Tag: Business Intelligence

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/ 

Read Related Articles:

Artificial Intelligence and Sustainability in the IT

Explainable AI Is Important for IT

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/

Read Related Articles:

Automated Driving Technologies Work

Ethics in the Era of Generative AI

We are living in an era of change, where industries are changing their traditional way of managing and streamlining organizational goals. SMEs and SMBs are gradually gaining market share and developing well-known brands, eliminating the term monopoly, as any business with an appropriate data strategy can create its own space in this competitive landscape.

To stay competitive, businesses are attracted to two potential technologies: artificial intelligence (AI) and business intelligence (BI). Combined, they offer a powerful tool that transforms raw data into implementable insight by making data accessible to BI managers. This collaboration between AI and BI enables companies to steer large-scale data efficiently and make quick business decisions.

This article provides an overview of the current landscape of AI and BI, highlighting the evolution of BI systems after integrating artificial intelligence. 

The Synergy Between BI and AI

The partnership between artificial intelligence and business intelligence has become the backbone of the modern business world.

In this competitive market, businesses across all industries strive to drive innovation and automation as an integrated strategy that reshapes organizations from a mindset of data and data-driven decision-making.

When BI managers integrate AI into BI systems in businesses, it harnesses big data’s power, providing previously inaccessible insights.

Traditionally, BI systems were focused on historical data analysis, which was collected and analyzed manually with the help of a data team, which tends to be a tedious job, and businesses often face data bias.

However, AI-powered BI systems have become a dynamic tool that uses predictive analysis and real-time decision-making skills to identify market patterns and predict future trends, providing a more holistic view of business operations and allowing your organization to make informed decisions.

The current landscape of AI-driven BI is a combination of big data analytics, machine learning (ML) algorithms, and AI in traditional BI systems, leading to a more sophisticated tool that provides spontaneous and automated analytical results.

As the AI field diversifies, the BI system will mature continuously, posing an integral role in shaping the future of business strategies across various industries.

Artificial intelligence is transforming business intelligence in numerous ways by making it a powerful tool for BI managers and their teams to work efficiently and effectively and have access to a wider range of customers. Even small businesses and enterprises are trying their hands at AI-powered BI software, intending to automate the maximum work of data analytics to make quick decisions.

In the coming years, we can expect more potential use cases of AI-powered business intelligence software and tools, helping businesses solve the greatest challenges and reach new heights.

To Know More, Read Full Article @ https://ai-techpark.com/transforming-business-intelligence-through-ai/

Read Related Articles:

Edge Computing TrendsImportance of AI Ethics

In AI-Tech Park’s commitment to uncovering the path toward realizing enterprise AI, we recently sat down with Chris Lynch, an esteemed figure in the industry and accomplished Executive Chairman and CEO of AtScale. With a remarkable track record of raising over $150 million in capital and delivering more than $7 billion in returns to investors, Chris possesses invaluable knowledge about what it takes to achieve remarkable results in the fields of AI, data, and cybersecurity.

Please give us a brief overview of AtScale and its origin story. What makes AtScale stand apart from its competitors?

AtScale was founded in 2013 as a highly scalable alternative to traditional OLAP analytics technologies like Microsoft SSAS, Business Objects, Microstrategy, or SAP BW.  However, our true breakthrough came with the enterprise’s shifting data infrastructure to modern cloud data platforms.  AtScale uniquely lets analytics teams deliver “speed of thought” access to key business metrics while fully leveraging the power of modern, elastic cloud data platforms.  Further, what sets AtScale apart is its highly flexible semantic layer.  This layer serves as a centralized hub for governance and management, empowering organizations to maintain control while avoiding overly constraining decentralized analytics work groups.

How do AtScale’s progressive products and solutions further the growth of its clients?

AtScale offers the industry’s only universal semantic layer, allowing our clients to effectively manage all the data that is important and relevant for making critical business decisions within the enterprise. This is so they can drive mission-critical processes off of what matters the most – the data!

To achieve this, AtScale provides a suite of products that enable our end clients to harness the power of their enterprise data to fuel both business intelligence (BI) and artificial intelligence (AI) workloads. We simplify the process of building a logical view of the most significant data by seamlessly connecting to commonly used consumption tools like PowerBI, Tableau, and Excel and cloud data warehouses like Google BigQuery, Databricks, and Snowflake.  

What potential do you think AI and ML hold to transform SMEs and large enterprises? How can companies leverage these modern technologies and streamline their processes?

AI and ML are going to have a profound impact on how we live, conduct our day-to-day business, and shape the global economy. It is imperative for every organization to leverage AI to streamline their operations and processes, improve their costs, and more importantly build and sustain competitive differentiation in the market. But without proper data, AI becomes inefficient and uneventful. The power of those AI models and their predictions rests in the organizational data and needs a universal semantic layer to create AI-ready data.

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

Read Related Articles:

Data Analytics Trends in 2023

Mental Health Apps for 2023