#Prescriptive analytics

[seenivasaniiabac]

Key Components of Prescriptive Analytics | IABAC

This image shows the main parts of prescriptive analytics: collecting data, analyzing and processing the data, applying algorithms and models, and providing actionable recommendations. It explains the steps clearly using simple illustrations in a circular layout. https://iabac.org/blog/what-is-prescriptive-analytics

[aretovetechnologies01]

In today's fast-paced digital landscape, data is the lifeblood of any successful enterprise. The ability to harness, analyze, and derive insights from data has become a critical competitive advantage. This is where data engineering comes into play, serving as the foundation for unlocking the full potential of data analytics. At Aretove Technologies, we understand the significance of data engineering, especially when combined with the robust capabilities of Amazon Web Services (AWS), to drive predictive and prescriptive analytics solutions.

[ict-123]

Rise in need for sophisticated market analytics solutions among businesses across the globe and emergence of advanced technologies such as big data and IoT has proliferated the adoption of analytics to analyze large data silos. 

[umarblog1]

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[smartinfologiks]

Predictive Analytics vs Descriptive Analytics vs Prescriptive Analytics

Here’s a three-minute guide to understanding and choosing the right descriptive, predictive, and prescriptive analytics for use across your business chain.

With enormous data available to businesses concerning the supply chain, companies are now adopting analytics solutions to extract meaningful and insightful volumes of data to help enhance decision-making.

Deeming all the analytics solutions can be a challenging task. Fortunately, these analytics alternatives are categorised extensively into three different types. None of them is better than another they co-exist and complement each other. For businesses to have a holistic view of the market and how a firm competes effectively within the market need a dynamic analytic environment which encompasses the:

Predictive Analytics: Predicting & Understanding the Future

Predictive analytics follows its roots to “predict” what might occur. These analytics are all about understanding the future. Predictive analytics is useful in providing firms with actionable insights based on real-time data. It also offers estimates concerning the likelihood of a future outcome. Note: no statistical algorithm can “predict” the future with 100 percent assurance. Businesses use these stats to forecast what might occur in the future. Because predictive analytics is based on probabilities. Predictive analytics combines historical data discovered in ERP, CRM, HR, and POS systems to identify data patterns and apply statistical models and algorithms to seize the relationship between multiple data sets. It can be used by companies to forecast customer behaviour and buying patterns to identify emerging trends in sales activities.

Descriptive Analytics: An Insight into the Past

Descriptive analytics does precisely what its name implies: they “describe,” or summarize, raw data and create it into something interpretable by humans. They’re merely the analytics that demonstrates the past. Descriptive analytics is helpful as they enable us to learn from past behaviours, and understand how they can influence future outcomes. This underlying data is a count or aggregate of a filtered data column to which typical math is applied. Leverage descriptive analytics to understand at an aggregate level what is happening in your firm, and when you are willing to summarize and describe multiple business facets.

Prescriptive Analytics: Aforethought on Possible Outcomes

This relatively novel stream of prescriptive analytics enables users to “prescribe” different possible actions and instruct them towards a solution. In short, these analytics are all about advice and suggestions. This analytics attempt to quantify the impact of future decisions to suggest possible outcomes before the decisions are made. At its best, prescriptive analytics not only predicts what will happen but also suggest why it will happen, offering suggestions concerning actions that will benefit from predictions. If implemented appropriately, they can greatly impact business decision-making as well as the company’s bottom line. Biggies in the market have already adopted prescriptive analytics to optimize production, scheduling, and inventory to ensure they’re providing the right products at the right time as well as optimizing the customer experience.

Still, in business, the most compelling areas sakes, marketing, operation, and finance departments are pedestals on descriptive and predictive analytics. Reach out to Smartinfologiks to optimize your business with the analytics solution, today!

[aditisposts]

Predictive vs Prescriptive vs Descriptive Analytics Explained 

Business analytics leveraging data patterns for strategic moves comes in three key approaches – descriptive identifying “what has occurred", predictive forecasting “what could occur” and prescriptive recommending “what should occur” to optimize decisions. We decode the science behind each for aspiring analytics professionals.

Descriptive analytics convert volumes of historical data into insightful summaries around metrics revealing business health, customer trends, operational efficiencies etc. using direct analysis, aggregation and mining techniques producing current reports. 

Predictive analytics forecast unknown future probabilities applying statistical, econometric and machine learning models over existing data to minimize uncertainties and capture emerging behaviors early for mitigation actions. Risk models simulate scenarios balancing upside/downside tradeoffs. 

Prescriptive analytics take guidance one step further by dynamically recommending best decision options factoring in key performance indicators for business objective improvements after predicting multiple futures using bell curve simulations. Optimization algorithms deliver preferred actions.

While foundational data comprehension and wrangling abilities fuel all models – pursuing analytics specializations focused on statistical, computational or operational excellence boosts career-readiness filling different priorities global employers seek!

Posted By:

Aditi Borade, 4th year Barch,

Ls Raheja School of architecture 

Disclaimer: The perspectives shared in this blog are not intended to be prescriptive. They should act merely as viewpoints to aid overseas aspirants with helpful guidance. Readers are encouraged to conduct their own research before availing the services of a consultant.

[half-man-half-lime]

A take that I'd like to bang like a drum over and over again:

Engaging with stories in a pedantic or analytical way isn't bad, it's just a different way of making, interacting with, and enjoying media that might work for some people but not everyone.

Being prescriptive that everyone should engage with stories the same way is bad. The CinemaSins people or whatever you find most annoying aren't a problem until they start telling people how stories should work.

If you're telling people that they should shut up about consistent worldbuilding or plate tectonics or weird implications of story logic because the story is really about the character dynamics, you're just a different flavor of annoying.

[pharmacrm]

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[techtinkerer]

What are the most popular Predictive Analytics techniques?

Data mining: Data mining is the process of sifting through massive amounts of raw data to find anomalies, patterns, and correlations that can help surface insights to inform decision-making. 

Decision trees: A tree-like structure that uses questions and decisions to predict based on past data. Decision trees can help identify critical features and understand relationships between input variables and their outcomes.

Neural networks: A machine learning algorithm that mimics the human brain to identify patterns in data. Neural networks can use multiple models, including regression, classification, clustering, and time series, to handle large amounts of data and complex relationships.

Linear regression: A simple and interpretable technique

Time series analysis: A method that uses previously observed data to predict future values. Time series models help predict metrics or behavior over time or when making decisions that involve uncertainty over time.

Clustering: A technique that groups similar data points together based on criteria. However, choosing the optimal number of clusters can be difficult, and clustering models may trade off simplicity for accuracy.

Although these methods vary in approach and complexity, they all serve one common purpose: to predict future outcomes that can help businesses capitalize on what lies ahead. 

Predictive analytics are mostly industry-agnostic. You can employ them to run your business smoothly, regardless of industry or market. 

[lorata]

This is so random, but what are some of your victors’ handwriting like? And how does their use of language differ from one another (regarding accents, vocabulary, cursing etc.)?

you know what, i've never considered their handwriting before

honestly they're probably pretty terrible! they're an uneducated bunch, most of them checked out around age 10-11, and everyone left school at 13. some finished their education post-victory (like odin or adessa) but most ....... did not*. a lot of them have undiagnosed learning disabilities as well (emory, brutus, claudius, likely others) and others have adhd (misha, devon at LEAST)

(*technically they have a super lopsided education because if you're a mentor you have to take stats, but you don't need like ....... the other mandatory high school classes, so they have these random analytics courses but have never written an essay longer than a hamburger)

odin and callista canonically have good penmanship. hera probably does also. adessa you'd THINK because she's so educated but no, she writes like a doctor's prescription pad, and only beetee can understand anything. emory has large, careful, childlike handwriting because she was illiterate and learned to write post-arena

the language question is REALLY hard to answer in an ask, lol. like, if you read a scene with a bunch of dialogue you'll see how the various characters speak, or even POVs will have their narrative voice, but trying to break it down outside of actual fiction is like ........... idk man

nero, brutus, emory and devon all have rural accents; the centre trains it out of them but they come back to it once they win, though devon is such a sponge that he keeps district standard more until he visits home and then suddenly oh there it is. lyme grew up with one but trained out of it herself, HARD. misha was a townie, ditto petra, enobaria and adessa. odin, hera, and calli all have the upper class accent. claudius would have if his mom hadn't kicked him out at seven - which hilariously means he sounds more like that in the no-Residential 14yo victor AU

vocabulary is more about persona. people expect adessa and odin to use larger words and so they do; brutus and nero, meanwhile, aren't, and so they don't, and this is mostly fine. but every so often brutus will drop a $5 word or adessa a real ugly swear just to remind you that what you're seeing isn't everything

(they can all perform district and panem standard as part of their official persona and mimic capitol dialect, though the latter is mostly for fun/mockery/sarcasm/making a point)

[aretovetechnologies01]

In today's fast-paced digital world, data has become the lifeblood of businesses, driving decision-making, innovation, and growth. However, the sheer volume and complexity of data generated daily present significant challenges for organizations seeking to harness its full potential. This is where Data Engineering Companies like Aretove Technologies step in, offering expertise in transforming raw data into actionable insights through advanced analytics techniques, particularly predictive and prescriptive analytics.

[beardedmrbean]

California's handling of sensitive health information is under scrutiny following a report that data entered by residents on the state's health insurance marketplace was shared with LinkedIn.

Covered California, which runs the state's marketplace, coveredca.com, shared sensitive personal data with LinkedIn, a subsidiary of Microsoft, through embedded tracking tools on the website, nonprofit news organization The Markup reported on Monday.

Covered California confirmed the data transmission in a news release later that day, saying "some sensitive data was inadvertently collected by the tags, including first names, the last four digits of Social Security numbers, and other sensitive health information like pregnancy status."

It added that all advertising-related tags on the website had been turned off as a "precautionary measure," and that it would review the extent of the data shared.

Representative Kevin Kiley, the Democrat from California has called for an investigation. "This is incredibly disturbing," he wrote on X, formerly Twitter.

Newsweek contacted Representative Kiley via social media and email, as well as the press offices of Health Secretary Robert F. Kennedy Jr. and California Governor Gavin Newsom via email outside of regular working hours on Wednesday.

Why It Matters

Concerns over personal data have grown in recent months after it emerged the government's Department of Government Efficiencyworked to gain access to the Social Security Administration's data systems, which hold sensitive personal data about approximately 70 million Americans.

California's sharing of sensitive data with LinkedIn will likely raise similar concerns about threats to Americans' privacy.

What To Know

Trackers on coveredca.com, which was created under the Affordable Care Act, captured users' answers to questions about blindness, pregnancy, high prescription use, gender identity and experiences with domestic abuse, The Markup reported.

The data was then transmitted to LinkedIn using Insight Tag, which uses code to track how visitors interact with websites.

Covered California said in a statement that it "leverages LinkedIn's advertising platform tools to understand consumer behavior;" however, LinkedIn notes on its website that Insight Tag "should not be installed on web pages that collect or contain Sensitive Data."

The LinkedIn campaign trackers began in February 2024 and were removed "due to a marketing agency transition" in early April, Covered California told CalMatters.

Covered California had more than 60 trackers on its site, compared to the average on other government sites of three, CalMatters reported.

What People Are Saying

Covered California said in a news release on Monday: "Covered California is reviewing its entire website and information security and privacy protocols to ensure that no analytics tools are impermissibly collecting or sharing sensitive consumer information. The LinkedIn Insight tags are no longer active and, as a precautionary measure, all active advertising-related tags across the CoveredCA.com website have been turned off.

"Covered California is committed to safeguarding the confidential information and privacy of its consumers. The organization will share additional findings from this investigation as they become available."

California Representative Kevin Kiley, wrote on X: "California's Obamacare website tracked users' personal health information—such as pregnancy and prescription drug use—and sent it to LinkedIn for a 'marketing campaign.' We are asking Secretary Kennedy to investigate for HIPAA violations."

What Happens Next

The Department of Health and Human Services has yet to respond publicly to Kiley's call for an investigation.

[umarblog1]

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[dailyanarchistposts]

Towards new encounters

Wherever they appear, common notions and transformative movements can fall prey to rigid radicalism. The shift can be subtle: what worked in a particular place and time can be converted into a fixed how-to list. A sense of experimentation and vitality can be sucked out of the air with a few words that induce a sense of paranoia and lack-finding. The shared capacity for encounters across difference can be converted into moral certainty and guilt-mongering. What was initially transformative in one context can be held up as the answer, a new duty, or a new set of responsibilities that are imposed on others. This can even manifest as a rigid insistence on autonomy and individual freedom that crushes the potential for collective responsibility and action.

Ethics and uncertainty cannot survive long in an atmosphere of stagnation and rigidity. Detached from the transformative relationships that animate them, common notions become fixed principles dropped on other people’s heads. They remain enabling and ethical only insofar as they retain the capacity to activate response-ability: the capacity to ask, over and over again, what might move things here and now, and to really take pause and listen to each other deeply. All of this is to say that ethical attunement, experimentation, and common notions are powerful, fragile, and precious. These sensibilities are already emerging in a lot of places, as people figure out how to sustain and defend joy against the crushing tendencies of both Empire and rigid radicalism.

Paranoid reading, moralism, and ideology aren’t going anywhere, and even naming and criticizing them can be ways of slipping into their poisonous grip, giving one a sense of superiority, of being above all those things. The critique of rigid radicalism can manifest as a new way of finding mistakes, or as contempt for places and people (including oneself) where rigid radicalism takes hold. It can become a paranoid critique of paranoia itself: criticism might be helpful to get a little distance from stifling and hurtful dynamics, or in figuring out how they work, but it will not necessarily activate other ways of being. Critiques are no use unless they create openings for joy and experimentation, and for feeling and acting differently. For us, the best way to do this analytically has been to affirm that openings are already happening and always have been, and that it is worth being grateful for these powerful legacies.

In our own experience and in talking to others, becoming otherwise is never a linear passage from one way of being to another, but a slow, uneven, messy process. Sometimes something new emerges only in the wreckage after groups have torn each other apart, or have people “burnt out.” Sometimes the flight from paranoid reading flips over into an everything-is-awesome attitude that refuses all forms of discernment and critique. Sometimes people sense that things are not working, find bits of joy, but then rigid radicalism takes over again in another guise. Sometimes a dramatic event leads to new common notions and joyful ways of relating, and rigid radicalism loses its grip. Sometimes people abandon rigid radicalism in favor of an attempt to live a “normal” life under Empire. Sometimes people travel and their encounters leave them changed, more capable of cultivating collective power and experimentation. There is no blueprint, no map for moving in other ways.

In telling these stories, we have tried to avoid generating prescriptions for others, and we hope to have made space for a proliferation of other stories about rigid radicalism, especially those about how and where people have been able to undo it or relate differently. New potentials can be activated by continuing these conversations with each other.

Ultimately, we think, what is at stake in undoing rigid radicalism is joyful transformation: a proliferation of forms of life that cannot be governed by Empire nor stifled by rigid radicalism. To be militant about this is to nurture and defend these shared powers that grow through people’s capacities to tune into their own situations, to remain open and experimental, and to recover and invent enabling forms of combat and intimacy.

[pameliciousss]

⋆𐙚₊˚⊹♡Imagine with Lawrence

(This is a t/n "your name" job by Lawrence #saw)

You had gone to the IMSS in Mexico because tuberculosis was killing you. You tried to cure it with remedies you saw on TikTok, but you only made things worse. After waiting four hours in the waiting room, the door to the office opened, and a tall man appeared in the doorway.

—T/N, come in —he said firmly.

You entered the cold consultation room, where the handsome Dr. Lawrence Gordon examined you professionally. He explained the proper treatment, but your mind was elsewhere. You didn’t know how to flirt with him, nor were you even sure if you wanted to.

On the outside, to everyone, you had always liked girls. What would happen if you confessed? Would he mock you? Or even worse, would his reaction be disgust? The uncertainty ate away at you, so you tried to focus on his explanation, although his deep voice and serious face didn't help at all.

Suddenly, the earthquake alert began to sound.

The clinic was filled with panic, but everyone evacuated carefully. As you stepped outside, you saw Lawrence in the distance, accompanied by a nurse. They didn't exchange a word, but the way they were together made you feel uncomfortable.

All your pain: tuberculosis, exhaustion, and now your heart. You knew he would never look your way, but seeing him so close to that "very good-looking" nurse only worsened that feeling.

A fleeting crush, an illness that was consuming you, and a doctor who didn't even notice your presence. Could fate be any crueller?

When the situation stabilized, everyone started returning to the hospital. You were about to follow the crowd when Lawrence's voice called you again.

—T/N, come. We're not finished yet.

You swallowed hard. You turned slowly, trying not to show your nervousness, but it was difficult. Why did he have to be so handsome? Why did his presence make everything so awkward? Your mind was flooded with confusing thoughts as he watched you.

With that analytical, doctor-like gaze.

You took a deep breath and re-entered the office, unsure whether your biggest problem was tuberculosis... or the doctor.

Lawrence finished writing the prescription for the painkillers. While you waited, unconsciously, your gaze dropped to his hand. And there you saw it.

A ring.

Your chest tightened, but there was no reason for it. It wasn't yours. You had no right to feel this way.

[ckmstudies]

June 24th:

Almost three and a half hours of studying done today! I need to hit just under three hours tomorrow to make my 20 hour weekly study goal! This will be the first time in four weeks that I've made it. I'll probably go for 25 hours next week considering I'm still behind. I'm supposed to have finished areas three and four including the second mini exam by tomorrow but I've just finished area three tonight. I also started my review of area three for my area three practice exam that I'm hoping to take tomorrow. Tomorrow's goals are to finish reviewing area three and take the practice exam as well as finish modules one and two of area four. I really need to grind it this next week to be anywhere near ready for this exam in just 25 days. My study course recommends over two weeks of revision before the exam and there's no way I can make that but I can try for a week and a half at least.

Today's accounting topic: Auditors use descriptive, prescriptive, predictive, and diagnostic analytics to analyze accounting information to help with understanding the client's course of business as well as identify material misstatements.

Other activity: I went to the craft store with my mom and looked at all the yarn which was super calming but I didn't buy any because I already have too much and I couldn't think of anything to make.