#Data-Based Decision-Making

[techyknow]

Top Pitfalls in Data-Based Decisions to Avoid in 2025

Data misuse, bias, and misinterpretation challenge leaders in 2025. Uncover how to navigate these issues for better strategic decisions.

[practicalsolutions]

I think about this image a lot more than what is probably healthy.

[ko-eko-ev-go-ms]

It continues to trip me up how much human brains are just weird organic computers

[indianreporter]

[thetrainingnetwork]

Data, once the golden thread of healthcare, is now on the chopping block. Performance indicators vanish, insight fades. In pursuit of efficiency, NHS risks flying blind into a future of false economies.

[technologyequality]

Make Smarter Moves, Not Just Faster Ones: The AI Decision Matrix You Didn’t Know You Needed

Make Smarter Moves, Not Just Faster Ones The AI Decision Matrix You Didn’t Know You Needed Ever felt like you were making business decisions with one eye closed, spinning the Wheel of Fortune, and hoping for the best? Yeah, me too. Let’s be honest: most entrepreneurs spend more time guessing than assessing. But here’s the plot twist, guesswork doesn’t scale. That’s where the AI-powered…

[fazmid]

there needs to be a nice way for me to say to users "what you're reporting isn't a tech issue you just don't know how to do your job and that's not really my problem past a certain point"

[lepton-software]

Revamping Business Intelligence with SmartMarket: Unlock Strategic Decision-Making with Location Insights

Discover how SmartMarket by Lepton Software revolutionizes business intelligence with powerful location-based insights. Learn how it empowers businesses to make strategic, data-driven decisions for growth and success.

[lunarlesbeing]

Ughhhh so way back at the end of last year I went for a regular check in with my psychiatrist and she was concerned about my heart rate being really rapid so that office basically just set me up the same day with the first regular doctor they could find that had an opening, I ended up wearing a heart rate monitor for a week and it confirmed that I have frequent racing heart but it’s otherwise normal rhythm. She changed me to a different medication (both the old and new meds are non-stimulant options for ADHD) to see if it was a side effect from the other one and nothing changed so she’s told me the last few times I’ve seen her that I should schedule a follow up just to check in on it again. (I’m hoping once I do I can get the clear to go back on the other med because it worked better and I really don’t think it was ever a factor with my heart tbh, I'm just anxious as fuck and also have hEDS/dysautonomia)

I’ve been putting it off because I’ve been super overwhelmed just in general (and also the new med doesn't do as much for executive dysfunction which doesn't help) but finally went to schedule something today. I actually was thinking of just seeing the same doctor as last time for the sake of continuity (and I don't normally like male doctors but he was actually pretty chill) but could only find him currently listed for a center a few towns over (my local hospital has a bunch of connected offices and it’s one of them but further than I was hoping to drive if I can avoid it) so I figured I’d try first with the doctor I’ve seen for other things in the past. But when I called they said she was booked up and I was like “yeah that’s fine I haven’t seen her in awhile” and agreed to see someone else with more availability.

But then when they asked what the appointment was for they were apparently super confused by my explanation (probably because of how much time has passed tbh) and ended up putting me on hold while they tried to figure it out and then said they’d call me back when they got more details from my psychiatrist and the other doctor I saw (who apparently might actually be practicing at the main center but just wasn’t listed there? Which would make things easier but it wasn’t really clear) about what they actually wanted me to do but I never heard back from them before the end of the day so…hopefully they call me back tomorrow? Ugh it sucks though, I get so much phone anxiety even under the best of circumstances so this is literally like my nightmare scenario, I had to have a stress cry about it once I got off even though the receptionist was super nice the whole time

[public-cloud-computing]

Revolutionize manufacturing decisions with AI-driven Business Intelligence. Dive into data insights, powering efficiency and innovation. Discover more!

[generative-ai-in-bi]

Smart Insights: AI Interfaces Driving BI Evolution

In such an ever-changing business arena, wisdom is one of the key assets and you have to rely not only on your intuition. Using Data Analytic Tools and Advanced Artificial Intelligence Models, one would be able to deduct the necessary strategic choices. The old trend was that the usage of such instruments mostly depended on particular experts to do it which happened to confine the utilization of these tools to most of the big organizations with their reliable data science teams. Yet the emergence of Generative AI Interfaces for instance is turning the tide beyond this traditional model of analytics democratizing access to advanced analytics and making it possible for small companies to be equipped with sophisticated analytics capabilities with unprecedented speed and thus making better decisions.

The AI Predictive Analytics has developed to a game changer in the realms of strategic decision making since it allows us to e pump up performance and business tremendously by predicting even the advanced matters of level. Leveraging AI-Based Forecasting Algorithms allows companies to react in a premitive manner to their mindsets, while foreclosed emerging opportunities and eliminating risks. By doing so, companies gain a significant competitive advantage in their industries. On the other hand, due to the complex nature of traditional analytics platforms, their adoption has been limited by the fact that to efficiently operate them one needs to be properly trained while an expert is preferable for navigation.

And these are the two areas in which Generative AI plays a major role, offering an innovative way to handle Business Intelligence (BI) problems through artificial intelligence, aimed at automating and simplifying the process. In contrast to traditional BI solutions that contains queries and examines fact-based static reports, Generative AI interfaces deploy machine intelligence which makes immediate relevant insights based on the unique needs and objectives of user. As a result, the individuals cut off from the loop manually for the model have required tasks been automated, not only has it eliminated human manipulation but also enabled users to discover the hidden patterns and correlations that were ignored by human analysts.

The use of Generative AI in BI applications proves to have various optimization options. First of all, these approaches increase scalability and accessibility of analytics solutions, thus granting organizations an opportunity to spread the advanced analytics capabilities not only across the departments but also across the functions without a need of specialised knowledge. AI-Empowered Business Analytics Software for Finance or Marketing teams will have AI-Powered Smart Interfaces which allow the users to derive actionable results with minimum learning.

Additionally, machine learning with AI, smart analytics and generative AI variation introduces a huge leap in the AI-driven wisdom capability allowing the organization to make informed decisions with certainty and lucidity. These systems transform huge data sets into valuable patterns which eventually amplify human decision-making proficiencies, thus, executives can tackle uncertain strategic issues hand in hand with analyzing capability. Whether it’s incorporating artificial supply chain management, demand forecasting or new revenue generation, AI Generative AI powered businesses in exploring new frontiers in their data resources.

The main example of the Generation AI is that, it is capable of adapting and developing new algorithms all the time as it’s instructed and the new information is provided. The ongoing cycle of continuous learning is the direct way towards improving the accuracy and reliability of AI-based forecasting. These points also give organizations the chance to stay nimble and adaptable to the dynamic business environment. Therefore, the companies will benefit from having a secured place in the future marketplace because generative AI is an effective and a revolutionary tool that enables them to stop losing to the competitors.

Besides, the democratization of Generative AI for Business Intelligence spurs innovation and entrepreneurship into a whole new dimension. It does this by lowering the door where most people can enter but in addition, it equips individuals with advanced analytics tools which they use to not only make data driven decisions but also try out new things. It isn’t sequestered to a small group of larger companies but rather is open to any business that wants to utilize it, and they are not discriminated against because of their size. Whether it is a startup looking to disrupt an industry or a small business seeking to optimise its operations, Generative AI interfaces level the playing field, enabling entities of every dimension to compete and succeed in digital economy.

In conclusion, the implementation of Generative AI inside Business Intelligence structure is a pivotal step for how to unlock the power of data with the purpose of encouraging the growth and creativity of organizations. Through the act of democratizing the advanced analytics tools and the premature artificial intelligence analytics, businesses gain wide access to advanced methods of decision making, faster. It can help unraveling hidden insights or making forecasts, or simply optimizing operations. This is how Generative AI interfaces become a new horizon of opportunities with which organization need to catch-up. Furthermore the journey of this disruptive technology is yet to discover its full extent and hence the future is highly satisfying.

[technologyequality]

AI-Powered Decision-Making: How to Execute with Precision and Confidence

AI-Powered Decision-Making How to Execute with Precision and Confidence Scaling a business is one thing, but making the right decisions at the right time? That’s the real challenge. We’ve already explored AI-powered leadership, customer experience, innovation, and strategic planning. Now, it’s time to connect the dots and focus on something that determines whether all of those efforts succeed���

[embershx]

hey maybe don't encourage trans people to allow their extremely transphobic government who are already pushing for anyone under 25 to have their gender affirming care taken from them and have them legally treated as a child unable to consent to "allow themselves to be used for research" when the entire thread you're sharing is about how that's a HORRIFIC IDEA!!

So maybe I wasn't clear enough here. Is the UK government transphobic? Yes, hugely. Oh my god yes. But it is very hard to have anything but patient information as a study point because you can't exactly run experiments most of the time. I'm not saying blindly agree to everything, but when the current studies they're using are super inconclusive and they're using that to say 'well lets not bother with gender affirming care we have no proof it works' having data points that say otherwise can be hugely benificial for anyone arguing for continued care. And I was explicit about saying I disliked the lack of consent involved. What I'm advicating for is people to be able to willingly provide data points if asked. All of this was coming not just off the post but from having read articles in response to Hilary Cass's Review ([Guardian summery]) about how the lack of high quality study data highlighting the benefit of gender affirming care was part of the reason that the 'no proof it helps' meta studies are able to successfully push for reallocating resources away from the programmes. Some of whice also look into the current treatment of patients. [The Guardian] [Aljazeera] [BBC News] And it coming relitively soon after the issue with The Tavistock seeming to funnel metal health patients into it's gender identity without any sort of due dilligence makes it particuarly difficult. [BBC News] [The Independant] [The Independant] In part because there's no way of tracking what treatments and results actually happened. Should we know Bob Smith had X treatment and Y result? No. But should we be able to say 'Z% of patients respond well to X'? Yes. And to do that we need data points.

[rightrev-revenuerecognition]

Revolutionizing Revenue Recognition: The Power of Automation

The answer lies in automating the decision-making process itself.

Revenue accounting automation involves pre-defining rules based on policies and desired outcomes. These rules can then be applied directly to data sourced from sales contracts and various systems capturing orders, fulfillment, and billing. The result? Precise revenue calculations and forecast schedules over the contract term.

So, how does it actually work?

Imagine a revenue analyst reviewing a contract to identify critical components for revenue recognition. Similarly, automation software can be configured to identify these components through data mapping—things like contract number, customer name, contract term, deliverables, and pricing details.

Instead of relying on cumbersome spreadsheets, an automated revenue sub-ledger takes on the task of aggregating data, applying rules, and recognizing revenue based on predefined criteria.

Think of it as building a roadmap for revenue recognition—a set of rules and guidelines that automate the process from start to finish.

[terotam]

How to Organize Maintenance Department?

Learn key strategies for organizing your maintenance department to boost efficiency, streamline operations, and enhance team productivity.

[us-enviro-comments]

The Public Comment Process (+ how to write effective public comments)

The US federal rule-making process is founded around the right to comment: the public's opportunity to publicly address the agency responsible for a decision. This right is enshrined by the Administrative Procedure Act (APA) of 1946 and reinforced by the National Environmental Policy Act of 1970, which both require that citizens be able to take part in pre-decision communication with a federal agency.

Public comments are important for a number of reasons:

Agencies must consider all new information received during the comment period and address that new information before publishing the final rule; this includes revising the proposed rule.

A good comment can be the basis for a court challenge. If the agency does not adequately address the new information in the comment, they can then be sued and the rule placed on hold until the issues raised by the new information are resolved.

Advocacy groups and journalists often scour public comments to get ideas for their own comments and campaigns, and to contextualize proposed rules.

Submitting a public comment on proposed federal rules and regulations is not like commenting on social media, though. Substantive comments that require agency response are those that contain information pertinent to the actions proposed in the regulation, such as community impacts, scientific evidence, or other data. Non-substantive comments ("I don't like this!") don't require any response beyond maybe a "Comment noted."

Here's a brief set of tips from the Public Comment Project:

“The most valuable public comments are unique, fact-based, and succinct. The agency will have to sort through many identical form letters and expressions of personal opinion.  

Your comment can report on scientific evidence that opposes or supports the theory behind the regulation. Providing additional supporting evidence helps strengthen the agency's position by creating a stronger scientific foundation for their action.

Use an opening sentence to establish your credibility. State who you are and summarize any of your experiences that are relevant to the topic of the proposal.

You do not have to come to a conclusion or judgement regarding the entirety of the regulation, but you do have to clearly communicate the implications of the research you present. Avoid leaving it up to the agency to infer how research or data relates to the regulation. 

Check out the agency's mission statement and any statutes relevant to the regulation. Federal agencies' actions are driven by their mission and held to the standards dictated by statutes, so make your comment stronger by explaining how your information contributes to their mission.”

You can also find templates here: https://publiccommentproject.org/comment-templates

Sources:

The Public Comment Project https://publiccommentproject.org

Democracy in Practice: Public Participation in Environmental Decisions, Beierle and Cayford 2002

Union of Concerned Scientists https://www.ucs.org/resources/participating-federal-rulemaking