Managerial insights on an article - "Simplify your analytics strategy"
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These are my insights on the article "Simplify your analytics strategy" by Narendra Mulani. This is an assignment, part of data analytics internship.
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Simplify your analytics strategy
The document discusses how businesses can overcome complexity and confusion with analytics by pursuing a simpler path to insights. It recommends creating a hybrid data environment to accelerate data delivery and insights. Businesses should delegate analytic work to technologies like business intelligence, data discovery, analytics applications, and machine learning. Each company's path to insights is unique, so they can take either a problem-focused or discovery-based approach depending on their needs. Uncovering opportunities requires making data-driven decisions once insights are found.
Simplify your analytics strategy
The document discusses simplifying analytics strategies for businesses dealing with big data. It identifies issues companies face in discovering opportunities in their data and achieving desired outcomes. It outlines various analytics technologies that can help including business intelligence, data visualization, data discovery, analytics applications, and machine learning. The key insights are that analytics solutions must provide the right data at the right time and place for users, allow users to test and discover patterns in data, and put analytics power in users' hands. It also notes there is no one-size-fits-all approach and strategies depend on a company's goals, technologies, data types, and culture. The document advocates for a simplified strategy to generate insights that lead to real outcomes through a hybrid technology environment
How business analytics is used in business
Business analytics is used in business to transform data into meaningful insights through statistical models. It analyzes data to recognize patterns and develop predictive models. Business analytics helps with decision making, problem solving, expansion planning, data mining, and finding customers. Common analytical tools used include programming languages like R and Python, self-serve tools like SAS and SPSS, data visualization tools like Tableau and Power BI, and auto ML platforms from AWS, Google Cloud, and Azure.
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This document introduces a course and discusses the key skills needed for an information technology career, including understanding technologies, designing architectures, managing projects, communicating effectively, thinking strategically, and adapting to change. It also outlines the core areas of information systems as content and system development, enterprise support systems, e-business and technology, and infrastructure management. Finally, it lists some of the top companies in Indonesia that graduates could work for.
"Simplify Your Analytics Strategy" by Narendra Mulani
Companies can get stuck trying to analyze all possible analytics opportunities instead of focusing on what matters most to customers, stakeholders, and employees. This document provides steps to simplify an analytics strategy and generate useful insights. It recommends accelerating data through a hybrid data platform and emerging technologies. A path to insight is unique for each organization and involves various elements like goals, data types and sources. Simplifying an analytics strategy can provide managerial insights through discovery-based or hypothesis-based approaches depending on whether the problem or solution is known.
Data & Analytics: A Point of View
This document discusses using data and analytics to improve learning and link it to business outcomes. It notes that currently only 30% of organizations can link learning programs to business performance. The document advocates extracting data from various learning and enterprise systems and analyzing it using techniques like predictive analytics. This could provide benefits like selecting the right employees, reducing time to proficiency, and personalized learning paths. The document proposes a plan to engage stakeholders, identify outcomes, collect and analyze data from various sources using an API, and provide analytical insights to take action.
Simplify your analysis strategy
Companies should simplify their analysis strategies by focusing on generating insights from data that lead directly to outcomes, accelerating the data collection and analysis process using business intelligence, data visualization, and data discovery techniques. Analytics applications and machine learning can also streamline analysis workflows and reduce human effort so companies spend less time analyzing data and more time taking data-driven actions.
Simplifying analytics
The document discusses simplifying analytics by focusing on important data and how to use it to improve business outcomes, rather than complex analytics. It recommends building an environment to accelerate data processing for faster insights and decisions. Companies should leverage business intelligence, data visualization, and data discovery tools, as well as machine learning models, to automate analysis and gain insights from large data sets. Different problems may require hypothesis-based or discovery-based approaches. The key is to identify important data, delegate analysis to tools when possible, visualize data for better understanding, uncover hidden patterns, and customize the approach to the specific problem and data.
Recommended
Simplify your analytics strategy
The document discusses how businesses can overcome complexity and confusion with analytics by pursuing a simpler path to insights. It recommends creating a hybrid data environment to accelerate data delivery and insights. Businesses should delegate analytic work to technologies like business intelligence, data discovery, analytics applications, and machine learning. Each company's path to insights is unique, so they can take either a problem-focused or discovery-based approach depending on their needs. Uncovering opportunities requires making data-driven decisions once insights are found.
Simplify your analytics strategy
The document discusses simplifying analytics strategies for businesses dealing with big data. It identifies issues companies face in discovering opportunities in their data and achieving desired outcomes. It outlines various analytics technologies that can help including business intelligence, data visualization, data discovery, analytics applications, and machine learning. The key insights are that analytics solutions must provide the right data at the right time and place for users, allow users to test and discover patterns in data, and put analytics power in users' hands. It also notes there is no one-size-fits-all approach and strategies depend on a company's goals, technologies, data types, and culture. The document advocates for a simplified strategy to generate insights that lead to real outcomes through a hybrid technology environment
How business analytics is used in business
Business analytics is used in business to transform data into meaningful insights through statistical models. It analyzes data to recognize patterns and develop predictive models. Business analytics helps with decision making, problem solving, expansion planning, data mining, and finding customers. Common analytical tools used include programming languages like R and Python, self-serve tools like SAS and SPSS, data visualization tools like Tableau and Power BI, and auto ML platforms from AWS, Google Cloud, and Azure.
Ead pertemuan-1
This document introduces a course and discusses the key skills needed for an information technology career, including understanding technologies, designing architectures, managing projects, communicating effectively, thinking strategically, and adapting to change. It also outlines the core areas of information systems as content and system development, enterprise support systems, e-business and technology, and infrastructure management. Finally, it lists some of the top companies in Indonesia that graduates could work for.
"Simplify Your Analytics Strategy" by Narendra Mulani
Companies can get stuck trying to analyze all possible analytics opportunities instead of focusing on what matters most to customers, stakeholders, and employees. This document provides steps to simplify an analytics strategy and generate useful insights. It recommends accelerating data through a hybrid data platform and emerging technologies. A path to insight is unique for each organization and involves various elements like goals, data types and sources. Simplifying an analytics strategy can provide managerial insights through discovery-based or hypothesis-based approaches depending on whether the problem or solution is known.
Data & Analytics: A Point of View
This document discusses using data and analytics to improve learning and link it to business outcomes. It notes that currently only 30% of organizations can link learning programs to business performance. The document advocates extracting data from various learning and enterprise systems and analyzing it using techniques like predictive analytics. This could provide benefits like selecting the right employees, reducing time to proficiency, and personalized learning paths. The document proposes a plan to engage stakeholders, identify outcomes, collect and analyze data from various sources using an API, and provide analytical insights to take action.
Simplify your analysis strategy
Companies should simplify their analysis strategies by focusing on generating insights from data that lead directly to outcomes, accelerating the data collection and analysis process using business intelligence, data visualization, and data discovery techniques. Analytics applications and machine learning can also streamline analysis workflows and reduce human effort so companies spend less time analyzing data and more time taking data-driven actions.
Simplifying analytics
The document discusses simplifying analytics by focusing on important data and how to use it to improve business outcomes, rather than complex analytics. It recommends building an environment to accelerate data processing for faster insights and decisions. Companies should leverage business intelligence, data visualization, and data discovery tools, as well as machine learning models, to automate analysis and gain insights from large data sets. Different problems may require hypothesis-based or discovery-based approaches. The key is to identify important data, delegate analysis to tools when possible, visualize data for better understanding, uncover hidden patterns, and customize the approach to the specific problem and data.
Simplify your analytics strategy
This presentation contains the key ideas from the article "Simplify your analytics strategy" by Narendra Mulani published in HBR. This presentation is a part of my internship under Prof. Sameer Mathur, IIM-L
Simplify your analytics strategy
Most companies get stuck analyzing large amounts of data. To overcome this, companies should pursue a simpler path to insights by accelerating data delivery in real-time, and delegating analytic work to technologies like business intelligence, data discovery, analytics applications, and machine learning. This allows the right data to reach decision-makers in a visual format tailored for each user, enabling data-driven decisions across departments to efficiently achieve organizational goals.
Impact of ai on workplace
The document discusses how artificial intelligence will impact the future workplace. It provides examples of technologies like Humanyze employee badges and Hitachi's happiness meter that collect employee data through sensors to provide insights into team interactions, productivity, and satisfaction. It also discusses Workday's talent retention tool that predicts employee turnover using past and real-time data points. While AI can optimize workplace layouts, communications, and engagement, it also poses risks like privacy concerns if data is misused or creates an Orwellian work environment under authoritarian regimes.
Simplify your analytics strategy
This presentation discusses simplifying analytics strategies for businesses. It suggests that while interest in analytics is growing, some businesses are overwhelmed by the complexity. It recommends pursuing a simpler path to uncover insights from data to make informed decisions. Fast data processing can provide fast insights and outcomes. Next-gen business intelligence and data visualization can help decision-makers explore opportunities. Data discovery alongside projects can uncover new patterns. Machine learning can reduce human elements and improve predictions. Each company's analytics journey depends on its unique culture and existing technologies. Companies can take discovery-based or known solution approaches depending on the problem.
Simply your analytics strategy
The document outlines 5 steps to simplify an analytics strategy: 1) Accelerate data delivery through a hybrid data platform; 2) Use next-gen business intelligence and data visualization; 3) Perform data discovery to uncover patterns; 4) Deploy industry-specific analytics applications; 5) Incorporate machine learning and cognitive computing. Taking these steps can generate insights that lead to improved decision-making and organizational performance. A manager must understand that analytics strategies require adapting to changing business needs, technologies, and data sources.
Simplify Your Analytics Strategy
The document discusses different approaches to analytics to solve business problems in a targeted manner. It describes accelerating data analysis in real time to provide immediate insights and adjust quickly. It also discusses business intelligence and data visualization to provide the right data at the right time and place visually, promoting data-driven decision making. Additionally, it covers data discovery to uncover patterns to drive business value, machine learning to personalize recommendations, and having an outcome-driven approach whether the problem is known or unknown.
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学校原版美国波士顿大学毕业证学历学位证书原版一模一样
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1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
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### Applications of TDM
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- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
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This presentation contains the key ideas from the article "Simplify your analytics strategy" by Narendra Mulani published in HBR. This presentation is a part of my internship under Prof. Sameer Mathur, IIM-L
Simplify your analytics strategy
Most companies get stuck analyzing large amounts of data. To overcome this, companies should pursue a simpler path to insights by accelerating data delivery in real-time, and delegating analytic work to technologies like business intelligence, data discovery, analytics applications, and machine learning. This allows the right data to reach decision-makers in a visual format tailored for each user, enabling data-driven decisions across departments to efficiently achieve organizational goals.
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Simplify your analytics strategy
This presentation discusses simplifying analytics strategies for businesses. It suggests that while interest in analytics is growing, some businesses are overwhelmed by the complexity. It recommends pursuing a simpler path to uncover insights from data to make informed decisions. Fast data processing can provide fast insights and outcomes. Next-gen business intelligence and data visualization can help decision-makers explore opportunities. Data discovery alongside projects can uncover new patterns. Machine learning can reduce human elements and improve predictions. Each company's analytics journey depends on its unique culture and existing technologies. Companies can take discovery-based or known solution approaches depending on the problem.
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Simplify Your Analytics Strategy
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学校原版美国波士顿大学毕业证学历学位证书原版一模一样
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留信网认证的作用:
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3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
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after readings
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The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
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Panel Session: Promoting Connection and Cooperation"
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Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
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The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
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The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
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