations. This democratization of data has allowed teams across various departments to glean insights, fostering a data-driven decision-making culture in organizations.

Predictive analytics is also gaining momentum, offering businesses a glimpse into future trends based on historical data. By employing these predictive models, companies can make informed decisions that minimize risks and capitalize on emerging opportunities. Retail giants, for example, are using predictive analytics to forecast demand, optimize inventory levels, and enhance supply chain efficiency.

Additionally, real-time analytics tools are proving to be game-changers, particularly in sectors like finance and healthcare. These tools allow businesses to make decisions based on live data streams, thereby improving their agility and responsiveness to changes in the market. Banks, for instance, are using real-time analytics to detect fraudulent transactions instantaneously, thereby safeguarding customer assets and enhancing trust.

As data continues to grow in importance, the adoption of advanced analytics tools will only become more pervasive. Embracing these tools is not just about staying competitive—it's about redefining how businesses operate and succeed in the data-driven landscape.

Discover the trend of new naming conventions in data science tools, enhancing clarity, accessibility, and inclusivity for better data-driven decisions.

In recent years, the rapid evolution of data science tools has transformed the way businesses operate and make informed decisions. Within this transformative landscape, a noteworthy trend has emerged – the shift towards new naming conventions for analytical tools. This trend reflects the dynamic changes in the field and signals a future of continuous innovation.

The adoption of intuitive and descriptive names for data science tools has gained traction as businesses seek more approachable and user-friendly analytical solutions. In the past, complex acronyms and jargon-heavy names made it challenging for non-technical professionals to navigate and effectively utilize these tools. Today, however, companies are gravitating towards names that convey functionality and simplify the user experience.

A notable example of this trend can be seen with Tableau, a data visualization tool that has embraced user-friendly terminology throughout its evolution. Instead of relying on technical acronyms, Tableau has focused on simplifying its product features, such as "Visual Explorer" and "Data Interpreter." By prioritizing clarity, Tableau empowers users across various departments to harness the power of data without needing deep technical expertise.

This shift aligns with the increasing demand for data democratization within organizations. As more departments seek to leverage analytical insights, the tools themselves must facilitate collaboration and ease of use. By employing clear and intuitive naming conventions, organizations encourage cross-departmental synergy and drive better business outcomes.

There's an evident parallel between the evolution of tool names and the growing emphasis on data literacy. As companies prioritize upskilling their workforce, simplifying tool nomenclature becomes integral to enabling employees to engage with data analytics confidently. This trend goes beyond mere branding; it's a strategic approach to foster engagement and encourage wider adoption of analytical tools.

Moreover, the new naming conventions reflect a commitment to accessibility and inclusivity. By adopting language that resonates with a broader audience, businesses remove barriers that have historically hindered data-driven decision-making. This approach empowers individuals to explore data independently, leading to innovative solutions that drive organizational growth.

As data science continues to evolve, embracing new naming conventions for analytical tools serves as a vital component of this evolution. By prioritizing clarity, accessibility, and inclusivity, companies enhance their competitive edge in an increasingly data-centric world. With an emphasis on empowering users, the future of analytical tools promises to be both transformative and engaging.

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.