Tag: Big Data

The first fundamental of Artificial Intelligence is data, with the Machine Learning models that feed on the continuously growing collections of data of different types. However, as far as it is a very significant source of information, it can be fraught with problems such as privacy limitations, biases, and data scarcity. This is beneficial in removing the mentioned above hurdles to bring synthetic data as a revolutionary solution in the world of AI.

What is Synthetic Data?

Synthetic data can be defined as data that is not acquired through actual occurrences or interactions but rather created fake data. It is specifically intended to mimic the characteristics, behaviors and organizations of actual data without copying them from actual observations. Although there exist a myriad of approaches to generating synthetic data, its generation might use simple rule-based systems or even more complicated methods, such as Machine Learning based on GANs. It is aimed at creating datasets which are as close as possible to real data, yet not causing the problems connected with using actual data.

In addition to being affordable, synthetic data is flexible and can, therefore, be applied at any scale. It enables organizations to produce significant amounts of data for developing or modeling systems or to train artificial intelligence especially when actual data is scarce, expensive or difficult to source. In addition, it is stated that synthetic data can effectively eliminate privacy related issues in fields like health and finance, as it is not based on any real information, thus may be considered as a powerful tool for data-related projects. It also helps increase the model’s ability to handle various situations since the machine learning model encounters many different situations.

Why is Synthetic Data a Game-Changer?

Synthetic data calls for the alteration of traditional methods used in industries to undertake data-driven projects due to the various advantages that the use of synthetic data avails. With an increasing number of big, diverse, and high-quality datasets needed, synthetic data becomes one of the solutions to the real-world data gathering process, which can be costly, time-consuming, or/and unethical.  This artificial data is created in a closed environment and means that data scientists and organisations have the possibility to construct datasets which correspond to their needs.

Synthetic data is an extremely valuable data product for any organization that wants to adapt to the changing landscape of data usage. It not only address practical problems like data unavailability and affordability but also flexibility, conforming to ethical standards, and model resilience. With a rising pace of technology advancements, there is a possibility of synthetic data becoming integral to building better, efficient, and responsible AI & ML models.

To Know More, Read Full Article @ https://ai-techpark.com/synthetic-data-in-machine-learning/

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In the context of the increasing prominence of decisions based on big data, companies are perpetually looking for the best approaches to effectively utilize their data resources truly. Introduce the idea of Unified Data Fabric (UDF), a new and exciting proposition that provides a unified view of data and the surrounding ecosystem. In this blog, we will uncover what UDF is, its advantages and thinking why it is set out to transform the way companies work with data.

What is Unified Data Fabric?

A Unified Data Fabric or Datalayer can be described as a highest form of data topology where different types of data are consolidated. It is an abstract view of the data accessible across all environment – on-premises, in the Cloud, on the Edge. Therefore, organizations are able to better leverage data and not micromanage the issues of integration and compatibility by abstracting over the underlying complexity through UDF.

The Need for UDF in Modern Enterprises

Today, elite business organizations are more involved in managing massive data from multiple fronts ranging from social media platforms to IoT, transactions, and others. Recent data management architectures have had difficulties in capturing and managing such data in terms of volume, variety, and velocity. Here’s where UDF steps in:

Seamless Integration: UDF complements the original set up by removing the barriers that create organizational and structural data separation.

Scalability: This makes it easy for UDF to expand with data as organizations carry out their activities without performance hitches.

Agility: It also enables an organization reposition itself rapidly when it comes to the data environment of an organization, hence it becomes easier to integrate new data sources or other analytical tools.

Unified Data Fabric for Seamless Data Access and Management

In the context of algorithmization of management and analytics-based decision making, more often than not, companies and enterprises are in a constant search for ways to maximize the value of their data. Introduce the idea of a Unified Data Fabric (UDF) – a relatively new idea that could help in achieving consistent and integrated data processing across various platforms. Let’s dive a bit deeper on understanding what is UDF, what it can bring to businesses, and why it will redefine data processing.

UDF is likely to be more significant as organizations proceed with the integration of advanced technology. The usefulness of being able to present and manipulate data as easily as possible will be a major force behind getting data back into dynamic uses whereby businesses can adapt to change and remain competitive in the market.

To Know More, Read Full Article @ https://ai-techpark.com/unified-data-fabric-for-data-access-and-management/

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Askia, can you share more about your role as Chief Growth Officer at DriveLine.ai and the key responsibilities associated with it?

In my role as Chief Growth Officer, I wear several hats, all focused on one critical goal: driving revenue growth and expansion. Through a multi-pronged approach that leverages strategic partnerships, comprehensive growth strategies, I am responsible for propelling DriveLine to market leadership.

My key responsibilities include the development of strategic partnerships and alliances, implementing comprehensive growth strategies, identifying and leveraging category and industry trends including new market opportunities, and the productization of our audience and location intelligence.

Beyond these key responsibilities, I also contribute to other areas which support our growth including working closely with our product and business development teams, to ensure alignment and collaboration across the organization.

With 17+ years of experience in consumer strategy, how has your journey shaped your approach to driving consumer behavior for brands?

Over the past 17+ years, my approach to consumer strategy has been profoundly reshaped a few times. My journey began in 2000 at KTLA-TV, where I dove headfirst into the bustling world of advertising sales, right as the digital advertising revolution converged with television. This early exposure to the nascent digital landscape, when monetization through consumer interaction was still largely uncharted territory, instilled in me a deep appreciation for innovation and a future-focused approach has become a defining characteristic of my strategic skill set ever since.

With almost two decades of experience navigating the ever-evolving media landscape, I have not only witnessed significant changes, but actively participated in shaping them. Through triumphs and setbacks, I have acquired a deep understanding of consumer behavior and the critical role it plays in successful media campaign outcomes. This valuable knowledge informs my strategic approach, ensuring that every campaign I develop is human-centered, data-driven, results-oriented, and impactful.

Can you elaborate on your future-focused approach to campaign performance and how it is applied across various client types, whether local, regional, national, or global?

Every component of advertising is related to a time period, timing and/or seasonality, making advertising campaigns intrinsically planned for the future. By focusing on the future, I help brands achieve their marketing goals in a sustainable and scalable way. By applying my future-focused approach to campaign performance, I help brands achieve success regardless of their size or location. This means focusing on long-term trends, anticipating future consumer behavior, and proactively adapting to stay ahead of the curve.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-askia-underwood/ 

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As we have entered the digital era, data and analytics strategies (D&A) have become important, as these technologies can transform any business during a massive data spike. According to global research, it was observed that around 2.5 quintillion bytes of data are produced by IT companies every day; therefore, to understand the importance of data, every employee must be data literate.

For a better understanding of data, the Chief Data Officers (CDOs) play an important role in making every employee data literate, i.e., able to understand, share, and have meaningful insight into data.  

With this mindset, organizations can seamlessly adopt emerging and existing technologies and transform their business outcomes across all departments while fostering quality decision-making, innovation, and a better customer experience. The CDOs

In this exclusive AI TechPark article, we will discuss the evolution of data literacy and how it can transform any organization into a data-literate one.

Read more about The Value of the Chief Data Officer in the Data Governance Framework

The Evolution of Data Literacy in the Technological Era

In the past few decades, data literacy has experienced a significant transformation with the introduction of new technologies and the explosion of data. This shift has ignited from traditional data analysis to a modern era of big data that has redefined the way organizations can make data-driven decisions.

To analyze data, data scientists and analysts were confined to basic statistics and simple datasets. Even to analyze the data, data professionals needed more tools, narrow, small-scale datasets, and internal data sources. However, in the late 20th century, there were a lot of technological advancements, such as the introduction of data storage, big data, and cloud computing. This helped data scientists collect and process massive amounts of data from complex, unstructured datasets that could be further analyzed for deeper insight.

Read more about Navigating the Future With the Integration of Deep Learning in Big Data Analytics

As a result of these technological advancements, the power of data has become a center point for developing strategic planning and seamlessly operating business efficiency in the IT industry. Thus, data literacy becomes equally important to developing a data-literate workforce and ensuring that professionals harness the full potential of data for competitive advantage in the data-driven landscape.

Data is necessary, empowering at both individual and organizational levels by creating a pathway to harness real-world data-driven decision-making and data-driven organizational strategy.

In an era where artificial intelligence, data analysis, machine learning, and big data are driving critical business decisions and the ability to steer through complex datasets and extract business insights, data literacy is the epitome of enhancing employability, making informed business decisions, driving innovation, and gaining a competitive edge.

To Know More, Read Full Article @ https://ai-techpark.com/understanding-data-literacy-in-the-digital-age/ 

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In the fast-growing digital world, deep learning (DL) and big data are highly used methods for data scientists. Numerous companies, such as Yahoo, Amazon, and Google, have maintained data in Exabytes, which helps generate large amounts of data with the help of big data analytics and deep learning tools and techniques.

Earlier data scientists used traditional data processing techniques, which came with numerous challenges in processing large data sets. However, with technological advancements in recent years, data scientists can utilize big data analytics, a sophisticated algorithm based on machine learning and deep learning techniques that process data in real-time and provide high accuracy and efficiency in business processes.

In recent times, it has been witnessed that DL methods are extensively used in healthcare, finance, and IT for speech recognition, learning methods in language processing, and image classification, especially when incorporated into various hybrid learning and training mechanisms for processing data with high speed.

Today’s exclusive AI Tech Park article aims to discuss integrating deep learning methods into big data analytics, analyze various applications of deep learning in big data analytics, and discuss the future of big data and deep learning.

Efficient Deep Learning Algorithms in Big Data Analytics

Deep learning is a subset of machine learning (ML), and it is considered the trendiest topic as DL is adopted in almost every field where big data is involved.

Every year, IT companies generate trillions of GBs of data, which makes extracting useful information a challenging task for them. Therefore, the answer to such a problem is deep learning, which automatically learns the hidden structure and patterns in the raw data using ML techniques.

Some deep learning models and algorithms show great potential in unleashing the complexity of patterns within big data analytics. In this section, we will take a glance at the effective ways data scientists can utilize deep learning techniques to implement big data analytics:

Preparing the Data

The initial step to implementing deep learning in big data analytics is data preparation. The quality of data used in training data learning models must be accurate to the model prepared by data scientists and IT professionals. Therefore, it is essential to ensure that the data is well structured and clean and should work as a problem solver.

To Know More, Read Full Article @ https://ai-techpark.com/deep-learning-in-big-data-analytics/

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In the fast-moving business world, IT professionals and enthusiasts cannot ignore the use of machine learning (ML) in their companies. Machine learning tends to give a better insight into improving business performance, like understanding trends and patterns that human eyes generally miss out on. Thus, Machine learning (ML) and artificial intelligence (AI) aren’t just words; rather, they have the potential to change the industry positively. Through this article, we will focus on the importance of implementing machine learning and its use cases in different industries that will benefit you in the present and future.

The Usefulness of ML in Different Industries

Machine learning is a game-changer, and let’s see here how different industries have made the best use of it:

Predictive Analytics for Recommendations

Predictive analytics are generally used to identify opportunities before an event occurs. For example, identifying the customers that have spent the most time on your e-commerce website will result in profit for your company in the long run. These insights are only possible through predictive analytics, which allows your company to optimize market spending and focus on acquiring customers that will generate profit.

 Automate Decision-making

Automated and intelligent decision-making solutions and tools can be used by you to make quick decisions for efficient teamwork. For instance, some industries require strict adherence to compliance, which can only be applied by decision-management tools that help in maintaining records of legal protocols. These tools can make quick decisions if the business fails to obey any compliance rules.

 Creating a Data-Driven Culture

Creating a data-driven culture helps in getting numbers and insights that are generated through data. A data-driven organization not only empowers your teams but also improves your decision-making efficiency and effectiveness. One such example of a data-driven culture is DBS Bank, which has embraced AI and data analytics to provide customers with personalized recommendations. This is helping the customers and the bank authorities make better financial decisions and also improving customer loyalty. By embracing a data-driven culture, DBS Bank has also invested in training employees in data analytics and big data.

Machine learning is an important tool for making automated decisions in various business processes. These models help you identify errors and make unbiased and informed decisions. By analyzing data through customer interaction, preference, and behavior, ML algorithms can help identify the correct patterns and trends, which will help your company in the long run.

To Know More, Read Full Article @ https://ai-techpark.com/ml-helps-make-decisions/ 

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Can you tell us about your background and how it led you to found Cyral?

Cyral is the intersection of my passions and proficiencies. I have been on a long entrepreneurial journey. I started out as an early hire at Aster Data, which was one of the first companies to talk about Big Data, where I ran most of the engineering team. Then I founded Instart, which was in the CDN space where we focused on managing infrastructure at cloud scale. Cyral presented itself as the intersection of these two experiences — managing data at cloud scale. When I saw that companies were moving their sensitive data off-premises to the cloud, I realized they need a different way to manage the security and governance of data, and the answer is Cyral.  

Can you explain the importance of data security governance and its impact on organizations?

The number one thing most security leaders are worried about is a data breach. Companies increasingly gather sensitive information about their customers that they are tasked with keeping out of the hands of hackers. When everything began migrating to the cloud, breaches became much more common since there are so many ways for a hacker to access a database. Data is everywhere, and there isn’t a structured enough system to protect it.

Data security governance is its own category like IT security or application security, and more organizations are finding a need to address it with a specialty team or service dedicated to protecting sensitive information.

How does Cyral’s solution differ from traditional security tools, and how does it address the challenges of securing modern cloud-based environments?

Modern technology solutions are an adaptation of the past. They either take the way a company functioned in a data center and move it to the cloud, commoditize technology from big, enterprise solutions for others, or have developers recreate the work that once belonged to an IT team. Cyral does something new.

Other security tools are not database aware and have no way of knowing what’s in a company’s database or whether a user should be allowed to access a specific field or record—it’s often all-or-nothing access. Cyral addresses this issue with its complete suite of discovery, authentication, authorization, and auditing controls. Several people within the same organization can input a query into their Cyral-protected database, and depending on their role or other defined factors, each would see a different result. In fact, Cyral is the first security solution to provide all the features of database activity monitoring (DAM), privileged access management (PAM), data loss prevention (DLP), and data security posture management (DSPM) for a company’s sensitive datasets from a single platform.

Can you discuss the role of generative AI in data security and the potential risks it poses to organizations?

Generative AI is a reality for technology, so I see it working in data security in two ways. As it stands, security products make a lot of noise. They send alerts and false positives often, driving security leaders to spend time across multiple dashboards and data streams just to understand what’s happening. I anticipate that generative AI will begin to be incorporated into security products to help reduce the noise and make security analysts more productive. It will more accurately pinpoint a threat and where it is then send security teams to the right place to investigate.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-manav-mital/ 

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Businesses today compete on their ability to quickly and effectively extract valuable insights from their data sets to produce goods, services, and ultimately–experiences. Customers make decisions on whether to buy from you or a competitor based on their experiences.

The faster you acquire insights from your data, the quicker you can enter your market. But how can you discover these insights when you are working with vast amounts of big data, various data sources, numerous systems, and several applications?

The solution is data integration!

Data Integration in a Nutshell!

Data integration is the process of combining information from many sources into a single, unified picture to manage data effectively, get an insightful understanding, and obtain actionable intelligence. It helps improve your business strategies, which would have a favorable effect on your bottom line.

Data integration solutions attempt to combine data regardless of its type, structure, or volume because data is increasing in amount, coming in various formats, and being dispersed more widely than before. Cleansing, ETL mapping, and transformation are a few of the processes that make up the integration, which starts with the ingestion procedure. Analytics technologies can finally create helpful, actionable business intelligence using data integration.

Data Integration Use Cases

Data Ingestion

Moving data to a storage place, such as a data warehouse or data lake, is a part of the data ingestion process. Ingestion involves preparing the data for a data analytics tool by cleaning and standardizing it. It can be broadcast in real-time or in batches. Building a data warehouse, data lake, or data lakehouse or moving your data to the cloud are examples of data ingestion.

Data Replication

Data is duplicated and moved from one system to another during the data replication process, for instance, from a database in the data center to a cloud-based data warehouse. As a result, accurate data is backed up and synchronized with operational needs. Replication can occur across data centers and the cloud in bulk, in scheduled batches, or in real-time.

Data Warehouse Automation

By automating the whole data warehouse lifecycle, from data modeling and real-time ingestion to data marts and governance, the data warehouse automation process speeds up the availability of analytics-ready data. It offers an effective substitute for traditional data warehouse design, as it takes less time to complete time-consuming operations like creating and distributing ETL scripts to a database server.

To Know More, visit@ https://ai-techpark.com/what-is-data-integration/ 

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