Saying, "Data is the new oil", is indeed true. As oil is essential for keeping the ball rolling across major industries, data has emerged as an invaluable tool for organizations seeking to leverage digitalization.But the real challenge is extracting value from the massive amount of data organizations generate daily. As of 2021, roughly 2.5 quintillion bytes of data are generated every day – data that can be stored in 2,500,000 Apple iPhone Pro Max with 1TB Capacity.However, data alone cannot provide the value organizations aspire to gain. With analytics and monitoring tools, data which was once considered to be of no value can be worth the gold. The worth is even greater when we analyze and monitor it in real-time. Real-time data monitoring enables organizations to act immediately on inputs and insights to accelerate growth and build trust among all entities involved.<h2>What is Real-Time Data Monitoring and Analytics?</h2>Real-time data monitoring helps organizations analyze data for the addition, deletion, and modification of data that exists in enterprise systems. Plus, it empowers organizations to review every process and function associated with data in real-time. With these insights, organizations can make proactive decisions and equip themselves with the right tools and resources to change strategies and maximize profitability.Embracing real-time data monitoring enables organizations to monitor better and manage the use of data across different enterprise systems. When organizations adopt real-time data monitoring and analytics solutions, they get unparalleled and valuable insights into the data extracted from different sources like running applications, networks, web servers, databases, etc.Today, companies – big and small – use real-time data monitoring. However, corporations with hefty budgets are using these solutions due to their expensive nature. Using real-time data monitoring provides numerous benefits:<ul><li>It can help improve customer satisfaction by enabling companies to process requests or complaints and act upon them.</li><li>It can improve employee satisfaction by offering more thorough feedback about an employee's performance.</li><li>It can help ensure compliance with industry regulations by identifying gaps before they become bigger issues. &nbsp;</li></ul><h2>What is Trend Monitoring?</h2>Organizations need to understand the past to be better prepared for the future. This is because consistent past events, also called trends, can help them predict what is likely to happen next – information beneficial that can help them stay ahead of the competition.That's why organizations are keen on adopting trend monitoring – a process or method that uses past data to forecast events. In other words, trend monitoring allows organizations to predict the future based on what has already happened in the past.Trend monitoring helps organizations compare data fetched from different periods to identify a pattern or trend. As a result, organizations can become future-proof while adapting to what works and what doesn't work for them.With the rapid change in consumer behaviour, the business landscape, and organizations' willingness to manage data, trend monitoring is going to help organizations aspiring to get a competitive advantage.<h2>How Can Real-Time Data Monitoring Benefit Your Business?</h2>If performed timely and accurately, real-time data monitoring can help organizations succeed in achieving their different objectives. There are multiple benefits that organizations can witness by implementing real-time data monitoring solutions.<h3>Better Customer Service</h3>Real-time data monitoring provides organizations with insights into how customers perceive and experience their business. Organizations can use these insights to make real-time changes to improve the customer experience. This could include providing personalized offers to customers if they are having trouble looking for something on your website.&nbsp;<h3>Higher Revenue</h3>Insights into customers' purchasing behaviour are important for any organization as they can change their strategies accordingly if they find any loopholes. Real-time data monitoring helps organizations identify these loopholes and gaps so that they better understand purchasing behaviour and make required changes to increase conversion rates.<h3>Enhanced Security</h3>Security has been a major concern for any organization, especially in the digital world where attackers are ready to permeate. With increasing cyber-attacks and data breaches, companies need to be more prepared to secure their digital assets.Real-time data monitoring can help organizations mitigate and prevent these threats by monitoring the network around the clock. This means that if something wrong happens, companies will know about it in real time and can respond to it before more damage is done.Furthermore, real-time data monitoring allows organizations to better protect their employees' data by giving them insights into what applications they are using and which networks they are connected to. Based on these insights, organizations can ensure the safety of their digital assets, employees' data, and enterprise systems by building an unbreakable perimeter around these tools. This helps organizations to effectively manage their network and ensure that only valid users have access to private information.<h2>Importance of Real-Time Data Monitoring</h2>Real-time data monitoring enables organizations to have deep insights into different business activities. These insights can help organizations to make better business decisions for higher productivity and more savings.Organizations must invest in real-time data monitoring because it provides a window to the business activities' inner workings, allowing them to review and understand what's happening at any moment. In addition, they will be in a better position to identify problems before they become too challenging to manage and see emerging patterns or trends, using which organizations can adjust their strategies. Investing in real-time data monitoring solutions has become necessary for organizations looking to understand their digital environments better.

Ankit Gupta

Real-time data monitoring has many advantages for modern businesses. If you're considering it for your company, this article will help you learn more about what to expect.

Why Invest in Real Time Data Monitoring? | Techfastly

Why Should Organizations Invest in Real-Time Data Monitoring?

<div>Currently, data analysts are one of the most in-demand
jobs due to the digital revolution, which has resulted in a massive amount of
data that many organizations are not familiar with. As a result, the data
analyst will be able to provide meaningful insight into the data and apply it
to the company's advantage. As a result of the broad scope of this job, it should
come as no surprise that companies are actively seeking data analysts. After
reading this article, you should have a better understanding of what it takes
to become a data analyst.&nbsp; <h2 style="text-align: justify; border-width: initial; border-style: none; border-color: initial;">What exactly is
the role of a Data Analyst?</h2>



In order to provide information that can be used to make
decisions, data analysts must collect, clean, transform, and model data. The
best use of available data, making the best investment decisions, analyzing
customer behavior, and identifying potential hazards are all tasks that data
analysts perform for every data-driven organization they are part of.&nbsp;

<h2 style="text-align: justify; border-width: initial; border-style: none; border-color: initial;">Are you
interested in learning how to become a Data Analyst?&nbsp;</h2>

As a data analyst, you must have the necessary technical
skills to perform more effectively daily, dealing with various problems in data
analysis and within the organization. The information provided here may help
choose a <a href="https://www.mygreatlearning.com/pg-program-data-science-business-analytics-course">Data Analytics
course for working professionals</a>.&nbsp;

Learn how to become an expert data analyst from the
ground up by following these simple steps!&nbsp;

<h3 style="text-align: justify; border-width: initial; border-style: none; border-color: initial;">Completion of the
Academic Prerequisites&nbsp;</h3>

A STEM
(Sciences, Technologies, Engineering, and Math) background is the best place to
begin learning the necessary skills for this vocation. Data analysts, on the
other hand, must have a bachelor's or master's degree in one of the following
fields:&nbsp;

●&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Business and information technology<o:p></o:p>

●&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Mathematics\sStatistics<o:p></o:p>

●&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Marketing\sFinance<o:p></o:p>

●&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Information Technology&nbsp;<o:p></o:p>

<o:p>&nbsp;</o:p>

<h3 style="text-align: justify; border-width: initial; border-style: none; border-color: initial;">Develop a Solid
Understanding of Programming Languages</h3>

Data analysts require less coding knowledge and
comprehension than data scientists. You must, however, be able to sort through
and evaluate large datasets. However, the majority of businesses cannot afford
these tools. As a result, Python and its numerous data visualization tools are
the most popular choice. If you're new to programming, Python is your best
bet.&nbsp;&nbsp;

●&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Working
knowledge of SQL fundamentals&nbsp;&nbsp;

As a data analyst, you've undoubtedly heard about this
particular skill. You will be able to obtain data from various sources if you
understand SQL. Understanding data recovery and integration may help you manage
your data more effectively.&nbsp;

●&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Hands-on
experience with Data Visualization Tools&nbsp;

Data analysts frequently employ data visualization
tools. These data visualization tools do not require any technical knowledge to
use. All you have to do is click and drag to get things to happen most of the
time. These technologies necessitate a thorough understanding of the numerous
characteristics required to evaluate and present data. A diverse skill set
increases your chances of landing fascinating, one-of-a-kind, and potentially
game-changing projects.<o:p></o:p>

<o:p>&nbsp;</o:p>Despite its ease of use, Microsoft Excel is used in
every industry and by every type of professional. Excel features such as pivot
tables, equations for data manipulation, and graphs for visualization can be
used to create genuinely exceptional dashboards.

●&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Be a
Story-teller&nbsp;

Dashboards must be not only visually appealing but also
functional. Each board item should have a solution-based narrative. This
implies that business analysts must be able to present their findings clearly
and concisely. The story should be brief, straightforward, and accurately
depict the subject matter.&nbsp;

●&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Know
about Machine Learning&nbsp;

When compared to traditional business intelligence
solutions, machine learning enables organizations to gain more insights from
both structured and unstructured data. It indeed provides leaders with the
tools they require to plan and execute. Using machine learning and analytics in
a broader sense allows you to respond to changing situations more quickly and
extract more value from the rapidly growing treasure mine of data, making it an
unavoidable tool for data analysis.<o:p></o:p>

<o:p>&nbsp;</o:p>●&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Improve
your Analytical Capabilities

Analytical abilities are, of course, essential for data
analysts. Using these abilities, raw data can be collected, categorized, and
evaluated. To be a successful professional data analyst, you must broaden your
analytical skills and thinking style.&nbsp;&nbsp;

●&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Gain
knowledge on a specific subject&nbsp;

Becoming a competent domain expert necessitates a
thorough knowledge of the industry and the business for which you work. A
company with an online store may require you to be familiar with the nuances of
e-commerce for you to do your job correctly. For example, if you're crunching
numbers on mechanical systems, you might need to understand how they work.&nbsp;

As for the wide variety of industries, specialized
knowledge varies from one to another. Working in the data analysis industry
necessitates extensive domain knowledge, which is a required skill for all data
analysts, regardless of location or business.&nbsp;

●&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Refresh
Your Reasoning and Logic Skills&nbsp;

To comprehend complex algorithms and algorithms, a data
analyst must have a strong understanding of logic. As a result, native
solutions such as CRM and ERP systems can be easily analyzed, and complex
reports can be generated. Working on your thinking skills will also make
dealing with commercial issues easier.&nbsp;

●&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Make
decisions and find solutions&nbsp;

These abilities serve as the foundation for analytical
approaches. The primary responsibility of a data analyst is to provide reliable
data for decision-making and problem-solving. As a result, it appears to be an
excellent skill for a data analyst.&nbsp;

●&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
Communicate
well&nbsp;

Data analysts must be able to communicate effectively
both verbally and in writing. To prepare, present, and explain data, you must
communicate effectively with co-workers. When working with people, it is
critical to communicate effectively.&nbsp;

<h2 style="text-align: justify; border-width: initial; border-style: none; border-color: initial;">Bottom-Line&nbsp;</h2>

A data analyst's job is very lucrative and well-paying,
making it a very desirable career. Consider enrolling in the <a href="https://www.mygreatlearning.com/data-science/courses">best courses to
learn Data Science</a> from&nbsp;<a href="https://www.mygreatlearning.com/">Great Learning</a>.&nbsp;Suppose
you want to work as a data analyst but don't have any prior experience. In that
case, you should learn the fundamentals of data analysis, get involved in some
data analysis projects, and build your résumé. Including project work
experience and new skills on your data analyst resume will help you stand out
from the crowd and land the job you want.<o:p></o:p></div>

Pushpendra Singh Rajput

Become a Successful Data Analyst from Scratch with These Valuable Tips!   

<div>When people think about data and computing, the first thing that comes to mind is Artificial Intelligence (AI). In the marketing sphere, AI has gained a considerable foothold, evident from the many SaaS products being launched. That being said, there is one aspect that is still amiss. Personalization. How can a company genuinely show that they care about their consumers when their products don’t reflect that? That’s where cognitive computing comes into the picture. &nbsp;Cognitive computing uses a combination of AI, neural networks, sentimental analysis, natural language processing, and the like to solve real problems. To implement a genuinely human-centric approach, it’s essential to use technology that mimics human emotions and actions better. This is precisely what cognitive computing does, and this is what differentiates it from AI-led approaches.<o:p></o:p>&nbsp;In addition to that, quantum computing and cognitive computing will soon be synonymous with companies such as IBM taking the next step forward. This could give cognitive computing the push needed in the marketing world as data-driven marketing seems to be getting more popular with time.<o:p></o:p>&nbsp;But before that, let’s see how cognitive computing works to bridge the human-computer gap.<o:p></o:p>&nbsp;<h4 style="margin: 0cm; line-height: 22px;">Bridging the Human-Computer Gap in Marketing</h4>When it comes to using computing technologies, the one thing that sets cognitive computing apart is that it accounts for human emotions. This makes it the perfect choice for marketing purposes since it goes beyond the generic responses. To do this, most marketers use sentimental analysis — a method used to gauge the audience’s sentiments and leverage them for your business.<o:p></o:p>&nbsp;An excellent example of sentimental analysis in action is a humanoid robot. They act like “cognitive assistants” and help you out with your immediate needs. You can find them either at conferences/ events where they’re checking you in, within hotel lobbies acting as your concierge, or even at nursing homes, helping elderly patients with their needs. The applications are far and wide but what’s important to note is that robots can understand our emotions now, all thanks to complex cognitive computing capabilities.<o:p></o:p>&nbsp;Even cognitive computers such as IBM’s Watson can improve business processes and customer interactions over time but use robotic process automation. They can identify emerging patterns and give marketers the competitive edge they need in the market. As cognitive computing allows the system to provide valuable and contextual information to its customers, they increase engagement over time.<o:p></o:p>&nbsp;Now that we know that human-computer interactions are being bridged let’s look at how sentiments play a role in that context.<o:p></o:p>&nbsp;<h4 style="margin: 0cm; line-height: 22px;">Leveraging Sentimental Analysis for Your Business</h4>Sentimental analysis is a process in which you gauge the emotions, attitudes, opinions of your audience concerning either your product, service, or brand from the business perspective. In today’s day and age, most customers expect personalized marketing content, which forces you to go above and beyond in your marketing efforts. But with sentimental analysis, it’s easier to gauge the emotions and attitudes of your customers and leverage that for your business.<o:p></o:p>&nbsp;For the most part, sentimental analysis focus on three aspects — engagement, decision, and discovery. Such systems can track data from multiple repositories to give better insight and assistance regarding engagement. This fosters the ability to have more profound and meaningful dialogues with humans, engaging the customer. Concerning decision making, they use reinforcement learning that helps it make better decisions through a closed loop of learning. Through deep learning and unsupervised machine learning models, cognitive computers tend to discover patterns and behavior that would not have been obvious otherwise. Using these three aspects, you can build relationships with your customers that would not have been possible otherwise.<o:p></o:p>&nbsp;A study showed that 68% of users are more likely to buy a product if it provides some form of personal value. This personal value cannot be gauged without genuinely understanding the customer, their needs, and what kind of situations they are in. To do this, sentimental analysis tends to be the best bet for business owners. Here, the analysis can either be direct (social media &amp; feedback) or indirect (automated sentimental analysis). When it comes to marketing, it can be used to track buyer sentiment, improve the ideal buyer persona and even manage any potential crisis — all powered through cognitive computing. That being said, it’s always important to employ a mix of direct and indirect analysis because, at the end of the day, a machine can only do so much.<o:p></o:p>&nbsp;<h4 style="margin: 0cm; line-height: 22px;">The Quantum Computing Push in 2021</h4>As mentioned earlier, cognitive and quantum computing have been said to go hand in hand with each other. Many quantum computers are being developed for this purpose because they offer a particular competitive advantage over other technologies. In conjunction with AI, marketers could spring forward in their ability to connect with their customers. As quantum algorithms can improve machine learning models (data clustering), customer and brand relationships will become more intuitive and thorough.<o:p></o:p>&nbsp;Many companies have already begun adopting these technologies because of their value in process optimization and marketing. It’s clear that the quantum push is already happening, with many mergers and acquisitions taking place in 2021. Some of them include Honeywell and Cambridge Quantum Computing, Rigetti Computing and Supernova Partners Acquisition Company ($1.5 billion), IonQ and DMY Technology ($2 billion), IBM and Raytheon Technologies, and many more.<o:p></o:p>&nbsp;These companies are spearheading the next generation of cognitive computing, which could become super beneficial for the marketing industry as a whole, especially for the early adopters of the technology. It’s is an amalgamation of all they need to succeed, from discovery to the decision to engagement.<o:p></o:p>&nbsp;<h4 style="margin: 0cm; line-height: 22px;">Final Takeaway</h4>Cognitive computing is the key to better and more competitive marketing strategies. Most marketing techniques rely on consumer behavior, and to ‘decode’ said behavior; it’s essential to build on models that can interpret actual human emotions. The use of sentimental analysis will only increase with time and will ensure that cognitive computing is truly the pinnacle of competitive marketing. Now is the time to invest in these technologies, as the benefits will become multifold with time.<o:p></o:p></div>

Tanaaz Khan

Sentimental Analysis — The Key to Changing Perceptions of Cognitive Computing in Marketing

<div>The best part about the new CDC for Astra DB is that it's now powered by advanced streaming technology and is built on the latest Apache Pulsar. It uses the event streams to process and deliver database changes in real-time. This real-time data changes then becomes available for use across data lakes, search, artificial intelligence, data warehouses, and machine learning. <o:p></o:p>With this advanced and powerful integration of event streaming with high-performance, high-scale Astra DB multi-cloud databases, any organization gets enabled to build more reactive and smarter applications. Applications that are fueled by real-time or, we can say, connected data.<o:p></o:p>According to the chief product officer at DataStax, Ed Anuff, the CDC for Astra DB is an entirely new and unique concept. The major areas it taps for the performance of the world's most scalable database and combines with the next-generation event streaming. Undoubtedly, working with the next-generation event streaming technology will fuel today's data-intensive applications with the best solutions for a seamless workflow.<o:p></o:p>Currently, only a fraction of real-time data is being leveraged, even though businesses have a huge urgency to use it. This is major because of the failure of legacy architectures that cannot meet the performance and scale requirements of real-time data processing. Due to this, we see the final result as data uploaded in batches which ultimately creates chronically stale data.<o:p></o:p>In regard to this, the enterprise architect of Alpha Ori Technologies, Praveen Viswanath, said that Alpha Ori Technologies develops real-time data solutions for maritime shipping. Also, he added that they are extremely excited about the availability of CDC for DataStax Astra DB. This will help real-time analytics by providing a real-time source of streaming data. He said CDC help our maritime clients save money on fuel, mitigate maintenance-related risks, and reduce carbon emissions by enabling them to alert and deliver analytics rapidly.<o:p></o:p>According to the report made by McKinsey, one of the seven characteristics predicted to define data-driven enterprises is the Data-Driven Enterprise of 2025, where data is processed and delivered in real-time. However, companies often choose between speed and computational intensity, which can delay more sophisticated analyses and inhibit the implementation of real-time use cases. WorkJam is the place of the business governor for actual forerunner orchestration: plan, duty administration, activities, communications, staffing, and several others.<o:p></o:p>The director of the R&amp;D at WorkJam had earlier stated in an interview that they're always hunting for different means to simplify their functions and operations with the help of Change Data Capture (also known as CDC). This will allow them to produce smarter and more responsive connected applications; live data would power.<o:p></o:p>With the help of Change Data Capture for Astra DB, companies could homogenize on the enormous scalable with no downtime Astra DB whatsoever. Apart from this, any modifications to the database are distilled and furnished in real-time to different portions of the data system without having any sort of downtime between rate, scale, and accessibility. This smooth incorporation facilitates corporations to initiate their real-time data to raise accumulative business significance from regions encompassing e-commerce, crime detection, consumer satisfaction, trades, stock and transport, and logistics.<o:p></o:p>The principal analyst at DBInsight stated that Through the CDC, Astra DB also unites functional and streaming data to evolve to be a bidirectional data center for live data. He added that the CDC would enable their consumers to combine Astra DB with cloud analytic places such as the Snowflake or the junction for Android application with further limited resistance.<o:p></o:p>DataStax is a live data corporation. Through DataStax, every business could be able to muster live data and rapidly construct the smart, top-notch apps needed to come to be a data-driven firm. DataStax Astra DB differently mixes the strength of Apache Cassandra—the highly scalable database across the globe—with the developed Apache Pulsar streaming tech in a harmonious bunch provided as assistance on any cloud.<o:p></o:p>Several giant corporations, comprising names such as ESL Gaming, Verizon, Audi, and several others, depend on DataStax to express the strength of real-time data to provide instant virtual experiences that could gain a victory in modern markets and shift businesses.<o:p></o:p>Apache Cassandra, Apache, Cassandra, Apache Pulsar, Pulsar, and Apache Kafka are either enrolled brand names or brands of the Apache Software Foundation or its associates in the Canadian region, the United States of America, and several different regions.<o:p></o:p></div>

Rahul Kumar

DataStax, The Real-time Data Company, Unveiled “Change Data Capture” (CDC) for Astra DB

AI has helped us reach Mars. Artificial intelligence often helps us address climate change, and most recently, discover cancer cures. We can easily communicate with Alexa or Siri to ask about the weather, play a song, or adjust the thermostat setting.In businesses, it helps us organize our day-to-day activities and save time by automating certain crucial tasks in no time.It may be challenging to envision the consequences of robots making judgments quicker and more accurately than humans, but one thing is certain: new developments and discoveries will continue to emerge in 2022, pushing the frontiers of AI further. Let's explore what artificial intelligence in business has to offer to us in 2022.<h3 id="top-7-ai-in-business-trends-2022">Top 7 AI in Business Trends 2022</h3>Since the advent of AI (artificial intelligence), our lives have transformed to a whole new level. The focus of business executives will switch from ?surviving? to 'thriving?. And this will require more long-term and scalable solutions. As a result of this transition, the following patterns will emerge in the current year:1. Data and analytics on the cloud2. AI Augmented Hyper Automation3. Enhanced Cybersecurity with AI and ML4. Emergence of Generative Artificial Intelligence or GAN5. Model-centric AI to data-centric AI6. Improved decision intelligence7. The continued evolution of AI and IoTLet us take a closer look at each of these artificial intelligence trends set to transform our lives and workplace in 2022.1. Data and analytics on the cloudArtificial Intelligence, according to Rico Burnett, director of client innovation at legal services provider Exigent, will play an important role in the wider implementation of Cloud Solutions in 2021.Artificial intelligence will make it easy to actively manage resources in the cloud as well as the large quantity of data accessible. In order to fuel data-driven business analytics at scale, data, cloud, AI, and machine learning (ML) needs to be combined. In 2022, cloud data will be the cornerstone for a large number of new digital practices.Businesses will be able to swiftly increase their predictive analysis capabilities by partnering with cloud analytics providers, which will eliminate the expenses and administration tied with on-premise technology.2. AI Augmented Hyper AutomationWhen it comes to innovation and automation in 2022, artificial intelligence and data science will be a component of a larger picture. Data ecosystems are scalable, lean, and supply data to a multitude of sources on schedule. But even so, a foundation must be established in order to conform and encourage innovation.Hyper automation, as the term implies, is automation taken to the next level. Hyper automation not only focuses on automating complicated operations but also assists companies and organizations in identifying procedures that may be automated. To accomplish so, it employs AI, machine learning, and robotic process automation (RPA) (Robotic Process Automation).In 2022, more enterprises and organizations will turn to hyper-automation to speed up and improve the accuracy of complex procedures.3. Enhanced Cybersecurity with AI and MLAs technology in computer systems grow increasingly complex, cybersecurity will become even more important in 2022. It's natural that as technology advances, hackers come up with new and more deadly ways to target people and businesses.Fortunately, increased cybersecurity based on Artificial Intelligence is going to be helping. AI/ML systems, for example, can scan greater network traffic and spot patterns of illicit virtual activity. This is anticipated to be the site of some of the most significant artificial intelligence technology advances in 2022. Since AI can rely on learning from their surroundings, AI-enabled internet security measures will revolutionize security and privacy in the digital realm.4. Emergence of Generative Artificial Intelligence or GANThe advent of generative AI is one of the rising business trends in 2022 with the integration of artificial intelligence. The term "generative artificial intelligence" refers to software that allows computer systems to produce content using text, audio data, and graphics.Business requires Generative Adversarial Networks (GAN) to create images that are indistinguishable from those produced by humans, including objects, humans, animals, and much more. This AI in business strategy will be able to analyze the real-time patterns of data distributions and develop useful concepts for improving artificial intelligence pathways using real-world examples.5. Model-centric AI to data-centric AIModel-centric AI is undergoing a transition towards data-centric AI in analytical systems.Model-centric methods maintain data fixed while continuously improving the code or model, whereas a data-centric strategy keeps the code stable while focusing on increasing the quality of data coming in. This ensures that high-quality data is available at all stages of the machine learning process.In 2022, we can expect this transition to be one of the rising artificial intelligence trends for businesses.6. Improved decision intelligenceBusinesses use AI in their processes to create quicker, more accurate, and regular decisions by using datasets with AI. This is referred to as AI decision-making.While employing frontline workers, for example, a bank may use machine learning to assess candidates' soft abilities in customer service. The bank will then be able to rapidly and efficiently hire the top individuals.Decision intelligence uses artificial intelligence to evaluate vast volumes of data in order to provide real-time findings to business executives. This concept is crucial because, in a rapidly changing environment, executives must move swiftly and grab new possibilities.7. The continued evolution of AI and IoTWith the combined use of AI, IoT, edge computing, and 5G networks, 2022 will be a year of improved internet of things (IoT) adoption, enabling smarter, more engaging goods and services. Data and analytics from the Internet of Things may be woven into the data fabric to enable company executives to innovate at scale.The Internet of Things, or IoT, refers to the billions of internet-connected gadgets that are constantly exchanging data over the internet. Technology experts believe that as AI developments enrich IoT devices, their capacity to interpret, transport, and communicate complicated data will improve at an exponential rate.In order to advance way faster than consumer needs or market demands, organizational leaders must continue to spend and innovate. They must keep ahead of the competition, and stay committed to their clients in 2022 with the support from reliable AI.<h3 id="final-thoughts">Final Thoughts</h3>As a wide range of AI domains develops and becomes simpler to construct and deploy, 2022 is quickly emerging to be a significant progressive year. Although businesses have already made significant progress with analytics and artificial intelligence as of 2021, the revolution is simply beginning. AI will become more crucial as businesses continue to automate day-to-day operations and better analyze COVID-affected datasets. Since the lockdown and work-from-home policies were implemented, industries are more technologically linked than ever before.

Toulika Das

Top 7 Artificial Intelligence in Business Trends for 2022

The amount of data available across businesses is staggering and gives rise to new trends every day. It's correct utilization can drive decision-making, impact strategies, and improve organizational performance. Despite this, a lot of data is riddled with redundancies, duplicates, and errors. It is imperative to have invaluable, tangible figures against which a business can measure its performance. As a result, demand for data analytics has soared. Yet only fewer insights are immediately actionable. Therefore, it could be equally valuable to think about how data analytics can enhance decision-making on a broad scale. No ado, let's get into the business right away!<h3 id="what-is-data-analytics">What is Data Analytics?</h3>Data analytics involves assessing data to uncover facts, find patterns, and extract insights. Data analytics is often referred to as business analytics in the business world. The process uses many frameworks, tools, and software to analyze data. Examples of these tools and technology are Google Charts, Microsoft Excel, Power BI, Infogram, Data Wrapper, Zoho Analytics, and Tableau. By using these tools, you can explore data in different ways and create visualizations that help illustrate your point.<h3 id="various-types-of-data-analytics">Various Types of Data Analytics</h3><h3 id="1-descriptive-analytics">1. Descriptive Analytics</h3>A descriptive analysis is the most straightforward analytics method and the foundation for all other types. You can identify trends in raw data and succinctly explain 'what happened or what is happening'.<h3 id="2-diagnostic-analytics">2. Diagnostic Analytics</h3>Diagnostic Analytics evaluates "why did this happen?" The process compares coexisting trends and movements. It uncovers correlations between variables and pinpoints causal relationships where possible. Diagnostic analytics gets to the root of an issue.<h3 id="3-predictive-analytics">3. Predictive Analytics</h3>With predictive analytics, you can forecast future trends or events and answer - What might happen? Historical data in conjunction with industry trends can help make informed predictions for the future. It helps to formulate strategies accordingly.<h3 id="4-prescriptive-analytics">4. Prescriptive Analytics</h3>Prescriptive analytics answers - How do you proceed further? A scenario is examined from all angles with this analytics, and actionable conclusions are drawn. It directly aims to make data-driven decisions.<h3 id="who-needs-data-analytics">Who Needs Data Analytics?</h3>It is still an understatement to emphasize who needs data analytics. Anyone in business-making decisions needs a solid understanding of data analytics. Data is more accessible than ever before. By forming strategies and making decisions without considering the data, you may overlook essential opportunities or red flags. A data analytics skill can benefit professionals such as:<ul><li>Marketers - Those who need a grip on industry trends, customer data, and past performance data can plan a fool-proof marketing strategy.</li><li>Product managers - Those who need to analyze various parameters of a product and improve its sales.</li><li>Finance professionals - Where historical performance data helps to forecast a business's financial trajectories</li><li>Human resources - Those responsible for gathering insights into the opinions, motivations, and behaviors of employees and matching those insights with industry trends to bring about meaningful changes in the workplace</li></ul>Every industry needs insightful data to change the whole business game, data-driven decision-making, and better business outcomes. So, what is this data-driven decision-making?<h3 id="data-driven-decision-making">Data-driven Decision Making</h3>Data-driven decision-making is an approach to using data and extracting insights to determine the best course of action. In business, it takes many forms. For example, a business might:<ul><li>Collect survey responses</li><li>Conduct user testing</li><li>Launch a new product or service</li><li>Analyze shifts in demographic data to find opportunities</li></ul>All this happens over data that can pull some trends, give visibility, and act as an element bridging those gaps strategically. How exactly that happens will depend on numerous factors, like business goals, types of data, the quality required, and accessibility.Data collection and analysis play an integral part in businesses. But with quintillion bytes of data produced every day across the globe, businesses have never had it easier to capture, process, and interpret data into real, actionable insights. Although data-driven decision-making has been around for centuries, it is a relatively new phenomenon.<h3 id="top-three-examples-of-data-driven-decision-making">Top Three Examples of Data-Driven Decision-Making</h3>Data enables today's most prosperous and largest organizations to make high-impact business decisions. Consider references of these well-known businesses to better understand the role of data analytics:GoogleGoogle pays close attention to "people analytics." As part of those initiatives, Project Oxygen, Google evaluated performance reviews of more than 10,000 employees and compared them with retention rates. As a result of this information, Google developed training programs to develop the competencies of high-performing managers.StarbucksStarbucks closed hundreds of locations in 2008, and Howard Schultz, then-CEO, promised to analyze future store locations. With the help of a location-analytics company, Starbucks identifies ideal store locations based on demographics and traffic patterns. It also consults regional teams before making decisions. It helped determine whether an investment in a particular location would succeed.AmazonAmazon uses data to find suitable product recommendations for the user base. Amazon's recommendation engine works with machine learning and data analytics. It pulls insights into previous purchases and buying behavior. McKinsey reported that, in 2017, 35% of Amazon's consumer purchases were the outcome of the recommendation tool.<h3 id="benefits-of-data-analyticsa-strategic-move">Benefits of Data Analytics - A Strategic Move</h3>With such examples on your table, let's reinforce why making a move towards integrating data analytics into businesses can give an edge in transformation.<h3 id="make-smarter-decisions">Make smarter decisions</h3>Data plays many roles. It can be a product launch, marketing campaign, launching a new store, or something else entirely. With efficient data collection and analysis, you can confidently make business decisions. Additionally, it helps to benchmark your current strategy, providing a better understanding of the impact of any future decision. Furthermore, data is logical and concrete than instincts. You can make more objective business decisions. It allows you to commit to a vision or strategy without thinking twice fully. Moreover, it controls the data to monitor flaws and measure its accuracy easily, which is challenging in traditional methods.<h3 id="take-advantage-of-customer-patterns">Take advantage of customer patterns</h3>Selling the right products to the right people is the first step in business. Here comes the need for business analytics. In this way, your business can ensure that it offers the right products and services. How can Data Analytics help with that?<ul><li>The first step is segmentation. The data is split into multiple categories using demographics, attitudes, and behavior.</li><li>The second step is forecasting. Predict specific patterns in the segmented category to determine the future of a product or service ahead.</li><li>The third step is pricing. Data gives an ability to understand the market value for a product or service and helps to set the right pricing.</li><li>The last step is customer satisfaction?data with the three steps mentioned above to meet customer expectations.</li></ul><h3 id="a-performance-driver">A performance driver</h3>Data analysis is a pivotal game-changer internally within a complex business scenario to make an informed decision for a holistic improvement. Operational costs, workforce planning, product development are the best examples. Streamlining these business operations will make them more time-efficient. An improved process results in enhanced profit margins.<h3 id="improved-risk-mitigation">Improved risk mitigation</h3>A strong reason businesses need a strategic data analytics team is to make better risk management decisions. A vast amount of unstructured data risks making a wrong decision unless adequately analyzed. Therefore, effective data analytics will predict risk and improve decision-making. It's also possible to create an actionable plan.<h3 id="end-note">End Note</h3>Data analytics is a disruptive technology. It's time for businesses to keep their systems up and running for a glass-breaking transformation. The reason - data is an integral part of making business decisions in today's market. Identify new opportunities with a significant competitive advantage and boosts the confidence to brace the digital disruption. Have you considered incorporating data analytics yet? You're just one thought away that leads to sustained success.

Sai Srivani Mandapaka

Data Analytics & Decision Making - A Strategic Move

Healthcare data analytics has become a vital part of healthcare, and in <a href="https://www.marketwatch.com/press-release/big-data-analytics-in-healthcare-market-is-anticipated-to-reach-at-a-cagr-of-124-globally-with-top-countries-data-analysis-by-trends-growth-and-forecast-2021--2027-2021-06-04?tesla=y">2021, the projected revenue for this industry is close to $9 billion</a>. Yes, you read that right. $9 billion. It?s only projected to increase from here on forth and could potentially rake in $20 billion by the year 2027. If this isn?t proof that it?s one of the most trending topics in healthcare, we don?t know what is!Data analytics in healthcare is not a new phenomenon but rather years of research that has culminated in intelligent systems being used across the globe. Without healthcare data analytics, we wouldn?t be able to retrieve our records at our whims and fancies. We won?t be able to diagnose complex diseases like pancreatic cancer in its early stages, and we definitely will not be able to get personalized plans for our treatment.I mean, let?s think about the time when the COVID-19 pandemic hit us. We wouldn?t identify and isolate people (contact tracing) unless we had data about who was infected. How do you think we identified hotspots? What do you think your e-vaccination certificate is all about? They?re all examples of the use of data analytics in healthcare! In 2020, <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4341817/pdf/13755_2013_Article_14.pdf">59% of people were happy to use AI-based devices</a> to diagnose their health issues, and with time, it?s increased.But how has healthcare data analytics evolved over time? Let?s break it down for you.<h2 id="what-is-healthcare-data-analytics">What is Healthcare Data Analytics?</h2>So, what is healthcare data analytics? Simply put, it?s a field that involves using data to make informed decisions or how we popularly know it now as data-driven decisions. Big data refers to the sheer volume of the data generated and our inability to store them physically. Data can be in any form?text, audio, video, log files, or even annotated records. If the data contributes to the decision-making process, it is considered relevant. It?s what we know as an Electronic Health Record or EHR.<figure class="kg-card kg-image-card kg-width-wide kg-card-hascaption"><a href="https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0217-0"><img src="https://lh4.googleusercontent.com/PFhUI0TSHBIevo0V116N58vq95xTOp_XVcW8tyCn6es_WS1DkLvxlb-42RR70GN_xlXbC9HJk8WDZl4sZ9Va7Gx0GytENx5k4exsR-8JY2H8fl9eucaB8DTH4IZ7ug6fpa5VBjce" class="kg-image" alt loading="lazy"></a><figcaption>The components of healthcare data analytics; Source: Dash et al., 2019</figcaption></figure>It?s has a significant impact on the healthcare industry. For example, it benefits doctors by allowing them to look at a problem from various perspectives and make better decisions for their patients. From the <a href="https://healthinformatics.uic.edu/blog/how-health-care-analytics-improves-patient-care/">patient?s perspective, they receive incredibly personalized plans with reduced costs</a> rendered impossible before big data analytics. From the administrative perspective, it has simplified the entire process. Instead of wasting time making manual written entries, they can focus on actually running the healthcare center.<h2 id="use-of-predictive-modeling-in-healthcare">Use of Predictive Modeling in Healthcare</h2>If you go to see, medicine is either about preventing health issues or treating health issues based on scientifically backed data. So, it?s not surprising that the next step in healthcare data analytics was predictive modeling. Doctors are humans, and there are only so many avenues they can think of at the end of the day. Predictive data analytics takes that burden off their shoulders and helps them make data-based decisions. It?s widespread to see the use of AI algorithms and the Internet of Things (IoT) to combine data from the past and present to find the best fitting solution. A few examples of such algorithms include:Artificial Neural Networks: Models the connections within our brain and is used for diagnostic purposes to identify patterns and develop patient-specific solutions.Logistic Regression: Used to determine potentially risky events through a series of analysis.Random Forest: Used to draw multiple trees or, in this case, possibilities of development of disease based on their medical history.It has completely revolutionized the healthcare space, and now that we see it, there?s no going back.<h2 id="how-has-data-analytics-in-healthcare-evolved-over-time">How Has Data Analytics in Healthcare Evolved Over Time?</h2>It was not uncommon to see that every little medical record would be handwritten and stored decades ago. Even now, doctors are required to put in their ?notes? which are filed in the patient?s folder. But that was a dangerous way to store data. Why, you ask? Simply because it could get stolen or destroyed easily. Just imagine that there?s a fire in the hospital, and suddenly, years of data are lost in seconds. This is precisely what healthcare data analytics aims to solve.The complete digitization of the medical workflow has allowed doctors and the administration alike to focus on much more important things. While initially, many companies were focused on creating the best technologies that can detect the most intriguing details within the shortest amount of time, something was amiss. Soon, in the 2010s, it was figured that while most of the big data in healthcare data analytics was solving a particular problem, the end user ? the patient was not necessarily reaping the intended benefits. Value-based<a href="https://healthitanalytics.com/news/is-healthcare-any-closer-to-achieving-the-promises-of-big-data-analytics"> healthcare has become a buzzword now,</a> with many healthcare management systems focusing on the patient?s perspective. For example, there are many applications to book an appointment instead of waiting for hours on end to get one; you can voice your concerns about the treatment and service that you receive; hospitals can follow up using automated systems which saves hours on their part and the best of all, online customer relationship management (CRM) powered by Artificial Intelligence (AI) allows us to map a patient?s journey within the system,A refreshing change, we must say!Another critical aspect that worries most about healthcare data analytics is cybersecurity. Initially, when scientists built these systems, security measures were sub-par at best. In 2021, the going rate for <a href="https://www.securelink.com/blog/healthcare-data-new-prize-hackers/">one EHR is $250</a>. That?s a massive amount to pay for one record, but it also goes to show why healthcare data is so prone to be attacked. It?s one of the reasons why most hospital systems prevent the use of third-party apps and have increased network visibility. The increasing focus on security has only helped the overall image of healthcare data analytics improve over time.<h2 id="the-covid-19-push-to-big-data-analytics-in-healthcare">The COVID-19 Push to Big Data Analytics in Healthcare</h2>Like every other technology, big data analytics also got the much-needed push during the pandemic. The true power of data analytics in healthcare was shown when <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8037067/">umpteen papers were published</a> describing the various ways in which the pandemic was being fought.From the diagnostic perspective, it was used for pre-symptomatic detection of the infection, tracking the disease in real-time also monitoring discharged patients in real-time. Epidemiologists also used it to identify hotspots and the spread of variants through the use of genetic repositories. Scientists eventually used this information to identify risks scores and conduct contact tracing activities all across the globe. It helped epidemiologists and helped hospital administrators make data-driven decisions on how and when to quarantine, what their current capacity is, what their resource capacity was like, and which patients they need to prioritize. It <a href="https://healthitanalytics.com/news/3-ways-healthcare-is-using-predictive-analytics-to-combat-covid-19">most definitely saved millions of lives </a>globally by using automated software powered by big data and AI.<h2 id="what-does-the-future-hold">What Does The Future Hold?</h2>We must work on improving the current systems to ensure better output shortly. Some initiatives include providing more data to algorithms for precision analysis; developing error-free and flawless algorithms that prevent bias; creating tighter security networks to protect data; and focusing on using omics data to provide personalized medical recommendations.All in all, the future of healthcare data analytics is on an upward trend and will only impact more lives soon. The implementation of big data analytics in healthcare has really given a new meaning to the phrase ?Prevention is better than cure.?

The Evolving Role of Data Analytics in the Healthcare Industry

<figure class="kg-card kg-image-card"><img src="https://iaxds62ruoojbg1k4180.cleaver.rocks/images/posts/2454fc7e1df2223fc2417108aeb9a0b9.png" class="kg-image" alt loading="lazy" width="643" height="321" srcset="https://techfastly.com/content/images/size/w600/2020/10/image.png 600w, https://iaxds62ruoojbg1k4180.cleaver.rocks/images/posts/2454fc7e1df2223fc2417108aeb9a0b9.png 643w"></figure>Web analytics evaluates both website and web marketing actions performances. It affords to understand how many users visit a website when they visit it and for how long they browse the website, which are the sources that carry the most traffic and more. There are several web analytics tools, and Google Analytics is always the first choice. Not only because it's free, but it provides lots of features that help to test, track, and measure customer behavior on a website.Let's give a short checklist on how to choose a useful analytics tool:-List the goals that need to be achieved from the information available on the tool. Check whether the tool can achieve the goals.-Check if the features match your web analytics needs. Check if it is easy to understand and use.-Check if it fits your budget, and it is in line with your expected ROI.-Check if it has a free-trial period.What is Google Analytics?Google Analytics is a free web analytics tool offered by Google that allows you to aggregate, filter, and visualize data to analyze how users interact with a website. It's a tracking software of people browsing a website retrieving data with valuable insights into user behavior, interests, and more to take action to improve a product or a service.<figure class="kg-card kg-image-card"><img src="https://iaxds62ruoojbg1k4180.cleaver.rocks/images/posts/fffe2aca5fd4800e4e88c24fbf4e0ca3.png" class="kg-image" alt loading="lazy" width="594" height="496"></figure>Why Digital Analytics?A web analytics tool's task is to make easily accessing data without having to read through online sales records, count how many times a page has been shared, or count comments on each post.People usually purchase goods in stages. There are different stages that describe customer interactions. A basic purchase includes the following steps:- Acquisition when users build awareness and interest.- Behaviour when users engage with the business.- Conversion when a user becomes a customer.In the offline world, this process can be hard to measure. But in the online world, there is the opportunity to measure several steps of the purchase using digital analytics, tracking what online behavior led to purchases and exploiting that data to make decisions about how to reach new and existing customers.How does Google Analytics collect data?The website data collection begins with a snippet of JavaScript tracking code that's included on every webpage where information is collected. The tracking code trails each user's interaction on the website. This code uses the domain of the website to track and put it in the analytics reports. In this way, Google Analytics will drop a cookie in the user's browser for that website and easily track the trail on a single website URL domain by default.With each user interaction on the website, the analytics tracking code sends a URL string, called "hit," with useful information about users.The URL string contains some useful data like:- the language of the user's browser- the name of the page visited- the screen resolution of the user's device- the Analytics ID that associates that hit to the right analytics accountThe hit will also include other parameters like a randomly-generated user identifier. This will allow Google Analytics to understand new and returning users.The three most common types of hits are:<ul><li>"pageview" hits</li><li>"event" hits</li><li>"transaction" hits</li></ul>A "pageview" hit is triggered every time a user opens a webpage with the tracking code. This is the most common type of hit sent to Google Analytics.An "event" hit is triggered every time a user interacts with a particular element on the website. Event hits send four parameters of data in the URL: event action, category, label, and value. It can be used to categorize interactions in reports that are specific to the website.A "transaction" hit can pass data to Google Analytics about ecommerce purchases.Setting up the Enhanced Ecommerce function, you can also pass additional ecommerce data like product category, whether items have been added or removed from a shopping cart, and how many times users viewed a product on a website.Additional hits such as "social hits" can pass likes, shares, or tweet data; and "page timing hits" that allow you to report on page timings.Google Analytics widens those data using other sources such as IP address, server-log files, and other ad-serving data. And in this way, it can understand things like:- a user's location- specifics about user's browser- user's age and gender- source/medium that referred users to a websiteOnce the hit is sent to Google Analytics and combined with additional data, they are ready for processing by the Analytics servers.<figure class="kg-card kg-image-card"><img src="https://iaxds62ruoojbg1k4180.cleaver.rocks/images/posts/40a35156950a37a80bdec8ef730bc885.png" class="kg-image" alt loading="lazy" width="643" height="331" srcset="https://techfastly.com/content/images/size/w600/2020/10/image-2.png 600w, https://iaxds62ruoojbg1k4180.cleaver.rocks/images/posts/40a35156950a37a80bdec8ef730bc885.png 643w"></figure>How to set up Google Analytics?Let's look at how Google Analytics accounts are organized.All company accounts can be grouped under an "Organization," which is optional. This allows us to manage multiple Google Analytics accounts under one grouping. Large businesses could have multiple accounts, while small-sized companies generally use one account. When an account is generated, it automatically generates a property and a view for that account. But each Analytics account can have multiple properties and each property can have multiple views. This organizes data collection in the best way for a business.The Google Analytics account determines how data is collected from websites and manages who can access that data. Generally, the best choice is to create separate accounts for distinct businesses or business units.Each account has at least one "property," and each property can collect data independently of each other using a unique tracking ID that appears in the tracking code. Just as each account can have multiple "properties," each property can have multiple "views." The Running Filters feature determines what data to include in the reports for each view.The view level also affords you set Google Analytics "Goals." Goals are a way to track business objectives from the website. For instance, a task could be how many users signed up for an email newsletter or how many users purchased a product. Pay attention when setting up your accounts, properties, and views because you can't change data once it's been collected and processed.Start your <a href="https://analytics.google.com/analytics/academy/">Google Analytics</a> trial.<figure class="kg-card kg-image-card"><img src="https://iaxds62ruoojbg1k4180.cleaver.rocks/images/posts/c3919dc4b0298fccb1b21b0cbea0d0da.png" class="kg-image" alt loading="lazy" width="643" height="279" srcset="https://techfastly.com/content/images/size/w600/2020/10/image-3.png 600w, https://iaxds62ruoojbg1k4180.cleaver.rocks/images/posts/c3919dc4b0298fccb1b21b0cbea0d0da.png 643w"></figure>

Claudio G. Giancaterino

How to collect data using Google Analytics

Data is on track to completely restructure and reform the agricultural market. Economic analysts predict that the global agriculture analytics market will jump from $585 million in 2018 to a whopping $1.236 billion in 2023. The fuel behind this drastic increase is the precision that the agriculture market has developed. As more farmers adopt technical solutions, like advanced analytics, artificial intelligence, and machine learning, the better they can track supplies, evaluate probability, generate predictive models, and increase yields. There are four ways data analytics can contribute to changing the way we approach agriculture.Food demand around the world is set to double in the next thirty years, and it will be up to farmers and their suppliers to exploit data and innovation to increase productivity to feed an ever-growing population. Farmers equipped with soil sensors, GPS tractors, and local weather apps can implement agricultural practices with unforeseen insights. They can better manage their resources ranging from seed, fertilizer, and pesticides, and at the same time, increase their productivity. One case study cites farmers using soil probes and sensors attached to their equipment and machinery to scan the farm as they move. The portal, the sensors are connected to will compare their data to other farmers, and offer suggestions based on various conditions. The journal of Nature found that ending nutrient overuse, in addition to sustainable intensification, could increase the production of corn and grains by upwards of 30%.Managing environmental challenges, particularly climate change, is another threat that data analytics can confront. Data-driven solutions can help farmers traverse shifts in environmental conditions as well as track crop health and various natural events such as pest or disease-based issues. By arming themselves with data, farmers can adjust their techniques to get ahead of adverse events and to alleviate the damages caused by environmental threats. According to Tufts University, by using smarter farming techniques, the industry could rake in $2.3 trillion in savings and business opportunities yearly. A not-so-insignificant chuck of those savings, $250 billion will come from artificial intelligence and analytics. These savings can act as a buffer against competition and a source for reinvestment in their farms.Farming, like most other businesses, is subjected to supply chains, and agriculture?s supply chain is predicted to have some of the most drastic changes. After the application of analytics, farmers will have a much simpler time tracing their products in the supply chain, and the retailers and distributors will be better prepared to customize their services to the needs of the agricultural market.The addition of analytics to agriculture is one of the essential technological additions in the last decade. As climate change threatening the world?s food supply, farmers need, now more than ever, to adapt to become more fruitful, economical, and they can do so by revolutionizing the agricultural sector.

Techfastly Team

Data Analytics Transforming Agriculture

Healthcare professionals are in a difficult position where the data that flows through their systems can mean life or death for the people they serve. Traditional business intelligence tools often fail to make data easily accessible and are unsuccessful at displaying the data in a meaningful way. The old way of doing things afforded multiple different systems across the data network which at best still didn?t solve the data flow problem. Currently, new progressive accountable models, particularly the Patient-Centered Medical Homes and Accountable Care Organizations, are making the sharing of patient data more urgent than ever.An Integrated Delivery Network can meet these challenges by implementing a strategic informatics platform. One such IDN was designed to take advantage of the current systems and the expansive quantity of data within them, and Active Analytics is the tool it uses to function. This IDN can collect data from multiple different systems, one of the pervasive problems while sharing that data in a secure manner. The data is also presented in a meaningful way with instantaneous analytics which will permit more informed decisions. Not only can an IDN cover the gaps left behind by the old intelligence tools but they eliminate the need to connect the multiple systems, relieving the resources they used. The advanced data processing and analytics can administer the unstructured data as well, and can take those reports, notes, or other written information and present it in a user-friendly way. The real-time analytics can also launch processes, alerts, and recommendations for timely action or inspire immediate action.Active Analytics, in our sample IDN, is essential because it pulls from a single centralized data store that endlessly pulls and optimizes data for analysis. The IDN can use Active Analytics to be fed data instantaneously for real-time analysis.

Driving Healthcare Outcomes Using Active Analytics

Big Data Analytics has significantly changed the face of business intelligence in the last decade. The big companies have been spending a fortune on technologies to utilize the enormous amount of data that consumers leave behind every second. Big data analytics has already made a big-shift in business, the field is continuously evolving, and innovations are marked every other day. Let?s have a look at what it holds for the year 2020.The increasing data is a big problem; we now have voice data, sensor data, mobile data, etc. All these data are getting bigger; it almost doubles every six months. The data can be unstructured and needs to be stored. The cloud-based technologies are already popular and is expected to rise by 60% in the coming years.Many of the more prominent companies are already using IoT technology to perform their daily operations using data analysis. In the year 2020, the IoT devices will be installed on many devices to collect data for analysis. It would not only provide relevant data but would also offer transparency.The Artificial Intelligence (AI) and Machine Learning (ML) algorithms would take the lead in data-driven organizations to automate the ease of the data governance process. The algorithms would be used to automate the processes of data engineering, data analysis and data sorting.It is expected that 90% of the larger companies will generate some revenue from the Data-as-a-service (DaaS) in 2020. DaaS is a cloud-based technology that allows sharing and accessing large files in real-time using the internet. It will improve the productivity within an organization considering its reliability and sharing speed. Augmented Analytics has already shown steady growth. It combines AI and MI technologies to automate many analytical processes and build models, making it easier to use.Data security and privacy protection would be a significant concern for various industries. The use of backups and snapshots to ensure data safety and reliability will increase manifolds. Though most of the data is private and confidential, the use of back-up systems will increase.A combination of all these technologies will undoubtedly develop more intelligent and smarter systems that will be customer-oriented.

Big Data Trends in 2020

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