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How AI Uses Big Data

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- Overview

How does AI work with big data? Artificial intelligence (AI) makes big data analytics easier by automating and enhancing data preparation, data visualization, predictive modeling and other complex analytical tasks that would otherwise be labor-intensive and time-consuming.

Big data and AI have a synergistic relationship. Big data provides the vast amounts of data that AI needs to learn and improve decision-making. AI uses big data to identify trends and hidden patterns to make predictions about the future.

Big data is a large amount of diverse dynamic data that can be mined for information. AI is a set of technologies that enable machines to simulate human intelligence. Machine learning is a subset of AI that combines algorithms, statistical models, and data to perform specific tasks. 

Big data and AI are interrelated in the following ways:

  • Research and technological innovation: Big data technology uses AI theories and methods.
  • Data preparation: AI tools make data preparation easier.
  • Cognitive capabilities: AI solutions can detect patterns and provide cognitive capabilities to large amounts of data.

Some applications of AI include:

  • Personalized shopping
  • Artificial intelligence assistant
  • Prevent fraud
  • Automate administrative tasks to help educators
  • Create smart content
  • Voice assistant
  • Personalized learning
  • Self-driving cars


- Data Science, Big Data, and AI

Data science is the process of extracting raw and unstructured data and transforming it into structured and filtered data by combining scientific methods and mathematical formulas. It uses a variety of tools and techniques to discover business insights and turn them into actionable solutions. Data scientists, engineers, and executives perform steps such as data mining, data cleaning, data aggregation, data manipulation, and data analysis.

Experts define data science as the interdisciplinary field of using scientific methods, processes, algorithms and systems to extract data. At the same time, they define artificial intelligence as the theory and development of computer systems capable of performing tasks that would normally require human intelligence. 

AI is a subset of data science and is often considered a representation of the human brain. It uses intelligence and intelligent systems to provide business process automation, efficiency and productivity. Here are some real-life AI applications: chatbot, voice assistance, Automatic recommendation, language translation, Image Identification.

Using data science and AI in companies can help them achieve incredible goals. It can also trigger automation and efficiencies in processes that require more labor and hours. Therefore, many industries have merged data science and artificial intelligence.


- How AI Fits with Big Data

The relationship between AI and Big Data is that of "give and take". AI uses the data sets to get better at the decision-making process, while Big Data uses smart AI systems for better data analysis. The more data we put through the machine learning models, the better they get. It’s a virtuous cycle. 

There’s a reciprocal relationship between big data and AI: The latter depends heavily on the former for success, while also helping organizations unlock the potential in their data stores in ways that were previously cumbersome or impossible.

Big data is definitely here to stay, and AI will be in high demand for the foreseeable future. Data and AI are merging into a synergistic relationship, and AI is useless without data, and data cannot be mastered without AI. By combining these two disciplines, we can begin to see and predict future trends in business, technology, commerce, entertainment, and everything in between.


- Management and Technical Challenges in AI

Currently, enterprises have implemented advanced artificial intelligence (AI) technology to support business process automation (BPA), provide valuable data insights, and promote employee and customer engagement. 

However, developing and deploying new AI applications poses several management and technical challenges. Management challenges include identifying appropriate business use cases for AI applications, lack of expertise in applying advanced AI technologies, and insufficient funding. 

Regarding technology challenges, organizations continue to encounter outdated existing information technology (IT)/information systems (IS) facilities; the difficulty and complexity of integrating new AI projects into existing IT/IS processes; AI infrastructure Immature and underdeveloped; Insufficient data volume and low learning quality requirements; Increasingly serious security issues/threats; and inefficient data preprocessing assistance.

- Data Trails and Data Environment

Data ecosystems are for capturing data to produce useful insights. As customers use products–especially digital ones - they leave data trails. Companies can create a data ecosystem to capture and analyze data trails so product teams can determine what their users like, don’t like, and respond well to. Product teams can use insights to tweak features to improve the product. 

Ecosystems were originally referred to as information technology environments. They were designed to be relatively centralized and static. The birth of the web and cloud services has changed that. Now, data is captured and used throughout organizations and IT professionals have less central control. 

The infrastructure they use to collect data must now constantly adapt and change. Hence, the term data ecosystem: They are data environments that are designed to evolve. There is no one ‘data ecosystem’ solution. Every business creates its own ecosystem, sometimes referred to as a technology stack, and fills it with a patchwork of hardware and software to collect, store, analyze, and act upon the data. 

The best data ecosystems are built around a product analytics platform that ties the ecosystem together. Analytics platforms help teams integrate multiple data sources, provide machine learning tools to automate the process of conducting analysis, and track user cohorts so teams can calculate performance metrics. 


Big Data and AI_100823A
[Big Data and AI - ISM]

- 6 Ways AI Fuels Better Insights

How is AI - and its prominent discipline, machine learning – helping deliver better business insights from big data? Let’s examine some ways – and peek at what’s next for AI and big data analysis. Let’s look at how they are connected in a detailed manner.

  • AI is creating new methods for analyzing data. Deriving insights from your data usually required a lot of manual effort. AI is creating new methods for doing so. In a sense, AI and ML are the new methods, broadly speaking. Historically, when it comes to analyzing data, engineers have had to use a query or SQL (a list of queries). But as the importance of data continues to grow, a multitude of ways to get insights have emerged. AI is the next step to query/SQL. What used to be statistical models now has converged with computer science and has become AI and machine learning.
  • AI and ML are tools that help a company analyze their data more quickly and efficiently than what could be done [solely] by employees. As a result, managing and analyzing data depends less on time-consuming manual effort than in the past. People still play a vital role in data management and analytics, but processes that might have taken days or weeks (or longer) are picking up speed thanks to AI.
  • AI and ML, among other emerging technologies, are critical to helping businesses have a more holistic view of all of that data, providing them with a way to make connections between key data sets, it’s not a matter of cutting out human intelligence and insight. Businesses need to combine the power of human intuition with machine intelligence to augment these technologies -- or augmented intelligence. More specifically, an AI system needs to learn from data, as well as from humans, in order to be able to fulfill its function. Businesses that successfully combined the power of human and technology are able to expand who has access to key insights from analytics beyond data scientists and business analysts while saving time and reducing potential bias that may result from business users interpreting data. This results in more efficient business operations, quicker insights gleaned from data and ultimately increased enterprise productivity.
  • AI and ML can be used to alleviate common data problems. The value of your data is inextricably linked to its quality. Poor quality means low (or no) value. If the data is dirty, any insights derived from it cannot be trusted, The ‘dirty’ secret of ML projects is that 80 percent of the time is spent cleansing and preparing the data. Fortunately, machine learning data can be cleansed using… machine learning!. ML algorithms can detect outlier values and missing values, find duplicate records that describe the same entity with slightly different terminology, normalize data to a common terminology, etc.
  • Analytics become more predictive and prescriptive. An ML algorithm can be taught to make a decision or take an action based on a forward-looking insight. In the past, data analytics was more postmortem than anything else: Future predictions were still essentially historical analyses. AI and ML are helping open a new front. Today, AI is moving big data decisions to points further down the timeline, in more accurate ways, by using predictive analytics. Traditionally, big data decisions were based on past and present data points, generally resulting in linear ROI. With AI, this has grown to epic and exponential proportions. Prescriptive analytics, leveraging AI, has the potential to provide company-wide, forward-looking strategic insights helping to advance the business. Using AI to make predictive or prescriptive business decisions based on inaccurate or inadequate data could have “catastrophic” outcomes. The value to the business increases with each progression through the analytics maturity model: beginning with process and data mapping, to descriptive analytics, to predictive analytics, and finally, to prescriptive analytics. 
  • What’s next for AI and big data? We’ve merely scratched the surface. If most teams are still learning to crawl (or walk), that might be OK because the combination of AI and big data is just beginning to reveal its possibilities. The future is intelligent software that leverages all of that data to solve problems and do work for us – providing context and answers rather than just nicer-looking reports. From an engineering perspective, intelligent enterprise applications will require that we connect individual AI and ML systems to other systems so that they can communicate with and learn from each other. Enterprises will finally see significant ROI from all of that data they’ve been storing.


[More to come ...]

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