Tag: AI

Security and Privacy Concerns with AI-driven Cloud Solutions

AI-based cloud services are becoming the first choice for many organizations due to the efficiency, scalability, and cost reduction they offer. It sounds very much tempting, isn’t it? However, it also brings a significant threat in the form of security and privacy. As we all rely on these cloud platforms to store data, there are various problems that emerge with time. It is also important for us to understand these issues to ensure the proper safety and security of our data while storing in these AI cloud platforms. In this article, we will share various data security and privacy concerns faced by experts of International SEO while storing their information in these cloud solutions.

Threats Concerning Security and Privacy in AI Cloud Solutions

While they provide the scalability and efficiency necessary for an organization to study its data, AI-driven cloud platforms are prone to the following security and privacy threats:

Data Breaches:

Despite having stringent security measures, the cloud can still suffer from data breaches. Hackers will develop new methods constantly to infiltrate the systems, which will definitely put sensitive data at risk. As this data can provide insights into AI, a breach can be really disastrous for your organization. Here are some of the things that can be exposed during data breaches in AI-based cloud platforms:

  1. Personally Identifiable Information: names, addresses, social security numbers, etc, which become vulnerable when they are used to train AI models.

  2. Proprietary information:  They include trade secrets, product formulas, and business strategies that can be compromised.

  3. Training data sets: These are the biases and vulnerabilities within the training data. These data can be exploited to influence outputs from AI.

How can you handle Data Breaches?

You can take the following steps to lower the risk of data breach:

  1. Implementing robust solutions for cloud security.

  2. Consulting reputable cloud providers.

  3. Lowering data storage.

  4. Anonymizing sensitive data before feeding it into AI models.

By doing these things, you can effectively lower the chances of data breaches in the AI-driven cloud solutions you are using to store sensitive data.

Privacy Issues That Happen in AI Cloud Computing

Privacy in various AI cloud platforms involves the usage and handling of data. Sometimes, AI won’t recognize whether the information given to it is private or public, which also raises concerns regarding privacy. To tackle such issues, you can do the following things:

  1. Establishing clear frameworks for AI development and deployment.

  2. Strictly collect the data that is important for AI-powered security solutions.

  3. Offering individuals control over their data and the option to opt out of AI-based surveillance, if they feel to do so.

This will help your organization and customers to ensure proper privacy without any compromise.

Bias and Discrimination Based on Algorithm

AI algorithms will act according to the data they are trained on. However, real-world data contains a lot of biases that can influence decisions made by AI. This will lead to discriminatory outcomes in areas like loan approvals, job applications, and several other aspects. For example, an AI model that is trained on loan data that favours specific demographics can cause those biases in the future. This will further raise ethical concerns and cause severe consequences for various individuals.

You can do the following things to tackle algorithmic bias:

  1. Identifying and removing biases in the data before using it for training.

  2. Check the outputs of AI for signs of bias and adjust algorithms accordingly.

  3. Ensure human involvement in important decision-making moments.

Lack of Transparency

Various AI systems are complex and non-transparent, which makes it difficult for us to understand how they arrive at any decision. Due to this lack of transparency, they have to face several following challenges:

  • Accountability:

If the AI you use makes a wrong decision, it is hard to find the cause or assign the responsibility.

  • Explainability:

Many users face challenges in understanding the logic behind the recommendation of an AI, which can also lower your trust and confidence.

  • Debugging and Improvement:

You can’t recognize and fix the flaws present in an AI model without understanding the process of decision-making.

You can follow the steps given below to make AI systems more transparent:

  1. Developing explainable AI methods to know how it reaches its conclusions.

  2. Documenting and maintaining clear records on how these AI models were built and trained.

  3. Providing users with explanations for AI-generated recommendations.

Malicious Ways In Which Your AI Can Be Manipulated.

Data breaches still continue to be a persistent threat. However, security concerns with AI-based cloud have extended these worries further. You should understand the following malicious aspects that can exploit the vulnerabilities in the AI models:

  • Adversarial Attacks:

As mentioned above, hackers will constantly try to find and develop various methods to generate inaccurate and incorrect outputs. Just imagine a fraudster feeding an AI loan approval system with financial data that is slightly altered to bypass security measures. This can result in people not getting enough financial support for their various purposes.

  • Model Poisoning:

Hackers can also infiltrate through the AI’s training process to inject data that is biased or corrupted. This can badly influence the AI’s decision-making ability and result in a predetermined outcome, which will also have unwanted consequences.

How Can You Face These Threats While Evolving Your Data Security?

As AI is experiencing an evolution, its security is also evolving constantly. You will also see various new forms of attack emerging at a time when AI capabilities are also getting more sophisticated. To tackle such issues, you should also keep an eye on the following security considerations:

  • Vulnerabilities in Supply Chains:

Security problems found in third-party softwares or hardware used within your AI system can cause vulnerabilities. To tackle that, you should examine the security strength of your entire AI ecosystem.

  • Physical Security:

It is important to have robust physical security measures for AI hardware and data centers. It will help you tackle any physical security breach that can compromise the integrity of AI models and the data they are dependent upon.

Conclusion

Ensuring the security of your data stored in AI-based cloud softwares can be a big challenge. However, understanding the complexities of these issues can definitely help you tackle them effectively and ensure the data used by your organization for analysis is secure. Furthermore, you should also ensure your AI model offers unbiased solutions with the help of filtering the data to ensure zero discrimination. This will further help you improve the functioning of your organization effectively.

About The Author

Amir Waheed is the co-founder & CEO of SEO Toronto Experts. He intends to bring a massive transformation to eCommerce SEO Services.  His team of talented IT professionals knows the secret of getting huge conversions.

Architecture and AI: Can AI Replace Architects?

Architectural buildings from early times withstand the test of time. When you observe how they were built, for what purposes, and the era they belong to, it becomes very interesting. The creative minds and functionality focus are blended to construct those buildings. As technology advances, AI is gradually becoming a part of architecture as well. In this article, we will discuss the relationship between architecture and AI, which is the hallmark of this combination.

Early and Modern Architecture

From early times, we can observe architectural structures that exhibit different styles, techniques, and purposes. These buildings served functions ranging from cultural to religious, each possessing its own unique characteristics and being constructed with artistic flair.

In the early to mid-20th century, there was a shift in architectural focus towards simplicity and practicality. Architects of the modern era started incorporating novel materials to create cutting-edge and inventive designs. They frequently aimed for a balance between structures and their environment, whether it be natural or urban, by carefully planning and designing the site.

AI and Architecture

Today, technology is making life easier in many aspects, and it is also influencing architecture. Artificial intelligence (AI) and architecture are related in numerous ways, and this relationship is transforming the way we work.

But what is the role of AI in architecture? What are its limitations that cannot replace human ability? Artificial intelligence finds applications across various sectors, such as healthcare, finance, retail, manufacturing, telecommunications, energy, agriculture, and architecture. Its integration is revolutionizing processes and operations in these fields, enhancing efficiency, accuracy, and innovation.

Let’s explore how AI is impacting the architecture industry and in what ways:

AI’s Effect on Architectural Design

As we can see from the early years, each historical structure has its own unique design. For example, the Great Pyramid is a monumental structure consisting of massive limestone blocks stacked to form a pyramid shape. The Taj Mahal is renowned for its symmetrical beauty and intricate detailing, while the Pantheon in Rome is characterized by its distinctive rotunda with a portico of Corinthian columns at the entrance. Each of the buildings we mentioned has a unique design. As we can see, in the early years, people paid attention to choosing and building unique designs, highlighting the importance of design in architecture. Design plays a pivotal role in architecture.

Nowadays, AI is being used in architecture in the design aspect. By using AI algorithms, architects can generate various design ideas based on specified parameters and constraints. This is very time-saving, and what makes it more preferable is the customization process, as architects need to consider several factors.

Some of the AI programs specifically designed for architectural design include: Autodesk Generative Design, NVIDIA Clara Architecture, Trimble SketchUp with Sefaira, Archistar AI.​​ These softwares offer a range of functionalities to support architectural design processes.

AR and VR at Architecture Design

Augmented Reality and Virtual Reality are other options that have a significant impact on making the architectural structure building process more effective, while also saving time and money for the company. They offer the opportunity to visualize and experience designs in immersive virtual environments.

AI’s Impact on Communication

Artificial intelligence significantly enhances collaboration among architects, engineers, and stakeholders. This streamlined approach ensures smoother and more efficient project execution, leading to better outcomes. Some of the ways how the AI improves communication include  by offering immediate feedback, automating routine tasks, and facilitating the seamless integration of design data across various software platforms.

Limitations of AI

AI has several advantages, but limitations are also present. Imagine the creativity of humans in early times, their manner of thinking, how they planned, and how perfectly they executed their plans into real projects. The human brain is just amazing. The creativity that humans possess, which is evident, AI cannot replace. AI still struggles to exhibit true creativity in the same way humans do. While AI can process and analyze vast amounts of data, it may struggle to interpret subtle cues, cultural nuances, or complex social dynamics that are essential for fully understanding a situation or problem.

The creative ideas that come from a Swiss architecture company cannot be replaced by AI. The idea of thinking outside the box and generating ideas from a personal perspective, as well as considering cultural aspects, can all be designed in a professional and creative manner.

Opportunities for Hybrid Approaches

When AI is combined with human activity and interaction, the future of this hybrid approach seems very promising. Hybrid approaches produce solutions that are more flexible, effective, and efficient than either one could be used alone. They offer a viable way to fully utilise AI technology’s potential while preserving human interaction and making sure that AI stays an instrument that enhances rather than replaces human abilities.

Conclusion

The architectural buildings that resist time show us in what powerful and creative manner they were built. Every element, from the design to the details, captures the historical and cultural context. Over time, the building process has evolved, and as technology evolves, the use of AI in the architectural process is improving in several ways. From design optimization to time and cost efficiency, AI is affecting the architecture field. However, the most promising approach for the future is a hybrid approach, which combines human creativity with the advantages of AI.

 

 

 

The Synergy Between Quantum AI and Blockchain

Quantum AI and blockchain are two groundbreaking technologies that have the potential to revolutionize multiple industries. While individually powerful, their true potential lies in their synergy and the unique possibilities they offer when combined. In this article, we will delve into the intricacies of quantumaitradingapp.org and blockchain, explore their intersection, discuss potential applications, highlight challenges in merging these technologies, and gaze into the future of this exciting field.

Understanding Quantum AI and Blockchain

In today’s rapidly evolving technological landscape, two fields that have garnered significant attention are Quantum AI and Blockchain. These cutting-edge technologies have the potential to revolutionize various industries, from finance to healthcare, by solving complex problems and enhancing security. Let’s delve deeper into the fascinating world of Quantum AI and Blockchain.

Defining Quantum AI

Quantum AI, also known as quantum artificial intelligence, is an interdisciplinary field that combines principles from quantum computing and artificial intelligence. It aims to tackle problems that are beyond the reach of classical computers. While classical computers rely on binary bits, which can represent either a 0 or a 1, quantum computers utilize qubits.

Qubits, short for quantum bits, take advantage of two unique phenomena: superposition and entanglement. Superposition allows qubits to exist in multiple states simultaneously, exponentially increasing the computational power. Entanglement, on the other hand, enables qubits to be interconnected, allowing for the transfer of information instantaneously.

By harnessing the power of qubits, quantum AI algorithms, such as quantum machine learning algorithms, can extract patterns and insights from vast datasets more efficiently than classical machine learning techniques. This has the potential to revolutionize fields such as drug discovery, optimization problems, and financial modeling.

The Basics of Blockchain Technology

While Quantum AI explores the realm of quantum computing, Blockchain technology emerged with the invention of cryptocurrencies like Bitcoin. At its core, blockchain is a decentralized and distributed ledger that securely records transactions across multiple computers, known as nodes.

Information on a blockchain is stored in blocks, which are linked together through cryptographic hashes, forming an immutable chain. This means that once a block is added to the chain, it becomes nearly impossible to alter or tamper with the recorded information. This attribute, known as immutability, ensures the integrity and trustworthiness of the data stored on the blockchain.

Blockchain’s transparency is another key attribute that sets it apart. Every transaction recorded on the blockchain is visible to all participants, creating a transparent and auditable system. This transparency has the potential to revolutionize industries such as supply chain management, where traceability and accountability are crucial.

Furthermore, blockchain technology provides enhanced security through its decentralized nature. Traditional centralized systems are vulnerable to single points of failure and hacking attempts. In contrast, blockchain’s decentralized architecture distributes the data across multiple nodes, making it highly resistant to attacks.

Given these attributes, blockchain technology has expanded beyond cryptocurrencies and found applications in various industries. From financial institutions exploring blockchain for secure and efficient cross-border transactions to healthcare providers leveraging blockchain for secure patient data management, the potential use cases are vast.

In conclusion, Quantum AI and Blockchain are two transformative technologies that hold immense promise for the future. Quantum AI’s ability to solve complex problems at an unprecedented speed and Blockchain’s potential to enhance security and transparency are reshaping industries and opening up new possibilities. As these technologies continue to evolve, we can expect to witness even more groundbreaking advancements in the years to come.

The Intersection of Quantum AI and Blockchain

In recent years, the convergence of quantum artificial intelligence (AI) and blockchain technology has sparked immense interest and excitement. This powerful combination has the potential to revolutionize various industries, from finance to healthcare, by offering enhanced security, improved computing capabilities, and accelerated development in both fields.

How Quantum AI Enhances Blockchain

Quantum AI brings several advantages to the world of blockchain technology. One significant benefit is improved security. Quantum computers possess an inherent capability to solve complex mathematical problems, including breaking conventional cryptographic algorithms that underpin classical blockchain networks.

Imagine a future where quantum AI algorithms can efficiently secure the blockchain against potential attacks, ensuring the integrity and confidentiality of transactions. This level of security would provide individuals and businesses with unparalleled trust in the blockchain, opening up new possibilities for secure digital interactions.

By leveraging quantum-resistant cryptographic techniques, such as lattice-based cryptography or multivariate cryptography, quantum AI can enhance the security of blockchain networks, safeguarding sensitive data and digital assets against quantum attacks. These advanced cryptographic methods can withstand the computational power of quantum computers, ensuring the longevity and reliability of blockchain systems.

Blockchain’s Role in Quantum AI Development

While quantum AI enhances blockchain, the reverse is also true. Blockchain technology can contribute to the development of quantum AI, addressing one of the key challenges in this field.

One key challenge in quantum AI research is the lack of accessible quantum computing resources. Quantum computing infrastructure is still limited, leaving researchers and developers with scarce resources to experiment and collaborate. However, blockchain’s decentralized nature offers a potential solution.

By leveraging blockchain’s decentralized nature, quantum AI enthusiasts can create distributed quantum computing architectures. These architectures can enable individuals and organizations to pool their quantum computing resources, facilitating faster advancements in quantum AI development. Through blockchain-based platforms, researchers can access a network of quantum computers, harnessing their collective power to tackle complex problems and accelerate scientific breakthroughs.

Furthermore, blockchain’s transparent and immutable nature can enhance the trust and collaboration among quantum AI researchers. By recording research progress, data, and results on the blockchain, scientists can establish a reliable and auditable record of their work. This transparency fosters an environment of open collaboration, where researchers can build upon each other’s findings and collectively push the boundaries of quantum AI.

As the intersection of quantum AI and blockchain continues to evolve, the possibilities for innovation and advancement are boundless. Together, these technologies have the potential to reshape industries, drive scientific progress, and unlock new frontiers in computing and data security.

Potential Applications of Quantum AI and Blockchain Synergy

Improving Security with Quantum AI and Blockchain

The combination of quantum AI and blockchain opens up new avenues for improving security in various domains. For example, secure authentication systems powered by quantum AI algorithms can protect sensitive data from hacking attempts. Additionally, supply chain management can benefit from enhanced security through blockchain’s transparency and quantum AI’s fraud detection capabilities.

Accelerating Transactions through Quantum AI-Blockchain Integration

In the financial industry, quantum AI can significantly enhance transaction speed and efficiency. Traditional financial systems often suffer from delays and intermediaries that slow down transactions. By integrating quantum AI algorithms into blockchain networks, transactions can be processed at a lightning-fast pace, eliminating unnecessary intermediaries and reducing transaction costs.

Challenges in Merging Quantum AI and Blockchain

Technological Limitations and Concerns

While the synergy between quantum AI and blockchain holds tremendous potential, there are several challenges that need to be addressed. One notable obstacle is the current limitations of quantum computing hardware. Quantum computers are still in their infancy, and scaling up their capabilities is an ongoing technological challenge.

Furthermore, there are concerns about the energy consumption of quantum computing, which can be substantially higher compared to classical computers. Researchers and engineers are actively working towards developing more efficient and scalable quantum computing architectures.

Ethical and Regulatory Implications

As with any emerging technology, the convergence of quantum AI and blockchain raises ethical and regulatory concerns. Privacy, data ownership, and security must be carefully considered to ensure the responsible implementation of these technologies.

Governments and regulatory bodies are grappling with the implications of quantum AI and blockchain in various sectors. Creating frameworks that strike a balance between innovation and protection is crucial for the widespread adoption of these technologies.

The Future of Quantum AI and Blockchain

Predicted Trends and Developments

Looking ahead, experts predict exponential growth in the synergistic application of quantum AI and blockchain. As quantum computers become more powerful and quantum AI algorithms advance, industries will adopt these technologies to gain a competitive edge.

Moreover, collaborations between researchers, developers, and industry leaders will accelerate the development of quantum AI and facilitate the creation of robust blockchain networks equipped to handle quantum threats.

Impact on Various Industries

Quantum AI and blockchain synergy will impact a wide range of industries. Finance, healthcare, logistics, energy, and cybersecurity are just a few sectors poised to benefit from the integration of these technologies.

For instance, personalized medicine can leverage quantum AI algorithms for genomic analysis and drug discovery, while blockchain can ensure the integrity and traceability of sensitive healthcare data.

As the potential of quantum AI and blockchain synergy unfolds, it is clear that these technologies have the capacity to reshape the world around us. By combining the computational power of quantum AI with the security and transparency of blockchain, limitless possibilities arise. However, addressing technological challenges, ethical concerns, and regulatory frameworks will be crucial to harness the full potential of this disruptive synergy.

The Advantages and Disadvantages of Developing a Cutting-Edge Gopuff Clone

Positioned at the forefront of the rapidly advancing artificial intelligence market is the GoPuff Clone App, a state-of-the-art virtual assistant. This groundbreaking app employs AI and natural language understanding to deliver an unmatched user experience. Representing the most recent innovation in AI-powered mobile apps, the Gopuff Clone App has the capability to revolutionize our interactions with technology.

A Brief Description of AI-Powered Virtual Assistant

The concept of virtual assistants dates back to the early days of computer science, but it was in recent years that significant strides were made. Early virtual assistants were limited in capabilities and primarily used for basic tasks like setting reminders or performing internet searches. However, with advancements in machine learning and natural language processing, modern virtual assistants have become increasingly sophisticated and can now engage in complex user interactions.

Comprehending Cutting-Edge Technology

The Evolution of AI in Mobile Applications

AI has brought about a transformative shift in the realm of mobile apps, empowering developers to craft software that is both intuitive and responsive to context. The influence of AI-powered applications spans a wide spectrum, ranging from chatbots to voice-activated aides, and they have become ingrained in various facets of our existence, amplifying convenience and efficiency.

Harnessing the advantages of this technology, creating an app Like Gopuff introduces a heightened level of engagement and customization. By scrutinizing user actions and preferences, the app acquires knowledge and adjusts itself to offer personalized support, establishing itself as an indispensable instrument for individuals from diverse backgrounds.

Here’s a technical breakdown of the Gopuff Clone App

At its core, the Gopuff Clone App relies on sophisticated algorithms that process natural language input from users. Leveraging machine learning models, the app recognizes speech patterns, intents, and context to generate appropriate responses and actions.

The app’s backend infrastructure is designed to handle large volumes of data and continuously improve its capabilities over time. With cloud-based storage and processing, the Gopuff Clone App can seamlessly integrate with various devices and services, ensuring a consistent and interconnected experience.

Advantages of Developing a Gopuff Clone App

As developers and businesses explore the potential of the Gopuff clone, discover a wide range of advantages that can revolutionise user experiences and organisational efficiencies.

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Seamless User Experience

Personalized Interactions with Users

One of the most significant advantages of the Gopuff Clone App is its ability to personalise user interactions. The app learns about individual preferences, behaviour, and habits through continuous data analysis. This allows it to offer tailored recommendations, reminders, and suggestions, creating a highly individualised experience for each user.

Natural Language Processing Capabilities

The Gopuff Clone App’s advanced natural language processing capabilities set it apart from traditional virtual assistants. Users can engage in conversations with the app, and it understands context, nuances, and even emotions. This human-like interaction makes the app more approachable and user-friendly, increasing user satisfaction and engagement.

Increased Productivity and Efficiency

Automating Repetitive Tasks

The Gopuff Clone App is a powerful productivity tool that automates repetitive tasks. From setting up appointments and managing to-do lists to sending emails and reminders, the app streamlines daily activities, freeing up valuable time for users to focus on more critical tasks.

Streamlining Daily Activities

The Gopuff Clone App simplifies daily activities by centralising various functions and services. Users can access multiple applications, services, and devices through a single interface, eliminating the need to switch between different platforms. This unified approach enhances efficiency and reduces the cognitive load on users.

Enhanced Customer Support

24/7 Availability and Quick Response Time

The Gopuff Clone App’s virtual nature provides round-the-clock customer support. Unlike human agents, the app does not require breaks or sleep, ensuring continuous availability. This real-time support ensures prompt responses to customer queries, enhancing overall customer satisfaction.

Handling Multiple Queries Simultaneously

Traditional customer support agents can handle only one query at a time, leading to potential delays in response. The Gopuff Clone App, on the other hand, can manage multiple queries simultaneously, addressing customer needs promptly and efficiently. This capability significantly improves customer service and reduces wait times.

Drawbacks of Developing a Gopuff Clone App

As groundbreaking as the Gopuff Clone App may be, it is essential to recognize and address its development and implementation’s potential drawbacks and challenges.

Privacy and Security Concerns

Storing Sensitive User Data

The Gopuff Clone App’s ability to personalize interactions relies on storing and analyzing user data. While this data is critical for providing a tailored experience, it raises concerns about privacy and security. Developers must prioritize robust data encryption and implement stringent data protection measures to safeguard user information from unauthorized access or breaches.

Preventing Unauthorized Access and Data Breaches

Given the vast amounts of personal and sensitive data the app processes, it becomes an attractive target for cybercriminals. Developers must fortify the app’s security architecture to prevent unauthorized access and potential data breaches. Regular security audits and updates are crucial to maintaining a secure environment for the app and its users.

Dependency on Internet Connectivity

Challenges in Offline Functionality

The Gopuff Clone App heavily relies on internet connectivity to process natural language queries and provide timely responses. However, this dependence poses challenges when users are in areas with poor or no internet connectivity. Developers must implement offline capabilities to ensure basic functionality even without an internet connection, striking a balance between online and offline experiences.

Mitigating Connectivity Issues

Even with offline capabilities, there might be instances where the app’s performance is affected by slow or unstable internet connections. To mitigate these issues, developers must optimize the app’s codebase to handle varying network conditions and provide graceful degradation when internet connectivity is limited.

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Addressing Ethical Considerations and Bias in AI

AI algorithms, like those integrated into the Gopuff Clone App, are susceptible to bias due to the data they’re trained on. Such bias can result in unjust or prejudiced behavior, disproportionately affecting specific user demographics. It’s imperative for developers to consistently work on recognizing and minimizing bias, guaranteeing impartial treatment for all app users.

Promoting Ethical Usage of the Gopuff Clone App

The functionalities of the Gopuff Clone App raise ethical queries concerning user agreement and data utilization. It’s pivotal that developers offer transparency regarding the app’s operations and gain explicit consent from users for the collection and application of their data. Furthermore, the app must adhere to stringent ethical standards to forestall any inappropriate use or mishandling of user information.

Putting it together

In conclusion, the Gopuff Clone App signifies a noteworthy leap in AI-powered technology, providing tailored experiences and heightened efficiency. Nevertheless, it’s crucial to address concerns about privacy, security, and ethical implications while considering the potential for job displacement. Through responsible development practices, we can unlock the app’s complete potential, empowering users and reshaping the trajectory of virtual assistants.

 

About The Author

Taylor Parker is an American-based writer and blogger. She is very passionate about family, fashion, health, and lifestyle. Taylor writes mostly lifestyle articles, but also you’ll find her in home improvement and other niches.  You can follow her on Facebook and Twitter

Do You Need a Business Intelligence Dashboard?

The advent of artificial intelligence and machine learning has advanced the capabilities of contemporary business intelligence (BI) tools considerably. With that said, the insights returned are inly as useful as the user’s ability to interpret them. 

BI dashboard software has made this easier to accomplish by providing operational insight with data visualization, reporting, analysis, and more. 

So, in that regard, the answer to the central question here — do you need a business intelligence dashboard — is yes, with a caveat.

Read on.

What is a BI Dashboard?

This tool puts the results of even the most in-depth analytics in the hands of those who need the information most. Moreover, the dashboard does so in a manner that makes it easy to both understand and apply to the situation at hand. Embeddable dashboard tools enable automated data visualization, which can deliver key takeaways in a way that can be readily digested. 

 

Even better, this makes it easy for anyone on your team, who is authorized to do so, to make requests, access the results and use them to effect decision-making. Moreover, flexibility is a core aspect of the best business intelligence dashboards, as they can be tailored specifically to the individual needs of a multitude of end users.

 

The Caveat 

Sudheesh Nair, CEO of ThoughtSpot, one of the industry’s leading data analytics firms, says the dashboard has an inherent limitation users need to keep in mind. The issue drawing his concern is predicated upon the way data has traditionally been used to enable companies to serve customers in an informed manner. 

 

To understand his perspective, one must look back at 2020 and all of the disruptions imposed by the COVID-19 pandemic. Businesses were suddenly forced to cope with conditions they’d never seen before, let alone anticipated. Nair says relying upon historical aggregated data going forward, given the past year’s disruptions have rendered it less relevant, is problematic. Therefore, a business intelligence dashboard relying upon the traditional analytics model is likely to be inaccurate. 

 

The Solution

Meanwhile, ThoughtSpot has positioned itself as something of a “Google” for data. Its platform works in a similar fashion to that of the venerable search engine, in that users can ask specific questions based upon the data provided and get answers to the question they asked. However, with ThoughtSpot, they also get alternative questions and answers that have some bearing on the query. 

 

As an example, Nair cites a scenario in which a financial institution is looking to get existing customers to use more of its products. In this case, the lender is trying to get holders of its car loans to take advantage of its mortgages too. So it runs a query to see which car loan customers might be considering buying a home. 

 

The results come back and customers identified by the algorithm get a mailer touting the benefits of the home loan. However, there is a group of car loan customers out there holding a bit of ill will against the lender for late charges imposed on payments that arrived one day after the due date. Irritated with the creditor, these borrowers ignore the offer altogether. 

 

Had the company used a platform that leveraged AI the way ThoughtSpot does, those customers would have been flagged, giving the lender the option of not sending them the offer, or better still — sending the offer with an apology and a refund of the late fee. 

 

Looking Forward as Well as Backward

Having this capability gives ThoughtSpot the ability to take a use case driven approach. This is going to be key in coming years because the COVID-19 disruption is going to skew results based upon the traditional aggregation models. While companies will still need the ability to ask “what”, they also need to be able to ask “what if” and “what’s next”. 

 

So, do you need a business intelligence dashboard? It is still useful, however, you really need to rethink the way it’s informed if you want the best actionable results going forward. 

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