Blue Trust Inc. grew its stake in shares of Murphy USA Inc. (NYSE:MUSA – Free Report) by 7.3% during the fourth quarter, according to the company in its most recent disclosure with the Securities and Exchange Commission. The fund owned 499 shares of the specialty retailer’s stock after ...

Blue Trust Inc. Buys 34 Shares of Murphy USA Inc. (NYSE:MUSA) - Defense World

Murphy USA Inc. (NYSE:MUSA – Get Free Report) has earned an average recommendation of “Hold” from the five analysts that are currently covering the firm, Marketbeat reports. One investment analyst has rated the stock with a sell rating, one has issued a hold rating and three have given ...

Murphy USA Inc. (NYSE:MUSA) Receives Consensus Rating of “Hold” from Analysts - Defense World

Mindy West will be promoted to executive vice president and chief operating officer as of March 1.

Murphy USA Creates New Role as It Prepares for Future Growth | Convenience Store News

The topic was brought before the Architectural Review Board Wednesday afternoon.

Will a Murphy USA gas station be coming to the Oakleigh Garden Historic District?

Murphy USA’s board of directors elected Mindy West to the newly created position of executive vice president and chief operating officer.

Murphy USA Names Mindy West Executive Vice President, COO

Funded, coordinated and organized by Choose New Jersey, the trip included an array of activity – ranging from partnerships, memorandums of understanding, meetings with top political and industry leaders,

Murphy-led delegation completes economic mission in East Asia (slideshow)

SouthernSun Asset Management, LLC, an investment management firm, released its “SouthernSun Small Cap Strategy” third quarter 2023 investor letter. A copy of the same can be downloaded here. In the third quarter,

Reasons for the Outstanding Performance of Murphy USA (MUSA)

Analysts fell to the sidelines weighing in on Murphy USA (MUSA – Research Report) and Hormel Foods (HRL – Research Report) with neutral

Wall Street Analysts Are Neutral on Top Consumer Goods Picks

The Board of Directors of Murphy USA Inc. (NYSE: MUSA) today declared a quarterly cash dividend on the Common Stock of Murphy USA Inc. of $0.41 per sh

Murphy USA Inc. Announces Dividend Increase

But Murphy will veto the proposed Turnpike Authority budget, the governor announced on social media Thursday. "I am not satisfied with the justification provided for the toll increases reflected in the budget and need more information for why the board is ...

Murphy makes surprise decision on big NJ toll hikes

Murphy USA

murphyusa

Google’s MADAs And RSAs Are Not Necessary For Android’s Success And Do Not Benefit Search Consumers

Google Has Not Shown That Pass-Through Effects Would Benefit Search Consumers

Easily Handle Faults And Retries With Polly In C#

Project estimation is a critical aspect of successful project management. Accurate estimation ensures that projects are completed on time, within budget, and with the desired quality. However, project estimation can be a complex and challenging process, often leading to deviations from the initial estimates that entail budget creep. In this comprehensive guide, I'll explore effective strategies for mastering project estimation. We'll delve into why projects frequently take longer than estimated and how to address this issue for both project teams and business owners. A staggering 91.5% of projects go over budget, over schedule, or both Project overruns are a common phenomenon across various industries. Let's explore some illuminating examples of how large-scale projects went off the tracks: The book titled "How Big Things Get Done: The Surprising Factors That Determine the Fate of Every Project, from Home Renovations to Space Exploration and Everything In Between" by Bent Flyvbjerg and Dan Gardner highlights a common trend in project management. It emphasizes that the majority of projects tend to deviate from their original plans. In particular, nuclear power construction and IT projects are notorious for their propensity to encounter disastrous and highly expensive setbacks. These examples represent just a few instances within the broader category of megaprojects, which the authors, Bent Flyvbjerg and Dan Gardner, define as projects with costs exceeding US$1 billion. Megaprojects, regardless of their nature, often experience significant budget overruns and delays, making them a cause for concern for business leaders, politicians, and organizations. Bent Flyvbjerg has compiled an extensive database on this topic, encompassing more than 16,000 projects spanning over 20 fields across 136 countries, with records dating back to 1910. These projects range from Olympic games to nuclear power plants, including complex endeavors like NASA's James Webb Space Telescope, which exceeded its budget by 450%. Shockingly, the research reveals that a staggering 91.5% of projects face either budget overruns, delays, or both. Moreover, less than 1% of projects manage to adhere to their original timelines and budgets while delivering the promised benefits. On average, projects experience a cost overrun of 62%. The distribution of these megaproject overruns exhibits a heavy tail, signifying the presence of catastrophic risks. To illustrate further, the authors point out that 18% of IT projects encounter cost overruns exceeding 50% in real terms, with an average overrun of 447%. These examples underscore the complexity of project estimation and the frequent occurrence of projects exceeding their initial estimates. However, it's crucial to understand that accurate estimation is attainable with the right approach, and by accurate, I mean an estimate with minimum possible deviation. The Two Sides of Project Estimation Project estimation is a delicate balancing act, requiring collaboration and understanding between the development team and the client or business owner. While the previous sections discussed the challenges faced by development teams and strategies for accurate estimation, it's equally crucial to consider the client's perspective. From the client/business owner's point of view, there are compelling reasons to add a buffer to the project estimate. Let's explore why this is essential and highlight scenarios where the initial project scope can be exceeded. The Client's Dilemma: Why Buffer Matters Clients and business owners have their own set of concerns when it comes to project estimation. They seek to achieve their objectives within a specified budget and timeframe, but they also understand that changes and uncertainties are inherent in any project. Here's why adding a buffer to the project estimate is vital from the client's viewpoint: 1. Evolving Design Requirements: Studies in project management have shown that design iterations are a common occurrence in project development. A client may have a vision for the project, but it often takes several iterations before the design aligns perfectly with their expectations. These design changes, while necessary for a successful project, can impact the project's timeline and budget. 2. Comprehensive Product Testing: Thorough testing is a fundamental part of project development to ensure the final product meets quality standards. However, extensive testing can reveal issues that were not initially anticipated. Addressing these issues may require additional time and resources, potentially extending the project's duration. 3. User Testing and Feedback: Incorporating real user feedback is essential for creating a user-friendly and effective product. Pilot groups and user testing often reveal usability challenges, logic gaps, or areas for improvement that were not evident during the initial planning stages. Adjustments based on user feedback can result in scope changes and project extensions. 4. Regulatory Compliance: For projects in regulated industries such as healthcare or finance, adhering to compliance standards is non-negotiable. Navigating regulatory requirements can introduce complexities and additional tasks that were not initially factored into the project plan. Strategies for Effective Project Estimation In this section, we delve into strategies for effective project estimation. We won't be covering the well-known practices that I am sure are already applied in your approach, such as proper preparation before committing to a project, working in areas of expertise or acquiring that expertise, breaking down work into smaller chunks (the Lego principle), using sprint methodologies and etc... Instead, I'll provide you with insights that go beyond the surface, offering practical approaches drawn from real-life experiences to help you tackle project estimation from a more detailed and low-level perspective, ultimately securing better results. At , we employ a dual-point estimation approach for our estimations. This practice is founded on two key pillars: optimistic and realistic estimates. These serve as powerful tools for effectively navigating the uncertainties and risks inherent in the estimation process. Uinno Below are detailed descriptions and examples of how we interpret and apply both optimistic and realistic scenarios: Optimistic Estimate (O) This is the "sunshine and rainbows" scenario. It's when all the stars align, the coffee tastes just right, and the Individual Contributor (IC) — that's you — is bouncing off the walls with an energy that puts the Energizer Bunny to shame. Here's a real-life scenario. You're tasked with implementing authentication functionality with the Awesome Auth Provider, and you've never worked with it before. Under our rosy optimistic scenario, the Awesome Auth Provider's manual looks like it's been crafted by angels — updated, user-friendly, and reads like your favorite novel. You might even get an easy-peasy development playground and a bunch of examples to play with. On this day, the birds are singing, you're fresh from a restful sleep, and you're so upbeat you feel like you can finish it without breaking a sweat. So you estimate this task to be finished within a single business day (8 hours). Realistic Estimate (R) This is the “Murphy’s Law” scenario where everything that can go wrong, does go wrong. Coffee's bitter, traffic's a nightmare, and you feel like you’ve been run over by a bus. Take the same task — implementing authentication functionality with the Awesome Auth Provider. In our not-so-sunny Realistic scenario, the Awesome Auth Provider's manual looks like it was written by a toddler — confusing, outdated, and features a dreaded XML API that could make grown developers weep. You've had a mere four hours of sleep, and there's a strong suspicion you're coming down with something. The thought of coding for more than two hours feels like a Herculean task, and you need a 15-20-minute siesta after each session. Well, life’s not all sunshine and unicorns, right? In this case, you feel like two business days of work (16 hours) is probably a better and more realistic guess. Balancing out the estimate. What it takes to deliver work really A common pitfall in estimations is the tendency to consider only the pure development time, without taking into account that additional factors will inevitably influence the actual feature delivery timeframe. In essence, to generate a more accurate and realistic estimation, it's necessary to expand the initial timeline to accommodate for these influencing variables. The act of coding is merely one piece of a larger puzzle that needs to be addressed when formulating an estimation. Let's see what are the other important parts you need to consider: Communication (С) Communication forms an integral part of every project, whether it be with the Project Manager, teammates, or other stakeholders. The number of individuals involved in the project or feature development typically correlates with the amount of communication required. It's a complex task to accurately anticipate the time required for communication, as it can vary considerably based on factors such as the project's pace, the meeting schedule, and the overall complexity of the project. Thus, it's crucial to take this into account when formulating an estimation. But as a starting point, keep in mind that at least an hour of your day is spent on some sort of daily meeting, retro or communication with your teammates. Testing (T) Regardless of its apparent simplicity, each piece of work needs to be rigorously tested before it's deployed in either a production or development environment. Even in the absence of a dedicated Quality Assurance Engineer, Individual Contributors are still required to ensure compliance with the acceptance criteria. This additional process significantly contributes to the overall estimation. It's essential to remember this critical step while formulating time estimations, as it ultimately guarantees the delivery of a high-quality, functional product. Deployment (D) A task isn't considered successfully completed in isolation, nor is it merely a piece of code on a local machine or git repository. Just as a well-crafted piece of machinery is of little use if left unassembled, so too does a task reach its full potential only when it's been deployed to the appropriate environment or at the very least, integrated with the main body of the application code. Remember, in the world of software development, a task should be considered done only when it becomes a functioning part of the greater system. Amendments (A) It's important to remember that the phases of testing and deployment can often be akin to opening Pandora's box of bugs. Therefore, the time allocation for bug fixing is an essential factor that should be incorporated into the general estimation. Environment (E) Despite our best efforts, none of us are machines capable of working an uninterrupted eight-hour shift without catering to life's necessities. Be it a much-needed coffee break, a trip to the restroom, or a quick chat with a colleague, these moments are a reality of the workday. Especially when a task is projected to take around eight hours or more, these "real-life" interruptions need to be factored into the overall estimation. Key Takeaways for Mastering Project Estimation Project estimation is a multifaceted endeavor that plays a pivotal role in the success of any project. Given that a staggering 91.5% of projects go over budget or schedule, mastering this skill is non-negotiable. Here's a concise step-by-step guide to enhance your project estimation skills: 1. Acknowledge the Complexity Understand that project estimation is not just about development time. It involves various other factors like design iterations, testing, user feedback, and regulatory compliance. 2. Employ Dual-Point Estimation Use both optimistic (O) and realistic (R) scenarios to navigate uncertainties. The optimistic estimate is your best-case scenario, while the realistic one prepares you for potential setbacks. 3. Factor in Additional Variables Don't just consider pure development time. Account for: [CADDET] Communication ( C) Amendments (ex Fixes) (A) Deployment (D) Development (D) Environment (ex Life) (E) Testing (T) 4. Add a Buffer Especially from the client's perspective, adding a buffer is crucial to accommodate evolving design requirements, comprehensive product testing, user feedback, and regulatory compliance. 5. Continual Learning and Adaptation Use past projects as a learning experience to refine future estimates. Keep track of where your estimates were accurate and where they fell short to continually improve your estimation skills. By incorporating these steps into your project estimation process, you'll be better equipped to deliver projects on time and within budget, thereby increasing your success rate significantly. Conclusion Project overruns are a reality, but they are not insurmountable. Accurate project estimation is attainable by adopting a comprehensive approach that considers both optimistic and realistic scenarios and accounts for influencing factors. Understanding the challenges of project estimation and learning from past experiences can lead to improved project planning and efficiency. It is also a collaborative approach. A collaborative approach is essential to address these challenges and maintain a positive working relationship between the development team and the client/business owner. Clients should be encouraged to include a reasonable buffer in their project budget and timeline to accommodate potential scope changes and unforeseen challenges. At the same time, development teams must strive for accurate estimation and transparent communication to minimize surprises.

A Comprehensive Guide to Effective Project Estimation

THE DIAMOND AS BIG AS THE RITZ

In this blog post, we will be covering how you can combine and leverage the open-source streaming solution, , with , to improve the quality of your streaming flows. Buckle up! bytewax ydata-profiling Stream processing enables real-time analysis of data in-flight and before storage and can be or . stateful stateless is used for recommendations, pattern detection, or complex event processing, where the history of what has happened is required for the processing (windows, joining by a key, etc.). Stateful stream processing real-time is used for transformation that doesn’t require knowledge of other data points in the stream like masking an email or converting a type. Stateless stream processing inline Overall, data streams are widely used in the industry and can be found applied to use cases such as , , or . fraud detection patient monitoring event predictive maintenance One Crucial Aspect That All Data Streams Must Consider Is the Quality of the Data Unlike traditional models where data quality is usually assessed during the creation of the data warehouse or dashboard solution, . streaming data requires continuous monitoring It is essential to maintain data quality throughout the entire process, from collection to feeding downstream applications. After all, the cost of bad data quality can be high for organizations: “The cost of bad data is an astonishing 15% to 25% of revenue for most companies. (…) Two-thirds of these costs can be eliminated by getting in front on data quality.” — Thomas C. Redman, author of “Getting in Front on Data Quality” Throughout this article, we will show you how you can combine with to profile and improve the quality of your streaming flows! bytewa ydata-profiling Stream Processing for Data Professionals With Bytewax is an OSS stream processing framework designed specifically for Python developers. Bytewax It allows users to with capabilities similar to Flink, Spark, and Kafka Streams while providing a friendly and familiar interface and build streaming data pipelines and real-time applications 100% compatibility with the Python ecosystem. Using built-in or existing Python libraries, (Kafka, RedPanda, WebSocket, etc.) and out to various downstream systems (Kafka, parquet files, data lakes, etc.). connectors you can connect to real-time and streaming data sources write transformed data For the transformations, Bytewax with , , and methods and comes with familiar features such as recovery and scalability. facilitates stateful and stateless transformations map windowing aggregation Bytewax and is p . facilitates a Python-first and data-centric experience to data streams urposely built for data engineers and data scientists It allows users to and create customizations necessary to meet their needs without having to learn and maintain JVM-based streaming platforms like Spark or Flink. build streaming data pipelines and real-time applications Bytewax is well suited for many use cases, namely, , , , and more. Embedding Pipelines For Generative AI Handling Missing Values in Data Streams Using Language Models in a Streaming Context to Understand Financial Markets For use case inspiration and more information like documentation, tutorials, and guides, feel free to check . the bytewax website Why Data Profiling for Data Streams? and refers to the step of : its structure, behavior, and quality. Data Profiling is key to a successful start of any machine learning task thoroughly understanding our data In a nutshell, involves analyzing aspects related to the data’s format and basic descriptors (e.g., number of samples, number/types of features, duplicate values), its (such as the presence of missing data or imbalanced features), and other complicating factors that may arise during data collection or processing (e.g., erroneous values or inconsistent features). data profiling intrinsic characteristics , where circumstances might change fast and may require immediate action (e.g., healthcare monitoring, stock values, air quality policies). Ensuring high data quality standards is crucial for all domains and organizations, but is especially relevant for domains operating with domains outputting continuous data For many domains, data profiling is used from an exploratory data analysis lens, considering historical data stored in databases. On the contrary, for data streams, , where data needs to be checked at different time frames or stages of the process. data profiling becomes essential for validation and quality control continuously along the stream By embedding an , we can on the current state of our data and be alerted for any potentially critical issues — whether they are related to (e.g., corrupted values or changing formats), or to (e.g., data drifts, deviation from business rules and outcomes). automated profiling into our data flows immediately get feedback data consistency and integrity events happening in short periods of time In real-world domains — — automated profiling might save us from multiple brain puzzles and systems needing to be taken out of production! where you just know Murphy’s law is bound to strike and “everything can definitely go wrong” In what concerns data profiling, has consistently been a , either for or data. And no wonder why — ydata-profiling crowd favorite tabular time-series it’s one line of code for an extensive set of analysis and insights. Complex and time-draining operations are done under the hood: ydata-profiling and depending on the feature types (either numeric or categorical), it that are shown in the profiling report. automatically detects the feature types comprised in the data, adjusts the summary statistics and visualizations Fostering a , the package also , focusing on their pairwise and , and provides a thorough , from or values to and features. data-centric analysis highlights the existing relationships between features interactions correlations evaluation of data quality alerts duplicate constant skewed imbalanced It’s really a — with minimal effort. 360º view of the quality of our data Putting It All Together: bytewax and ydata-profiling Before starting the project, we need to first set our Python dependencies and configure our data source. First, let’s install the and packages ( bytewax ydata-profiling You might want to use a virtual environment for this — if you need some extra guidance!) check these instructions pip install bytewax==0.16.2 ydata-profiling==4.3.1 Then, we’ll upload the (License — CC0: Public Domain), which contains several measurements of from different IoT devices: Environmental Sensor Telemetry Dataset temperature, humidity, carbon monoxide liquid petroleum gas, smoke, light, and motion wget https://raw.githubusercontent.com/bytewax/air-quality-sensor/main/data/iot_telemetry_data_1000 , and the input would look like what we expect in a streaming platform . In this article, and create a dataflow using bytewax. In a production environment, these measurements would be continuously generated by each device such as Kafka to simulate the context we would find with streaming data, we will read the data from the CSV file one line at a time (As a quick side note, a dataflow is essentially a data pipeline that can be described as a directed acyclic graph — DAG) First, let’s make some : necessary imports from datetime import datetime, timedelta, timezone

from bytewax.dataflow import Dataflow
from bytewax.connectors.stdio import StdOutput
from bytewax.connectors.files import CSVInput
from bytewax.testing import run_main Then, we define our dataflow object. Afterward, we will use a stateless map method where we pass in a function to convert the string to a DateTime object and restructure the data to the format (device_id, data). The map method will make the change to each data point in a stateless way. The reason we have modified the shape of our data is so that we can easily group the data in the next steps to profile data for each device separately rather than for all of the devices simultaneously. flow = Dataflow()
flow.input("simulated_stream", CSVInput("/content/iot_telemetry_data_1000"))

# parse timestamp
def parse_time(reading_data):
    reading_data["ts"] = datetime.fromtimestamp(float(reading_data["ts"]), timezone.utc)
    return reading_data

flow.map(parse_time)


# remap format to tuple (device_id, reading_data)
flow.map(lambda reading_data: (reading_data['device'], reading_data)) Now, we will take advantage of the stateful capabilities of to gather data for each device over the duration of time that we have defined. expects a snapshot of the data over time, which makes the window operator the perfect method to use to do this. bytewax ydata-profiling In , we are able to produce summarizing statistics for a dataframe that is specified for a particular context. For instance, in our example, we can produce snapshots of data referring to each IoT device or to particular time frames: ydata-profiling from bytewax.window import EventClockConfig, TumblingWindow

# This is the accumulator function, and outputs a list of readings
def acc_values(acc, reading):
    acc.append(reading)
    return acc


# This function instructs the event clock on how to retrieve the
# event's datetime from the input.
def get_time(reading):
    return reading["ts"]

  
# Configure the `fold_window` operator to use the event time.
cc = EventClockConfig(get_time, wait_for_system_duration=timedelta(seconds=30))

# And a tumbling window
align_to = datetime(2020, 1, 1, tzinfo=timezone.utc)
wc = TumblingWindow(align_to=align_to, length=timedelta(hours=1))

flow.fold_window("running_average", cc, wc, list, acc_values)

flow.inspect(print) After the snapshots are defined, leveraging is as simple as calling the for each of the dataframes we would like to analyze: ydata-profiling ProfileReport import pandas as pd
from ydata_profiling import ProfileReport

def profile(device_id__readings):
    print(device_id__readings)
    device_id, readings = device_id__readings
    start_time = readings[0]['ts'].replace(minute=0, second=0, microsecond=0).strftime('%Y-%m-%d %H:%M:%S')
    df = pd.DataFrame(readings)
    profile = ProfileReport(
        df,
        tsmode=True,
        sortby="ts",
        title=f"Sensor Readings - device: {device_id}"
    )

    profile.to_file(f"Ts_Profile_{device_id}-{start_time}.html")
    return f"device {device_id} profiled at hour {start_time}"


flow.map(profile) In this example, we are writing the images out to local files as part of a function in a map method. These could be reported out via a messaging tool or we could save them to some remote storage in the future. Once the profile is complete, the dataflow expects some output so we can use the built-in to print the device that was profiled and the time it was profiled at that was passed out of the profile function in the map step: StdOutput flow.output("out", StdOutput()) There are multiple ways to execute Bytewax dataflows. In this example, we use the same local machine, but Bytewax can also run on multiple Python processes, across multiple hosts, in a , using a , and . Docker container Kubernetes cluster more In this article, we’ll continue with a local setup, but we encourage you to check our helper tool which manages Kubernetes dataflow deployments once your pipeline is ready to transition to production. waxctl Assuming we are in the same directory as the file with the dataflow definition, we can run it using: python -m bytewax.run ydata-profiling-streaming:flow We can then use the profiling reports to validate the data quality, check for changes in schemas or data formats, and . compare the data characteristics between different devices or time windows In fact, we can leverage the that highlights the differences between two data profiles in a straightforward manner, making it easier for us to detect important patterns that need to be investigated or issues that have to be addressed: comparison report functionality snapshot_a_report = ProfileReport(df_a, title="Snapshot A")
snapshot_b_report = ProfileReport(df_b, title="Snapshot B")

comparison_report =snapshot_a_report(snapshot_b_report)
comparison_report.to_file("comparison_report.html") Ready to Explore Your Own Data Streams? Validating data streams is crucial to identify issues in data quality in a continuous manner and compare the state of data across distinct periods of time. For organizations in , , , and — all working with continuous streams of data — a , from quality assessment to data privacy. healthcare energy manufacturing entertainment utomated profiling is key to establishing data governance best practices This requires the analysis of snapshots of data which, as showcased in this article, can be achieved in a seamless way by combining and . bytewax ydata-profiling takes care of all the processes necessary to handle and structure data streams into snapshots, which can then be summarized and compared with through a comprehensive report of data characteristics. Bytewax ydata-profiling Being able to appropriately process and profile incoming data opens up a plethora of use cases across different domains, from the to the highlighting and mitigation of additional issues that derive from real-world activities, such as (e.g., fraud or intrusion/threats detection), , and other events that deviate from the expectations (e.g., data drifts or misalignment with business rules). correction of errors in data schemas and formats anomaly detection equipment malfunction Now, you’re all set to start exploring your data streams! Let us know what other use cases you find, and as always, feel free to drop us a line in the comments, or find us at the for further questions and suggestions! Data-Centric AI Community See you there! Acknowledgments ) and Oli Makhasoeva (Developer Relations @ ) -- developing . This article was written by Fabiana Clemente (Co-founder & CDO @ ) and Miriam Santos (Developer Relations @ ) -- developing -- and Zander Matheson (CEO & Founder @ YData YData ydata-profiling Bytewax Byetwax bytewax You may find additional information about the OSS packages in the respective documentations: & . ydata-profiling docs bytewax docs Also published here

How to Understand Your Data in Real-Time Using bytewax and ydata-profiling

Cómo entender sus datos en tiempo real usando bytewax y ydata-profiling

Comment comprendre vos données en temps réel en utilisant bytewax et ydata-profiling

बाइटवैक्स और वाईडेटा-प्रोफाइलिंग का उपयोग करके वास्तविक समय में अपने डेटा को कैसे समझें

bytewax と ydata-profiling を使用してデータをリアルタイムで理解する方法

Como entender seus dados em tempo real usando bytewax e ydata-profiling

Cách hiểu dữ liệu của bạn trong thời gian thực bằng cách sử dụng bytewax và ydata-profile

如何使用 bytewax 和 ydata-profiling 实时了解您的数据

According to the , inflation in the 12 months up to April 2023 was 4.9%. This is a slight decrease on the 2022 annual inflation rate of 6.6%. US Inflation Calculator The impact of is clear to see for shoppers who are adapting purchasing behavior to limit financial challenges. For many, it seems the effects of inflation on consumers are intensifying. inflation The U.S. Bureau of Labor Statistics reports the Consumer Price Index for April 2023 rose after a 0.1% increase in March. This data shows shoppers have less to spend on essential items due to the impact of inflation and . 0.4% shrinkflation The shock of shrinkflation However, it’s not just rising prices and inflation that are creating stressful shopping conditions for Americans. Reduced product sizes (shrinkflation) are also affecting consumer shopping habits, with shoppers buying items at their normal price but ending up with a smaller product. In fact, the recent inflation and shrinkflation survey found that respondents said shrinkflation is affecting their wallets. 83% of Shoppers are buying what they need but it isn’t going as far as it used to. Survey respondents stated that they are selecting cheaper, larger items to get by. How is inflation affecting consumer choices? In March 2023, PissedConsumer.com surveyed 400 U.S. consumers of different ages, genders, and backgrounds to find out how inflation is influencing their consumer preferences: 94.5% found inflation impacts their shopping habits. 76.5% noticed price gouging on groceries. 94.3% saw an increase in egg prices. 33.6% said Walmart is the cheapest store, 46.3% thought Whole Foods was the most expensive. Consumers notice price increases Shoppers are running into price rises everywhere they look, so it’s a real challenge to maintain standards and buy essential items. Some consumers are leaving online reviews about significant price hikes, such as this Papa Murphys’ shopper: “Today I bought a pizza I've been paying $14 for at $19. This is a $5 or 35% increase in price. I think they are simply trying to take advantage and blame inflation.” Maximizing the power of the reviews website, Walmart shoppers are also leaving about price rises to their favorite products over a short time period: comments “WALMART has increased its prices dramatically on food items. For example, their GREAT VALUE 16 oz. peanuts went from $1.98 to $2.28, now they are $2.44-up over 42%!” Shrinkflation concerns shoppers More and more consumers are recognizing the tell-tale signs of shrinkflation affecting the size and nature of their products. Store shoppers are leaving online reviews to talk about this issue, including this Nabisco customer: “Today I purchased a family sized package of lemon Oreos. When I got home there were noticeable gaps in each of the trays … large enough for 10 cookies!” Another consumer review reports that bottles they bought in The Epoxy Resin Store had been reduced in volume: “There is probably 20 ounces missing in each bottle.” How are consumers coping? Consumers are cutting back on those splash-out items, eating out less, and considering fewer trips. But they are saving for that feel-good splurge to keep them feeling great to maintain their lifestyle as much as possible. Shoppers need to keep their loved ones healthy with groceries and small extras to help in challenging times. Many consumers are trading down, buying bargain brands, and purchasing less to keep their finances in check. One way consumers can stay ahead of the curve is to check online review websites to ensure they can pick up any coupons, money off items and discover stores that are keeping prices in check.

When Prices Rise and Products Shrink: How Inflation Alters Consumer Preferences

The Kidnapped Prime Minister

It’s time for the next generation of Scrum: no Sprints needed. If you’re a small cross-functional team aiming to quickly deliver working value to your customer, pick the number one business and user need and build it. Get feedback, adjust, and release. Then pick the next one. Just deliver. Don’t throw in whatever a [time box] can fit. It’s not a container – it’s the lowest risk period of time teams try to manage. If you think the more tasks you throw in, the higher the chance to release – you won’t deliver anything. Waterfall One can only understand Sprint Planning and time-boxed Sprints if one has worked in a pure waterfall project that lasted, let’s say, a year and a half before anything was released. What is released at the end of such a cycle is basically a new product, almost completely incompatible with the previous version. The release notes are five pages long (that no one reads), and it takes another year to get existing customers accustomed to new versions. Most customers are unlikely to upgrade because the minor inconveniences a new release fixes isn’t worth the investment. I remember one of the most impressive presentations about Agile Transformations. Large enterprise virtualization company. They used to release a new version once every two years. But the average time a customer used to get a new version with a change request of theirs in it was … wait for it .. four years. Because, just like enterprise vendors, enterprise customers are not agile. It was impressive because they managed – the vendor – to transform and went all Scrum after months of training and experimentation. At the time of the presentation, they had teams that had started releasing directly to their customers every two weeks. Good Sprint Four years versus two weeks – that’s the power of Scrum when done with the right intentions and the right way. What is the right way? Decide and commit on all levels. The risk mitigation superpowers of the time-boxed Sprints are based on simple mathematics and faith in destiny. You need to be a little superstitious to make it work. Remember – if anything can go wrong, it will go wrong. Murphy’s law Now, don’t get me wrong (unintended) – this is not about fear. This is about being reasonable. Information technologies are the fastest industry today. It’s one of the most complex ecosystems. There are tons of dependencies agile teams navigate through every day, most of them unplanned. You need while dealing with the inevitable daily surprises. practices to help you be productive There’s another famous law – a deficiency in any one of a number of factors dooms an endeavour to failure. Consequently, a successful endeavour (subject to this principle) is one for which every possible deficiency has been avoided. And here’s the question – to make sure your endeavour is going to be successful? for how long can you avoid every possible deficiency Can you make sure that all internal and external dependencies to your product and team – people, tools, resources, world economy —  will remain stable and in a condition favouring your goals for six months, one year, two years? No, you can’t. That’s why here comes Scrum with its safety net of time-boxed Sprints. One to three weeks sounds foreseeable, feasible, and low risk. We might try to get all conditions in place to deliver high-value and high-quality to our customers within two weeks or less. Sounds achievable, right? We have the time to consider the risks and continuously get feedback on customers’ top needs. The developers confirm they understand those top priorities and have everything they need to implement and deliver as expected. The expectation is to work and to receive it sooner than four years. Release early and release often. Once you master this, remove the Sprints and release earlier and more often – several times a day, for example. Sprints enable to happen at all. continuous delivery Scrum and Sprint Planning are a good step toward limiting and avoiding the risks of delays, not delivering at all, rework and waste and throwing months of work in the bin because no customer will get it. What’s sadder – no customer might need it anymore at the time we release it already if there were no Sprints. Bad Sprint The problems with Sprints begin when we start mixing Time and Space. And instead of perceiving “time-boxed” as a limit for time, we interpret it as a container for tasks and start throwing as many tasks in the “box” as it can fit and some more on top in case we finish some earlier. As if it has ever happened. And this is when our Scrum fails. Еngineers are not superstitious. They are opportunistic. They believe nothing can ever go wrong, and their plans are foolproof. They don’t believe in risk. That’s what lets them drive world innovation and technology but this approach has some downsides. The worst thing we can do in Sprint Planning is to initiate it with the question, “How many tasks can we fit in this Sprint?” This is the root cause of all evil we’re witnessing in Product and Engineering management nowadays. It doesn’t matter how many tasks your team they can do in one Sprint. What matters is which task they can get done that your customer can begin using at the end of the sprint. thinks If you don’t have a strong Product Goal turned into Useful, Meaningful, Feasible, Releasable that your team can commit to delivering with , and if you don’t have a clear vision and mechanisms how to handle the that will inevitably occur during the Sprint, your Sprint Planning is useless and a waste of time. descriptions of users’ needs first high quality unplanned deficiencies Throwing tens of items in that box doesn’t increase the chance to . at least release something On the contrary, it’s a sure way to failure and team demoralisation. There are two scenarios for bad Sprints. The one above is the most common occurrence – trying to add as many work items in the Sprint as the suggests. Have you noticed how we never plan as many stories as is the average velocity despite the Velocity charts? We always add more. velocity I cannot count how many times I’ve heard: “Oh, we have for some more story points. Which tasks should we add?” a capacity “Let’s leave some for bug-fixing.” capacity “Nah, let’s Sprint, and we can always the ones at the bottom if we see we can’t complete them at the meeting.” plan the whole remove grooming Guess how the Sprint ends. We manage to get a couple of tasks done, 18 stories are to the next Sprint, and 6 are moved back to the backlog. almost moved That’s why it’s called a backlog – it’s the log of everything we moved back. The other scenario of Sprint Planning failure is when the . This is actually the best thing if it’s on purpose, but when it is because the product manager does everything except figuring out what the most important features, fixes, and improvements are, which we should do first in order to meet customer expectations, grow our business and advance with our technology, this is a symptom of a burned-out product manager, wasted team effort and motivation, and irritated stakeholders. This is when the product manager gets fired. product backlog is almost empty The Next Generation Scrum We need new Scrum terminology for the 2025 edition to get people to get it. Let’s try to reimagine the next generation of Scrum: Timebox becomes . Low-Risk High-Efficiency – Streamlined E.g., The daily meetings are streamlined. The Sprint becomes good old because, with Sprints, we forget to build and get feedback and then build and get feedback again until the user is happy and we’ve reached our goals. Iteration iterate – E.g., What customer value are we going to deliver this iteration? Rather than sprinting (literally being in a rush because it’s a huge story) and coding for five Sprints and eventually caring to get feedback. That’s not agile. The Product Backlog should be straightened to because apparently, repeating a thousand times that the Product Backlog should be ordered by priorities is not enough. That’s why we should start talking only about Product (Business, Customer) Priorities. It implies thinking about what’s most essential and game-changing for the business compared to a To-Do list with random notes. Priorities E.g., Which are our top priorities as a company? No Sprints. Just deliver. Now, we clarified that all that matters is the company’s strategic goals, the business development plans, the customer needs, the technology evolution, and the product vision – we have to just start working on them. the Priorities – Which one to choose? Which one is the top priority? I like the idea of the one that will make the most significant difference – in quality and quantity. The one that will enable the . The one that will open the largest market expansion. The one that will bring more customers. In terms of quality, it’s called (appropriateness, completeness, correctness). highest growth Functional Suitability The one that will and increase your product (maturity, availability, fault tolerance, recoverability) and Performance Efficiency. If your system had a significant downtime last month, then you need to invest in your infrastructure – also Reliability. retain all customers Reliability The one that will offload customer success teams so they can really focus on success . instead of support. The quality characteristic to make this happen is (learnability, operability, user error protection) Usability The one that will make the product (modularity, reusability, analysability, modifiability, testability) more efficient. That’s the one that will of bug-fixing, lower the regression risk, and by enabling continuous delivery. Maintainability reduce the cost speed up the releases It might be that the last big feature was heavily delayed because of unforeseen regression issues. Then the one you should probably prioritise is cleaning tech debt and heavy refactoring. It will again increase your maintainability. There will be a few items in that priority list that are equally important. The prioritisation strategies differ depending on your business context at the moment. It might be that a big fish customer is knocking on your door. Then you’d go with the new big feature they are waiting for. This is the of your product. And the entire Team should start working on it together. Functional Appropriateness Whatever your current product situation is, account for it and always lean on the one that increases the quality of your product and service. It’s always the right choice. Once you decide what your goals are, it gets easy. . Let them think and ideate, let them draw and scratch, let them get loud, let them play, let them build features they are proud of, you are proud of, and your customers are recommending. Let them deliver your five-stars product to your thousands of happy customers. Gather the entire team together and let them create You don’t need Sprints to do that. You need a clear vision and Priorities. Decide. Commit. Succeed. Also published . here

The Next Generation of Scrum: No Sprints Needed. Just Deliver

Technology appears to be developing more quickly every day. With innumerable movies, TV shows, and songs being streamed practically every second, streaming has become one of the most widely used digital entertainment technologies in the UK and worldwide. Because of technological improvements, our viewing and entertainment consumption habits have changed, and streaming sites have undoubtedly transformed as well. Owing to high demand for streaming in the UK market, various streaming alternatives are presently available for subscription. So, what kinds of technological advances make streaming worthwhile? Let’s find out about the most important changes in technology for streaming videos. Video Streaming Industry and Technology 1- High-Resolution Artificial intelligence, which is currently changing numerous industries, has significantly impacted video streaming. AI models could learn how to convert low-resolution photographs into high-resolution images by studying several images. A high-resolution image can be created from a low-resolution one using the "Super-Resolution" technique. Super-Resolution is a type of generative algorithm, which is an approach to creating useful learning.  The network can use a low-resolution visual, and the data can be extrapolated to produce a high-quality visual. AI models that are fully trained on a large amount of visual data make it much easier to scale up images whenever a new image is given. The same concept may be extended to videos with a few minor adjustments. Although the new higher frame in movies is created by combining the old low-resolution frame with the old high-resolution frame in films, the basic idea is the same. With the help of such a development, videos can be accessed in various resolutions, including 1080p on the user's device, while streaming at 480p. Recent improvements in detail learning and the ability to add more and more powerful computing resources to consumer devices have made this kind of technology possible. TecoGAN generated high-resolution outputs in a study that produced results that remarkably matched the graphics that could be seen in reality. This strategy improves the user experience by reducing bandwidth demand by about 30%. 2- Peer to Peer Streaming The client-server model is used to stream video. The data is sent out through websites on the edge that are marked as CDN and cache the server's content. All of these CDNs are used to get the media, which is then streamed to client devices like laptops, TVs, and tablet computers. But, as you can see below, CDN-based streaming video does have some limits. 1) There is a lot of lag when the number of viewers goes up. 2) CDN coverage isn't good enough for streaming media to faraway places. The problems mentioned above make it challenging for a streaming video service to properly scale its offering and maintain a positive user experience. Each of these problems might be solved by combining a P2P network with a CDN streaming technology known as a hybrid CDN. Installing a peer-to-peer barrier on top of a traditional CDN makes the primary CDN less busy by sending files from the peers around it that already work as CDNs and by moving the perimeter much closer to the user. The customer experience is better because the video is pulled from a nearby peer instead of a faraway CDN. Also, because there is less traffic on CDNs, costs have gone down by 40%. 3- Multi CDN As you can see, the content is sent to users' devices through CDNs, which temporarily store the content from the server. To distribute material, a CDN requires data centres spread across numerous regions. In this, we talk about the "Points of Presence" (POP). The best thing is to have as many POPs as you can. But not every CDN has the same reach around the world. For example, some of the most popular CDNs don't have a POP in China. Most of the time, the effectiveness of each CDN is random and changes over time. Murphy's law says that anything can go wrong with a CDN at any time. Maybe the best way to deal with each of these risks is to use more than one CDN at the same time. You could do this by making separate deals with each CDN, or you could hire a Multi-CDN vendor who might take care of everything for you. Hosting your streaming video platform on a Multi-CDN will provide benefits that improve your overall user experience. 1) You might avoid unplanned service interruptions by switching to a trustworthy CDN. 2) Using " " (RUM) parameters to improve "Quality of Service" while getting content from the best CDN available. Real User Monitoring 3) Using mid-stream swapping can cut CDN costs by a lot. 4- Per Title Encoding It is a method of efficient encoding that changes the bitrate pyramid of each video based on how hard it is to understand. The main goal is to choose a bitrate that gives the codec just enough space to hold the data needed for a perfect viewing experience, but not more. Another way to look at it is to assume that the streamlined, responsive package only has content that users will find useful. The "best" bitrate for each type of content is different. Most of the time, a higher bitrate is needed to hold the information in a sport or fight scene because there is more action and less repetition, making each scene more complicated. It also gives you fewer options for compression techniques, which keeps the quality of the information from looking worse. On the other hand, documentaries tend to have a lot less movement in each scene. This means that a codec might be able to deliver more information in less time without sacrificing accuracy. By taking these things into account and changing the encoding profile accordingly, you can lower the bitrate while keeping the relative value of your content at a very high level. To choose the best option for each different piece of content, you need a good way to compare bitrates. Getting this data usually takes a lot of encodings with different bitrate ratios. Each encoding should use a different content type. You can set up a specific bitrate cascade for each content file and use it to encode it. This method is effective, will improve the user experience, and will almost always lead to less bandwidth being used. Companies that sell digital content, especially those that offer VoD and OTT platforms, naturally focus on this. In 2015, Netflix was able to improve Per-Title Encoding while cutting costs related to the extra analysis phase. As a result, they improved the user experience and cut bandwidth use considerably. Conclusion In the end, we've seen a lot of different . With the arrival of 5G and esports' growth, the use of video bandwidth will go through the roof.  It is important to use the latest innovations to keep up with this growth and keep it going. technological improvements in video streaming

Stories, News & Mentions about MURPHYUSA

Murphyusa’s Stories on HackerNoon

Murphyusa's News & Mentions