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What Is Business Intelligence? Business Intelligence (BI) is a multifaceted approach that involves the gathering, processing, analysis, and visualization of data to derive actionable insights. At its core, BI aims to transform raw data into meaningful information, facilitating informed decision-making within organizations. Components of Business Intelligence: begins with the collection of diverse data from multiple sources such as databases, spreadsheets, CRM systems, and more. This data is integrated and organized into a structured format suitable for analysis. Data Collection and Integration: BI Data warehouses are repositories that store consolidated data from various sources. They enable easy access and retrieval of data, supporting analytical processing and reporting. Data Warehousing: BI tools employ various analytical techniques to scrutinize data, including statistical analysis, data mining, predictive modeling, and more. The insights gained from these analyses are presented through reports, dashboards, and visualizations. Data Analysis and Reporting: Visualization plays a crucial role in BI by presenting complex data in visually comprehensible formats like charts, graphs, and heat maps. make it easier for stakeholders to grasp insights quickly. Data Visualization: Visual representations BI serves as a decision support system, offering executives and decision-makers a clear view of trends, patterns, and anomalies within the data. This information aids in strategic planning and decision-making processes. Decision Support: The Intersection of Business Intelligence and Artificial Intelligence (AI) The integration of Business Intelligence with Artificial Intelligence (AI) represents a powerful synergy, propelling organizations into an era of enhanced data-driven decision-making and predictive capabilities. AI Augmenting Business Intelligence: AI augments BI by integrating advanced analytics and machine learning algorithms. This fusion empowers BI systems to forecast trends, identify patterns, and predict future outcomes more accurately. Advanced Analytics and Predictive Capabilities: AI-driven automate the analysis process, providing real-time insights and recommendations. Natural Language Processing (NLP) enables users to interact with BI systems using everyday language, making insights accessible to a broader audience. Automated Insights and Recommendations: BI services AI algorithms improve data processing efficiency by handling vast volumes of data rapidly. AI-driven BI tools can process unstructured data, such as text and images, enabling a comprehensive analysis of diverse data types. Enhanced Data Processing: The amalgamation of AI with BI facilitates personalized insights tailored to individual user preferences. Adaptive intelligence learns from user interactions, delivering increasingly relevant and customized recommendations. Personalization and Adaptive Intelligence: AI-powered BI tools streamline data preparation tasks by automating data cleansing, normalization, and enrichment processes. This automation accelerates the data preparation phase, allowing analysts to focus more on deriving insights. Augmented Data Preparation: The Impact and Benefits of BI and AI Integration The convergence of BI and AI brings about transformative advantages for organizations across various sectors: The synergy between BI and AI equips decision-makers with highly accurate predictive insights, enabling proactive decision-making and strategy formulation. Enhanced Decision-Making: AI-driven BI automates repetitive tasks, optimizing processes and resource allocation. This efficiency leads to cost savings and improved productivity. Operational Efficiency: AI-infused BI solutions analyze customer behavior patterns, enabling personalized and targeted marketing strategies that enhance overall customer satisfaction. Improved Customer Experience: The fusion of AI and BI fosters innovation by uncovering new opportunities, anticipating market trends, and positioning organizations ahead of competitors. Innovation and Competitive Edge: Predictive analytics powered by AI within BI frameworks identify potential risks, allowing organizations to proactively mitigate and manage risks effectively. Risk Mitigation: Challenges and Considerations Despite the potential benefits, integrating AI with BI presents some challenges: Maintaining data quality and ensuring data privacy remain paramount. AI-driven BI systems require accurate and reliable data sources to produce meaningful insights. Data Quality and Privacy Concerns: Utilizing AI within BI necessitates specialized skill sets. Organizations need to invest in training their workforce to leverage these advanced technologies effectively. Skills Gap and Training: AI algorithms can inadvertently perpetuate biases present in historical data. Ensuring ethical use and preventing biases in AI-driven BI solutions are critical considerations. Ethical and Bias Considerations: Future Outlook The synergy between Business Intelligence and Artificial Intelligence marks a significant paradigm shift in how organizations harness data. As both fields continue to evolve, the collaboration between BI and AI promises to revolutionize decision-making processes, drive innovation, and redefine the competitive landscape across industries. In conclusion, the integration of AI with BI represents a transformative leap forward, empowering organizations to derive deeper insights, make data-driven decisions, and gain a competitive advantage in an increasingly data-centric world.

The Role of Business Intelligence in AI

In today's dynamic digital landscape, data is frequently dubbed as the new oil. However, as with crude oil, the true value of data lies not in its raw state but in its refinement: processing, analyzing, and leveraging it to inform decisions. and Data Engineering often intertwine, especially as DevOps principles are increasingly applied in the data domain, giving rise to the term "DataOps." As data volumes have surged and the pace of business has intensified, the methodologies used to manage and harness data have consequently evolved. Enter DataOps. DevOps Introduction to DataOps At its core, DataOps can be understood as the application of DevOps principles to data workflows. If DevOps is about enhancing software development through continuous delivery, integration, and collaboration, DataOps takes a leaf out of that book but specifically targets the unique challenges and intricacies of the data domain. The focus here is not just on the data itself but on the processes, systems, and teams that handle it, ensuring a streamlined flow of data through its lifecycle. Definition: The concept of DataOps didn't emerge in isolation. It was born out of a confluence of needs. With businesses increasingly relying on data-driven insights and with data sources becoming more varied and voluminous, traditional data management practices began to show their limitations. Speed, scalability, and collaboration became paramount. DataOps, as an approach, traces its roots back to these industry demands and the successful paradigms established by DevOps. Over time, as the industry started recognizing the gaps between data teams (from engineers to scientists to analysts) and the operational challenges they faced, DataOps began to solidify as a distinct discipline in data management. Evolution: Parallels between DevOps and DataOps The continuous evolution in technology and business demands has brought about various methodologies and practices. While DevOps primarily emerged as a bridge between software development and IT operations, ensuring swift and efficient software releases, its foundational principles found resonance in another crucial domain: data engineering. This synergy paved the way for DataOps. To grasp the essence of DataOps, one must first understand its parallels with DevOps. Shared Principles: Just as DevOps emphasizes the and infrastructure changes to ensure consistent and rapid delivery, DataOps recognizes the need for automating data pipelines. This automation reduces manual intervention, minimizes errors, and accelerates data processing. For instance, where DevOps might automate code testing and deployment, DataOps automates data validation, transformation, and loading processes. Imagine a global e-commerce platform. Where DevOps might ensure that the platform itself remains bug-free and user-friendly through automated testing, DataOps ensures that when a customer views a product, the stock count, price, and other product details are up-to-date and accurate, achieved through automated data validation and loading. Automation: automation of software deployment The heart of DevOps lies in the , which ensures that code changes are continuously integrated, tested, and delivered to production. Similarly, DataOps adopts CI/CD but in the realm of data. It ensures that data from various sources integrates seamlessly, is consistently refined, and is delivered to data warehouses or analytics tools without hiccups. This continuous flow ensures that businesses always have access to timely and reliable insights. Continuous Integration and Continuous Delivery (CI/CD): CI/CD pipeline DevOps introduced a culture where developers and IT operations teams collaborated closely, breaking down silos. DataOps extends this collaborative approach to include data engineers, data scientists, and business stakeholders. The objective is the same: fostering an environment where cross-functional collaboration results in optimal outcomes, whether it's software products in DevOps or data-driven insights in DataOps. For instance, a tech company launching a new device might need both software updates (managed by DevOps) and real-time market data for pricing and promotion (handled by DataOps). Seamless collaboration between these teams can ensure a successful launch. Collaboration: Customization for Data: While the foundational principles might be shared, it's essential to understand that data has its own set of complexities. This is where DataOps customizes these principles. Unlike code repositories, which are relatively standardized, data can come from a plethora of sources: IoT devices, user interactions, databases, third-party APIs, and more. DataOps ensures that the principle of continuous integration is agile enough to handle this diversity, integrating data from varied sources into a cohesive dataset. Diverse Data Sources: Data isn't always clean or reliable. Unlike software bugs that are generally consistent in nature, data anomalies can be random and multifaceted. DataOps, drawing from the automation principle, incorporates automated data quality checks and validation processes, ensuring that the data utilized is accurate and trustworthy. Data Quality and Integrity: Data volumes are ever-growing. While DevOps deals with the scalability of applications and infrastructure, DataOps must ensure that and pipelines scale efficiently, processing ever-larger datasets without performance bottlenecks. Scalability and Performance: data infrastructures In essence, DataOps is not just a mere adoption of DevOps principles but a meticulous adaptation, ensuring these principles serve the unique demands and challenges of the data world. It's this careful melding of principles and customizations that makes DataOps a vital methodology in today's data-driven enterprises. Importance of DataOps In the evolving landscape of data-driven decision-making, organizations are under constant pressure to efficiently utilize their data resources. With the sheer volume and diversity of data at play, traditional data management techniques often fall short of delivering timely insights. DataOps, as a solution, steps into this arena, emphasizing not just the methods but the value it brings to the table. The importance of DataOps is manifold, addressing time-to-value, collaborative efficiency, and data quality with remarkable efficacy. Time-to-Value: In the world of business, time is money. The quicker raw data is converted into actionable insights, the faster organizations can make informed decisions, be it launching a new product, refining marketing strategies, or identifying operational inefficiencies. DataOps plays a pivotal role here: Streamlined Workflows: By applying continuous integration and delivery principles, DataOps ensures that data pipelines are smooth, reducing the lag between data acquisition and data consumption. Data validation, transformation, and loading are automated, minimizing manual interventions that often lead to delays. Automated Processes: Airlines use DataOps to quickly process vast amounts of flight data, weather conditions, and passenger preferences to optimize flight schedules, pricing, and in-flight services. This immediate processing can lead to improved customer experience and efficient operations. Collaborative Efficiency: The true potential of data is realized when diverse teams —including data engineers, data scientists, and business stakeholders—work in unison. DataOps fosters this synergy. DataOps encourages the use of platforms where teams can collaboratively view, access, and work on data. This shared workspace reduces back-and-forths and promotes parallel processing. Unified Data Platforms: With clear communication channels, teams are better aligned in their goals, ensuring that the data engineering process serves the larger business objectives. Shared Objectives: Data Quality and Reliability: High-quality insights require high-quality data. With the vastness and varied nature of data sources, ensuring data consistency and reliability is paramount. This is where DataOps shines: Just as code undergoes tests in DevOps, DataOps incorporates automatic data quality assessments, ensuring anomalies are detected and rectified early in the pipeline. Consider financial institutions like , where trading algorithms are based on vast amounts of data. It's not just about quantity; the quality of this data is paramount. A single discrepancy could lead to significant financial discrepancies. Through DataOps' automated quality checks, the bank ensures that its algorithms operate on accurate, validated data, reducing potential risks. Automated Quality Checks: JP Morgan Borrowing from DevOps, DataOps often uses version control for data, ensuring that every stakeholder accesses the most recent, consistent version of the dataset. Version Control: Feedback Loops: Continuous monitoring of data pipelines means that any discrepancies in data quality are flagged and fed back into the system for improvements. This iterative approach enhances the reliability of the data over time. In the midst of an information age where data is plentiful but actionable insights are gold, DataOps stands as a beacon, guiding organizations to effectively harness their data potential. By focusing on quick turnarounds, collaboration, and quality, DataOps ensures that data engineering is not just about managing data but truly empowering businesses. Challenges Addressed by DataOps In today's digital era, businesses are inundated with data. Yet, while data is undeniably a valuable asset, it comes with its own set of challenges. These challenges, if not addressed, can hinder an organization's ability to make informed decisions, strategize effectively, and maintain a competitive edge. DataOps, as a forward-thinking methodology, aims to alleviate these pain points. Let's delve into the core challenges DataOps addresses: Scalability: With data streaming in from a myriad of sources, data infrastructures often buckle under the strain of ever-expanding data volumes. Traditional systems might be ill-equipped to handle this influx, leading to performance degradation and bottlenecks. DataOps encourages the use of cloud-based solutions and containerization, allowing data infrastructures to dynamically scale as per the data volume. This ensures smooth and efficient data processing irrespective of the data load. For instance, Netflix, known for its massive user base, leverages DataOps principles to handle petabytes of data daily, ensuring that their recommendation algorithms and content delivery networks run efficiently. Dynamic Scaling: Through continuous monitoring and feedback loops, DataOps ensures that resources are optimally utilized, preventing over-provisioning and wastage. Resource Optimization: Data Variety: Data heterogeneity poses another challenge. Organizations handle data that ranges from structured datasets in relational databases to unstructured data from social media, logs, and IoT devices. DataOps promotes the creation of platforms where diverse datasets can be integrated, transformed, and standardized, thus offering a cohesive view of data. Coca-Cola, for example, integrates data from various sources such as sales figures, social media feedback, and supply chain data using DataOps techniques to gain a holistic view of its global operations. Unified Data Platforms: DataOps practices often emphasize robust metadata management, aiding in understanding, categorizing, and utilizing diverse datasets effectively. Metadata Management: Real-time Processing: In an age where real-time insights can be a game-changer, the demand for real-time data processing has surged. This requires handling data streams efficiently and processing them without delays. DataOps ensures that data pipelines are designed for low latency, handling data streams efficiently and delivering real-time insights. utilize DataOps to process real-time data on traffic, driver availability, and user demand to efficiently match drivers with riders. Streamlined Pipelines: Companies like Uber DataOps often leans on event-driven architectures, which respond in real-time to data changes or specific events, ensuring timely data processing and analytics. Event-Driven Architectures: Data Security and Compliance: With data breaches becoming increasingly common and regulations like GDPR in place, data security and compliance cannot be overlooked. DataOps emphasizes encrypting data at rest and in transit, ensuring that sensitive information is always secure. End-to-End Encryption: With tools integrated into the DataOps workflow, compliance checks are automated, ensuring data handling adheres to regulatory standards consistently. Automated Compliance Checks: Access Control: Role-based access controls are instituted, making sure that only authorized personnel can access and modify sensitive data. In addressing these challenges, DataOps transforms the daunting task of data management into a streamlined, efficient, and secure process. Organizations armed with DataOps are better positioned to harness their data's potential, ensuring that data-related challenges are not roadblocks but mere stepping stones toward a data-driven future. Best Practices for Implementing DataOps Implementing DataOps can significantly refine an organization's data engineering processes, but the effectiveness of this methodology hinges on the adoption of best practices. These practices act as guideposts, ensuring that DataOps not only integrates smoothly into the existing data ecosystem but also realizes its transformative potential. Here's a comprehensive look at these best practices: Setting Objectives: Defining the North Star: Before diving into DataOps, organizations must outline their primary goals. Whether it’s streamlining data processing, bolstering data quality, or fostering inter-departmental collaboration, a well-articulated objective is pivotal. Companies like Airbnb have emphasized the importance of setting clear objectives when transitioning to DataOps, which allowed them to streamline their vast property and user data more efficiently. Key Performance Indicators (KPIs) act as tangible metrics of success. By setting quantifiable targets, organizations can gauge the efficacy of their DataOps practices and iteratively refine their approach. Without a set target, many organizations find themselves adrift in the vast sea of data, leading to wasted resources and missed opportunities. Quantifying Success: Building the Right Team: The essence of DataOps lies in interdisciplinary teamwork. Assembling a diverse group comprising data scientists, engineers, and operations experts ensures a holistic approach to data challenges. Tech giants like Google stress the importance of diverse teams, drawing on varied expertise to tackle complex data scenarios. Cross-functional Collaboration: The fluid nature of data mandates that the team remains updated with prevailing trends and methodologies. Regular workshops and training sessions help maintain a cutting-edge team. Moreover, fostering soft skills like adaptability, effective communication, and problem-solving enhances the team's ability to navigate the challenges of DataOps efficiently. Continuous Training: Choosing the Right Tools: The market is inundated with tools designed for containerization, orchestration, version control, and monitoring. Organizations should carefully assess their requirements, conduct pilot tests, and choose tools that align with their objectives and infrastructure. The plethora of available tools can be overwhelming. Prioritizing tools like Apache Airflow for orchestration or Docker for containerization, after meticulous assessment, can be beneficial. It’s also prudent to be wary of tools that promise the moon but might not align with the organization's specific needs or existing infrastructure. Assessment and Evaluation: The chosen tools should seamlessly integrate with existing systems, ensuring that the transition to a DataOps approach is smooth and devoid of disruptions. Integration Capabilities: Promoting a DataOps Culture: Collaboration is the bedrock of DataOps. Creating a milieu where open dialogue is the norm and where teams from diverse domains unite to solve data challenges is paramount. Organizations that sideline this collaborative ethos often find themselves grappling with inefficiencies, even if they possess advanced tools. Fostering Collaboration: Just as continuous integration and delivery are integral to DataOps, so is continuous feedback. Regularly collecting feedback from team members and stakeholders and acting on it refines the DataOps process over time. Feedback Loops: In the fast-evolving world of data, learning never stops. Promoting a culture where team members are encouraged to learn, experiment, and innovate ensures that the organization remains at the forefront of data management best practices. Lifelong Learning: In summary, while DataOps holds the promise of revolutionizing , the key to unlocking this potential lies in adhering to these best practices. They act as the bedrock upon which successful DataOps implementation is built, ensuring that data processes are agile, efficient, and in perfect harmony with business objectives. data engineering Conclusion The Transformative Potential of DataOps As we've journeyed through the intricacies of DataOps, one thing is clear: its transformative potential in the realm of data engineering is immense. This isn't just another buzzword or fleeting trend. It represents a paradigm shift. By incorporating DevOps principles into data workflows, organizations can experience heightened efficiency. Gone are the days of siloed operations, where data engineers, data scientists, and business stakeholders worked in isolation. DataOps bridges these divides, fostering a collaborative ecosystem where data-driven decisions are expedited, aligned with business objectives, and rooted in high-quality, reliable data. Future Outlook of DataOps Looking ahead, the future of DataOps seems promising and exciting. As technology continues to advance, we can anticipate the emergence of even more sophisticated tools that further simplify data engineering tasks, promoting automation and ensuring even tighter integration of data processes. Moreover, as organizations globally recognize the merits of DataOps, we might witness the development of new methodologies, best practices, and standards that further refine this discipline. Furthermore, the growing emphasis on and machine learning will likely intertwine with DataOps. This union will give birth to intelligent data operations, where predictive analytics, automation, and adaptive data workflows become the norm. artificial intelligence In conclusion, DataOps stands at the cusp of revolutionizing the world of data engineering. Its principles, methodologies, and practices hold the key to navigating the complexities of today's data-driven world. As organizations continue to embrace and evolve with DataOps, the future of data engineering looks bright, collaborative, and extraordinarily efficient.

DataOps: the Future of Data Engineering

ডেটাঅপস: ডেটা ইঞ্জিনিয়ারিংয়ের ভবিষ্যত

DataOps: die Zukunft des Data Engineering

DataOps: el futuro de la ingeniería de datos

DataOps : l'avenir de l'ingénierie des données

डेटाऑप्स: डेटा इंजीनियरिंग का भविष्य

DataOps: データ エンジニアリングの未来

DataOps: 데이터 엔지니어링의 미래

DataOps: o futuro da engenharia de dados

DataOps: будущее инженерии данных

DataOps: Veri Mühendisliğinin Geleceği

DataOps: Tương lai của Kỹ thuật dữ liệu

DataOps：数据工程的未来

132 Stories To Learn About Robotics

How to Tell the Difference Between Data Warehouses, Data Lakes and Data Lakehouses

According to a recent study from , there's an immense appetite for learning from your company data. Regardless of industry, decision-makers worldwide know they can harness data to make the right strategic calls, whether they are corporate leaders, salespeople, nurses, or technicians. Harvard Business Review The only problem is that many can't extract the correct data from their complex systems. Either they lack the technical skills required or the systems aren't suitable for reliable data gathering and extraction. The latter is mostly due to the age of the systems, making them harder to connect to others. This makes it difficult to extract and compare data quickly, resulting in data silos. Robotic Process Automation (RPA) and Business Intelligence (BI) solutions are the answer here. provides many benefits and allows more instant access to data. RPA BI Addressing The Myths While RPA and BI may be the future solution to all many problems, there is still a great deal of confusion around this topic. When it comes to robotics and AI people have misconceptions about the , its potential, and its limitations. Here are some common myths around RPA BI. technology Robots Will Replace Humans RPA and business intelligence tools allow companies to gain deep insights into their operations, as well as automate processes through robotic means. While RPA may be able to carry out certain jobs more efficiently than a human worker, they will never be able to actually replace the human element. is not actually intelligent, and it cannot make decisions based on common sense. Instead, robots are restricted to the rule-based operations in which they are coded. RPA Digital workers are great for getting certain tasks done in order to help humans and increase efficiency. However, RPA will never actually be able to fully replace the human element. RPA Is Always Correct Automating tasks through RPA aims for total efficiency. However, just like humans, robots can make mistakes too. In fact, these mistakes can become even worse with RPA BI, because the technology will not know that it made a mistake, and could repeat the error. Certain process tools and technology can help to monitor this. There is plenty of sophisticated process intelligence available to keep an eye on this. Although RPA can create incredibly successful repetition tasks, there is always the risk of an error. Just like humans, this needs monitoring. management RPA Aims To Only Reduce Costs One of the major benefits of implementing RPA is saving money over the long run. However, this is not always the primary reason that businesses choose to follow this route. RPA can improve all levels of business processes. This can include task efficiency, time-saving, and often improved quality in the outcome. For example, systems can help users to retrieve data from a database using only Natural Language (as opposed to technical SQL API). This means that end-users don’t require specialized training and that the business process becomes faster and easier for all. Here, RPA is not just working to reduce costs, but to make processes easier for humans. RPA BI RPA Doesn’t Pay Off Upfront costs for RPA can be pretty large - something that can scare away many businesses. However, implementing RPA can offer substantial increases in ROI. Automatic back-office tasks can result in huge savings. RPA is also easy to implement, and commonly produces massive investment returns. Why Automation For BI? A big problem with older systems is noisy data. Modern software is more or less built around centralizing data entry. Let's say, for example, you enter a new account in your online CRM. This is immediately visible to every other user, regardless of where they are or what device they happen to use. While it's still possible to input faulty values, mistakes are easier to spot and fix. Compare this to using systems that aren't connected, either online or with each other—say, your CRM with your customer support and accounting systems. The worst-case scenario? Individual, error-prone files like spreadsheets. This opens the door to duplicate data entries spread over multiple or hard drives, which makes it very hard to know with absolute certainty that your data is reliable. And who wants to make decisions based on incomplete data? databases But even if you use modern software, it can still be complex to find or churn out the right data. That's where automation for Business Intelligence (BI) comes in, in the form of Robotic Process Automation or RPA. Anyone with RPA BI can drill down into data using only Natural Language for getting basic details instantly in order to double-check and make sure about data accuracy. Adding A Natural Language Interface There are two approaches to RPA BI. Files and other content get the index-based approach, while the NLSQL ( ) engine is used for numbers. Natural Language to SQL By adding an intuitive Natural Language Interface to your data and using this in combination with SQL transformation logic, anyone can find everything they need instantly via apps like , or . Slack Microsoft Teams webchat Finding information becomes as easy as Googling for it. This helps to increase productivity and faster decision-making. Instead of waiting hours for colleagues to respond, you can find all the data you need through the NLSQL engine. Depending on the use case, the approaches can be combined or used separately. In practice, we’ve observed that users prefer to work with a Google Search-like web page for finding files and other content within corporate systems. When it comes down to the numbers, (AI) webchat rules the user experience charts. The system can ask for additional input from the user, making sure everything that needs to be in the output is there. You can have all the numbers and figures you need to seal a deal right at your fingertips. All in all, NLSQL is a practical upgrade that increases both productivity and usability and helps you make decisions faster than ever. Step Into The 21st Century: Upgrade Your BI With RPA Upgrading your BI with RPA may seem like a daunting task to some. However, this process focuses on automation and the smart use of strictly coded technology to help maximize your business. RPA can provide plenty of benefits across various industries. In particular, with NLSQL, information is more accessible allowing for processes to become more efficient.

Robotic Process Automation BI Facts And Myth

Are your algorithms transparent those they impact? Is your technology reinforcing or amplifying existing bias? In January 2019, Gartner that AI adoption tripled in the last year alone, with an estimated 37% of firms now implementing AI in some form. Based on responses from over 3,000 CIOs across a wide range of industries, the survey strongly showed how is becoming a crucial component of enterprise strategies, regardless of the industry. reported AI AI is one of the most disruptive technologies of the modern era. Enterprises are constantly exploring and finding new ways to harness data and identify business opportunities using AI. But as AI becomes more commonplace in the enterprise, IT leaders need to consider the ethical implications. Since 2017, more than two dozen national governments have released AI strategies or plans to develop ethics standards, policies, and regulations. Organizations, universities, and governments are calling on companies using AI to discuss and outline ethical principles to govern their use of the technology. Since 2017, more than national governments have released AI strategies or plans to develop ethics standards, policies, and regulations. two dozen Today, major technology companies, such as and have published their own internal principles and are dedicating new to address ethical issues such as bias and lack of transparency. Other initiatives by the world’s largest tech companies include: Google Microsoft teams * A new partnership between Facebook and the Technical University of Munich (TUM) to form , with an initial investment of $7.5 million. The Institute for Ethics in Artificial Intelligence * Amazon and the National Science Foundation recently earmarked $10 million for AI fairness research. * Salesforce announced its tackling bias in AI with new additions to its . Trailhead developer education platform In a recent Deloitte survey, of executives named ethical risks as one of the top three potential concerns related to AI. However, organizing an ethics committee or research center is a costly undertaking. one-third Companies can take a more practical approach first, outlining the mission of their AI-related work and forming their ethics principles around that. One way business leaders can jumpstart this conversation is with the Ethical OS Toolkit. The Ethical OS Toolkit The Ethical OS Toolkit - - was developed by the , a Palo Alto-based think tank, and the , an initiative from the impact investment firm . The toolkit provides technologists with information about new risks related to the technology they are building as well as guidelines for how they can keep their customers, communities, and society safe. available to download for free here Institute of the Future Tech and Society Solutions Lab Omidyar Network The toolkit outlines seven ‘future-proofing’ strategies which help technologists prioritize identified risks, determine their biggest and hardest-to-address threats, and provides guidance on where and how to develop strategies to mitigate those risks. More than 20 technology companies, schools, and startups, including Techstars and Mozilla, are already using the toolkit to address and ensure ethical technology initiatives. Questions enterprises must address to ensure ethical AI For business leaders and developers seeking a practical way to ensure their AI efforts are in line with their company mission, the Ethical OS toolkit outlines several questions to consider. - Does this technology make use of deep data sets and machine learning? If so, are there gaps or historical biases in the data that might bias the technology? How could these have been prevented or mitigated? - Have you seen instances of personal or individual bias enter into your product’s algorithms? - Is the technology reinforcing or amplifying existing bias? Is there a lack of diversity in the people responsible for the design of the technology? - Who is responsible for developing the algorithm? (the assumption that AI-based systems and decisions are correct and don’t need to be verified or audited)? - How will you push back against a blind preference for automation Is there any recourse for people who feel they have been incorrectly or unfairly assessed? - Are your algorithms transparent to the people impacted by them? Taking the right steps toward ethical AI There are plenty of stories about AI ethics boards and committees almost as quickly as they were put together. And though many of these initiatives may be flawed, and in the absence of regulatory and industry AI standards, they are still a move in the right direction. falling apart and a good first step is for IT leaders and their teams to discuss the questions above openly and honestly together. As Stephen Hawking said, “The short-term impact of AI depends on who controls it, the long-term impact depends on whether it can be controlled at all.” Enterprises must consider the ethical implications of the AI products and services they are building, Image source

A Free Ethical OS Toolkit for Woke AI Enterprises  

Do you face difficulties in providing more accurate reporting and analysis?Are you unable to identify the latest trends, market conditions and respond quickly to change? Having problems in optimizing internal business process and driving decisions? What about obtaining important business reports? Is it challenging to convert raw data into actionable insights? If yes, then this blog is definitely for you! Well, if you are using Excel, it may be difficult to work with the large data which ultimately creates roadblocks for data-driven enterprises. And if you are using any other tools which have fewer competencies, it may be difficult to harness data for driving the business decision. So, how to make an informed decision with the use of data? Well, in such a scenario, the powerful Business Intelligence system can come to rescue. Gartner has recognized Microsoft as a leader in Analytics and BI platforms for 11 consecutive years. Why do organizations prefer to use Power BI? Before going in-depth let’s start with basics. What Is Power BI? is a cloud-based business analytics service by Microsoft enabling anyone to visualize and analyze data with greater speed, efficiency, and high understanding. Power BI Moreover Power BI:- Is an interactive data visualization tool which makes it easy to share data across the organization. Delivers valuable insights to make quick and informed decisions Transforms raw and unprocessed data into insightful and stunning visuals for officials to communicate and understand the business better. Provides access to the customized reports and interactive dashboards which help to have a complete 360-degree view of the business. Companies like Dell, Wipro, Metro Bank PLC, Edsby, Oraylis, Vizion solutions and many more are already using Power BI to grow their businesses. Have a look at this video <a href="https://medium.com/media/be842c09282afcb9593226e2f9e1dab9/href">https://medium.com/media/be842c09282afcb9593226e2f9e1dab9/href</a> Who Can Use Power BI? It can be used by to- create interactive data visualization from multiple data sources- deliver real-time insights across the organization Busines Analysts and IT personnel It can be used by to- simplify management- achieve compliance- get accurate and reliable reports- detect anomalies- streamline the workflow Enterprises can use to automate application lifecycle. Moreover, they can- Embed stunning visuals in apps, portals, websites, etc.- Develop and test visuals with open-source JavaScript libraries like D3, jQuery, and more. Developers Power BI REST API But, How Professionals Can Have Access To This Tool? Well, there are three ways to use Power BI : You can connect to data through multiple data sources, combine them into a data model and create compelling visuals. Power BI Desktop : It is a cloud-based, online service for collaboration with the teams and corporations. The reports created in Power BI desktop can be shared in Power BI service, where you can continue modifying them. You can also create dashboards based on your reports in Power BI service. Power BI Service : Power BI offers a set of mobile apps for iOS, Android and Windows 10 mobile devices, where you can view, interact and securely access reports, dashboards, and other data on the go — all in one app. Power BI Mobile Apps To be precise, the desktop is for creating content; the Service is for sharing the content; the mobile apps are for getting insights anywhere on any device. Image Credit: https://docs.microsoft.com To understand in a better way, you need to know the . Power BI architecture But before that, you must be aware of . Power BI components where each component plays an important part in Power BI solution. Power BI is a combination of components Overview Of Power BI’s Components Power BI offers several components that enable users to create and share data reports which include: : Facilitates data search capabilities and transform & merge data from multiple data sources. Power Query : In-memory data modeling component which enables highly-compressed data storage and extremely fast aggregation and calculation. Power Pivot : An interactive visualization tool which enables drag-and-drop interface to the users. Power View : a 3D geospatial data visualization tool Power Map : A natural language question and answering engine Power Q&A : By using this component you get periodic data refreshers and data feeds. Data Management Gateway : User can easily discover and reuse queries using Data Catalog. Metadata can be facilitated for search functionality. Data Catalog Let’s have an overview of the Power BI architecture Power BI Architecture: Sharing And Deploying Files For Power BI solution Connecting Data Power BI can connect data from a variety of data sources. There are two primary options for sourcing data: (1) Importing the data to power BI (2) Using the Direct Query. If you are importing a file to Power BI, it limits the data sets to 1 GB, but if the size is more than 1 GB, you can use Direct Query (you can build reports based on extremely large datasets). Transforming The Data Once the data is connected, you can transform it as per your needs. Transforming data includes changing the name of columns or tables, modifying the text to numbers, removing rows, adjusting rows, headers and much more. There is a number of available to shape your data. transformation options Reporting And Publishing Here reports are created which represent different findings and insights from that dataset. Basically, it is in the form of a , , , etc. It is highly interactive and customizable; which updates as the underlying data changes. After creating a report, you can publish them to Power BI services. graph pie-charts slicers Creating A Dashboard A power BI dashboard consists of a single page, often called canvas that tells a story through visualizations. You can create a dashboard by pinning the individual element or live report page. As it is limited to one page, a well-designed dashboard contains only the overview. Power BI Experience Your Data. Any Data, Any Way, Any Where Power BI: How It Can Be A Game-Changer For Enterprise? Gathers Data From All The Data Sources Power BI makes it easy to fetch data from the cloud services including Microsoft Dynamics, Salesforce, Google Analytics, Zen desk, Facebook and more. No matter where your data is available, power BI enables to shape the data from different sources; enabling you to create visual reports that you can easily share with others. Image Credit: https://powerbi.microsoft.com/en-us/desktop Rich Power BI Dashboards To Make Informed Decisions With Power BI, you can create personalized dashboards enabling it to monitor the business needs in real-time. It makes easy to solve the problems as they occur and provides a holistic view of the business empowering the teams to make informed decisions. You can easily embed the dashboards and BI reports in the applications to provide the unified user experience. Image Credit: https://community.powerbi.com Ask Questions- Get Answers In Real Time How about asking queries in natural language and getting answers in stunning interactive visualization? Well, with Power BI, you can ask a question and get an answer in the form of right chart and right graph. It has auto-search functionality which lets you scan the hidden insights in seconds. It stores all the information in a centralized location and makes interaction with fast and quick data. Image Credit: https://powerbicdn.azureedge.net Tell Stories With Interactive Reports The timeline storyteller is one of the trending custom visuals in Power BI that convey information and details that vary over time. It presents data in the form of circle, grids, spirals or any custom shapes, or even a simple linear list. It also has layouts, different scale representation like logarithmic and relative, controls for filtering, highlighting and annotation. You can decide how you want your data to be represented, scaled and designed in a timeline. Image Credit: https://timelinestoryteller.com Make Business Decisions From Anywhere Power BI offers native BI mobile apps for Android, iOS and Windows where anyone can securely access and view live Power BI dashboards and reports on any device. It is easy to access on-premise data stored in SQL server or data in the cloud; no matter wherever you are. With Power BI Mobile App You Can: View live dashboards and reports on a mobile device Directly share reports and dashboards from the app, which helps to keep the team on the same page Get push notifications for personal data alerts to your device Image Credit: https://powerbicdn.azureedge.net Other Perks Of Power BI is needed to implement Power BI; No technical support is required Little assistance Service provided by Microsoft and has tight integration with its other business tools such as Office 365, Microsoft Excel, Azure, and SQL Server with your existing business environment Integrates easily Helps you to set up automatic data refresh Users can on social media posts and emails, making them accessible to whoever may be interested in them publish their visualizations as Power BI can connect to the software like Spark, Hadoop, etc. and combine the data into a centralized dashboard Data is more accessible No memory and speed constraints Provides with new features and functions frequent updates The has grown significantly over the years community Looking At The Other Side Although Power BI shapes the business world and offers numerous advantages but with every advantage, there is always a corresponding disadvantage. Dashboards and reports can only be shared with users who have the same email domains or email domains listed with your Office 365 tenant. The list of Power BI components is very long and sometimes it becomes difficult to understand which component is required. Microsoft Power BI doesn’t allow you to build scheduled reports, personalized user views, personalized notifications, personalized security views Not all reports can be shared with colleagues as it needs a pro-license to work collaboratively Although, Power BI has few disadvantages; its unique features and functionalities are aligned with the business decisions. Thus it is loved by most of the organizations. How Power BI Is Different From Other BI Tools? To simplify this, let’s compare Power BI with Tableau and QlikView which are the industry-leading BI tools. Tableau doesn’t have a free version. The Personal version of Tableau cost $420 and the Professional version will cost you $840 annually. Whereas, the PRO version of Power BI will only $120 but the free version itself covers a lot more than Tableau. QlikView doesn’t support Predictive Analytics which makes it difficult to make data-based decisions whereas Power BI can help you to create predictive models and with the content pack of Power BI, you can visualize the predictive results. The Future Of Power BI The future belongs to the solution that is Flexible Affordable Innovative Integrates with other solution hassle-free Easy to configure and can be viewed anywhere on any device. Most importantly, it should have the features and functionalities that enable to make quick business decisions. And Power BI fits all these criteria. So, of-course future holds the best for Power BI and it is here to stay for many more years ahead. What is your opinion? Do you think businesses should use Power BI? Please express your opinion below or connect with us to know more about Power BI. : This post was previously published on our blog: Note here <a href="https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href">https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href</a>

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