Tag: Data Management

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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Data veracity and quality are equally essential in the current world that is characterized by unbounded data generation and utilization. Analyzing the information being used in the operations of companies today, it is apparent that businesses have developed a high dependence on data, hence the need to make it accurate and reliable. Two ideas central to this effort are data governance and lineage. Data availability, usability, integrity, and security of data within an organization can collectively be referred to as data governance. Contrarily, lineage tracking refers to one’s ability to follow the life cycle of the data, starting from its source to the current status. Altogether, these practices are needed for a small business as well as for large and middle-scale enterprises, as they offer the base for managing the information and meeting the requirements.

Understanding Data Governance

What is data governance?

Data governance refers to the culture, norms, rules, and guidelines that govern the organization’s data resources. These are ownership of data, quality assurance, and governance, where components include obtaining commitments on ownership of data/information, defining data quality requirements, and ensuring the agreed data access and security provisions are in place. Because of this, data governance’s crucial function is in covering the regulatory requirements and sources of risk that need to be addressed so that better decisions can be made.

Benefits of Data Governance

Ensuring Data Quality and Accuracy: Enumerating best practices as well as standardizing procedures promotes the credibility, integrities, and sanity of the data.

Enhancing Decision-Making and Operational Efficiency: Optimization of workflow solutions improves with high-quality data, leading to overall effectiveness of operations.

Protecting Sensitive Information and Maintaining Privacy: Data governance is aimed at data security so that the organization complies with the data privacy laws and reduces the risks of data leakage.

Data governance and data lineage are crucial methods that add value to an organization through data accountability. This means that through the formulation of appropriate data governance frameworks, as well as timely and accurate lineage solutions, business value can be realized from data assets.

Evaluate your current data management state and think about implementing the data governance and data lineage management concepts for your organization’s profitable improvement.

Availing more details to more people in the future, the trends in data governance and lineage tracking will include AI and ML.

Thus, by optimizing such practices, it will be possible to manage the effectiveness of organizational data and use it as a lever for the company’s success.

To Know More, Read Full Article @ https://ai-techpark.com/optimizing-data-governance-and-lineage/

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Leslie, can you please introduce yourself and share your experience as a CEO and Founder at TurinTech?

As you say, I’m the CEO and co-founder at TurinTech AI. Before TurinTech came into being, I worked for a range of financial institutions, including Credit Suisse and Bank of America. I met the other co-founders of TurinTech while completing my Ph.D. in Computer Science at University College London. I have a special interest in graph theory, quantitative research, and efficient similarity search techniques.

While in our respective financial jobs, we became frustrated with the manual machine learning development and code optimization processes in place. There was a real gap in the market for something better. So, in 2018, we founded TurinTech to develop our very own AI code optimization platform.

When I became CEO, I had to carry out a lot of non-technical and non-research-based work alongside the scientific work I’m accustomed to. Much of the job comes down to managing people and expectations, meaning I have to take on a variety of different areas. For instance, as well as overseeing the research side of things, I also have to understand the different management roles, know the financials, and be across all of our clients and stakeholders.

One thing I have learned in particular as a CEO is to run the company as horizontally as possible. This means creating an environment where people feel comfortable coming to me with any concerns or recommendations they have. This is really valuable for helping to guide my decisions, as I can use all the intel I am receiving from the ground up.

To set the stage, could you provide a brief overview of what code optimization means in the context of AI and its significance in modern businesses?

Code optimization refers to the process of refining and improving the underlying source code to make AI and software systems run more efficiently and effectively. It’s a critical aspect of enhancing code performance for scalability, profitability, and sustainability.

The significance of code optimization in modern businesses cannot be overstated. As businesses increasingly rely on AI, and more recently, on compute-intensive Generative AI, for various applications — ranging from data analysis to customer service — the performance of these AI systems becomes paramount.

Code optimization directly contributes to this performance by speeding up execution time and minimizing compute costs, which are crucial for business competitiveness and innovation.

For example, recent TurinTech research found that code optimization can lead to substantial improvements in execution times for machine learning codebases — up to around 20% in some cases. This not only boosts the efficiency of AI operations but also brings considerable cost savings. In the research, optimized code in an Azure-based cloud environment resulted in about a 30% cost reduction per hour for the utilized virtual machine size.

To Know More, Read Full Interview @ https://ai-techpark.com/ai-tech-interview-with-leslie-kanthan/ 

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In the evolving landscape of data management, age-old approaches are gradually being outpaced to match the demands of modern organizations. Enter as a savior: Data Mesh, a revolutionary concept that modern organizations harness to reshape their business models and implement “data-driven decisions.” Therefore, understanding and implementing Data Mesh principles is essential for IT professionals steering this transformative journey.

At its core, data mesh is not just a technology but a strategic framework that addresses the complexities of managing data at scale, as it proposes a decentralized approach where ownership and responsibility for data are distributed across numerous domains.

This shift enables each domain or department to manage data pipelines, maintain and develop new data models, and perform analytics across all interconnected integrations to facilitate infrastructure and tools that empower domain teams to manage their data assets independently.

At the core of the data mesh architecture lies a robust domain team that is the powerhouse behind the creation, delivery, and management of data products. This team comprises professionals with domain-specific knowledge who will epitomize the decentralized nature of data mesh to foster greater ownership, accountability, and agility within the organization.

This AITech Park article will explore how to build a data mesh team by outlining roles and responsibilities to drive success in an organization.

Data Product Owner (DPO)

The DPO, or Data Product Manager, is an emerging role in the field of data science that manages the roadmap, attributes, and importance of the data products within their domain. The DPO understands the use cases in their domain to serve as per UX and gets acquainted with the unbounded nature of data use cases to create combinations with other data in numerous forms, some of which are unforeseen.

Data Governance Board

After infrastructure, the data governance board is a critical part of the data mesh as they oversee the enforcement of data governance policies and standards across data domains. The board represents data product managers, platform engineers, security, legal, and compliance experts, along with other relevant stakeholders, who will tackle data governance-related problems and make decisions across the various domains within the business.

Building and maintaining a data mesh team needs careful planning, strategies, and commitments to develop talents across all boards. Therefore, organizations must adopt a hybrid organizational structure so that they can establish roles and responsibilities that help drive innovation, agility, and value creation in the digital age.

To Know More, Read Full Article @ https://ai-techpark.com/data-mesh-team/ 

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As we have stepped into the digital world, data science is one of the most emerging technologies in the IT industry, as it aids in creating models that are trained on past data and are used to make data-driven decisions for the business.

With time, IT companies can understand the importance of data literacy and security and are eager to hire data professionals who can help them develop strategies for data collection, analysis, and segregation. So learning the appropriate data science skills is equally important for budding and seasoned data scientists to earn a handsome salary and also stay on top of the competition.

In this article, we will explore the top 10 data science certifications that are essential for budding or seasoned data scientists to build a strong foundation in this field.

Data Science Council of America (DASCA) Senior Data Scientist (SDS)

The Data Science Council of America’s (DASCA) Senior Data Scientist (SDS) certification program is designed for data scientists with five or more years of professional experience in data research and analytics. The program focuses on qualified knowledge of databases, spreadsheets, statistical analytics, SPSS/SAS, R, quantitative methods, and the fundamentals of object-oriented programming and RDBMS. This data science program has five trackers that will rank the candidates and track their requirements in terms of their educational and professional degree levels.

IBM Data Science Professional Certificate

The IBM Data Science Professional Certificate is an ideal program for data scientists who started their careers in the data science field. This certification consists of a series of nine courses that will help you acquire skills such as data science, open source tools, data science methodology, Python, databases and SQL, data analysis, data visualization, and machine learning (ML). By the end of the program, the candidates will have numerous assignments and projects to showcase their skills and enhance their resumes.

Open Certified Data Scientist (Open CDS)

The Open Group Professional Certification Program for the Data Scientist Professional (Open CDS) is an experienced certification program for candidates who are looking for an upgrade in their data science skills. The programs have three main levels: level one is to become a Certified Data Scientist; level two is to acquire a Master’s Certified Data Scientist; and the third level is to become a Distinguished Certified. This course will allow data scientists to earn their certificates and stay updated about new data trends.

Earning a certification in data science courses and programs is an excellent way to kickstart your career in data science and stand out from the competition. However, before selecting the correct course, it is best to consider which certification type is appropriate according to your education and job goals.

To Know More, Read Full Article @ https://ai-techpark.com/top-5-data-science-certifications-to-boost-your-skills/ 

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As we have stepped into the realm of 2024, the artificial intelligence and data landscape is growing up for further transformation, which will drive technological advancements and marketing trends and understand enterprises’ needs. The introduction of ChatGPT in 2022 has produced different types of primary and secondary effects on semantic technology, which is helping IT organizations understand the language and its underlying structure.

For instance, the semantic web and natural language processing (NLP) are both forms of semantic technology, as each has different supportive rules in the data management process.

In this article, we will focus on the top four trends of 2024 that will change the IT landscape in the coming years.

Reshaping Customer Engagement With Large Language Models

The interest in large language models (LLMs) technology came to light after the release of ChatGPT in 2022. The current stage of LLMs is marked by the ability to understand and generate human-like text across different subjects and applications. The models are built by using advanced deep-learning (DL) techniques and a vast amount of trained data to provide better customer engagement, operational efficiency, and resource management.

However, it is important to acknowledge that while these LLM models have a lot of unprecedented potential, ethical considerations such as data privacy and data bias must be addressed proactively.

Importance of Knowledge Graphs for Complex Data

The introduction of knowledge graphs (KGs) has become increasingly essential for managing complex data sets as they understand the relationship between different types of information and segregate it accordingly. The merging of LLMs and KGs will improve the abilities and understanding of artificial intelligence (AI) systems. This combination will help in preparing structured presentations that can be used to build more context-aware AI systems, eventually revolutionizing the way we interact with computers and access important information.

As KGs become increasingly digital, IT professionals must address the issues of security and compliance by implementing global data protection regulations and robust security strategies to eliminate the concerns.  

Large language models (LLMs) and semantic technologies are turbocharging the world of AI. Take ChatGPT for example, it's revolutionized communication and made significant strides in language translation.

But this is just the beginning. As AI advances, LLMs will become even more powerful, and knowledge graphs will emerge as the go-to platform for data experts. Imagine search engines and research fueled by these innovations, all while Web3 ushers in a new era for the internet.

To Know More, Read Full Article @ https://ai-techpark.com/top-four-semantic-technology-trends-of-2024/ 

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Data has always been at the core of a business, which explains the importance of data and analytics as core business functions that often need to be addressed due to a lack of strategic decisions. This factor gives rise to a new technology of stitching data using data fabrics and data mesh, enabling reuse and augmenting data integration services and data pipelines to deliver integration data.

Further, data fabric can be combined with data management, integration, and core services staged across multiple deployments and technologies.

This article will comprehend the value of data fabric architecture in the modern business environment and some key pillars that data and analytics leaders must know before developing modern data management practices.

The Evolution of Modern Data Fabric Architecture

Data management agility has become a vital priority for IT organizations in this increasingly complex environment. Therefore, to reduce human errors and overall expenses, data and analytics (D&A) leaders need to shift their focus from traditional data management practices and move towards modern and innovative AI-driven data integration solutions.

In the modern world, data fabric is not just a combination of traditional and contemporary technologies but an innovative design concept to ease the human workload. With new and upcoming technologies such as embedded machine learning (ML), semantic knowledge graphs, deep learning, and metadata management, D&A leaders can develop data fabric designs that will optimize data management by automating repetitive tasks.

Key Pillars of a Data Fabric Architecture

Implementing an efficient data fabric architecture needs various technological components such as data integration, data catalog, data curation, metadata analysis, and augmented data orchestration. Working on the key pillars below, D&A leaders can create an efficient data fabric design to optimize data management platforms.

Collect and Analyze All Forms of Metadata

To develop a dynamic data fabric design, D&A leaders need to ensure that the contextual information is well connected to the metadata, enabling the data fabric to identify, analyze, and connect to all kinds of business mechanisms, such as operational, business processes, social, and technical.

Convert Passive Metadata to Active Metadata

IT enterprises need to activate metadata to share data without any challenges. Therefore, the data fabric must continuously analyze available metadata for the KPIs and statistics and build a graph model. When graphically depicted, D&A leaders can easily understand their unique challenges and work on making relevant solutions.

To Know More, Read Full Article @ https://ai-techpark.com/data-management-with-data-fabric-architecture/ 

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Neda, please share some key milestones and experiences from your professional journey that have shaped your perspective and approach as Chief Product Officer at Stibo Systems.

I have the honor of mentoring a couple of young leaders, and they ask this question a lot. The answer I always give is that I approach each day as an opportunity to learn, so it’s difficult to pinpoint a specific milestone. However, there have been some crucible moments where I made radical decisions, and I think those moments have influenced my journey. One of them was my shift towards computer science. Despite having a background in linguistics and initially aspiring to be a teacher, I took a complete shift and decided to explore computer science. Programming was initially intimidating for me, and I had always tried to avoid math throughout my student life. I saw myself more as an art and literature person, and that gutsy shift turned out to be a great decision in the long term. The decision was made by me, but it wouldn’t have turned into a success without support from my mentors and leaders – it’s super important to have champions around you to guide you, especially early in your career.

Another significant moment was when I accepted a consulting job that involved phasing out legacy systems. This required negotiating with users who would lose functionalities they had been using for years. These conversations were often challenging, and I was tempted to quit. However, I made the decision to stay and tackle the problem with a more compassionate approach towards the application users. It was during this time that I truly understood the nature of change management in the product development process. People find it difficult to let go of their routines and what has made them successful. The more successful users are with their apps, the less likely they are to embrace change. However, sometimes solutions become outdated and need to be replaced – plain and simple. The challenge is how to build a changing product while ensuring that users come along. This story applies to Stibo Systems. We have been around for over 100 years and have managed to transform our business. Stibo Systems is a perfect example of how to build lasting products, be open to change and transformation and make sure you aren’t leaving any customers behind.

Could you provide an overview of Stibo Systems’ mission and how it aligns with the concept of “better data, better business, better world”?

Our heritage extends far back, but we are a cutting-edge technology company. We specialize in delivering data management products that empower companies to make informed decisions, resulting in remarkable outcomes. This approach not only contributes to our sustainable growth but also supports our profitability, allowing us to reinvest and expand.

Our mission statement encapsulates our business ethos – one with a strong sense of conscientiousness. Our primary focus revolves around doing what’s right for our customers, employees and the environment. Customer satisfaction is at the forefront of our priorities, evident in our high software license renewal rates, a testament to our commitment to delivering top-notch products and services.

Moreover, we hold a unique position in the market as one of the few major companies headquartered in Europe. Europe is facing increasing pressure to embrace sustainable practices, and we are actively engaged in leading this transformation.

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

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In the early 2000s, many companies and SMEs had one or more C-suites that were dedicated to handling the IT security and compliance framework, such as the Chief Information Security Officer (CISO), Chief Information Officer (CIO), and Chief Data Officer (CDO). These IT leaders used to team up as policymakers and further implement rules and regulations to enhance company security and fight against cyber security.

But looking at the increased concerns over data privacy and the numerous techniques through which personal information is collected and used in numerous industries, the role of chief privacy officer, or CPO, has started playing a central role in the past few years as an advocate for employees and customers to ensure a company’s respect for privacy and compliance with regulations. 

The CPO’s job is to oversee the security and technical gaps by improving current information privacy awareness and influencing business operations throughout the organization. As their role relates to handling the personal information of the stakeholders, CPOs have to create new revenue opportunities and carry out legal and moral procedures to guarantee that employees can access confidential information appropriately while adhering to standard procedures.

How the CISO, CPO, and CDO Unite for Success

To safeguard the most vulnerable and valuable asset, i.e., data, the IT c-suites must collaborate to create a data protection and regulatory compliance organizational goal for a better success rate.

Even though the roles of C-level IT executives have distinct responsibilities, each focuses on a single agenda of data management, security, governance, and privacy. Therefore, by embracing the power of technology and understanding the importance of cross-functional teamwork, these C-level executives can easily navigate the data compliance and protection landscape in their organizations.

For a better simplification of the process and to keep everyone on the same page, C-suites can implement unified platforms that will deliver insights, overall data management, and improvements in security and privacy.

Organizational data protection is a real and complex problem in the modern digitized world. According to a report by Statista in October 2020, there were around 1500 data breaching cases in the United States where more than 165 million sensitive records were exposed. Therefore, to eliminate such issues, C-level leaders are required to address them substantially by hiring a chief privacy officer (CPO). The importance of the chief privacy officer has risen with the growth of data protection in the form of security requirements and legal obligations.

To Know More, Read Full Article @ https://ai-techpark.com/data-privacy-with-cpos/

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We believe that the journey of developing software is as tough as quality assurance (QA) engineers want to release high-quality software products that meet customer expectations and run smoothly when implemented into their systems. Thus, in such cases, quality assurance (QA) and software testing are a must, as they play a crucial role in developing good software.

Manual testing has limitations and many repetitive tasks that cannot be automated because they require human intelligence, judgment, and supervision.

As a result, QA engineers have always been inclined toward using automation tools to help them with testing. These AI tools can help them understand problems such as finding bugs faster, and more consistently, improving testing quality, and saving time by automating routine tasks.

This article discusses the role of AI in the future of QA engineering. It also discusses the role of AI in creating and executing test cases, why QA engineers should trust AI, and how AI can be used as a job transformer.

The Role of AI in Creating and Executing Test Cases

Before the introduction of AI (artificial intelligence), automation testing and quality assurance were slow processes with a mix of manual and automatic processes.

Earlier software was tested using a collection of manual methodologies, and the QA team tested the software repetitively until and unless they achieved consistency, making the whole method time-consuming and expensive.

As software becomes more complex, the number of tests is naturally growing, making it more and more difficult to maintain the test suite and ensure sufficient code coverage.

AI has revolutionized QA testing by automating repetitive tasks such as test case generation, test data management, and defect detection, which increases accuracy, efficiency, and test coverage.

Apart from finding bugs quickly, the QA engineers use AI by using machine learning (ML) models to identify problems with the tested software. The ML models can analyze the data from past tests to understand and identify the patterns of the programs so that the software can be easily used in the real world.

AI as a Job Transformer for QA Professionals

Even though we are aware that AI has the potential to replace human roles, industrialists have emphasized that AI will bring revolutionary changes and transform the roles of QA testers and quality engineers.

Preliminary and heavy tasks like gathering initial ideas, research, and analysis can be handled by AI. AI assistance can be helpful in the formulation of strategies and the execution of these strategies by constructing a proper foundation.

The emergence of AI has brought speed to the process of software testing, which traditionally would take hours to complete. AI goes beyond saving mere minutes; it can also identify and manage risks based on set definitions and prior information.

To Know More, Read Full Article @ https://ai-techpark.com/ai-in-software-testing/

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