#spotfire oracle connection

[cybersecurityict]

Advanced Analytics Market Trends, Size, Share & Forecast to 2032

The Advanced Analytics Market was valued at USD 62.2 Billion in 2023 and is expected to reach USD 554.3 Billion by 2032, growing at a CAGR of 24.54% from 2024-2032.

Advanced Analytics Market is witnessing transformative growth as businesses increasingly adopt data-driven decision-making strategies. The demand for predictive, prescriptive, and diagnostic analytics is soaring across sectors including healthcare, finance, manufacturing, and retail. Organizations are leveraging advanced analytics tools to enhance operational efficiency, gain competitive advantages, and deliver personalized customer experiences. As digital transformation accelerates globally, the integration of artificial intelligence (AI), machine learning (ML), and big data technologies further propels the market’s evolution, shaping the future of enterprise intelligence.

Advanced Analytics Market continues to gain momentum with the proliferation of cloud-based analytics platforms and real-time data processing capabilities. Enterprises are focusing on agile analytics solutions to meet evolving consumer expectations and complex business environments. The convergence of analytics with Internet of Things (IoT), robotic process automation (RPA), and blockchain is expanding the possibilities of data insight and actionability, unlocking new growth avenues across industries.

Get Sample Copy of This Report: https://www.snsinsider.com/sample-request/5908 

Market Keyplayers:

Microsoft – Power BI

IBM – IBM Watson Analytics

SAP – SAP Analytics Cloud

Oracle – Oracle Analytics Cloud

Google – Google Cloud BigQuery

SAS Institute – SAS Viya

AWS (Amazon Web Services) – Amazon QuickSight

Tableau (Salesforce) – Tableau Desktop

Qlik – Qlik Sense

TIBCO Software – TIBCO Spotfire

Alteryx – Alteryx Designer

Databricks – Databricks Lakehouse Platform

Cloudera – Cloudera Data Platform (CDP)

Domo – Domo Business Cloud

Zoho – Zoho Analytics

Market Analysis

The advanced analytics market is driven by the increasing need for real-time decision-making, risk management, and performance optimization. Key industry players are investing in innovative technologies and strategic partnerships to stay competitive. The rise in structured and unstructured data from multiple digital touchpoints has amplified the demand for sophisticated analytical tools. Furthermore, government and enterprise investments in digital infrastructure are accelerating the deployment of advanced analytics solutions across emerging economies.

Market Trends

Growing adoption of AI and ML-powered analytics for enhanced data interpretation

Surge in demand for cloud-based analytics platforms due to scalability and flexibility

Expansion of self-service analytics tools for non-technical users

Integration of predictive analytics in supply chain and risk management functions

Increasing use of natural language processing (NLP) in business intelligence

Shift towards augmented analytics to automate insight generation

Strong focus on data governance, privacy, and regulatory compliance

Market Scope

The market spans a wide array of applications including fraud detection, customer analytics, marketing optimization, financial forecasting, and operational analytics. It serves multiple industries such as BFSI, IT & telecom, retail & e-commerce, healthcare, manufacturing, and government. With the expansion of IoT devices and connected systems, the scope continues to widen, enabling deeper, real-time insights from diverse data streams. Small and medium enterprises are also emerging as significant contributors as advanced analytics becomes more accessible and cost-effective.

Market Forecast

The advanced analytics market is expected to continue its upward trajectory driven by innovation, increased digital maturity, and widespread application. Continued advancements in edge computing, neural networks, and federated learning will shape the next phase of analytics evolution. Organizations are likely to prioritize investments in unified analytics platforms that offer scalability, security, and end-to-end visibility. The market outlook remains robust as businesses focus on leveraging analytics not just for insights, but as a strategic enabler of growth, resilience, and customer engagement.

Access Complete Report: https://www.snsinsider.com/reports/advanced-analytics-market-5908 

Conclusion

The rise of the advanced analytics market signals a paradigm shift in how data is harnessed to unlock strategic business value. From real-time insights to predictive foresight, the impact of analytics is becoming foundational to every industry. As technology progresses, the market is poised for a future where data isn’t just a tool—but the engine of innovation, agility, and transformation. Organizations ready to embrace this shift will be the frontrunners in tomorrow’s digital economy.

About Us:

SNS Insider is one of the leading market research and consulting agencies that dominates the market research industry globally. Our company's aim is to give clients the knowledge they require in order to function in changing circumstances. In order to give you current, accurate market data, consumer insights, and opinions so that you can make decisions with confidence, we employ a variety of techniques, including surveys, video talks, and focus groups around the world.

Contact Us:

Jagney Dave - Vice President of Client Engagement

Phone: +1-315 636 4242 (US) | +44- 20 3290 5010 (UK)

[differenttimemachinecrusade]

Big Data and Analytics in GCC Market: Size, Share, Scope, Analysis, Forecast, Growth and Industry Report 2032 – Retail and E-commerce Trends

Big Data and Analytics are transforming the operational frameworks of Global Capability Centers (GCCs) across the globe. As businesses increasingly recognize the pivotal role of data in driving strategic initiatives, Global Capability Centers are evolving into centers of excellence for data-driven decision-making. According to research 76% of Global Capability Centers identified data as a critical area for future growth,

Big Data and Analytics in GCC Market is experiencing rapid growth due to the region’s digital transformation initiatives. Governments and enterprises are leveraging data to drive innovation, optimize services, and improve decision-making. As a result, demand for data-driven strategies is surging across sectors.

Big Data and Analytics in GCC Market continues to evolve with the rising adoption of AI, cloud computing, and IoT technologies. From smart cities to healthcare and finance, businesses in the Gulf Cooperation Council (GCC) are embracing analytics to remain competitive, improve operational efficiency, and enhance customer experiences.

Get Sample Copy of This Report: https://www.snsinsider.com/sample-request/4716 

Market Keyplayers:

IBM Corporation (IBM Watson, IBM Cloud Pak for Data)

Microsoft Corporation (Microsoft Azure, Power BI)

Oracle Corporation (Oracle Analytics Cloud, Oracle Big Data Service)

SAP SE (SAP HANA, SAP BusinessObjects)

SAS Institute Inc. (SAS Viya, SAS Data Management)

Google LLC (Google Cloud Platform, BigQuery)

Amazon Web Services (AWS) (Amazon Redshift, Amazon EMR)

Tableau Software (Tableau Desktop, Tableau Online)

Teradata Corporation (Teradata Vantage, Teradata Cloud)

Cloudera, Inc. (Cloudera Data Platform, Cloudera Machine Learning)

Snowflake Inc. (Snowflake Cloud Data Platform)

MicroStrategy Incorporated (MicroStrategy Analytics)

Qlik Technologies (Qlik Sense, QlikView)

Palantir Technologies (Palantir Foundry, Palantir Gotham)

TIBCO Software Inc. (TIBCO Spotfire, TIBCO Data Science)

Domo, Inc. (Domo Business Cloud)

Sisense Inc. (Sisense for Cloud Data Teams, Sisense Fusion)

Alteryx, Inc. (Alteryx Designer, Alteryx Connect)

Zoho Corporation (Zoho Analytics, Zoho DataPrep)

ThoughtSpot Inc. (ThoughtSpot Search & AI-Driven Analytics)

Trends Shaping the Market

Government-Led Digital Initiatives: National visions such as Saudi Arabia’s Vision 2030 and the UAE’s Smart Government strategy are fueling the adoption of big data solutions across public and private sectors.

Growth in Smart City Projects: Cities like Riyadh, Dubai, and Doha are integrating big data analytics into infrastructure development, transportation, and citizen services to enhance urban living.

Increased Investment in Cloud and AI: Cloud-based analytics platforms and AI-powered tools are gaining traction, enabling scalable and real-time insights.

Sector-Wide Adoption: Industries including oil & gas, healthcare, finance, and retail are increasingly utilizing analytics for predictive insights, risk management, and personalization.

Enquiry of This Report: https://www.snsinsider.com/enquiry/4716 

Market Segmentation:

By Type

Shared Service Centers

Innovation Centers

Delivery Centers

By Industry Vertical

Banking and Financial Services

Healthcare

Retail

Manufacturing

Telecommunications

By Functionality

Descriptive Analytics

Predictive Analytics

Prescriptive Analytics

Real-time Analytics

By Technology Type

Data Management

Analytics Tools

Artificial Intelligence & Machine Learning

By End-User

Large Enterprises

Small and Medium Enterprises (SMEs)

Market Analysis

Accelerated Digital Transformation: Organizations across the GCC are shifting to digital-first operations, creating vast amounts of data that require robust analytics solutions.

Public and Private Sector Collaboration: Joint efforts between governments and tech firms are fostering innovation, resulting in smart platforms for public services, energy, and education.

Data-Driven Decision Making: Businesses are leveraging data to improve ROI, streamline operations, and personalize offerings—especially in e-commerce, banking, and telecommunications.

Cybersecurity and Data Privacy Awareness: With the increase in data generation, there’s a growing emphasis on securing data through advanced governance and compliance frameworks.

Future Prospects

The Big Data and Analytics in GCC Market is expected to witness exponential growth over the next five years. With increasing internet penetration, 5G rollout, and continued focus on digital infrastructure, data-driven technologies will become even more central to economic and social development in the region.

Talent Development and Upskilling: Governments are investing in training programs and digital literacy to prepare a workforce capable of managing and interpreting big data.

Emerging Startups and Innovation Hubs: The GCC is witnessing a rise in homegrown analytics startups and incubators that are driving localized solutions tailored to regional needs.

AI Integration: The convergence of AI with big data will unlock new insights and automate complex tasks in sectors such as logistics, healthcare diagnostics, and financial modeling.

Regulatory Frameworks: Future success will depend on the creation of robust regulatory policies ensuring data privacy, cross-border data flows, and ethical AI usage.

Access Complete Report: https://www.snsinsider.com/reports/big-data-and-analytics-in-gcc-market-4716 

Conclusion

The Big Data and Analytics in GCC Market stands at the forefront of digital transformation. With strong government backing, sector-wide adoption, and a growing tech ecosystem, the region is well-positioned to become a data-driven powerhouse. As the market matures, the focus will shift from data collection to intelligent utilization—empowering smarter decisions, better services, and sustainable growth across the GCC.

About Us:

SNS Insider is one of the leading market research and consulting agencies that dominates the market research industry globally. Our company's aim is to give clients the knowledge they require in order to function in changing circumstances. In order to give you current, accurate market data, consumer insights, and opinions so that you can make decisions with confidence, we employ a variety of techniques, including surveys, video talks, and focus groups around the world.

Contact Us:

Jagney Dave - Vice President of Client Engagement

Phone: +1-315 636 4242 (US) | +44- 20 3290 5010 (UK)

[learnstransformation]

https://learntransformation.com/top-business-intelligence-tools/

The top 10 Business Intelligence tools offer diverse features for data analysis and visualization. Microsoft Power BI integrates AI and Excel compatibility for customized reports. Tableau simplifies complex data with drag-and-drop functionality and real-time results. Qlik provides multi-cloud connectivity and user-friendly interfaces without coding expertise. ThoughtSpot's AI simplifies complex tasks with full-stack design and in-memory computation. Sisense offers self-service intelligence solutions and enterprise-grade applications. Oracle BI Suite aids in faster decision-making on-the-go. MicroStrategy focuses on hyper-intelligence applications for enterprises. TIBCO Spotfire and Jaspersoft offer powerful visualization and traditional reporting. SAP delivers ample reporting and analytics with cloud or on-premises options. Domo BI integrates data with pre-built applications and supports predictive modeling tasks. These tools enhance business efficiency and performance across various functionalities.

[jtenney4-blog]

Tableau Suite Analysis

architecture of the system

Live Query Engine interprets abstract queries generated by VizQL into language that is understood by popular database systems, such as SQL and MDX syntax. Thus, increased data accessibility and usability of databases through a uniform user interface that interacts with a diverse range of databases, formats, and sizes.  Live Query Engine allows users to query databases without having to first import the data, the query is instead interpreted and run by the database with only the results rendered.  This technology provides for data consistency and avoids data movement while still being scalable, secure, and flexible.  Further, it allows Tableau connects to open-source Hadoop databases, proprietary MapReduce technologies, and cloud data warehouses like Amazon Redshift and Google BigQuery.  Column stores, databases designed to process unstructured data, and web applications such as Salesforce and Google Analytics are also able to be connected (Form 10-K, 2016).  

Tableau’s In-Memory Data Engine further supports user’s ability to analyze large amounts of data independent of database systems.  Much of the today’s data is not stored in databases or stored in databases that are too slow for interactive analysis, hence, the need for analysis outside of the database.  The In-Memory Data Engine uses column-based storage and compressed representations of data while leveraging RAM-based indices to provide users with fast calculations without the complications, costs, and delays of a database system (Form 10-K, 2016).  

Tableau has developed their own visual query language (VizQL) that translates drag-and-drop actions into data queries and then expresses the information visually.  Queries and visualizations used to be separate tasks and the queries often required scripts, chart wizards, or dialogue boxes.  The VizQL technology increases speed and flexibility, provides a creative and engaging experience, and brings a significant improvement in the ability to gain insights from data (Form 10-K, 2016).  

The VizQL, Live Query Engine and In-Memory Data Engine work harmoniously to form Tableau’s Hybrid Data Architecture, allowing users to fully exploit flexibility and power without any programming or scripting. Flexibility to access and analyze data from a range of sources while optimizing speed and performance for each source is the core of the hybrid strategy. Customers are able to integrate live data with in-memory data on a single visualization or dashboard through these amazing technologies.  The In-Memory Data Engine could be used to import a data sample from a large database in order to ask a question from a visualization.  This visualization can then be queried against the entire database using the Live Query Engine to answer another question or find a new pattern/trend (Form 10-K, 2016).  The breadth of usability is truly magnificent and is what makes Tableau stand out from its competitors.

data sources

Tableau suite can interact over 40 data sources (Form 10-K, 2016), on premise or in the cloud (Tableau, 2017) to including those from the top five database vendors.  

data mining functions

Tableau is very short-sited in overall scope; however, it is extremely advanced within its limited scope. The software itself does not offer any ETL technologies nor does it consolidate, clean, transform, or reduce data. Tableau imports already cleaned, consolidated, transformed, and reduced data to ask a question and create a visualization from it.  This visualization can then be used for another query; however, the platform focuses more on the visualization of the data rather than the actual mining of it. It prides itself on such seamless integration with other BI tools in order to supplement fully without having to provide all of the other functions.  

data mining methodologies

Tableau does offer data mining through classification, clustering, and association rules within the drag-and-drop interface.  

coupling with database or data warehouse systems

Tableau’s hybrid architecture allows the software to run outside of the database.  See ‘architecture of the system’ above for a more in-depth explanation.  

scalability

Tableau suite can be dialed into the perfect combination of user flexibility and control.  Existing security protocols can be seamlessly integrated to provide central governance of metadata and security rules.  User and group level authentication options are available as well as pass-through data connection permissions and row-level filtering (Tableau, 2017).

visualization tools

Tableau has incredibly strong live visual analytics that allow users unrestricted data exploration capabilities.  The drag-and-drop interface provides the ability to use reference lines, forecasts, and statistical summaries to tell a visual story through trend analysis, regressions, and correlations.  This method of storytelling appeals to the psychological aspect of learning and calling for action.  Users are able to capture emotion and logic, taking the viewers on a journey through the data.  Viewers are more likely to digest and retain the dat.  Further, viewers are more likely to identify with the data, which drives change.  Static slides and boring presentations are no longer relevant or captivating (Tableau, 2017).

Tableau has strengthened its portfolio with a new, free application, Vizable, that turns data into interactive graphs that can be shared from an iPad and explored on the go without the need for a server or any cloud-based services.  The technology queries data, aggregates, and generates a visualization on the tablet within seconds.  The exciting interface uses hand gestures such as dragging, swiping, and pinching to receive instant feedback.  

graphical user interfaces. 

Insights can be embedded into workflows for employees, customers, partners, and suppliers to provide analytics anywhere needed.  Interactive dashboards can be embedded into existing business portals including applications like Salesforce, SharePoint, and Jive.  Users are able to switch between extracts and live connections to data with just one click, or schedule automatic extractions.  Team members can securely access published dashboards from any mobile device or external browser (Tableau, 2017).  

Can you propose one improvement to such a system and outline how to realize it?

It appears the industry-wide recognized weakness of the Tableau system is its inability to load data for preparation before use.  In the beginning of my research, I thought this should be improved upon and could be realistically strategized.  However, after digging deeper, this seems to be a characteristic that Tableau prides themselves on and leverages to provide flexibility and efficiency to their customers.  Tableau has developed a hybrid architecture to fully emphasize the advantages of this approach.

Tableau focuses on visualization of data, while others feel this may limit them, they are interested on developing new technologies and features for visualization rather than expanding the functionality.  They are pioneers in their field of expertise, they know what they are good at and they are sticking with it.  There is nothing wrong with this approach, it is just viewed as lacking by many who try to be ‘do-all’ technologies.

While I can appreciate Tableau’s approach, they should remain guarded as others are quickly implementing new technologies to match their level of visualization capability, they may need to consider expanding their portfolio.  

Read the company annual report (or 10K) and give an overview of the company, their competitors, their customers, their products and overall strategy. You do not need to include any financial analysis; however, you are strongly encouraged to evaluate the performance of Tableau and its overall direction financially for your own benefit.

Tableau version 9.0 is currently available in 8 languages with over 39,000 customers in over 150 countries.  This statement alone is a testament to the mission of the company, help people see and understand their data.  Distribution strategy is designed to capitalize on the ease of use, low up-front investment, and collaborate facets of the software usually evolving from a free trial to different departments and potentially to an enterprise level.  Total revenues have increased to $653.6 million from $412.6 million in 2014. Tableau is committed to constantly innovating and advancing, they spent $204.1 million in R&D for 2015.

Tableau cites its primary competitors into three categories; large technology companies (IBM, Oracle, Micorsoft), business analytics software companies (Qlik, MicroStrategy, Spotfire), and SaaS-based products or cloud-based analytics providers.  Tableau expects competition to increase and realizes many of their competitors outweigh in resources and history, further understanding this could lead to a loss in market share or price cuts.  Beyond relentless development, Tableau further recognizes their weaknesses and other uncontrollable factors that could impair or diminish success.  It is understood that there is a fine balance of development and retaining revenue for success that must be juggled going forward. Tableau currently has 16 issued U.S. patents and 35 pending patent applications (Form 10-K, 2016).  

competitive analysis of Tableau and two of their competitors

IBM Cognos

These two products are both well-known in the BI software market but they are distinct in the markets they target.  Tableau is a leader visualization tool with its drag and drop modern interface.  Users of all levels can create meaningful dashboards and reports.  Cognos obviously uses visualizations, however, providing a complete, enterprise level BI platform is their focus.  I think this is the true discrimination between the two. Cognos is excellent for multidimensional and relational data sources that can be used by experts to improve strategy and monitor performance.  However, this complexity the product is valued for also makes it difficult for all levels of users to access the insight they need.  So, while this is Tableau’s strength, Tableau is fragile in terms of integrating data from different sources in preparation for analysis. Data preparation would instead be a strength of Cognos (Scavicchio, 2016).

Spotfire

Spotfire BI solutions parallels Tableau’s goal of allowing users to quickly visualize data from various sources, however, their approach is unique. Spotfire requires a more advanced user to make predictions with data whereas Tableau allows less advanced users to drill down into data without statistical analysis.  Spotfire can be troublesome when attempting to customize visualizations and drilling down to specific data details.  Spotfire is recommended for companies looking to improve sales, marketing, and customer experience (Spotfire, 2016).  

QlikView

Like Tableau, QlikView emphasizes data visualization and analytics with easy to use GUI and the ability to integrate data from a plethora of data sources.  However, QlikView also encompasses other BI tools like QlikView Expressor (a metadata intelligence solution) and NPrinting (report generation, scheduling, and distribution).  Users say the interface is clean, easy to understand, and easily integrates with Excel. Therefore, this solution is effective at an enterprise level where different features can be utilized in different departments.  Other features may be stronger than Tableau’s parallel such as good third-party integration, advanced data filtering options, and data manipulation.  QlikView can be difficult to learn and operate because of its many facets and intricacies, but Tableau also comes with a learning curve.  Data management and mapping can require IT assistance with QlikView, visually appealing reports can be difficult to create, and hardware can be extremely costly.  As previously noted, Tableau does not offer ETL capabilities which is a huge shortcoming. QlikView is also able to integrate more data sources than Tableau (Foley, 2015).  

The conversation is not one of which is best, rather that of what the end goal is.  This ties into last week’s conversation surrounding Data Scientists versus Data Analysts.  If you are going to have data analysts and end users gathering what they need from the data available, Tableau is an excellent option.  However, if you have an enterprise with a data scientist, you likely will choose a solution that allows an expert to use statistics for predictive and prescriptive analytics.  Drilling down into the data available will no longer suffice, more information of the data will be needed to find new data sets for users to query.

References

Foley, A. (2015). QlikView Vs. Tableau: Software Showdown. ClearPoint Strategy. Retreived from https://www.clearpointstrategy.com/qlikview-vs-tableau/

Form 10-K. (2016). Tableau Software, Inc. United States Securities and Exchange Commission. Retrieved from http://d1lge852tjjqow.cloudfront.net/CIK-0001303652/893d1eb0-642d-4226-b2ff-853d712155e6.pdf

Scavicchio, J. (2016). Tableau vs. IBM Cognos: Compare Key Features and Functionality. BetterBuys. Retreived from https://www.betterbuys.com/bi/tableau-vs-ibm-cognos-differences/

Scavicchio, J. (2016). Tableau vs. Spotfire: Price and Feature Comparison. BetterBuys. Retreived from https://www.betterbuys.com/bi/tableau-vs-spotfire/

Tableau. (2017). Business Intelligence and Analytics. Retrieved from https://www.tableau.com

[viztips]

Connecting Spotfire to Oracle

Spotfire connection strings to an Oracle database are slightly less straightforward than connecting to a SQL server dbs.

Inside SQL developer, in the pop up for connection properties, a connection string is generated on the top left that contains a concatenation (merging together) of the user name, host name, port, and SID. In that connection string, the user will see that after the user id, the string is as follows: hostname:port/SID (please note the colon and backslash). This is the correct string to input in the server name box that pops up in Spotfire.

A sample below shows how Oracle itself generates the string.

[differenttimemachinecrusade]

Connected Device Analytics Market Evolution, Emerging Trends, and Forecast 2032

TheConnected Device Analytics Market Size was valued at USD 26.94 Billion in 2023 and is expected to reach USD 173.96 Billion by 2032 and grow at a CAGR of 23.0% over the forecast period 2024-2032

The Connected Device Analytics Market is experiencing rapid expansion as businesses and industries increasingly rely on IoT-driven data insights. With billions of connected devices generating vast amounts of real-time data, organizations are leveraging analytics to enhance efficiency, improve decision-making, and drive innovation. Growing demand for predictive analytics, AI-driven insights, and real-time monitoring is propelling this market forward.

The Connected Device Analytics Market continues to evolve as industries such as healthcare, manufacturing, retail, and smart cities integrate IoT devices into their operations. The ability to process, analyze, and derive actionable insights from connected devices is revolutionizing business models. As digital transformation accelerates, the demand for sophisticated analytics solutions that provide enhanced security, automation, and data-driven intelligence is expected to surge.

Get Sample Copy of This Report: https://www.snsinsider.com/sample-request/3727 

Market Keyplayers:

Microsoft (US) (Azure IoT, Power BI)

Oracle Corporation (US) (Oracle IoT Cloud, Oracle Analytics Cloud)

IBM Corporation (US) (IBM Watson IoT, IBM Cognos Analytics)

SAP SE (Germany) (SAP Leonardo IoT, SAP Analytics Cloud)

PTC (US) (ThingWorx, Kepware)

Cisco (US) (Cisco Kinetic, Cisco Jasper)

Google (US) (Google Cloud IoT Core, Google BigQuery)

SAS Institute (US) (SAS IoT Analytics, SAS Visual Analytics)

Adobe (US) (Adobe Analytics, Adobe Sensei)

Teradata (US) (Teradata Vantage, Teradata IoT Analytics)

AWS (US) (AWS IoT Analytics, Amazon QuickSight)

HPE (US) (HPE Edgeline, HPE Vertica)

Hitachi (Japan) (Hitachi Lumada, Pentaho)

Software AG (Germany) (Cumulocity IoT, TrendMiner)

GE (US) (GE Digital Predix, GE APM (Asset Performance Management))

Cloudera (US) (Cloudera DataFlow, Cloudera Machine Learning)

Guavus (US) (Guavus AI-based Analytics, Guavus Reflex)

Splunk (US) (Splunk Industrial IoT, Splunk Enterprise)

TIBCO Software (US) (TIBCO Spotfire, TIBCO Streaming)

Qlik (US) (Qlik Sense, Qlik Data Integration)

Salesforce (US) (Salesforce IoT Cloud, Tableau)

Infor (US) (Infor IoT, Infor Birst)

Mnubo (Canada) (Mnubo SmartObjects, Mnubo Data Science Studio)

Arundo Analytics (US) (Arundo Edge, Arundo Analytics Platform)

Key Trends Driving Market Growth

1. Rise of Edge Computing and AI-Driven Analytics

With the increasing number of IoT devices, edge computing has emerged as a crucial trend. Organizations are leveraging AI-driven analytics at the edge to process data closer to the source, reducing latency and enhancing real-time decision-making. This approach enables faster responses in critical applications such as healthcare, autonomous vehicles, and industrial automation.

2. Expansion of Predictive and Prescriptive Analytics

Businesses are shifting from traditional descriptive analytics to predictive and prescriptive analytics to anticipate trends and optimize operations. Connected devices equipped with advanced analytics capabilities can forecast equipment failures, monitor energy usage, and improve supply chain efficiency, significantly reducing operational costs.

3. Growing Adoption of 5G Connectivity

The rollout of 5G networks is significantly enhancing the capabilities of connected devices. With ultra-low latency and high-speed data transfer, 5G enables seamless real-time analytics, making applications like smart cities, autonomous vehicles, and remote healthcare monitoring more efficient and reliable.

4. Increasing Focus on Cybersecurity and Data Privacy

As connected devices collect vast amounts of sensitive data, cybersecurity and data privacy have become critical concerns. Organizations are investing in advanced encryption, AI-powered threat detection, and blockchain technology to ensure data integrity and compliance with global security regulations such as GDPR and CCPA.

5. Integration of IoT with Cloud and Hybrid Analytics

Many enterprises are adopting cloud-based and hybrid analytics models to handle massive datasets generated by connected devices. Cloud platforms enable scalability, while hybrid approaches offer a balance between security and efficiency, ensuring businesses can analyze IoT data in real-time while maintaining control over sensitive information.

Enquiry of This Report: https://www.snsinsider.com/enquiry/3727 

Market Segmentation:

By Component  

Solution

Service

 By Application  

Sales and Customer Management

Security & Emergency Management

Remote Monitoring

Predictive Maintenance Asset Management

Inventory Management

Energy Management

Building Automation

Others

 By Organization Size

Large Enterprise

Small and Medium Size Enterprise

By Deployment Mode

On-premises

Cloud

Market Analysis and Current Landscape

Surging IoT Adoption: The number of IoT-connected devices is projected to exceed 30 billion by 2030, generating massive amounts of analyzable data.

Rising Need for Operational Efficiency: Companies are leveraging analytics to optimize processes, reduce downtime, and enhance predictive maintenance.

Government and Industry Regulations: Compliance with data security standards and regulations is prompting businesses to adopt robust analytics solutions to manage and secure IoT-generated data.

Competitive Industry Landscape: Tech giants such as Microsoft, IBM, Google, AWS, and SAP are investing in advanced connected device analytics platforms, intensifying market competition.

Despite these promising trends, challenges such as data silos, interoperability issues, and the high cost of analytics implementation remain barriers to widespread adoption. However, as technology advances, businesses are finding scalable and cost-effective solutions to overcome these obstacles.

Future Prospects: What Lies Ahead?

1. Evolution of AI-Powered Autonomous Systems

The next phase of connected device analytics will witness the rise of AI-powered autonomous systems capable of making real-time decisions without human intervention. These systems will be widely used in smart factories, healthcare, transportation, and logistics, driving unprecedented efficiency.

2. Growth of Digital Twins Technology

Digital twins, virtual replicas of physical assets, are becoming mainstream in industries such as manufacturing, construction, and energy. These AI-driven models use connected device analytics to simulate scenarios, predict failures, and optimize asset performance in real time.

3. Blockchain for Secure Data Transactions

Blockchain technology will play a crucial role in securing IoT transactions by ensuring transparency, immutability, and authentication. This will be particularly beneficial for industries dealing with sensitive data, such as financial services, healthcare, and supply chain management.

4. Expansion into Smart Homes and Consumer Electronics

As IoT adoption grows in the consumer segment, smart home devices, wearables, and connected appliances will rely on analytics to improve user experiences. AI-powered assistants, personalized recommendations, and home automation solutions will redefine how consumers interact with their devices.

5. Industry-Specific Analytics Solutions

Companies are increasingly demanding industry-tailored analytics solutions that cater to their specific operational needs. Custom-built analytics platforms for automotive, energy, retail, and telecom sectors will drive deeper market penetration and growth.

Access Complete Report: https://www.snsinsider.com/reports/connected-device-analytics-market-3727 

Conclusion

The Connected Device Analytics Market is set for significant expansion, driven by technological innovations, increasing IoT adoption, and the rising demand for real-time data insights. As industries embrace AI, edge computing, and predictive analytics, businesses that invest in advanced analytics solutions will gain a competitive edge. The future of this market will be shaped by the seamless integration of cloud, AI, and cybersecurity measures, ensuring connected devices operate efficiently and securely. With continued advancements, connected device analytics will not only enhance business operations but also transform how industries leverage data for smarter decision-making and automation.

About Us:

SNS Insider is one of the leading market research and consulting agencies that dominates the market research industry globally. Our company's aim is to give clients the knowledge they require in order to function in changing circumstances. In order to give you current, accurate market data, consumer insights, and opinions so that you can make decisions with confidence, we employ a variety of techniques, including surveys, video talks, and focus groups around the world.

Contact Us:

Jagney Dave - Vice President of Client Engagement

Phone: +1-315 636 4242 (US) | +44- 20 3290 5010 (UK)