#Data Visualisation Solutions

[datavisualisationservices]

Choosing the Right Data Visualisation Solution for Your Business Needs

Introduction

Businesses rely heavily on data visualisation solutions to extract meaningful insights and facilitate informed decision-making in the modern data-driven world. However, selecting the right solution can be overwhelming with the vast array of data visualisation tools available. A well-chosen visualisation tool can enhance business efficiency, improve collaboration, and provide deeper insights.

Understanding Data Visualisation Solutions

Data visualisation solutions transform raw data into graphical representations such as charts, graphs, dashboards, and maps. These solutions help businesses interpret complex data patterns, trends, and correlations more effectively. Choosing the right tool requires a thorough understanding of business objectives, data complexity, and user requirements.

Key Considerations When Choosing a Data Visualisation Solution

1. Business Objectives and Use Cases

Before selecting a tool, businesses must define their primary use cases. Are they looking to improve internal reporting, track key performance indicators (KPIs), or create real-time interactive dashboards? Understanding these objectives will narrow down suitable options.

2. Ease of Use and User Experience

A data visualisation solution should be user-friendly, catering to both technical and non-technical users. A tool with an intuitive interface and drag-and-drop functionalities can empower business users to generate insights without extensive technical expertise.

3. Integration with Existing Systems

The chosen tool must integrate seamlessly with existing databases, business intelligence (BI) platforms, and cloud services. Compatibility with data sources such as SQL databases, spreadsheets, and cloud applications ensures smooth data flow.

4. Customisation and Scalability

Businesses should consider whether the tool can be customised to meet specific needs and scale as the company grows. A scalable solution will support increasing data volumes and more complex analytics over time.

5. Advanced Analytical Capabilities

Some businesses require more than basic visualisation; they need predictive analytics, artificial intelligence (AI)-driven insights, and machine learning capabilities. Selecting a tool that provides these features can give businesses a competitive edge.

6. Collaboration and Sharing Features

For organisations with multiple teams, a tool that allows collaborative data exploration, report sharing, and real-time updates is essential. Cloud-based solutions enhance teamwork and remote access.

7. Security and Compliance

Data security is a top priority. Businesses must evaluate whether the solution meets compliance regulations (such as GDPR or HIPAA) and offers encryption, role-based access control, and secure authentication methods.

8. Cost and ROI Consideration

While budget constraints are important, businesses should weigh the cost against potential benefits. A higher-priced tool with robust features may offer greater long-term value compared to a cheaper, limited-scope solution.

Popular Data Visualisation Tools in the Market

Several tools cater to different business needs. Some of the most popular include:

Tableau – Best for advanced analytics and interactive dashboards.

Power BI – Ideal for Microsoft ecosystem users and enterprise-level reporting.

Google Data Studio – Great for free, cloud-based reporting and collaboration.

D3.js – Suitable for developers who need custom visualisation capabilities.

Looker – Best for integrating BI and data analytics into workflows.

Conclusion

Choosing the right data visualisation solution requires careful evaluation of business needs, technical requirements, and budget. By considering factors such as ease of use, scalability, integration, and security, businesses can select a tool that maximises the value of their data. Investing in the right data visualisation solution empowers organisations to turn raw data into actionable insights, driving business growth and success.

[vertexcom]

IT solutions for insurance companies provide tailored technologies to streamline operations, enhance customer experience, and ensure regulatory compliance. These solutions often include policy management systems, claims processing software, CRM platforms, and data analytics tools to improve decision-making and risk management. By integrating digital tools, insurers can automate processes, reduce costs, and deliver personalized services efficiently.

[powerbisolutions-blog]

Enhancing Project Management with Power BI Reporting and Dashboards 

Efficient project management is the cornerstone of successful businesses, and having the right tools can make all the difference. Power BI, a leading business analytics platform, is revolutionizing project management by offering robust reporting and dashboard capabilities that provide real-time insights and data-driven decision-making. 

Transforming Project Management with Power BI 

Power BI empowers project managers by consolidating data from multiple sources into interactive dashboards. These dashboards enable teams to monitor project progress, track key performance indicators (KPIs), and identify potential bottlenecks—all in real time. With customizable reports, managers can tailor insights to align with specific goals, ensuring that every stakeholder has the information they need at their fingertips. 

For example, Power BI’s real-time monitoring allows project managers to oversee task completion rates, budget utilization, and resource allocation. Alerts and automated notifications can be set up to identify risks early, ensuring timely interventions. By offering a centralized view of project data, Power BI eliminates the need for time-consuming manual updates, freeing teams to focus on what matters most—delivering successful projects. 

Seamless Integration and Scalability 

One of Power BI’s standout features is its ability to integrate seamlessly with tools like Microsoft Excel, Azure, and even ERP systems like Acumatica. This means project teams can work within a familiar ecosystem while leveraging the advanced analytics and visualization capabilities of Power BI. Whether your business is a small startup or a large enterprise, Power BI’s scalable solutions grow with you, ensuring consistent performance and reliability. 

Empowering Teams with Exceptional Support 

At Power BI Solutions, we specialize in delivering tailored dashboards and reports designed to meet the unique needs of your business. Our certified professionals provide end-to-end implementation and ongoing training, ensuring your team is equipped to unlock the full potential of Power BI. From automating reporting processes to creating actionable insights, we are dedicated to empowering businesses with data-driven project management solutions. 

Conclusion 

With its interactive dashboards, real-time reporting, and seamless integration, Power BI is transforming project management. By simplifying data analysis and enhancing decision-making, Power BI equips businesses to achieve their project goals efficiently and effectively. 

[pilog-group]

How to Optimize Data Management for a Seamless Digital Transformation and ERP Implementation?

Digital transformation and ERP (Enterprise Resource Planning) strategies have become pivotal for businesses aiming to stay competitive in today’s fast-evolving landscape. At the core of these strategies lies data management — a critical aspect that ensures accurate, reliable, and actionable insights for decision-making.

Watch this comprehensive video on starting data management in your ERP strategy for an in-depth understanding.

If you’re wondering how to start data management in your digital transformation and ERP strategy, this guide provides actionable steps to help you begin your journey effectively.

Why Data Management is Crucial in Digital Transformation

Before diving into the “how,” it’s essential to understand the “why.” Here’s why data management is indispensable:

Data as the Backbone of ERP Systems: ERP systems thrive on clean, structured, and well-organized data. Without proper data management, the efficiency of ERP systems diminishes.

Informed Decision-Making: Reliable data leads to better analytics, which fuels strategic decisions.

Cost Optimization: Effective data management reduces redundancies, eliminates errors, and cuts costs in operations.

5 Steps to Start Data Management in Your Digital Transformation Journey

1. Assess Your Current Data Landscape

Before implementing any strategy, audit your current data repositories. Identify duplicate, incomplete, or irrelevant data that might be affecting your systems.

2. Define Your Data Governance Framework

A strong governance framework ensures consistency and accountability. This includes setting rules for data collection, usage, storage, and security.

3. Choose the Right Tools and Technologies

Invest in master data management (MDM) tools that integrate seamlessly with your ERP system. These tools enable:

Centralized data storage

Automated data cleansing

Real-time data updates

4. Involve Key Stakeholders

Data management isn’t an IT-only responsibility. Include leaders from finance, operations, and supply chain departments to ensure holistic alignment.

5. Provide Continuous Training

Educate your employees about the importance of clean data and how their inputs impact larger systems like ERP. A data-driven culture begins with informed employees.

How ERP Systems and Data Management Work Hand-in-Hand

High-Impact Benefits of Starting Data Management Now

Enhanced Business Agility: With structured data, businesses can respond quickly to changes.

Improved Compliance: Regulatory requirements like GDPR and CCPA demand clean and transparent data practices.

Better Customer Experiences: When your systems are fueled by high-quality data, you deliver superior services.

Conclusion

Starting your data management journey may seem daunting, but with the right approach and resources, it can transform your digital transformation and ERP strategy into a powerful business enabler.

To gain deeper insights and actionable advice on this topic, make sure to watch this detailed video here.

Take the first step today — because the future of your business depends on how well you manage your data today!

[spacetimewithstuartgary]

Deep in the Mediterranean, in search of quantum gravity

A study published in JCAP places new limits on quantum gravity using data from the underwater detector KM3NeT

Quantum gravity is the missing link between general relativity and quantum mechanics, the yet-to-be-discovered key to a unified theory capable of explaining both the infinitely large and the infinitely small. The solution to this puzzle might lie in the humble neutrino, an elementary particle with no electric charge and almost invisible, as it rarely interacts with matter, passing through everything on our planet without consequences.

For this very reason, neutrinos are difficult to detect. However, in rare cases, a neutrino can interact, for example, with water molecules at the bottom of the sea. The particles emitted in this interaction produce a “blue glow” known as Čerenkov radiation, detectable by instruments such as KM3NeT.

The KM3NeT (Kilometer Cube Neutrino Telescope) is a large underwater observatory designed to detect neutrinos through their interactions in water. It is divided into two detectors, one of which, ORCA (Oscillation Research with Cosmics in the Abyss), was used for this research. It is located off the coast of Toulon, France, at a depth of approximately 2,450 meters.

However, merely observing neutrinos is not enough to draw conclusions about the properties of quantum gravity—we must also look for signs of “decoherence”.

As they travel through space, neutrinos can “oscillate”, meaning they change identity—a phenomenon scientists refer to as flavor oscillations. Coherence is a fundamental property of these oscillations: a neutrino does not have a definite mass but exists as a quantum superposition of three different mass states. Coherence keeps this superposition well-defined, allowing the oscillations to occur regularly and predictably. However, quantum gravity effects could attenuate or even suppress these oscillations, a phenomenon known as “decoherence”.

“There are several theories of quantum gravity which somehow predict this effect because they say that the neutrino is not an isolated system. It can interact with the environment,” explains Nadja Lessing, a physicist at the Instituto de Física Corpuscular of the University of Valencia and corresponding author of this study, which includes contributions from hundreds of researchers worldwide.

“From the experimental point of view, we know the signal of this would be seeing neutrino oscillations suppressed.” This would happen because, during its journey to us—or more precisely, to the KM3NeT sensors at the bottom of the Mediterranean—the neutrino could interact with the environment in a way that alters or suppresses its oscillations.

However, in Lessing and colleagues’ study, the neutrinos analyzed by the KM3NeT/ORCA underwater detector showed no signs of decoherence, a result that provides valuable insights.

“This,” explains Nadja Lessing, “means that if quantum gravity alters neutrino oscillations, it does so with an intensity below the current sensitivity limits.” The study has established upper limits on the strength of this effect, which are now more stringent than those set by previous atmospheric neutrino experiments. It also provides indications for future research directions.

“Finding neutrino decoherence would be a big thing,” says Lessing. So far, no direct evidence of quantum gravity has ever been observed, which is why neutrino experiments are attracting increasing attention. “There has been a growing interest in this topic. People researching quantum gravity are just very interested in this because you probably couldn’t explain decoherence with something else.”

IMAGE: The visualisation of a simulated event in the KM3NeT/ORCA detector.  Credit CC BY-NC 4.0 https://creativecommons.org/licences/by-nc/4.0 Credits KM3NeT

[concerningwolves]

Thank you for all your help with this! your solution for the project totals worked perfectly :D

As for the difficulty deleting projects, thanks again for looking into it. I’m using Google sheets, and when I delete a project - any of them - the top stats sheet starts looking like this:

(The clipped words image was already like that) The chart sheet otherwise deleted the data just fine, but looks like this:

And totals like this:

The 2024 word count in yearly comps has that same REF image instead of a number, and the chart instead says ‘Add a series to start visualising your data’. In totals and comps data the information for the deleted project also shows the REF message, unless I manually delete that column as well (idk if that’s an issue but best be thorough). Daily graph and chart remove the data fine, and if I delete a project that has its own sheet it’s replaced with the add a series message, which I assume is supposed to happen. I haven’t noticed and other adverse affects.

(Seriously, thank you for helping me so much. You’re amazing, and even if you don’t find/know a work around for this you’ve been a huge help!)

Ah, okay, I seeeeeee. So, anywhere you see that #REF! message, it's because it's trying to reference information that's not there any more. In this case, you did the right thing by deleting the project column from the Totals sheet! If you delete a project column in the Daily sheet, you also need to delete its corresponding columns in the Comps. Data and Totals sheets. This is because both of those sheets pulled information from the deleted project column in the Daily sheet, and they'll still be trying to fulfil a formula that no longer has the necessary information to work.

Once those columns are deleted, follow the instructions for deleting a series from a chart on page 7 of the instructions booklet, and delete the project's series from the Totals chart.

Something you could do in the future, instead of deleting project columns from the Daily sheet, is to hide them (right-click on the letter above the column and choose "hide column" from the drop-down menu). Choosing this method means that a) you don't need to delete any project columns elsewhere, and b) "adding" more columns, should you ever need them, becomes a matter of unhiding the column. The only change you'll need to make then is deleting the project series from the Totals chart, as explained in previous paragraph.

(And yes, deleting a project column will affect its corresponding sheet, if it has one. Pages 23–24 of the instructions explain how to make a new project-specific sheet, and you can follow those same instructions to link the sheet to a different project column, if you want to. Otherwise, feel free to hide/delete the sheet.)

However. I noticed that for some reason, removing any data from the totals chart converts the previously-invisible line for the totals into a stacked column instead. To fix that, right-click on the chart and select "Chart style" to open the Chart editor window. Then, under the "customise" tab, scroll down and click on the option that says "series". Pick "Totals" from the drop-down menu of series, and change where it says "column" to "line" and set the line opacity to 0 (Shown in the left image).

Below this section, there is a list of three tick-boxes labelled Error bars, Data labels and Trend line. Click the box for Data labels, and make sure the text colour is set to black. You'll then get a number for total words written that month appearing above the stacked columns. (Shown in right image)

Fixing the Top Stats sheet:

In the case of the current streak counter, the issue is in two places: with the formula that counts the current streak:

and the formula that decides whether to say "to date" or "as of yesterday":

I've underlined the bits you'll need to change. You can find more details on how to do this and how it works on pages 9–10 (Top Sheets explanation) and 18 (how the streak counter works) of the instructions document.

also. if those squashed "words" textboxes are annoying you (they would drive me nuts lol), I think the problem is that the text box isn't set to resize itself with the text (even though I thought i did that...). Try clicking on the box, then on the little three dots symbol and choose "Edit" from the menu that appears. When the Drawing window opens, select that icon next to the box (circled), and choose "Resize shape to fit text".

[edurlearning10]

Future of data science?

Exciting deve­lopments await the future of data scie­nce as it evolves. Data scie­ntists can expect to see­ greater integration of artificial inte­lligence (AI) and machine le­arning, facilitated by automation and AutoML, which will make data analysis more acce­ssible. Proficiency in big data and real-time­ analytics will be crucial as the volume and comple­xity of data continue to expand. Additionally, ethical conside­rations such as privacy protection, bias detection, and re­sponsible AI implementation will re­main important focal points. Furthermore, data science­ will involve increased inte­rdisciplinary collaboration that bridges technical expe­rtise with domain knowledge and e­thics.

Explainable AI will be necessary for the development of trust and compliance, while edge and IoT analytics will cater for the increased demand of IoT devices. In this way, data visualisation and storytelling skills will still be vital. Data scientists will need to adapt as organisations shift to hybrid and multi-cloud environments. This field will require continuous learning for success. The next quantum leap for data science with quantum computing. Customization and personalization will also define data science; this will deliver specific solutions for particular business needs. In the data-driven era, it’s the data scientists that will excel.

If you are considering preparing for this promising future, Edure provides a wide variety of comprehensive data science courses designed for students of all skill levels. Edure has a comprehensive curriculum that includes introductory courses in basic data science concepts, tools and techniques for beginners and advanced programs targeted for experienced professionals. These courses will give you the knowledge and the skills that will enable you to succeed in the data-driven world of tomorrow. Therefore, whether a beginner or an expert desiring to remain up to date in data science, Edure courses are valuable for launching into or deepening one’s involvement in this promising domain.

For more information contact as

Edure | Learn to Earn

Aristo Junction, Thampanoor, 

Thiruvananthapuram, Kerala , 695001

info@edure.in

+91 9746211123 +91 9746711123

[shantitechnology]

The Role of Business Intelligence in ERP Software

Enterprise Resource Planning (ERP) software providers like STERP (Shanti Technology), an excellent ERP software company in Madhya Pradesh, understand the value of Business Intelligence (BI) within this context.  STERP, a leading provider of manufacturing ERP software in Indore, recognises the potential of business intelligence (BI) to turn collected data into a competitive advantage.

Business intelligence (BI) in the context of enterprise resource planning (ERP) refers to the processes involved in collecting, preparing, and analysing data from a wide variety of ERP subsystems.  This suite of state-of-the-art methods and technologies produces insightful reports that may be used for strategic planning, performance monitoring, and operational optimisation.

STERP, a leading ERP software company in Madhya Pradesh and one of the top ERP solution providers in Indore understands the significance of a robust BI system for monitoring key performance indicators (KPIs), tracking trends, identifying patterns, and uncovering hidden opportunities and risks.  Data analytics can be put to use in businesses for potential gains in productivity, cost savings, customer satisfaction, and innovation.

STERP, one of the most distinguished ERP software companies in Madhya Pradesh, promises cutting-edge BI tools in all of its ERP packages.  By providing intuitive dashboards, customizable reports, and real-time analytics, STERP provides its customers with a bird's eye view of their operations.  Let's explore the role that business intelligence plays in enterprise resource planning systems.

Data Integration and Consolidation for Informed Decision-Making:

Integrated and consolidated data is crucial for businesses like STERP, one of the most reliable ERP software providers in Madhya Pradesh, to make well-informed decisions.  As an industry leader in manufacturing ERP software in Indore, STERP is well aware of the need of combining and integrating data from several sources.

The term "consolidation" refers to the process of collecting and harmonizing data from several locations.  In its capacity as one of the leading ERP software firms in Madhya Pradesh and ERP solution providers in Indore, STERP facilitates the consolidation of data from disparate sources into a single repository.  Data centralization ensures that all firm decision-makers and executives are using the same, reliable information.

Reporting and Analytics for Performance Monitoring:

In order to generate reports, it is necessary to construct and present organised data in an understandable and unambiguous way.  STERP's ERP software makes it simple for businesses to tailor reports to their specific requirements, allowing for deeper analysis of sales, inventory, production, and finances. 

By evaluating data and providing reports, STERP, a well-known manufacturing ERP software provider in Indore, aids numerous firms in gaining insight into their processes.  Real-time dashboards and visualisations allow executives to identify bottlenecks, allocate resources effectively, streamline processes, and make educated strategic decisions.

Predictive Analytics and Forecasting for Strategic Planning:

Strategic decision-making at STERP, a distinct ERP software company in Madhya Pradesh, is significantly influenced by analytics and forecasting.  As one of the most distinguished ERP solution providers in Indore, STERP recognises the significance of analytics and forecasting in directing business growth and strategy.

Utilising historical information, statistical algorithms, and machine learning techniques, predictive analytics allows for precise forecasting and prediction.  In order to stay ahead of the competition, businesses can use predictive analytics to forecast demand, identify risks, determine the most efficient use of resources, and make other proactive decisions.

Self-Service BI and Empowering End Users:

Being one of the trusted ERP solution providers in Indore and a top manufacturing ERP software company in Indore, STERP appreciates the importance of self-service BI in empowering end users to make better, more efficient decisions.

Self-service BI allows end users to access and update data without involving IT or data analysts.  To make data exploration, report preparation, and insight production accessible to users of all skill levels, STERP offers intuitive interfaces and clear tools.  Users are empowered to make decisions at the moment based on accurate data without relying on IT, thanks to self-service BI.

Final Thoughts:

Business intelligence (BI) is crucial in ERP.  Companies like STERP (Shanti Technology), a distinct ERP software company in Madhya Pradesh, appreciate the value of BI since it helps them to leverage data for strategic decision-making and planning.  When businesses are able to consolidate and integrate their data, they are able to view the big picture of their operations, and the reporting and analytics functions give them insight into KPIs.  In addition, businesses can use forecasting and predictive analytics to anticipate future trends, mitigate risks, and seize opportunities.  Self-service BI provides end users with straightforward tools to access and analyse data on their own, fostering a culture of data-driven decision-making and increasing productivity.

[someblakearts]

System Shock 2 in Unreal Engine 5

Tools, tools, tools

Back when I worked in the games industry, I was a tools guy by trade. It was a bit of a mix between developing APIs and toolkits for other developers, designing database frontends and automated scripts to visualise memory usage in a game's world, or reverse engineering obscure file formats to create time-saving gadgets for art creation.

I still tend to do a lot of that now in my spare time to relax and unwind, whether it's figuring out the binary data and protocols that makes up the art and assets from my favourite games, or recreating systems and solutions for the satisfaction of figuring it all out.

A Shock to the System

A while back I spent a week or so writing importer tools, logic systems and some basic functionality to recreate System Shock 2 in Unreal Engine 5. It got to the stage where importing the data from the game was a one-click process - I clicked import and could literally run around the game in UE5 within seconds, story-missions and ship systems all working.

Most of Dark engine's logic is supported but I haven't had the time to implement AI or enemies yet. Quite a bit of 3D art is still a bit sketchy, too. The craziest thing to me is that there are no light entities or baked lightmaps placed in the levels. All the illumination you can feast your eyes on is Lumen's indirect lighting from the emissive textures I'd dropped into the game. It has been a fun little exercise in getting me back into Unreal Engine development and I've learnt a lot of stuff as usual.

Here is a video of me playing all the way up to the ops deck (and then getting lost before I decided to cut the video short - it's actually possible to all the way through the game now). Lots of spoilers in this video, obviously, for those that haven't played the game.

What it is

At it's core, it's just a recreation of the various logic-subsystems in System Shock 2 and an assortment of art that has been crudely bashed into Unreal Engine 5. Pretty much all the textures, materials, meshes and maps are converted over and most of the work remaining is just tying them together with bits of C++ string. I hope you also appreciate that I sprinkled on some motion-blur and depth of field to enhance the gameplay a little. Just kidding - I just didn't get around to turning that off in the prefab Unreal Engine template I regularly use.

Tool-wise, it's a mishmash of different things working together:

There's an asset converter that organises the art into an Unreal-Engine-compatible pipeline. It's a mix of Python scripting, mind numbingly dull NodeJS and 3dsmaxscript that juggles data. It recreates all the animated (and inanimate) textures as Unreal materials, meshifies and models the map of the ship, and processes the objects and items into file formats that can be read by the engine.

A DB to Unreal converter takes in DarkDBs and spits out JSON that Unreal Engine and my other tools can understand and then brings it into the Engine. This is the secret sauce that takes all the levels and logic from the original game and recreates it in the Unreal-Dark-hybrid-of-an-engine. It places the logical boundaries for rooms and traps, lays down all the objects (and sets their properties) and keys in those parameters to materialise the missions and set up the story gameplay.

Another tool also weeds through the JSON thats been spat out previously and weaves it into complex databases in Unreal Engine. This arranges all the audio logs, mission texts and more into organised collections that can be referenced and relayed through the UI.

The last part is the Unreal Engine integration. This is the actual recreation of much of the Dark Engine in UE, ranging all the way from the PDA that powers the player's journey through the game, to the traps, buttons and systems that bring the Von Braun to life. It has save-game systems to store the state of objects, inventories and all your stats, levels and progress. This is all C++ and is built in a (hopefully) modular way that I can build on easily should the project progress.

Where it's at

As I mentioned, the levels themselves are a one-click import process. Most of Dark engine's logic, quirks and all, is implemented now (level persistence and transitions, links, traps, triggers, questvars, stats and levelling, inventory, signals/responses, PDA, hacking, etc.) but I still haven't got around to any kid of AI yet. I haven't bought much in the way of animation in from the original game yet, either, as I need to work out the best way to do it. I need to pull together the separate systems and fix little bugs here and there and iron it out with a little testing at some point.

Lighting-wise, this is all just Lumen and emissive textures. I don't think it'll ever not impress me how big of a step forward this is in terms of realistic lighting. No baking of lightmaps, no manually placing lighting. It's all just emissive materials, global/indirect illumination and bounce lighting. It gets a little overly dark here and there (a mixture of emissive textures not quite capturing the original baked lighting, and a limitation in Lumen right now for cached surfaces on complex meshes, aka the level) so could probably benefit with a manual pass at some point, but 'ain't nobody got time for that for a spare-time project.

The unreal editor showcasing some of the systems and levels.

Where it's going

I kind of need to figure out exactly what I'm doing with this project and where to stop. My initial goal was just to have an explorable version of the Von Braun in Unreal Engine 5 to sharpen my game dev skills and stop them from going rusty, but it's gotten a bit further than that now. I'm also thinking of doing something much more in-depth video/blog-wise in some way - let me know in the comments if that's something you'd be interested in and what kind of stuff you'd want to see/hear about.

The DB to JSON tool that churns out System Shock 2 game data as readable info

Anyway - I began to expand out with the project and recreate assets and art to integrate into Unreal Engine 5. I'll add more as I get more written up.

[govindhtech]

New developments in Access Risk, Cloud Governance And IAM

Google Cloud's mission is to assist you meet shifting policy, regulatory, and commercial goals. It routinely releases new cloud platform security features and controls to strengthen your cloud environment.

Google Cloud Next introduced IAM, Cloud Governance, and Access Risk capabilities. Google Cloud launched numerous new features and security upgrades, including:

Access and Identity Management

Context-Aware Access,

Identity Threat Detection and Response, and VPC Service Controls mitigate access risk.

Using Organisation Policy Service for Cloud Governance and Resource Management

It also introduced new AI technologies to enable cloud operators and developers throughout the application lifecycle. New Gemini Code Assist and Gemini Cloud Assist functionalities provide application-centered AI help throughout the application development lifecycle.

Identity and Access Management updates

Workforce Identity Federation

Workforce Identity Federation extends Google Cloud's identity capabilities with syncless, attribute-based single sign-on. Over 95% of Google Cloud products support Workforce Identity Federation. FedRAMP High government standards were supported to help manage compliance.

Increased non-human identity security

Due to microservices and multicloud deployments, workload and non-human identities are growing faster than human identities. Many large organisations contain 10 to 45 times more non-human identities than human identities, which often have wide rights and privileges.

Google Cloud is announcing two new features to strengthen access control and authorisation to secure non-human identities:

X.509 certificates provide keyless Google Cloud API access, enhancing workload authentication.

Managed Workload Identities allow workload-to-workload communication using SPIFFE-based mutual TLS (mTLS) encryption, secure identification, and authentication.

CIEM for multicloud infrastructure

Google Cloud is fighting excessive and unjustified security permissions. Google Cloud offers comprehensive protection across all tiers and tools to manage permissions to proactively address the permission issue.

Cloud Infrastructure Entitlement Management (CIEM), its main authorisation solution, is currently available for Azure and broadly available for Google Cloud and AWS.

IAM Admin Centre

It also included IAM Admin Centre, a role-specific single pane of glass for tasks, recommendations, and notifications. Additional services are accessible from the console.

IAM Admin Centre lets organisation and project administrators discover, learn, test, and use IAM functionalities from one place. It provides contextual feature discovery, daily work focus, continuing learning tools, and well designed beginning instructions.

IAM functionality enhancements

Other IAM features expanded and became more robust.

Google Cloud previously unveiled the Principal Access Boundary (PAB) and IAM Deny policies, which are effective resource access limitations. As these important controls gain service coverage and acceptance, planning and visualisation tools are needed.

It previewed Deny, PAB, and troubleshooters to fix this.

Privileged Access Manager (PAM) now has two authorisation levels with several principals. Scope entitlement grants may now be customised to apply just to the relevant resources, roles, projects, and folders.

Updates on Access Risk

Comprehensive security requires ongoing monitoring and control, even with authenticated users and workloads with the necessary privileges and active session participation. Google Cloud's access risk portfolio protects people, workloads, and data with dynamic features.

Improved session and access security

CAA protects Google Cloud access based on user identification, network, location, and corporate-managed devices, among other things.

CAA will soon include Identity Threat Detection and Response (ITDR) capabilities using activity signals like questionable source activity or new geolocations. These features automatically detect problematic conduct and initiate security validations like MFA, re-authentication, or rejections.

Automatic re-authentication sends a request when users change billing accounts or perform other sensitive tasks. Although you may disable it, Google Cloud recommends leaving it on by default.

Increased VPC Service Control coverage

You can protect your data, resources, and designated services using VPC Service Controls. It introduced Violation Analyser and Violation Dashboard to help diagnose and debug access denial events using VPC Service Controls.

Changes to Cloud Governance with Organisation Policy Service Increased Custom Organisation Policy coverage

Google Cloud's Organisation Policy Service allows programmatic, centralised resource management. Organisation policy provides constraints, but you may create custom policies for additional control. With 62 services, custom organisation policy covers more.

Increased Custom Organisation Policy coverage

Google Cloud promises to simplify high-security outcomes. Google Cloud launched its Google Cloud Security Baseline, a stronger set of security settings, as part of this effort. Due to positive response, it is now advertising them to all current consumers. Last year, all new customers received them by default.

Users' consoles have seen Google Cloud Security Baseline implementation recommendations since this year. You may also use a simulator to mimic how these restrictions affect your environment.

Updates on resource management

Resource Manager app capability

The Google Cloud Resource Manager was likewise application-centric. App-enabled folders, presently in preview, simplify administration, organise services and workloads into a single manageable unit, centralise monitoring and management, and show an application-centric perspective.

[mywordsolutionedu-blog]

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Découvrez les Outils d’IA Gratuits les Plus Utiles pour le Travail, l’Éducation et la Création Numérique

L’intelligence artificielle a connu une évolution fulgurante ces dernières années, rendant accessibles à tous des outils puissants qui autrefois étaient réservés aux grandes entreprises technologiques. Aujourd’hui, une grande variété d’outils d’IA gratuits sont disponibles en ligne et couvrent des usages très divers, allant du commerce à l’éducation, en passant par la création artistique, la programmation ou encore les services juridiques. Ces outils permettent à chacun d’optimiser ses tâches quotidiennes, de gagner du temps et de décupler sa créativité, quel que soit son domaine d’activité.

Les professionnels de la vente s’appuient désormais sur des outils gratuits d’assistants commerciaux IA qui peuvent analyser des données clients, proposer des scripts de vente personnalisés, automatiser des emails et même prédire les comportements d’achat. Ces outils transforment la relation client et permettent d’améliorer les performances commerciales. Du côté du secteur juridique, l’outil IA pour les assistants juridiques est une révolution. Il aide les avocats et juristes à traiter rapidement des documents, à analyser des décisions de justice et à générer des textes juridiques cohérents, tout en assurant une conformité stricte aux normes.

Dans un monde où la productivité personnelle est clé, le meilleur outil IA pour assistants personnels vous permet d’organiser votre emploi du temps, de planifier vos tâches, de gérer vos emails ou encore de rappeler des rendez-vous importants. C’est un assistant virtuel intelligent capable de vous libérer de nombreuses contraintes quotidiennes. Pour ceux qui souhaitent renforcer leur présence en ligne, l’outil gratuit de génération d’avatar IA offre la possibilité de créer des représentations visuelles uniques, qu’elles soient réalistes ou stylisées, pour les réseaux sociaux, les plateformes de jeux ou les espaces de réalité virtuelle.

La recherche d’emploi est également facilitée grâce à l’outil IA pour la création de CV en ligne, qui permet de générer des CV professionnels, adaptés aux standards modernes, et optimisés pour les systèmes de recrutement automatisés. Ces outils augmentent considérablement les chances d’attirer l’attention des recruteurs. Les auteurs et créateurs de contenus narratifs bénéficient eux des outils IA de création d’histoires, capables de proposer des récits originaux, des structures de scénario ou même des dialogues convaincants en quelques secondes.

La lutte contre le plagiat et l’usage abusif de l’intelligence artificielle est assurée grâce à l’outil de détection IA gratuit en ligne, un service indispensable dans les milieux éducatifs. Il permet de vérifier si un texte a été rédigé par un humain ou par une IA, contribuant ainsi à la transparence et à l’intégrité des travaux académiques. Par ailleurs, les étudiants et les enseignants tirent pleinement profit de l’outil IA gratuit en ligne pour les études et l’éducation. Ce dernier propose des résumés de cours, des explications sur des concepts complexes, des solutions d’exercices ou encore des sessions interactives d’apprentissage.

Les analystes financiers, quant à eux, peuvent compter sur les meilleurs outils d’IA gratuits pour l’analyse financière afin d’interpréter les données du marché, prédire les tendances économiques et prendre des décisions éclairées. Dans un registre voisin, les outils d’IA gratuits pour la science des données sont essentiels pour les data scientists souhaitant explorer, visualiser et modéliser des ensembles de données sans devoir coder chaque étape manuellement.

Les tâches automatisables en entreprise sont désormais simplifiées grâce aux outils d’IA gratuits pour l’automatisation, qui prennent en charge des fonctions comme l’envoi de mails, la classification de documents, la saisie de données ou la création de rapports. Ces outils d’IA gratuits pour le travail en entreprise permettent une efficacité renforcée dans tous les services, de la comptabilité aux ressources humaines. Pour les professionnels techniques, l’outil IA en ligne pour les solutions de feuilles de calcul facilite la gestion de données complexes dans Excel ou Google Sheets, tandis que l’outil IA pour la génération de code aide à écrire des scripts dans différents langages à partir d’une simple commande en langage naturel.

L’intelligence artificielle ne se limite pas au monde professionnel. Les créateurs vidéo trouvent leur bonheur avec les meilleurs outils d’IA gratuits pour la création vidéo, capables de générer automatiquement des clips, d’ajouter des effets visuels ou sonores, et même de faire du montage intelligent sans effort. Dans le domaine artistique, les meilleurs outils d’IA gratuits pour le dessin sont très appréciés. Ils permettent de produire des illustrations originales, d’améliorer des croquis ou d’explorer de nouveaux styles graphiques en quelques clics, tout en inspirant les artistes en manque d’idées.

Avec autant de possibilités offertes par l’intelligence artificielle, il devient évident que ces outils changent la manière dont nous travaillons, apprenons et créons. Que vous soyez à la recherche du meilleur outil IA pour assistants personnels, d’un outil IA pour la création de CV en ligne, d’un outil IA pour la génération de code ou encore d’un outil gratuit de génération d’avatar IA, il existe une solution adaptée à vos besoins. Ces innovations, gratuites et faciles à utiliser, rendent l’IA accessible à tous et représentent une véritable révolution numérique dans notre quotidien.

Souhaitez-vous que je rédige une version anglaise de cet article également ?

Veuillez visiter les outils d'IA suivants pour plus d'informations:

Outils gratuits d'assistants commerciaux IA

Outil IA pour les assistants juridiques

Meilleur outil IA pour assistants personnels

Outil gratuit de génération d'avatar IA

Outil IA pour la création de CV en ligne

Outils IA de création d'histoires

Outil de détection IA gratuit en ligne

Outil IA gratuit en ligne pour les études et l'éducation

Outil IA en ligne pour les solutions de feuilles de calcul

Outil IA pour la génération de code

Meilleurs outils d'IA gratuits pour l'analyse financière

Outils d'IA gratuits pour la science des données

Outils d'IA gratuits pour l'automatisation

Outils d'IA gratuits pour le travail en entreprise

Meilleurs outils d'IA gratuits pour la création vidéo

Meilleurs outils d'IA gratuits pour le dessin

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Data visualization solutions refer to software tools and platforms that enable users to create meaningful visual representations of data. These solutions range from simple charting tools to sophisticated analytics platforms that can handle large datasets and create interactive visualizations. They are essential for businesses and organizations seeking to uncover insights, communicate trends, and make data-driven decisions more effectively. Popular examples include Tableau, Power BI, Google Data Studio, and D3.js, each offering unique features tailored to different user needs and levels of technical expertise.

[hyderabadnew]

Inside the Development Cycle: Editors, Runtimes, and Notebooks

In the evolving world of data science, knowing algorithms and models is only part of the story. To truly become proficient, it’s equally important to understand the development cycle that supports data science projects from start to finish. This includes using the right editors, managing efficient runtimes, and working with interactive notebooks—each of which plays a vital role in shaping the outcome of any data-driven solution.

If you’re beginning your journey into this exciting field, enrolling in a structured and comprehensive data science course in Hyderabad can give you both theoretical knowledge and practical experience with these essential tools.

What Is the Development Cycle in Data Science?

The data science development cycle is a structured workflow that guides the process of turning raw data into actionable insights. It typically includes:

Data Collection & Preprocessing

Exploratory Data Analysis (EDA)

Model Building & Evaluation

Deployment & Monitoring

Throughout these stages, data scientists rely on various tools to write code, visualise data, test algorithms, and deploy solutions. Understanding the development environments—specifically editors, runtimes, and notebooks—can make this process more streamlined and efficient.

Code Editors: Writing the Blueprint

A code editor is where much of the data science magic begins. Editors are software environments where developers and data scientists write and manage their code. These tools help format code, highlight syntax, and even provide autocomplete features to speed up development.

Popular Editors in Data Science:

VS Code (Visual Studio Code): Lightweight, customisable, and supports multiple programming languages.

PyCharm: Feature-rich editor tailored for Python, which is widely used in data science.

Sublime Text: Fast and flexible, good for quick scripting or data wrangling tasks.

In most data science classes, learners start by practising in basic editors before moving on to integrated environments that combine editing with runtime and visualisation features.

Runtimes: Where Code Comes to Life

A runtime is the engine that executes your code. It's the environment where your script is interpreted or compiled and where it interacts with data and produces results. Choosing the right runtime environment is crucial for performance, compatibility, and scalability.

Types of Runtimes:

Local Runtime: Code runs directly on your computer. Good for development and testing, but limited by hardware.

Cloud-Based Runtime: Services like Google Colab or AWS SageMaker provide powerful cloud runtimes, which are ideal for large datasets and complex models.

Containerised Runtimes: Using Docker or Kubernetes, these environments are portable and scalable, making them popular in enterprise settings.

In a professional data science course in Hyderabad, students often gain experience working with both local and cloud runtimes. This prepares them for real-world scenarios, where switching between environments is common.

Notebooks: The Interactive Canvas

Perhaps the most iconic tool in a data scientist's toolkit is the notebook interface. Notebooks like Jupyter and Google Colab allow users to combine live code, visualisations, and explanatory text in a single document. This format is ideal for storytelling, collaboration, and experimentation.

Why Notebooks Matter:

Interactivity: You can run code in segments (cells), making it easy to test and modify individual parts of a script.

Visualisation: Direct integration with libraries like Matplotlib and Seaborn enables real-time plotting and analysis.

Documentation: Notebooks support markdown, making it simple to annotate your work and explain results clearly.

These features make notebooks indispensable in both academic learning and professional development. Many data science courses now revolve around notebook-based assignments, allowing students to document and share their learning process effectively.

Putting It All Together

When working on a data science project, you’ll often move fluidly between these tools:

Start in an editor to set up your script or function.

Run your code in a suitable runtime—either local for small tasks or cloud-based for heavier jobs.

Switch to notebooks for analysis, visualisation, and sharing results with stakeholders or collaborators.

Understanding this workflow is just as important as mastering Python syntax or machine learning libraries. In fact, many hiring managers look for candidates who can not only build models but also present them effectively and manage their development environments efficiently.

Why Choose a Data Science Course in Hyderabad?

Hyderabad has quickly emerged as a tech hub in India, offering a vibrant ecosystem for aspiring data professionals. Opting for data science courses in Hyderabad provides several advantages:

Industry Exposure: Access to companies and startups using cutting-edge technologies.

Expert Faculty: Learn from instructors with real-world experience.

Career Support: Resume building, mock interviews, and job placement assistance.

Modern Curriculum: Courses that include the latest tools like Jupyter notebooks, cloud runtimes, and modern editors.

Such programs help bridge the gap between classroom learning and real-world application, equipping students with practical skills that employers truly value.

Conclusion

The success of any data science project depends not only on the strength of your algorithms but also on the tools you use to develop, test, and present your work. Understanding the role of editors, runtimes, and notebooks in the development cycle is essential for efficient and effective problem-solving.

Whether you’re an aspiring data scientist or a professional looking to upskill, the right training environment can make a big difference. Structured data science classes can provide the guidance, practice, and support you need to master these tools and become job-ready.

Data Science, Data Analyst and Business Analyst Course in Hyderabad

Address: 8th Floor, Quadrant-2, Cyber Towers, Phase 2, HITEC City, Hyderabad, Telangana 500081

Ph: 09513258911

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Rural-urban Divide Hampering Digital Health Innovation in Kenya

Just as the Ebola and now the Covid-19 crises galvanised digital health innovation, across Africa, the Covid-19 pandemic brought with it a surge of Kenyan digital health innovation in the health sector. However, infrastructure and other challenges continue to limit the reach of health digital innovations.

Tibu Health, Zuri Health, Nadia, TeleAfya and Penda Health are examples of telemedicine apps that connect patients to doctors and other primary healthcare workers, local pharmacies, ride share apps etc affordably. Damu-Sasa helps hospitals source blood from donors and from other hospitals, while tracking the donation histories.

Ilara Health offers affordable mobile phone and tablet-based diagnostic equipment to patients and healthcare providers in peri-urban areas. Afya Poa allows users to pay for health insurance premiums on a daily basis for inpatient care and save for outpatient care.

The product extends the cover to the user’s family. CEMA, a platform developed in a partnership between innovation lab Qhala, the University of Nairobi and Kenya’s Ministry of Health, aggregates epidemiological data from government and other sources and uses data engineering and data visualisation to make the data accessible to the general public for research purposes.

This surge was made possible partly by a supportive regulatory framework. Kenya’s National E-Health Policy 2016–2030 prioritises, among others, the development and use of mobile technologies for health (M-Health), as a means of delivering quality health care to all.

There is, arguably, space for more digital health innovation in the areas of maternal health, contact tracing, mental health, health data and others that would strengthen Kenya’s chronically underfunded health sector. However, systemic challenges continue to deepen a pre-existing urban-rural divide, hampering the ability of digital health innovation to scale.

Systemic Challenges Affecting Digital Health Innovation Growth

Infrastructure

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Digital innovations, including health innovations, in Kenya tend to be concentrated in urban and peri-urban areas for reasons related to infrastructure. Grid power is uneven in the rural areas, making the cost of internet connectivity higher in rural areas than it is in urban areas.

This high cost, combined with the fact that incomes are generally lower in rural areas, is part of the reason why only 17% of rural populations in Kenya have access to the internet, compared to 44% in urban areas, according to World Bank statistics. Access to 4G internet is still largely concentrated in and around urban centres[1].

Innovation ecosystem

In addition to infrastructure challenges, as Jacob Mugendi of Engineering for Change points out, innovation hubs tend to be located in urban areas not rural areas, contributing to the lack of an innovation ecosystem outside of the cities. This locks out of the digital scene potential local innovators who understand local problems that need solutions.

Low digital literacy

Despite the roll-out of the Kenyan government’s Digital Literacy Programme, many schools across the country, particularly primary schools, still do not have computers or access to them. Digital literacy levels remain low. Where digital tools are in use there is low availability of support, particularly for older, less savvy users[2]. Despite Kenya’s high smartphone penetration, digital health innovation in general tend to be the preserve of residents of urban and peri-urban areas.

These challenges are, in reality, three aspects of the same persistent rural-urban divide, deepened by systemic factors. Until these challenges are addressed, or until the digital health apps are designed to be more inclusive, the digital health innovation will not be able to scale within the country, no matter how much funding they are able to secure.

[1] https://www.engineeringforchange.org/news/challenges-implementing-digital-technologies-rural-kenya/

[2] https://www.engineeringforchange.org/news/challenges-implementing-digital-technologies-rural-kenya/

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Transforming real-time monitoring with AI-enhanced digital twins

New Post has been published on https://thedigitalinsider.com/transforming-real-time-monitoring-with-ai-enhanced-digital-twins/

Transforming real-time monitoring with AI-enhanced digital twins

A recent McKinsey report found that 75% of large enterprises are investing in digital twins to scale their AI solutions. Combining digital twins with AI has the potential to enhance the effectiveness of large language models and enable new applications for AI in real-time monitoring, offering significant business and operational benefits.

What are digital twins?

Digital twins, originally developed to aid in the design of complex machinery have evolved significantly over the last two decades. They track and analyse live systems in real-time by processing device telemetry, detecting shifting conditions, and enhancing situational awareness for operational managers. Powered by in-memory computing, they enable fast, actionable alerts. Beyond real-time monitoring, digital twins also can simulate intricate systems like those for use in airlines and logistics, supporting strategic planning and operational decisions through predictive analytics.

Integrating digital twins with generative AI creates new opportunities for both technologies: The synergy can boost the prediction accuracy of generative AI, and can enhance the value of digital twins for system monitoring and development.

Proactively identifying anomalies with AI-powered digital twins

Continuous, real-time monitoring is a strategic necessity for organisations that manage complex live systems, like transportation networks, cybersecurity systems, and smart cities. Emerging problems must never be overlooked because delayed responses can cause small problems to become large ones.

Enhancing digital twins with generative AI reshapes how real-time monitoring interprets massive volumes of live data, enabling the reliable and immediate detection of anomalies that impact operations. Generative AI can continuously examine analytics results produced by digital twins to uncover emerging trends and mitigate disruptions before they escalate. While AI enhances situational awareness for managers, it can also pinpoint new opportunities for optimising operations and boosting efficiency.

At the same time, real-time data supplied by digital twins constrains the output of generative AI to avoid erratic results, like hallucinations. In a process called retrieval augmented generation, AI always uses the most up-to-date information about a live system to analyse behaviour and create recommendations.

Transforming data interaction with AI-driven visualisations

Unlocking insights from digital twin analytics should be intuitive, not technical. Generative AI is redefining how teams interact with massive datasets by enabling natural language-driven queries and visualisations. Instead of manually constructing intricate queries, users can simply describe their needs, and generative AI immediately visualises relevant charts and query results that provide new insights. This capability simplifies interactions and gives decision-makers the data they need. As organisations handle increasingly complex live systems, AI-powered intelligence allows them to efficiently sift through vast data pools, extract meaningful trends, and optimise operations with greater precision. It eliminates technical barriers, enabling faster, data-driven decisions that have a strategic impact.

Incorporating machine learning with automatic retraining

Digital twins can track numerous individual data streams and look for issues with the corresponding physical data sources. Working together, thousands or even millions of digital twins can monitor very large, complex systems. As messages flow in, each digital twin combines them with known information about a particular data source and analyses the data in a few milliseconds. It can incorporate a machine learning algorithm to assist in the analysis and find subtle issues that would be difficult to describe in hand-coded algorithms. After training with data from live operations, ML algorithms can identify anomalies and generate alerts for operational managers immediately.

Once deployed to analyse live telemetry, an ML algorithm will likely encounter new situations not covered by its initial training set. It may either fail to detect anomalies or generate false positives. Automatic retraining lets the algorithm learn as it gains experience so it can improve its performance and adapt to changing conditions. Digital twins can work together to detect invalid ML responses and build new training sets that feed automatic retraining. By incorporating automatic retraining, businesses gain a competitive edge with real-time monitoring that reliably delivers actionable insights as it learns over time.

Looking forward

Integrating digital twin technology with generative AI and ML can transform how industries monitor complex, live systems by empowering better real-time insights and enabling managers to make faster, more informed decisions. ScaleOut Software’s newly-released Digital Twins™ Version 4 adds generative AI using OpenAI’s large language model and automatic ML retraining to move real-time monitoring towards the goal of fully-autonomous operations. 

(Image source: Unsplash)