1. Power BI Community Forums:  

Dive into the vibrant Power BI Community Forums where users converge to share knowledge, troubleshoot issues, and geek out over data. Get ready to immerse yourself in a world of Q&A sessions, brainstorming discussions, and the occasional meme or two.

  2. YouTube Channels:  

Tune in to YouTube channels like “Guy in a Cube” and “The BIccountant” for a dose of Power BI goodness served with a side of humor and practical insights. From basic tutorials to advanced tips and tricks, these channels have got you covered.

  3. Microsoft Power BI Documentation:  

Explore the holy grail of Power BI knowledge – the official Microsoft Power BI Documentation. Navigate through a treasure trove of guides, tutorials, and documentation covering everything from data modeling to DAX functions.

  4. Power BI Guided Learning:  

Embark on a guided learning journey with interactive tutorials and exercises tailored to your skill level. Get hands-on experience and level up your Power BI game at your own pace.

  5. Power BI Blog:  

Stay updated with the latest Power BI news, updates, and insights straight from the source – the official Power BI Blog. Get ready for a rollercoaster ride of feature announcements, customer success stories, and behind-the-scenes peeks into the world of Power BI development.

  6. Udemy Courses:  

Unlock a treasure trove of Power BI courses on Udemy, where knowledge is just a click away. Whether you’re a data rookie or a seasoned pro, there’s a course for everyone, covering topics from data visualization to advanced analytics.

  7. LinkedIn Learning:  

Dive into the world of Power BI with LinkedIn Learning and explore a curated collection of courses taught by industry experts. Elevate your skills and boost your career prospects with in-depth courses on data analysis, dashboard design, and more.

  8. GitHub Repositories:  

Venture into the realm of open-source Power BI projects on GitHub and discover a world of possibilities. From custom visuals to advanced data connectors, there’s no shortage of gems waiting to be unearthed.

  9. Power BI Challenges:  

Challenge yourself and put your Power BI skills to the test with community-driven challenges. Get ready to roll up your sleeves, tackle real-world data problems, and showcase your expertise to the world.

  10. Power BI User Groups:  

Join forces with fellow Power BI enthusiasts in local or virtual user groups and embark on a journey of knowledge-sharing and collaboration. From meetups to workshops, there’s no shortage of opportunities to connect with like-minded individuals.

  11. Power BI Templates and Sample Reports:  

Kickstart your Power BI projects with ready-to-use templates and sample reports. From financial dashboards to sales analytics, these templates are your ticket to faster insights and smarter decisions.

  12. Power BI Community Webinars:  

Immerse yourself in a world of knowledge with Power BI community webinars covering a wide range of topics. From deep dives into advanced features to case studies showcasing real-world applications, there’s always something new to learn.

As you embark on your Power BI journey, remember that learning is a never-ending adventure. With the plethora of free resources available, mastering Microsoft Power BI has never been more accessible. So dive in, explore, and unleash the full potential of your data with Power BI

The post Power BI Unveiled: Top 12 Free Resources for Learning in 2024 first appeared on AITechTrend.

Navigating the Data Quagmire: The Role of Open Source Reporting Tools

The ever-expanding significance of big data in today’s economy has paved the way for a crucial shift in data management strategies. It’s imperative for businesses to not only analyze company data but also present it in a comprehensible and precise format. This is where open source reporting tools come to the rescue. These tools seamlessly integrate with databases, collecting data to create visually appealing insights in the form of graphs and charts. By harnessing the power of reporting tools, users can ensure the value of their data, construct visual dashboards, and generate profound data insights.

Cost-Efficiency and Functionality: The Dual Advantages of Open Source Reporting Tools

One of the primary reasons that enterprises are increasingly adopting open source reporting tools is the remarkable cost-saving benefits they offer. These tools provide robust functionalities without the hefty price tag often associated with proprietary solutions. This level of accessibility and affordability is a game-changer, particularly for small and medium-sized businesses aiming to maximize their analytical potential without breaking the bank.

Furthermore, the adaptability of open source reporting tools to diverse business requirements is a significant draw. These tools can be tailored to meet specific needs, ensuring a seamless fit within existing infrastructures. Moreover, many of these tools provide dedicated discussion boards, fostering a collaborative environment where users can troubleshoot challenges, share insights, and enhance their utilization of the tools.

Unveiling the Titans: Top Open Source Reporting Tools of 2023

In the realm of open source reporting tools, several prominent contenders have risen to prominence, each offering a unique set of features and capabilities. Let’s explore some of the standout players that have carved their niche in the industry:

1. BIRT: Empowering Visual Report Creation

BIRT, an acronym for Business Intelligence and Reporting Tools, is a powerful open source technology platform under the umbrella of the Eclipse Foundation. It encompasses a visual report designer and a runtime component designed for Java and Java EE environments. This tool enables the creation of reports, data visualizations, and charts, all of which can be seamlessly integrated into web-based and rich client applications. BIRT‘s versatility makes it indispensable for real-time decision-making and historical data analysis alike.

2. FineReport: The Bridge to Visual Excellence

FineReport, a versatile business dashboard software, is available in both on-premise and SaaS versions. Notably, its distinct advantage lies in its compatibility with large screens and TV dashboards, ensuring smooth and captivating visualizations. Furthermore, FineReport facilitates the hassle-free import of data from Excel, transforming raw data into visually compelling elements. With FineReport, converting Excel data into stunning visual insights becomes an effortless endeavor.

3. Pentaho: Orchestrating Comprehensive Reporting

Pentaho emerges as a robust suite of open source reporting tools tailored for creating relational and analytical reports from a diverse array of data sources. The suite encompasses the Report Designer, Reporting Engine, and Reporting SDK. A pivotal component of the Pentaho BI Platform, the reporting suite seamlessly integrates with the platform’s analytical module, boasting an Analysis Engine (ROLAP) and advanced analytical views. This integration empowers businesses to explore complex data relationships and gain valuable insights.

4. JasperReports: Pioneering Dynamic Content Generation

At the heart of dynamic content generation lies JasperReports, an open source reporting engine. This versatile tool enables the delivery of rich content in various formats such as PDF, HTML, XLS, and more. Its Java-based nature allows for its integration into Java-enabled applications, making it a powerful resource for generating dynamic and print-ready documents. JasperReports shines in its ability to effortlessly create page-oriented documents, simplifying the process of document creation.

5. KoolReport: Transforming Data into Visual Narratives

KoolReport serves as an intuitive and flexible open source reporting tool that breathes life into data analysis. With a focus on visual reporting and dynamic dashboards, KoolReport acts as a conduit between raw data and actionable insights. Its versatility extends to its ability to connect with various databases, including MySQL, SQL Server, Oracle, MongoDB, as well as CSV and Excel files. Additionally, KoolReport boasts an array of data manipulation processes, fostering data cleansing, manipulation, and analysis.

6. JFreeChart: Elevating Data Visualization

In the realm of Java charting libraries, JFreeChart emerges as an open source powerhouse. Its extensive range of chart types, including X-Y charts, Pie charts, Gantt charts, and more, provides users with a comprehensive toolkit to represent complex data in an easily digestible manner. JFreeChart’s diverse capabilities empower businesses to visually communicate intricate data relationships, aiding in decision-making processes.

7. ReportServer: Seamlessly Integrating BI Reporting Engines

ReportServer stands as an open source reporting tool that streamlines the integration of diverse BI reporting engines within a unified user interface. This approach empowers users to select the most suitable analytics tool for their specific needs, enhancing efficiency and insights. The software’s Dynamic List feature enables on-the-fly generation of list-like reports, ensuring agility in reporting tailored to immediate requirements.

8. Seal Report: Your Daily Reporting Companion

Written entirely in C# for the Microsoft.Net framework, Seal Report offers a holistic package for crafting daily reports and dashboards from various databases. The tool prioritizes ease of installation and report design, enabling users to swiftly build and publish reports. With a focus on accessibility, Seal Report empowers users to effortlessly harness the power of their data, fostering informed decision-making.

9. QueryTree: Empowering Ad-Hoc Reporting

QueryTree emerges as a web-based, open-source ad-hoc reporting tool compatible with Microsoft SQL Server, MySQL, and PostgreSQL databases. Its intuitive interface enables users to construct reports on diverse devices, including mobile, tablet, and desktop platforms. The tool’s responsive report building widget facilitates the addition of filters, grouping, and calculated columns, creating an environment akin to Excel pivot tables for data exploration.

10. iReport: Designing Complexity with Ease

iReport, a free and open source report designer, finds its purpose in simplifying the creation of intricate layouts containing charts, images, crosstabs, and more. Aiding users in accessing data through various sources, including JDBC, JavaBeans, XML, and more, iReport streamlines the report creation process. By empowering users to effortlessly design sophisticated layouts, iReport serves as an invaluable asset in presenting complex data narratives.

The post Empowering Decision-Making: The Rise of Open Source Reporting Tools in Modern Business first appeared on AITechTrend.

Introduction to DAX Expressions

DAX (Data Analysis Expressions) is a formula language used in Power BI to create custom calculations and measures. DAX expressions allow you to manipulate data and create complex formulas that can be used to control the colors in your report. DAX expressions are similar to Excel formulas and follow the same syntax.

Using DAX Expressions to Control Colors

There are several DAX functions that can be used to control colors in Power BI. These functions allow you to set the color of a data point based on a specific condition or a measure value. Let’s look at some of the commonly used DAX functions for color control.

1. SWITCH Function

The SWITCH function allows you to test multiple conditions and return a different result for each condition. It can be used to set the color of a data point based on a specific value or condition. Here’s an example:

Color =
SWITCH(
    TRUE(),
    [Sales] > 100000, "Green",
    [Sales] > 50000, "Yellow",
    [Sales] <= 50000, "Red"
)

In this example, we are using the SWITCH function to set the color of a data point based on the value of the [Sales] measure. If the [Sales] value is greater than 100,000, the color will be set to “Green.” If the [Sales] value is between 50,000 and 100,000, the color will be set to “Yellow.” If the [Sales] value is less than or equal to 50,000, the color will be set to “Red.”

2. Color Function

The Color function allows you to set the color of a data point based on a specific RGB value. Here’s an example:

scssCopy codeColor =
Color.FromRGB(255,0,0)

In this example, we are using the Color function to set the color of a data point to red. The RGB values are specified as arguments in the function. The first argument represents the red value, the second argument represents the green value, and the third argument represents the blue value.

3. Color Scale Function

The Color Scale function allows you to set the color of a data point based on a specific color scale. Here’s an example:

cssCopy codeColor =
ColorScale.RedYellowGreen([Sales])

In this example, we are using the Color Scale function to set the color of a data point based on the [Sales] measure. The function uses a red-yellow-green color scale, where red represents low values, yellow represents moderate values, and green represents high values.

Conclusion

In this article, we discussed how to control colors with DAX expressions in Power BI. DAX expressions provide a powerful way to customize the color scheme of your report and make it more visually appealing to the end-users. We looked at some of the commonly used DAX functions for color control, such as SWITCH, Color, and Color Scale. By using these functions, you can create a custom color scheme that fits your needs and enhances the overall user experience.

The post How to Customize Your Power BI Report’s Color Scheme with DAX Expressions first appeared on AITechTrend.

Introduction

Data scientists are often required to make decisions based on statistical analysis. However, the accuracy of the decisions depends on the accuracy of the statistical analysis. Therefore, it is important to identify and avoid common statistical errors that data scientists make.

Using the wrong statistical test

One of the most common statistical errors made by data scientists is using the wrong statistical test. It is important to select the correct statistical test for the type of data being analyzed. Using the wrong test can lead to incorrect conclusions and decisions.

Solution

Data scientists should have a good understanding of the different types of statistical tests and their applications. They should also consult with statisticians when necessary.

Ignoring outliers

Outliers are data points that are significantly different from the rest of the data. Ignoring outliers can lead to incorrect conclusions and decisions.

Solution

Data scientists should identify and analyze outliers before making any conclusions. They should also consider the cause of the outlier and whether it should be included or excluded from the analysis.

Failing to check assumptions

Statistical tests often have assumptions that must be met for the test to be valid. Failing to check these assumptions can lead to incorrect conclusions and decisions.

Solution

Data scientists should check the assumptions of the statistical test before conducting the analysis. If the assumptions are not met, they should use a different test or transform the data.

Using small sample sizes

Using small sample sizes can lead to incorrect conclusions and decisions. Small sample sizes do not provide enough information to make accurate conclusions.

Solution

Data scientists should ensure that the sample size is large enough to make accurate conclusions. They should also consider using techniques such as bootstrapping to generate more data.

Confusing correlation with causation

Correlation is a measure of the relationship between two variables. However, it does not imply causation. Confusing correlation with causation can lead to incorrect conclusions and decisions.

Solution

Data scientists should be cautious when interpreting correlation. They should consider other factors that may influence the relationship between variables before making any conclusions.

Overfitting the data

Overfitting is a common problem in machine learning. It occurs when a model is too complex and fits the data too well. Overfitting can lead to incorrect conclusions and poor generalization to new data.

Solution

Data scientists should use techniques such as cross-validation to prevent overfitting. They should also consider using simpler models that are less prone to overfitting.

Not considering the context

Data scientists often analyze data without considering the context in which it was collected. Failing to consider the context can lead to incorrect conclusions and decisions.

Solution

Data scientists should consider the context in which the data was collected before conducting any analysis. They should also consider external factors that may influence the data.

Not accounting for missing data

Missing data is a common problem in data analysis. Failing to account for missing data can lead to incorrect conclusions and decisions.

Solution

Data scientists should identify and account for missing data before conducting any analysis. They should also consider the reason for the missing data and whether it should be imputed or excluded from the analysis.

Not considering the impact of outliers on the results

Outliers can significantly impact the results of a statistical analysis. Failing to consider their impact can lead to incorrect conclusions and decisions.

Solution

Data scientists should identify outliers and consider their impact on the results of the analysis. They should also consider alternative methods for analyzing the data that may be less sensitive to outliers.

Using p-values incorrectly

P-values are commonly used in statistical analysis to determine the significance of a result. However, they can be misinterpreted, leading to incorrect conclusions.

Solution

Data scientists should have a good understanding of p-values and their limitations. They should also consider using other methods, such as effect sizes, to supplement p-values.

Over-reliance on statistical significance

Statistical significance does not always imply practical significance. Over-reliance on statistical significance can lead to incorrect conclusions and decisions.

Solution

Data scientists should consider the practical significance of their results in addition to their statistical significance. They should also consider other factors, such as the cost and feasibility of implementing any recommendations based on the results.

Conclusion

Data scientists play an important role in analyzing and interpreting data. However, they can make mistakes that lead to incorrect conclusions and decisions. By understanding and avoiding the common statistical errors discussed in this article, data scientists can ensure that their analyses are accurate and reliable.

The post The Top 10 Statistical Errors Data Scientists Make (and How to Avoid Them) first appeared on AITechTrend.

Neo4j^®, the world’s leading graph data platform, crossed $100 million in annual recurring revenue (ARR) during 2021. The year was marked by strategic product innovation that drove customer and partner excellence, strong community engagement, and super-sized venture funding investments.

“Neo4j has pioneered the graph space for a number of years, with critical deployments among major credit card firms for fraud detection, as well as use cases in areas driven by the pandemic, including product testing and supply chain analysis,” said Carl Olofson, Research Vice President at IDC.

Neo4j continued to grow in popularity throughout 2021 as the world’s most widely deployed graph database, maintaining its position as a top 20 database overall. Momentum drivers include the accelerated adoption of Neo4j AuraDB, a fully managed service that reduces friction as complex applications shift to the cloud, as well as the success of Neo4j Graph Data Science, a complete toolset for data scientists to apply graph algorithms for more effective machine learning and better predictions.

Over 1,000 organizations depend on Neo4j for mission-critical applications, and many thousands more experiment, prototype, and deploy Neo4j’s expanding portfolio of cloud services. Notable customers include Pfizer, PepsiCo, Inc., World Health Organization (WHO), Cable News Network, Inc. (CNN), and BMW Group.

Neo4j’s success in helping customers across industries such as Financial Services, Retail, and Healthcare caught the attention of investors, leading to $390 million in new investments raised in 2021, and launching Neo4j to a $2 billion valuation. On top of being the largest single funding round to date in the database space, Neo4j also welcomed GV (formerly Google Ventures) as a strategic investor and added former Google CFO, Patrick Pichette, to its board to offer increased industry expertise for the next phase of growth.

Patrick Pichette, Inovia Capital Partner and Neo4j Board Member, touched upon Neo4j’s momentum over the past year.

“2021 marked an incredible year for Neo4j and graph technology at large,” said Pichette. “What really sets Neo4j’s graph technology apart is that it uniquely solves some of the world’s most complex challenges. Neo4j is poised for strong, consistent growth leading into 2022, and we’re excited to be part of that journey.”

Emil Eifrem, CEO and Co-Founder of Neo4j, reflected on the past year and leading one of only a handful of private database companies to cross $100 million in ARR.

“In 2021, we demonstrated that Neo4j is a mainstay of modern data infrastructure, grounded in a global community of developers and data scientists, empowered with a rich portfolio of technology to address complex challenges, and scale without barriers,” said Eifrem. “We enter 2022 with the wind at our backs, and the right talent and leadership in place. We’re poised to deliver Neo4j to a fast-growing user base, and continue to delight our customers as their use cases become more exacting.”

The company ended 2021 with over 600 employees, representing the largest collective of graph expertise in the world. During the course of the year, Neo4j expanded rapidly in Asia-Pacific (Shanghai, Singapore, Sydney, Jakarta, and Bangalore), and Latin America (São Paulo).

Notable Neo4j 2021 milestones include:

Technology Leadership

• Breaking the Graph Scale Barrier: As part of NODES 2021, Neo4j demonstrated its super-scaling technology to show real-time query performance against a graph with over 200 billion nodes and more than a trillion relationships, running on over one thousand machines.
• Graphs and AI: Neo4j Graph Data Science was adopted by over 50 customers to build sophisticated AI, machine learning, and advanced analytics applications.
• AuraDB Enterprise: The most deployed and trusted graph technology platform was made generally available as a fully managed service, helping organizations including Levi Strauss & Co. and Adeo to radically accelerate time to value and get to production faster.
• Knowledge Graphs Accelerate Adoption: Two-thirds of Neo4j customers – including NASA – are implementing knowledge graphs to redefine what’s possible in data management and analytics.

Demonstrable Customer Value

• Unsurpassed ROI: The Neo4j Graph Data Platform pays for itself more than 4x in the span of three years (417% ROI), according to a recent Forrester TEI report.
• Accelerated Time to Value: According to Forrester, Neo4j showed 60% accelerated time to value, as average development time shrunk from 12 months to four.
• Digital Transformation: The TEI study was based on Forrester’s in-depth interviews with Neo4j customers who realized substantial cost savings from IT modernization and rationalization.

Commercial Impact

• Neo4j on Azure, GCP, and AWS: Neo4j is now globally available on Microsoft Azure, Google Cloud Platform (GCP), and Amazon Web Services (AWS) marketplaces. Customers can now seamlessly deploy Neo4j on the cloud platform of their choice.
• New Executives and Board Members: Neo4j welcomed Kristin Thornby as Chief People Officer. Nathalie Kornhoff-Bruls of Eurazeo and Patrick Pichette of Inovia Capital both joined Neo4j’s board.
• Partner Traction: Neo4j trained and certified over 1,000 graph practitioners from leading global system integrators including Accenture, Deloitte, EY, Capgemini, and PwC, in addition to closing new business with nine U.S. Federal Programs. The company expanded its partner leadership in emerging markets including Brazil, China, India, and Australia.

Market Expansion

• Top 20 Database: Neo4j is the leading graph database and ranks among the 20 most popular databases globally, as tracked by DB-Engines, an independent ranking of all database management systems.
• Award Wins: Neo4j was named one of CRN’s Big Data 100 Coolest Database System Companies, 2021 Data Breakthrough Awards Graph DB Solution of the Year, and 2021 Datanami Readers’ and Editors’ Choice Awards for Best Data and AI Technology.
• Rise in Cybersecurity Graph Adoption: Companies like Deloitte and Uplevel Security increasingly sought to leverage graph databases to strengthen cybersecurity strategies in response to increasingly sophisticated threats. Graphs equip cybersecurity teams with the competitive advantage of rapid screening, improved turnaround time, quality of recommendations, and reduced false positives, ultimately allowing them to mitigate cyber threats.
• Inclusion in U.S. Department of Defense (DoD) Iron Bank: Neo4j collaborated with The DoD’s Platform One organization to develop a hardened container for its graph database technology and joins a repository of digitally signed, binary container images that have been hardened according to the Iron Bank’s Container Hardening Guide.

Community Engagement

• Growing Developer Base: The global Neo4j community surpassed 240,000 members over the last year. During 2021, developers downloaded Neo4j more than 36 million times and launched more than 150,000 Neo4j Sandbox instances. Upwards of 53,000 professionals list Neo4j as a skill on their LinkedIn profiles.
• The Pandora Papers: The International Consortium of Investigative Journalists (ICIJ) released the Pandora Papers, which used Neo4j to generate visualizations and make searchable records of the hidden riches of world leaders. Neo4j has been working with the ICIJ since the 2016 Panama Papers investigation.
• Graphs4Good: The efforts of the Neo4j community to collaborate and help fight against the spread of COVID-19 were recognized by two honorable mentions in the AI and Data and Software categories of Fast Company’s 2021 World Changing Ideas Awards.
• Largest Graph Event: Neo4j Online Developer Expo and Summit (NODES 2021) welcomed over 12,000 registrants to listen to presentations from Fujitsu Research Labs, Dataiku, BASF, Apiax, Linkurious, and more.
• 2021 Graphie Award Winners: This year’s nominations eclipsed all prior years, with Neo4j receiving nominations spanning more than 10 countries and awarding 27 winners including Pfizer, Qualicorp S.A., Commonwealth Bank of Australia, Lenovo, Volvo Cars, Levi Strauss & Co., and many more.

 Resources

• Neo4j ROI
• Neo4j Products
• Neo4j Customers
• Neo4j Community
• Neo4j on Twitter
• Neo4j on LinkedIn
• Neo4j on YouTube
• Neo4j is Hiring

About Neo4j
Neo4j is the world’s leading graph data platform. We help organizations – including Comcast, ICIJ, NASA, UBS, and Volvo Cars – capture the rich context of the real world that exists in their data to solve challenges of any size and scale. Our customers transform their industries by curbing financial fraud and cyber crime, optimizing global networks, accelerating breakthrough research, and providing better recommendations. Neo4j delivers real-time transaction processing, advanced AI/ML, intuitive data visualization and more. Find out more at neo4j.com and follow us at @Neo4j.

The post Neo4j Closes Banner Year Marked by Customer Successes, Continued Industry Validation, Community Engagement, and Major Funding first appeared on AITechTrend.

Our artificial intelligence services turn data into useful information. With personalized analytics and easily accessible reporting tools like dashboards and indicators, you can see real-time results, identify growth opportunities, and make informed decisions.

Artificial Intelligence for Customer Insights

Companies use CRM technology to uncover the right customers and personalize their experiences in real-time. Nurturing customer relationships is as much about understanding individual customers as it is about knowing your customers. Insights in CRM are used to design the most relevant products and services to drive brand loyalty and drive top-line growth. Getting the most from CRM is about getting insights from your data. Insights derived from AI can uncover all sorts of insights, from customer lifetime value to correlation of historical trends with current data to engagement patterns and trends.

How AI Helps

Thanks to AI-powered tools, quality control is as simple as scanning barcodes with a camera. Or, you can use object and image recognition to detect defects in products or make automated quality control adjustments. With machine learning capabilities, robots can reduce the workload of human labor, decreasing costs and saving time.

Bringing AI to your product and service means speed and cost savings. With insights into your customers’ buying patterns and sentiments, you can adjust to meet them where they are. Or, you can change how products are sold to maximize profits. No matter what your next steps are, be sure to work with an expert and ask questions to avoid getting bogged down in the hype of the AI trend.

What to Know When Utilizing AI

AI tools can reduce time spent on administrative tasks, empowering you to focus on growth. As machine learning and data science advance, the use of AI will be a no-brainer for hospitals, providing technology to bolster patient care. Data analytics and AI will help optimize hospital staff performance through predictive tools and predictive tools and provide on-the-go support for medical professionals. AI offers unprecedented transparency into operations and patient services. Integrating and applying AI services will help increase revenue, help reduce costs, improve patient outcomes and ultimately improve the quality of care. By employing the right artificial intelligence, hospitals will be able to make informed decisions, improve patient care and drive growth. Ovid Therapeutics 1.

The Value of Data

Data is the new oil. Every company needs data to drive growth, but few have the bandwidth to commit to managing the volume and variety of data they have today. Despite the fact that most companies invest in data analytics software, few are able to harness their data to create actionable insights that help grow the bottom line. In addition, most large organizations have struggled to put together a holistic data strategy that addresses their organizations’ business goals. And yet, many companies are committed to advancing digital transformation, which relies heavily on data to drive innovation, growth, and innovation.

The Importance of the Data Architecture in AI

The industry is moving to a new era of rapid innovation with advancements in the use of advanced technology and increased usage of artificial intelligence. CIOs need to transform business models and rework existing technologies to leverage the power of AI.

Companies that are looking to implement AI should take the following steps:

Saturate your data with AI-related use cases: If you want to leverage AI technology, you must first identify which use cases will be the most beneficial to your business and focus your efforts on that.

Developing data models is the first step.

Create a data hierarchy using hierarchical dimensions (e.g., top-to-bottom, left-to-right, left-to-right-middle, bottom-to-top) that represent common data dimensions.

The Importance of Data Analysis

From analyzing product and customer feedback to improving patient outcomes, data analysis and data visualization is the key to success. Businesses rely on data to drive progress, innovation, and growth. It’s time to break away from a spreadsheet-driven world and re-write your data. Everything starts with data analysis, whether you’re thinking about a possible merger or buying a competitor, or evaluating how well you’re performing in today’s competitive landscape.

From customer surveys to converting traffic into leads and improving patient outcomes, AI can help you cut through the clutter and make data-driven decisions.

Conclusion

Building a strong product, improving customer experience, and establishing a competitive edge through innovative technology is no longer enough to succeed in today’s competitive market. Both the product and customer experience need to be top-notch in order to stay relevant and generate growth. Insight AI is an analytical approach to delivering insights on critical business and technology issues.

The post Insight Artificial Intelligence: How To Make The Most of AI Technology first appeared on AITechTrend.