Author Archives: Evelina Rosser

WHAT ARE SOME EXAMPLES OF BUSINESS INTELLIGENCE TOOLS THAT CAN BE USED FOR ANALYZING CUSTOMER DATA

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Microsoft Power BI: Power BI is a powerful and popular BI tool that allows users to connect various data sources like Excel, SQL databases, online analytical processing cubes, text files or Microsoft Dynamics data and perform both standard and advanced analytics on customer data. With Power BI, you can visualize customer data through interactive dashboards, reports and data stories. Some key capabilities for customer analytics include segmentation, predictive modeling, timeline visualizations and real-time data exploration. Power BI has intuitive data modeling capabilities and strong integration with the Microsoft ecosystem and Office 365 which has led to its widespread adoption.

Tableau: Tableau is another leading visualization and dashboarding tool that enables effective analysis of customer data through interactive dashboards, maps, charts and plots. It has an easy to use drag-and-drop interface for quickly connecting to databases and transforming data. Tableau supports a variety of data sources and database types and has advanced capabilities for univariate and multivariate analysis, predictive modeling, time series forecasting and geospatial analytics that are highly useful for customer insights. Tableau also offers analytics capabilities like account profiling, adoption and retention analysis, next best action modeling and channel/campaign effectiveness measurement.

SAP Analytics Cloud: SAP Analytics Cloud, previously known as SAP BusinessObjects Cloud, is a modern BI platform delivered via the cloud from SAP. It provides a rich feature set for advanced customer data modeling, segmentation, predictive analysis and interactive data discovery. Some key strengths of SAP Analytics Cloud for customer analytics are predictive KPIs and lead scoring, Customer 360 360-degree views, customizable dashboards, mobility and collaborative filtering features. Its connectivity with backend SAP systems makes it very useful for large enterprises running SAP as their ERP system to drive deeper insights from customer transaction data.

Qlik Sense: Qlik Sense is another powerful visualization and analytics platform geared towards interactive data exploration using associative data indexing technology. It allows users to explore customer datasets from different angles through its Associative Data Modeling approach. Businesses can build dashboards, apps and stories to gain actionable insights for use cases like customer journey modeling, campaign performance tracking, Churn prediction and more. Qlik Sense has strong data integration capabilities and supports various data sources as well as free-form navigation of analytics apps on mobile devices for intuitive data discovery.

Oracle Analytics Cloud: Oracle Analytics Cloud (previously Oracle BI Premium Cloud Service) is an end to end cloud analytics solution for both traditional reporting and advanced analytics use cases including customer modeling. It has pre-built analytics applications for scenarios like customer experience, retention and segmentation. Key capabilities include embedded and interactive dashboards, visual exploration using data discoveries, predictive analysis using machine learning as well as integration with Oracle Customer Experience (CX) and other Oracle cloud ERP solutions. Analytics Cloud uses in-memory techniques as well as GPU-accelerated machine learning to deliver fast insights from large and diverse customer data sources.

Alteryx: Alteryx is a leading platform for advanced analytics and automation of analytical processes using a visual, drag-and-drop interface. Apart from self-service data preparation and integration capabilities, Alteryx provides analytic applications and tools specifically for customer analytics such as customer journey mapping, propensity modeling, segmentation, retention analysis among others. It also supports predictive modeling using techniques like machine learning, statistical analysis as well as spatial analytics which enrich customer insights. Alteryx promotes rapid iteration and has strong collaboration features making it suitable for both analysts and business users.

SAS Visual Analytics: SAS Visual Analytics is an enterprise grade business intelligence and advanced analytics platform known for its robust and comprehensive functionality. Some notable capabilities for customer intelligence are customer value and portfolio analysis, churn modeling, segmentation using R and Python as well as self-service visual data exploration using dashboards and storytelling features. It also integrates technologies like AI, machine learning and IoT for emerging use cases. Deployment options range from on-premise to cloud and SAS Visual Analytics has deep analytics expertise and industry specific solutions supporting varied customer analytics needs.

This covers some of the most feature-rich and widely applied business intelligence tools that organizations worldwide are leveraging to perform in-depth analysis of customer and consumer data, driving valuable insights for making informed strategic, tactical and operational decisions. Capabilities like reporting, visualization, predictive modeling, segmentation and optimization combined with ease-of-use, scalability and cloud deployment have made these platforms increasingly popular for customer-centric analytics initiatives across industries.

CAN YOU PROVIDE MORE DETAILS ABOUT THE MARS SAMPLE RETURN CAMPAIGN AND HOW IT RELATES TO PERSEVERANCE’S MISSION?

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The Mars Sample Return (MSR) campaign is an ambitious multi-year collaborative effort between NASA and the European Space Agency (ESA) to return scientifically selected rock and soil samples from Mars to Earth. Bringing samples back from Mars has been a priority goal of the planetary science community for decades as samples would provide a wealth of scientific information that cannot be obtained by current robotic surface missions or remote sensing from orbit. Analyzing the samples in advanced laboratories here on Earth has the potential to revolutionize our understanding of Mars and help answer key questions about the potential for life beyond Earth.

Perseverance’s role in the MSR campaign is to collect scientifically worthy rock and soil samples from Jezero Crater using its drill and sample caching system. Jezero Crater is a 28-mile wide basin located on the western edge of Isidis Planitia, just north of the Martian equator. Billions of years ago, Jezero was the site of an ancient lake filled by a river delta. Scientists believe this location preserves a rich geological record that could provide vital clues about the early climate and potential for life on Mars.

Perseverance carries 43 sample tubes that can each store one core sample about the size of a piece of chalk. Using its 7-foot long robotic arm, drill, and other instruments like cameras and spectrometers, Perseverance will identify and study geologically interesting rock formations and sedimentary layers that could contain traces of ancient microbial life or preserve a record of past environments like a lake. Under careful sterile conditions, Perseverance’s drill will then take core samples from selected rocks and the rover will transfer them to sealed tubes.

The carefully cached samples will then remain on the surface of Mars until a future MSR mission can retrieve them for return to Earth, hopefully within the next 10 years. Leaving the samples on the surface minimizes the risk of contaminating Earth with any Martian material and allows the scientific study of samples to happen under optimal laboratory conditions here with sophisticated equipment far beyond the capabilities of any Mars surface mission.

Perseverance began caching samples in its first session at “Rochette” in October 2021 and as of March 2022 had already cached 9 samples. It plans to continue collecting samples at Jezero Crater through at least 2033 to ensure the most scientifically compelling samples are returned to Earth for detailed analysis. The tubes will be deposited in carefully documented “cache” locations along the rover’s route so future missions know where to retrieve them. In total, Perseverance has the capability to cache up to 38 samples by the end of its prime mission.

The ambitious MSR architectural plan currently envisions three complex separate missions to retrieve and return the cached Perseverance samples. The first mission, currently targeted for launch in 2028, is the Mars Ascent Vehicle/Orbiting Sample (MAV/OS). This rocket and spacecraft combo would land near Perseverance’s cached samples, lift off from the Martian surface, and deploy the Sample Retrieval Lander containing the Mars Orbiting Sample canister.

The Sample Retrieval Lander would then touch down, deploy a small rover to retrieve the cache tubes left by Perseverance at the designated cache location(s), and transfer the samples to the Sample Orbiting Sample canister. The MAV would then lift back into Martian orbit where it would rendezvous with the orbiter and transfer the Sample Orbiting canister into the secure containment orbiting Mars.

The next critical MSR mission is the Earth Return Orbiter (ERO) launch, targeted for 2030. The ERO spacecraft would travel to Mars and capture the orbiting sample container left by the MAV/OS mission. The ERO would then depart Mars and begin the seven-month 230-million-mile trip back to Earth carrying the priceless samples. To prevent terrestrial contamination, the samples would remain sealed in the containment orbiter for re-entry.

The third mission planned is the Earth Entry Vehicle (EEV) targeted to launch in 2031. This mission would capture the returning ERO spacecraft and utilizing a capsule, heat shield, and parachutes, would safely land the sample containers in Utah’s west desert where scientists can extract the Mars samples under strict planetary protection protocols in new laboratories built specifically for this purpose.

The unprecedented MSR campaign has the potential to revolutionize our understanding of Mars and address questions that have intrigued scientists for generations like whether Mars ever supported microbial life. Careful caching by Perseverance and meticulous retrieval and return by the future MSR elements provides the best opportunity for scientific discovery while ensuring planetary protections. Perseverance’s diligent efforts at Jezero Crater to select and cache compelling rock core samples in its ambitious multi-year exploration leaves promising potential for future scientists to examine Martian treasures from the safety of Earth.

WHAT ARE SOME OF THE CHALLENGES THAT MICROSOFT’S AI FOR GOOD PROGRAM AIMS TO ADDRESS?

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Microsoft launched its AI for Good initiative in 2017 with the goal of using artificial intelligence technology to help address major societal challenges. Some of the key challenges the program focuses on include:

Improving Global Health Outcomes – One of the primary focuses of AI for Good is applying AI to help improve health outcomes worldwide. This includes using machine learning models to help accelerate medical research and discover new treatments. For example, Microsoft is working with researchers to use AI to analyze genetics and biomedical data to help develop personalized medicine approaches. AI tools are also being developed to help tackle global health issues like improving early detection of diseases. By helping medical professionals more accurately diagnose conditions, AI could help save more lives.

Addressing Environmental Sustainability – Another major challenge AI for Good works on is supporting environmental sustainability efforts. Microsoft is developing AI solutions aimed at issues like monitoring climate change impacts, improving agricultural sustainability, and aiding conservation efforts. For example, computer vision models are being used with satellite imagery to track changes to forests, glaciers and other natural areas over time. AI is also being applied to help farmers optimize crop yields while reducing water and land usage. By aiding environmental monitoring and more efficient resource management, AI for Good’s goal is to help address the threat of climate change and encourage sustainable practices.

Improving Education Outcomes – Gaps in access to quality education is another societal problem AI for Good seeks to help solve. Microsoft is researching how to apply AI to personalized learning approaches and make education more widely available. This includes developing AI teaching tools and adaptive learning software that can tailor lessons to individual students’ needs and learning styles. Natural language processing is also being used to help automate essay grading and feedback to enhance learning assessments. By helping expand access to customized, data-driven education approaches, AI for Good’s vision is to help improve learning outcomes worldwide, especially in underserved communities.

Fostering More Inclusive Economic Growth – More inclusive and sustainable economic development is another focus challenge area. AI solutions are being explored that can help address issues like accessibility of employment and workforce retraining needed for new skillsets. For example, Microsoft is researching how AI career coaches and virtual agents could provide personalized guidance to help jobseekers of all backgrounds. Computer vision is also being applied to tasks like manufacturing to automate certain physical jobs in a way that creates new types of employment, rather than replacement. By aiding the transition to emerging industries, AI for Good’s aim is to foster stronger, more shared economic prosperity.

Enhancing Accessibility for People with Disabilities – Applying AI to push forward accessibility efforts and expand opportunities for those with disabilities is another key goal. Microsoft is researching uses of AI like computer vision, speech recognition and intelligent interfaces to develop new assistive technologies. This includes exploring how AI could help the blind or visually-impaired better navigate environments and access digital information. AI is also being researched as a way to aid communication for those with mobility or speech impediments. By removing barriers and enhancing inclusion through technology, AI for Good seeks to uphold principles of accessibility and equal access.

Promoting More Responsible and Trustworthy AI – Ensuring the responsible, safe and fair development and application of AI itself is another core challenge area AI for Good was launched to directly address. Microsoft actively researchers issues like mitigating algorithmic bias, increasing transparency in machine learning models, and fostering more accountable and well-governed uses of emerging technologies. The company also helps other organizations apply principles like fairness, reliability and privacy through initiatives assisting with AI safety, management and oversight practices. By advocating for and supporting the development of trustworthy, well-managed AI, Microsoft’s program aims to help guide emerging technology advances in a way that properly serves and benefits humanity.

Through its AI for Good initiative Microsoft is applying artificial intelligence to help address major challenges across a wide range of areas including global health, environmental sustainability, education, economic opportunity, accessibility, and governance of AI itself. By fostering innovative, responsible and data-driven technological solutions, the program’s overarching goal is to promote more inclusive progress on issues that are important to people and the planet. AI for Good demonstrates how emerging technologies, guided by principles of trustworthiness and service to humanity, could help achieve societal benefits at a large scale. The initiative reflects Microsoft’s vision of building AI tools to help advance important challenges facing communities worldwide.

WHAT ARE SOME POTENTIAL CHALLENGES THAT STUDENTS MAY FACE WHILE WORKING ON THE SMART AGRICULTURE USING IOT PROJECT?

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One of the main challenges students may face is collecting and sourcing the necessary hardware components to build out their IoT network for the smart agriculture system. While there are many off the shelf sensors available that can measure things like soil moisture, ambient temperature and light levels, others like pH sensors or those that measure nutrients may need to be sourced from specialty equipment suppliers. Sourcing the right components within a student’s budget can prove difficult.

Another related challenge is properly integrating the various hardware components together into a cohesive network. Students will need to select an IoT networking protocol like Zigbee, LoRaWAN or WiFi to connect their sensors to a central gateway device. They’ll then need to determine how to interface each sensor to the gateway, which may involve soldering connectors or writing custom code. Ensuring reliable communication between all the nodes in the network across a field setting is challenging.

Once the basic hardware network is established, a big challenge is collecting and managing the volume of data that will be generated from multiple sensor readings occurring periodically across the deployment area. Students will need to store this influx of data cost effectively, likely in a cloud-based database. They’ll then need to process and analyze the data to derive meaningful insights, which requires programming and data science skills that students may not yet possess.

Visualizing the data for farmers in a simple dashboard is also difficult. Students must design easy to read graphics and reports that distill key information about field and crop conditions clearly without overwhelming the user. Integrating the dashboard into a web or mobile app platform adds another layer of complexity to the project.

The sensors themselves may also pose challenges. Ensuring they remain calibrated over the long-term as they are exposed to varying environmental conditions like precipitation or temperature fluctuations in the field is difficult. Sensors can drift out of calibration, leading to inaccurate readings. Students need to devise ways to periodically check and recalibrate sensors to maintain data integrity.

Powering the remote sensor nodes sustainably also presents a formidable challenge. Batteries will need to be regularly replaced in hard to access areas, and solar panels and energy harvesting technologies may be required. Managing energy usage of the nodes to maximize uptime adds complexity.

Testing and validating the full system under real world farming conditions is a major undertaking. Students must work closely with an actual farm to deploy the network and systematically evaluate whether it provides useful insights over seasons or years. This level of long-term field testing is difficult for a student project.

Regulatory compliance issues may also arise depending on the country or region of the project. Using wireless networks for agricultural applications may require certifications for things like spectrum use or equipment regulations. Students need to fully understand applicable compliance rules which can be intricate.

Convincing farmers to adopt a new IoT system developed by students also poses challenges. Farmers are conservative about new technologies and students must prove how their solution will meaningfully help operations or improve yields. Designing an adoption strategy and pilot program takes savvy community engagement skills.

Budget and timeline constraints are always a reality for student projects too. Completing such an ambitious multi-disciplinary IoT and agriculture project within a single academic term or year limits what can realistically be achieved. Maintaining motivation and momentum with inevitable setbacks is difficult.

Integrating machine learning or predictive analytics capabilities would elevate a smart agriculture project but requires even more advanced coding and math skills that students may struggle with. Basic data monitoring without predictive functions has limited long-term value. Finding the right scope and complexity balance is a challenge.

Developing a fully functional smart agriculture IoT system poses immense logistical, technical, engagement and integration challenges for students. Proper planning, clear definition of objectives, flexibility, and help from industry mentors would be needed to successfully overcome these barriers. While ambitious, the learning outcomes for students tackling such a meaningful project could be invaluable and help address critical needs in global agriculture. Carefully scoping the project to match available time and resources is key to achieving success.

Some of the major potential challenges students may face in this type of smart agriculture IoT project involve procuring and integrating diverse hardware components, managing large streams of real-time sensor data, ensuring system reliability over the long term in outdoor conditions, gaining farmer adoption of new technologies, and addressing regulatory compliance and budget constraints. Taking on such a complex multi-disciplinary endeavor would provide students invaluable hands-on experience that transfers to many careers, so long as they are supported and the scope remains realistic for their capacity. With proper planning and focus, they could achieve meaningful outcomes and learning despite inevitable setbacks along the way.

CAN YOU PROVIDE MORE INFORMATION ON THE BENEFITS OF BLENDED LEARNING MODELS?

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Blended learning combines traditional face-to-face classroom methods with more modern online approaches in a way that allows students to learn both online and offline, using the best aspects of each method. This relatively new model of education has several potential benefits over solely online or in-person instructional approaches.

One key benefit of blended learning is flexibility and individualization. By blending online and in-person learning, students are able to choose when and where they access online content and resources. This allows them to learn at their own pace and according to their individual schedules. Students who need to review material can do so online at their convenience instead of having to wait for the next classroom lesson. They also have more freedom to learn in different environments that suit their learning styles, such as at home or in the library in addition to the traditional classroom.

Blended models also leverage technology to offer students additional learning tools and resources that allow them to access engaging multimedia content, interactive lessons, self-assessments, and individualized feedback. Well-designed blended programs can differentiate instruction based on student needs and performance data, identifying areas where students need additional support or enrichment. Students are able to spend more time on those focus areas through targeted online activities. This level of tailored, data-driven instruction would be very difficult to achieve with only face-to-face teaching.

Research has found that the blended approach may lead to improved student engagement and motivation. By incorporating digital tools and online learning components, students are exposed to material in a more interactive way that holds their attention. They are able to access information in multiple modalities like video and games in addition to traditional textbook-based learning. This variety in instructional methods keeps students mentally engaged and interested in their studies. The flexibility of blended models also allows students to learn in ways that match their interests and strengths. All of these aspects can increase student enthusiasm for learning.

Blended learning has been shown to positively impact academic achievement as well. Multiple meta-analyses that reviewed the effects of blended models compared to solely online or face-to-face classes found blended students consistently outperformed their traditionally-taught peers. This is likely due to a combination of the individualized practice and feedback online tools provide as well as the benefits of face-to-face teaching including immediate guidance from an instructor. When used appropriately to enhance – rather than replace – classroom instruction, blended approaches may foster deeper learning and understanding.

Blended learning can reduce absence issues since students have the ability to access content online if they miss class. This reduces learning loss that might otherwise occur from absence. Blended environments also allow for “flipped classroom” approaches where students watch lecture videos before class, then spend class time on more engaging applied activities like projects and discussions. Some research indicates this mode of instruction may lead students to perform better on conceptual understanding tests since class is used for higher-order tasks rather than passive content delivery.

From an instructor standpoint, blended learning offers advantages as well. Teachers are able to spend more class time engaged in interactive discussions, activities and one-on-one support rather than lecturing. They have data on student performance and areas of struggle from the online system to guide face-to-face lessons. Online tools also allow automated grading of assessments freeing up time for more personalized attention. Teachers can create engaging multimedia lessons once that can be reused with different classes, requiring less overall planning time. Blended models may alleviate classroom space and resource constraints since online work can be done anywhere with an internet connection.

From a financial viewpoint, blended approaches are potentially cost-effective compared to building additional physical classroom space or hiring extra teachers for growing enrollments since class sizes may be increased with some learning done remotely. The upfront and ongoing costs of online courseware may be offset by longer term facility and staffing budget savings. For students, blended programs open up access to advanced courses that might not otherwise be offered at their schools due to low demand.

Blending online and in-person learning offers students a highly customized education experience with engaging digital resources that research indicates leads to better outcomes. For teachers and schools, blended models provide data-driven instructional tools alongside the benefits of face-to-face interaction in a way that could have long term cost and efficiency advantages over traditional instructional formats. When thoughtfully designed and implemented, the blended learning approach maximizes the upsides of both digital and physical learning environments.