Category: Data Strategy

AI, Artificial Intelligence (AI), Data Strategy, Machine Learning (ML)

AI in Healthcare: Transforming Patient Care and Clinical Operations

Artificial Intelligence (AI) is redefining healthcare at every level—speeding and improving patient care, enhancing diagnostic accuracy, personalizing treatments, speeding drug delivery, and streamlining hospital operations. As global health systems grapple with rising costs, staff shortages, and complex patient needs, AI has emerged as a critical force for innovation. From radiology to drug discovery, AI is no longer experimental; it is becoming a cornerstone of modern medicine.

AI in healthcare is expected to exceed a whopping $600B by 2034, according to one forecast. The explosion of digital healthcare records has created vast opportunities for AI to uncover and use patterns and insights across the spectrum of healthcare.

How AI Is Being Used in Healthcare

AI’s applications in healthcare are vast and growing. Here are a few examples:

  • Medical Imaging and Diagnostics: AI algorithms can analyze X-rays, MRIs, and other medical images to detect diseases, such as cancer and heart conditions.
  • Virtual Health Assistants: As in every industry, AI chatbots and symptom checkers (e.g., Ada Health, Babylon Health) provide patients with 24/7 support proving information and answers to questions, triage guidance, medication refills, and appointment scheduling.
  • Predictive Analytics: Hospitals use AI to forecast patient admissions, identify those at risk for readmission, and anticipate Emergency / ICU demand. AI can predict the risk of developing certain diseases based on patient data, such as medical history, lifestyle, and genetic information.
  • Personalized Medicine: AI will help to develop with better precision personalized treatment plans and medications/supplements for patients.
  • General Research & Development: AI is being used in medical research to analyze massive datasets in order to develop new treatments and improve patient care and outcomes.
  • Drug Discovery and Development: AI models from companies like BenevolentAI and Insilico Medicine accelerate the identification of potential compounds, reducing the timeline from years to months.
  • Robotic Surgery: AI-powered robotic systems can assist surgeons with complex procedures, improving precision and minimizing invasiveness. Systems like the da Vinci Surgical System use AI-assisted robotics to improve surgical precision and minimize recovery times.
  • Administrative Automation: AI streamlines billing, coding, and claims management to reduce paperwork and human error. AI-powered systems can also automate appointment scheduling and reminders. This will help with staff shortages and reduce staff burnout.
  • Billing, coding and health insurance processing: AI can analyze medical records and automate insurance processing and billing, thus improving efficiency, reducing errors, and reducing staff hours.
  • Fraud detection: AI algorithms can identify suspicious patterns in healthcare claims, leading to reducing in fraud.
  • Pregnancy Management: AI applications are used to monitor the health of both mother and fetus through wearable devices and monitored data.

And the healthcare use cases go on and on.

Tools, Technologies, and Methods Behind AI in Healthcare

Healthcare AI encompasses a wide variety of solutions and, therefore, draws on a mix of advanced technologies and methods:

  • Machine Learning (ML) for patient risk stratification, disease progression modeling, and outcome prediction.
  • Natural Language Processing (NLP) to analyze unstructured clinical notes, extract insights from medical records, and power conversational interfaces.
    • for example: Healthcare Text Analysis: In the healthcare sector, Azure’s language solutions are used to extract clinical information from unstructured medical documents. Features like entity recognition and text analytics for health help identify symptoms, medications, and diagnoses, supporting faster and more accurate decision-making.
  • Computer Vision in radiology, pathology, and dermatology for image-based diagnostics.
  • Robotics for surgeries, rehabilitation support, and hospital logistics (e.g., delivery robots for medications).
  • Cloud AI Platforms such as Microsoft Azure Health Data Services, AWS HealthLake, and Google Cloud Healthcare API for data integration and analysis.
  • Generative AI for drug molecule design, synthetic medical data creation, and personalized patient communication.
  • Google’s DeepMind and IBM Watson Health assist radiologists in detecting conditions such as cancer, stroke, and heart disease earlier and with higher precision.
  • IDx-DR, an FDA-approved AI tool, diagnoses diabetic retinopathy by analyzing eye images, helping to prevent irreversible damage through early detection.
  • AI-powered drug discovery tool, Atomwise, has successfully identified potential treatments for diseases like Ebola within a day.
  • Aidoc, an AI-driven radiology platform, prioritizes critical cases and detects abnormalities in medical images, significantly enhancing diagnosis and treatment.
  • Dragon Medical One uses speech recognition and speech-to-text features to assist healthcare providers with documenting patient notes, leading to time savings and better accuracy.

Benefits of AI in Healthcare

The adoption of AI has brought measurable benefits across the sector:

  • Improved Diagnostics: AI-powered imaging tools have demonstrated higher sensitivity in detecting certain conditions than human experts alone.
  • Personalized Care: AI helps tailor treatment plans to individual patient profiles, improving outcomes.
  • Operational Efficiency: Predictive analytics optimize staffing, reduce wait times, and cut costs.
  • Faster Drug Development: AI-driven discovery platforms shorten the drug development cycle, lowering costs and improving pipeline success.
  • Accessibility: Virtual assistants extend care access to underserved populations by providing round-the-clock guidance.

Pitfalls and Challenges of AI in Healthcare

Despite the proven results and huge promise, several challenges and risks persist:

  • Data Quality: While significant amounts of data are available, not all of it is of high quality, and significant efforts are needed to ensure that the data driving AI solutions is clean and accurate.
  • Data Bias and Inequality: AI models trained on non-diverse datasets may produce biased outcomes, particularly for underrepresented populations.
  • Regulatory Hurdles: The FDA and other agencies require rigorous testing and approval, slowing AI adoption.
  • Failed Projects: IBM Watson Health, once hyped as a revolutionary tool, failed to meet expectations in oncology due to overpromising and underdelivering.
  • Data Privacy Concerns: With vast amounts of sensitive data, breaches or misuse pose serious risks.
  • Integration Challenges: Many hospitals face difficulties embedding AI into legacy systems and workflows.
  • High AI Costs: AI solutions are rarely cheap, and not all healthcare companies can afford what they desire. Companies need to carefully and strategically choose which solutions to implement.
  • Overreliance on AI: Excessive trust in algorithms could lead to errors if not combined with human oversight.

The Future of AI in Healthcare

The trajectory of AI points toward deeper integration into healthcare delivery:

  • Precision Medicine at Scale: AI will increasingly guide genomics-driven treatments, tailoring care to a patient’s DNA profile.
  • Real-Time Monitoring: Wearables and IoT devices paired with AI will continuously track patient health and alert clinicians to early warning signs.
  • Generative AI in Research: AI will help simulate clinical trials and accelerate hypothesis generation.
  • Holistic Care Platforms: AI-powered systems will unify patient data from hospitals, clinics, and home devices into seamless health records.
  • Ethical AI Frameworks: Future AI systems will be built with fairness, accountability, and transparency at their core.

How Healthcare Organizations Can Gain an Advantage

To stay competitive and maximize AI’s potential, healthcare providers and companies should:

  1. Invest in High-Quality Data: Ensure datasets are diverse, representative, and securely stored.
  2. Adopt AI Incrementally: Start with specific use cases—such as imaging, scheduling, or claims processing—before scaling enterprise-wide.
  3. Prioritize Human-AI Collaboration: Position AI as a support tool to augment, not replace, clinicians.
  4. Strengthen Compliance and Ethics: Build governance frameworks around data privacy, bias mitigation, and transparency.
  5. Train and Upskill Staff: Equip medical professionals and administrators with the skills to effectively use AI.
  6. Foster Partnerships: Collaborate with AI startups, academic research labs, and technology providers for faster innovation.

Conclusion

AI in healthcare represents both extraordinary promise and complex challenges. It is already improving patient outcomes, optimizing hospital operations, and reducing the time and cost of drug development. Yet, for every breakthrough, there are lessons in bias, regulation, and integration that remind us AI is not a silver bullet. However, its adoption and success rates in healthcare, and across the board, is expected to grow significantly. Not using AI is not an option. The future belongs to healthcare organizations that use AI responsibly and effectively—balancing innovation with ethics, automation with compassion, and efficiency with equity.

This article is a part of an “AI in …” series that shares information about AI in various industries and business functions. Be on the lookout for future (and past) articles in the series.

Thanks for reading and good luck on your data (AI) journey!

Other “AI in …” articles in the series:

AI in the Hospitality Industry: Transforming Guest Experiences and Operations

AI in Gaming: How Artificial Intelligence is Powering Game Production and Player Experience

Analytics, Business Intelligence, Data Analysis, Data Strategy

Developing metrics for your analytics project

When starting an analytics project, one of the most important decisions you will make is identifying the right metrics. Metrics serve as the compass for the initiative—they show whether you are on the right track, communicate achievements, highlight challenges, uncover blind spots, and ultimately, along with guiding future decisions, they demonstrate the value of the project to stakeholders. But designing metrics is not as simple as picking a single “success number.” To truly guide decision-making, you need a holistic set of measures that reflect multiple dimensions of performance.

Why a Holistic View Matters

Analytics projects sometimes fall into the trap of focusing on only one type of metric. For example, a project might track quantity (e.g., number of leads generated) while ignoring quality (e.g., lead conversion rate). Or it may measure cost savings but fail to consider user satisfaction, leading to short-term wins but long-term disengagement.

Develop Metrics from Multiple Dimensions

To avoid this pitfall, it’s critical to develop a balanced framework that includes multiple perspectives:

  • Quantity: How much output is produced? Examples include number of units produced, sales revenue, or number of new customers added.
  • Quality: What is the quality of the output? Examples include accuracy rates, defect counts, or error percentages.
  • Time: How long does it take to achieve the output? Or in other words, what timeframe is the quantity and quality measured over? Is it Sales revenue per hour, per day, per month, or per year?
  • Costs: What resources are being consumed? Metrics might include infrastructure costs, labor hours and costs, materials costs, or overall project spend.
  • Satisfaction: How do stakeholders, customers, or employees feel about the results? Feedback surveys, adoption rates, product ratings, and net promoter scores (NPS) are common ways of identifying this information.

Each of these perspectives contributes to the full story of your analytics project. If one dimension is missing, you risk optimizing for one outcome at the expense of another.

Efficiency, Effectiveness, and Impact Metrics

Another way you can classify your metrics to achieve a holistic view is with three overarching categories: Efficiency, Effectiveness, and Impact.

  • Efficiency Metrics
    • These measure how well resources are used and answers “are we doing things right?“. They focus on inputs versus outputs.
      • Example: “Average work hours per product” shows how quickly work gets done.
      • Example: “Cost per customer acquired” reflects the efficiency of your sales operations.
    • Efficiency metrics often tie directly to quantity, cost, and time.
  • Effectiveness Metrics
    • These measure how well goals are achieved—whether the project delivers the intended results, and answers “are we doing the right things?“.
      • Example: “Customer satisfaction” demonstrates how happy customers are with our products and services.
      • Example: “Actual to Target” shows how things are tracking compared to the goals that were set.
    • Effectiveness metrics often involve quality, satisfaction, and time.
  • Impact Metrics
    • These measure the broader business or organizational outcomes influenced by some activity.
      • Example: “Market share and revenue growth” shows financial state from a broader market and overall standpoint.
      • Example: “Return on Investment (ROI)” is the ultimate metrics for financial performance.
    • Impact metrics communicates how we are doing with our long-term, strategic goals. They often combine quantity, quality, satisfaction, and time dimensions.

The Significance of the Time Dimension

Among all the dimensions used in metrics, time is especially powerful because it adds critical context to nearly every metric. Without time, numbers can be misleading. Just about all metrics are more relevant when the time component is added. Time transforms static measures into dynamic insights. For instance:

  • A quantity metric of “100 new customers” becomes far more meaningful when paired with “this month” versus “since company founding.”
  • A quality metric of “95% data accuracy” is less impressive if it takes weeks to achieve, compared to real-time cleansing.
  • A cost metric of “$100,000 project spend” raises different questions depending on whether it’s a one-time investment or a recurring monthly expense.

By always asking, “Over what time frame?”, you unlock a truer understanding of performance. In short, the time dimension transforms static measures into dynamic insights. It allows you to answer not just “What happened?” but also “When did it happen?”, “How long did it take?”, and “How is it changing over time?”—questions that are generally crucial for actionable decision-making.

Time adds context to every other metric. Think of it as the axis that brings your measures to life. Quantity without time tells you how much, but not how fast. Quality without time shows accuracy, but not whether results are timely enough to act upon. Costs without time hide the pace at which expenses accumulate. And satisfaction without time misses whether perceptions improve, decline, or stay consistent over an initiative’s lifecycle.

The Significance of the Timeliness

Another important consideration is timeliness. Metrics must be accessible to decision makers in a timely manner to allow them to make timely decisions. For example:

  • A metric may deliver accurate insights, but if it takes three weeks to refresh the data and the dashboard that displays it, the value erodes.
  • A machine learning model may predict outcomes with high accuracy, but if the scoring process delays operational decisions, the benefit diminishes.

Therefore, in addition to deciding on and building the metrics for a project, the delivery mechanism of the metrics (such as a dashboard) must also be thought out to ensure that the entire process, from data sourcing to aggregations to dashboard refresh for example, can all happen in a timely manner to, in turn, make the metrics available to users in a timely manner.

Putting It All Together

When developing metrics for your analytics project, take a step back and ensure you have a comprehensive, multi-angle approach, by asking:

  • Do we know how much is being achieved/produced (quantity)?
  • Do we know how well it is being achieved/produced (quality)?
  • Do we know how fast results are being delivered (time)?
  • Do we know how much it costs to achieve (costs)?
  • Do we know how it feels to those affected (satisfaction)?
  • Do we know whether we are efficiently using resources?
  • Do we know whether we are effective in reaching goals?
  • Do we know what impact this work is having on the organization?
  • And for the above questions, always get a perspective on time … when? over what timeframe?
  • When are updates to the metrics needed by (real-time, hourly, daily, weekly, monthly, etc.)?

By building metrics across these dimensions, you create a more reliable, meaningful, and balanced framework for measuring success. More importantly, you ensure that the analytics project supports not only the immediate technical objectives but also the broader organizational goals.

Thanks for reading! Good luck on your analytics journey!

AI, Artificial Intelligence (AI), Data Analysis, Data Strategy, Machine Learning (ML), Natural Language Processing (NLP)

AI in Gaming: How Artificial Intelligence is Powering Game Production and Player Experience

The gaming industry isn’t just about fun and entertainment – it’s one of the largest and fastest-growing industries in the world. Valued at over $250 billion in 2024, it’s expected to surge past $300 billion by 2030. And at the center of this explosive growth? Artificial Intelligence (AI). From streamlining game development to building creative assets faster to shaping immersive and personalized player experiences, AI is transforming how games are built and how they are played. Let’s explore how.

1. AI in Gaming Today

AI is showing up both behind the scenes (in development studios and in technology devices) and inside the games themselves.

  • AI Agents & Workflow Tools: A recent survey found that 87% of game developers already incorporate AI agents into development workflows, using them for tasks such as playtesting, balancing, localization, and code generation PC GamerReuters. For bug detection, Ubisoft developed Commit Assistant, an AI tool that analyzes millions of lines of past code and bug fixes to predict where new errors are likely to appear. This has cut down debugging time and improved code quality, helping teams focus more on creative development rather than repetitive QA.
  • Content & Narrative: Over one-third of developers utilize AI for creative tasks like dynamic level design, animation, dialogue writing, and experimenting with gameplay or story concepts PC Gamer. Games like Minecraft and No Man’s Sky use AI to dynamically create worlds, keeping the player experience fresh.
  • Rapid Concept Ideation: Concept artists use AI to generate dozens of initial style options—then pick a few to polish with humans. Way faster than hand-sketching everything Reddit.
  • AI-Powered Game Creation: Roblox recently announced generative AI tools that let creators use natural language prompts to generate code and 3D assets for their games. This lowers the barrier for new developers and speeds up content creation for the platform’s massive creator community.
  • Generative AI in Games: On Steam, roughly 20% of games released in 2025 use generative AI—up 681% year-on-year—and 7% of the entire library now discloses usage of GenAI assets like art, audio, and text Tom’s Hardware.
  • Immersive NPCs: Studios like Jam & Tea, Ubisoft, and Nvidia are deploying AI for more dynamic, responsive NPCs that adapt in real time—creating more immersive interactions AP News. These smarter, more adaptive NPCs react more realistically to player actions.
  • AI-Driven Tools from Tech Giants: Microsoft’s Muse model generates gameplay based on player interaction; Activision sim titles in Call of Duty reportedly use AI-generated content The Verge.
  • Playtesting Reinvented: Brands like Razer now embed AI into playtesting: gamers can test pre-alpha builds, and AI tools analyze gameplay to help QA teams—claiming up to 80% reduction in playtesting cost Tom’s Guide. EA has been investing heavily in AI-driven automated game testing, where bots simulate thousands of gameplay scenarios. This reduces reliance on human testers for repetitive tasks and helps identify balance issues and bugs much faster.
  • Personalized Player Engagement: Platforms like Tencent, the largest gaming company in the world, and Zynga leverage AI to predict player behavior and keep them engaged with tailored quests, events, offers, and challenges. This increases retention while also driving monetization.
  • AI Upscaling and Realism
    While not a game producer, NVIDIA’s DLSS (Deep Learning Super Sampling) has transformed how games are rendered. By using AI to upscale graphics in real time, it delivers high-quality visuals at faster frame rates—giving players a smoother, more immersive experience.
  • Responsible AI for Fair Play and Safety: Microsoft is using AI to detect toxic behavior and cheating across Xbox Live. Its AI models can flag harassment or unfair play patterns, keeping the gaming ecosystem healthier for both casual and competitive gamers.

2. Tools, Technologies, and Platforms

Let’s take a look at things from the technology type standpoint. As you may expect, the gaming industry uses several AI technologies:

  • AI Algorithms: AI algorithms dynamically produce game content—levels, dialogue, music—based on developer input, on the fly. This boosts replayability and reduces production time Wikipedia. And tools like DeepMotion’s animation generator and IBM Watson integrations are already helping studios prototype faster and more creatively Market.us
  • Asset Generation Tools: Indie studios like Krafton are exploring AI to convert 2D images into 3D models, powering character and world creation with minimal manual sculptingReddit.
  • AI Agents: AI agents run thousands of tests, spot glitches, analyze frame drops, and flag issues—helping devs ship cleaner builds fasterReelmindVerified Market Reports. This type of AI-powered testing reduces bug detection time by up to 50%, accelerates quality assurance, and simulates gameplay scenarios on a massive scale Gitnux+1.
  • Machine Learning Models: AI tools, typically ML models, analyze player behavior to optimize monetization, reduce churn, tailor offers, balance economies, anticipate player engagement and even adjust difficulty dynamically – figures range from 56% of studios using analytics, to 77% for player engagement, and 63% using AI for economy and balance modeling Gitnux+1.
  • Natural Language Processing (NLP): NLPs are used to power conversational NPCs or AI-driven storytelling. Platforms like Roblox’s Cube 3D and Ubisoft’s experimenting with AI to generate dialogue and 3D assets—making NPCs more believable and story elements more dynamic Wikipedia.
  • Generative AI: Platforms like Roblox are enabling creators to generate code and 3D assets from text prompts, lowering barriers to entry. AI tools now support voice synthesis, environmental effects, and music generation—boosting realism and reducing production costs GitnuxZipDoWifiTalents
  • Computer Vision: Used in quality assurance and automated gameplay testing, especially at studios like Electronic Arts (EA).
  • AI-Enhanced Graphics: NVIDIA’s DLSS uses AI upscaling to deliver realistic graphics without slowing down performance.
  • GitHub Copilot for Code: Devs increasingly rely on tools like Copilot to speed coding. AI helps write repetitive code, refactor, or even spark new logic ideas Reddit.
  • Project Scoping Tools: AI tools can forecast delays and resource bottlenecks. Platforms like Tara AI use machine learning to forecast engineering tasks, timelines, and resources—helping game teams plan smarter Wikipedia. Also, by analyzing code commits and communication patterns, AI can flag when teams are drifting off track. This “AI project manager” approach is still in its early days, but it’s showing promise.

3. Benefits and Advantages

Companies adopting AI are seeing significant advantages:

  • Efficiency Gains & Cost Savings: AI reduces development time significantly—some estimates include 30–50% faster content creation or bug testing WifiTalents+1Gitnux. Ubisoft’s Commit Assistant reduces debugging time by predicting where code errors may occur.
  • Rapid Concept Ideation: Concept artists use AI to generate dozens of initial style options—then pick a few to polish with humans. Way faster than hand-sketching everything Reddit.
  • Creative Enhancement: Developers can shift time from repetitive tasks to innovation—allowing deeper storytelling and workflows PC GamerReddit.
  • Faster Testing Cycles: Automated QA, asset generation, and playtesting can slash both time and costs (some developers report half the animation workload gone) PatentPCVerified Market Reports. For example, EA’s automated bots simulate thousands of gameplay scenarios, accelerating testing.
  • Increased Player Engagement & Retention: AI keeps things fresh and fun with AI-driven adaptive difficulty, procedural content, and responsive NPCs boost immersion and retention—users report enhanced realism and engagement by 35–45% Gitnux+2Gitnux+2. Zynga uses AI to identify at-risk players and intervene with tailored offers to reduce churn.
  • Immersive Experiences: DLSS and AI-driven NPC behavior make games look better and feel more alive.
  • Revenue & Monetization: AI analytics enhance monetization strategies, increase ad effectiveness, and optimize in-game economies—improvements around 15–25% are reported Gitnux+1.
  • Global Reach & Accessibility: Faster localization and AI chat support reduce response times and broaden global player reach ZipDoGitnux+1.

For studios, these benefits and advantages translate to lower costs, faster release cycles, and stronger player engagement metrics, resulting in less expenses and more revenues.

4. Pitfalls and Challenges

Of course, it’s not all smooth sailing. Some issues include:

  • Bias in AI Systems: Poorly trained AI can unintentionally discriminate—for example, failing to fairly moderate online communities.
  • Failed Investments: AI tools can be expensive to build and maintain, and some studios have abandoned experiments when returns weren’t immediate.
  • Creativity vs. Automation: Overreliance on AI-generated content risks creating bland, formulaic games. There’s worry about AI replacing human creators or flooding the market with generic, AI-crafted content Financial Times.
  • Legal Risks, Ethics & Originality: Issues around data ownership, creative rights, and transparency are raising developer anxiety ReutersFinancial Times. Is AI stealing from artists? Activision’s Black Ops 6 faced backlash over generative assets, and Fortnite’s Vader stirred labor concerns WikipediaBusiness Insider.
  • Technical Limitations: Not all AI tools hit the mark technically. Early versions of NVIDIA’s G-Assist (now patched) had performance problems – it froze and tanked frame rates – but is a reminder that AI isn’t magic yet and comes with risks, especially for early integrators of new tools/solutions. Windows Central.
  • Speed vs. Quality: Rushing AI-generated code without proper QA can result in outages or bugs—human oversight still matters TechRadar.
  • Cost & Content Quality Concerns: While 94% of developers expect long-term cost reductions, upfront costs and measuring ROI remain challenges—especially given concerns over originality in AI-generated content ReutersPC Gamer.

In general, balancing innovation with human creativity remains a challenge.

5. The Future of AI in Gaming

Looking ahead, we can expect:

  • More Personalized Gameplay: Games that adapt in real-time to individual player styles.
  • Generative Storytelling: Entire narratives that shift based on player choices, powered by large language models.
  • AI Co-Creators: Game development may become a hybrid of human creativity and AI-assisted asset generation.
  • Smarter Communities: AI will help moderate toxic behavior at scale, creating safer online environments.
  • Games Created from Prompts: Imagine generating a mini-game just by describing it. That future is teased in surveys, though IP and ethics may slow adoption PC Gamer.
  • Fully Dynamic Games: AI-generated experiences based on user prompts may become a reality, enabling personalized game creation—but IP concerns may limit certain uses PC Gamer.
  • NPCs That Remember and Grow: AI characters that adapt, remember player choices, and evolve—like living game companions WIREDFinancial Times.
  • Cloud & AR/VR Boost Growth: AI will optimize streaming, drive immersive data-driven VR/AR experiences, and power e-sports analytics Verified Market ReportsGrand View Research.
  • Advanced NPCs & Narrative Systems: Expect smarter, emotionally adaptive NPCs and branching narratives shaped by AI AP NewsGitnux.
  • Industry Expansion: The AI in gaming market is projected to swell—from ~$1.2 billion in 2022 to anywhere between $5–8 billion by 2028, and up to $25 billion by 2030 GitnuxWifiTalents+1ZipDo.
  • Innovation Across Studios: Smaller indie developers continue experimenting freely with AI, while larger studios take a cautious, more curated approach Financial TimesThe Verge.
  • Streaming, VR/AR & E-sports Integration: AI-driven features—matching, avatar behavior, and live content moderation—will grow more sophisticated in live and virtual formats Gitnux+2Gitnux+2Windows Central.

With over 80% of gaming companies already investing in AI in some form, it’s clear that AI adoption is accelerating and will continue to grow. Survival without it will become impossible.

6. How Companies Can Stay Ahead

To thrive in this fast-changing environment, gaming companies should:

  • Invest in R&D: Experiment with generative AI, NPC intelligence, and new personalization engines. Become proficient in the key tools and technologies.
  • Focus on Ethics: Build AI responsibly, with safeguards against bias and toxicity.
  • Upskill Teams: Developers and project managers need to understand and use AI tools, not just traditional game engines.
  • Adopt Incrementally: Start with AI in QA and testing (low-risk, high-reward) before moving into core gameplay mechanics.
  • Start with High-ROI Use Cases: Begin with AI applications like testing, balancing, localization, and analytics—where benefits are most evident.
  • Blend AI with Human Creativity: Use AI to augment—not replace—human designers and writers. Leverage it to iterate faster, then fine-tune for quality.
  • Ensure IP and Ethical Compliance: Clearly disclose AI use, respect IP boundaries, and integrate transparency and ethics into development pipelines.
  • Monitor Tools & Stay Agile: AI tools evolve fast—stay informed, and be ready to pivot as platforms and capabilities shift.
  • Train Dev Teams: Encourage developers to explore AI assistants, generative tools, and optimization models so they can use them responsibly and creatively.
  • Focus on Player Trust: Transparently communicating AI usage helps mitigate player concerns around authenticity and originality.
  • Scale Intelligently: Use AI-powered analytics to understand player behavior—then refine content, economy, and retention strategies based on real data.

There will be some trial and error as companies move into the new landscape and try/adopt new technologies, but companies must adopt AI and become good at using it to stay competitive.

Final Word

AI isn’t replacing creativity in gaming—it’s amplifying it. From Ubisoft’s AI bug detection to Roblox’s generative tools and NVIDIA’s AI-enhanced graphics, the industry is already seeing massive gains. As studios continue blending human ingenuity with machine intelligence, the games of the future will be more immersive, personalized, and dynamic than anything we’ve seen before. But it’s clear, AI will not be an option for game development, it is a must. Companies will need to become proficient with the AI tools they choose and how they integrate them into the overall production cycle. They will also need to carefully choose partners that help them with AI implementations that are not done with in-house personnel.

This article is a part of an “AI in …” series that shares information about AI in various industries and business functions. Be on the lookout for future (and past) articles in the series.

Thanks for reading and good luck on your data (AI) journey!

Other “AI in …” articles in the series:

AI in Hospitality

Business Intelligence, Data Analysis, Data Development, Data Governance, Data Integration, Data Quality Assurance, Data Security, Data Strategy, Data Warehousing, Performance Tuning

Quality Assurance (QA) for Data Projects or Data Applications

This post discusses Quality Assurance (QA) activities for data projects.

What is Quality Assurance (QA)?  Simply put, Quality Assurance, also called QA, Testing or Validation, is about testing an application or solution to ensure that all the stated/promised/expected requirements are met. It is a critically important activity for all software application development or implementations. Data applications are no different. They need to be tested to ensure they work as intended.

QA stands between development and deployment. And QA makes the difference between a delivered product and a high quality delivered product.

There are a number of things to keep in mind when you plan your Quality Assurance activities for data solutions. I present some of them in this post as suggestions, considerations, or prompting questions. The things mentioned here will not apply to all data applications but can be used as a guide or a check.

People / Teams

The number of people and teams involved in a project will vary depending on the size, scope and complexity of the project.

The technical team building the application needs to perform an initial level of validation of the solution.

If there is a Quality Assurance team that performs the validation tasks, then that team will need to perform the “official” validation.

The business analysts and end-users of the application also need to validate. Where possible, work with as many end users as efficiently possible. The more real users you have testing the application, the better the chances of finding issues early.

Where it makes sense, Test IDs that simulate various types of users or groups should be used to help test various usage and security scenarios. This is particularly useful in automated testing.

On large projects where there is a lot to be tested, it is best to break up the testing across multiple people or teams. This will help to prevent testing fatigue and sloppy testing and result in higher quality testing.

Plan ahead to ensure that access for all the relevant users is set up in the testing environments.

Communication

With all the teams and people involved, it is important to have a plan for how they will communicate. Things to consider and have a plan for include:

  • How will teams communicate within? Email, Microsoft Teams, SharePoint, Shared Files, are some options.
  • How will the various teams involved communicate with each other? In other words, how will cross-team communication be handled? As above, Email, Microsoft Teams, SharePoint, Shared Files, are some options.
  • How will issues and status be communicated? Weekly meetings, Status emails or documents, Shared files available on shared spaces are options.
  • How will changes and resolutions be tracked? Files, SDLC applications, Change Management applications are options.
  • How will teams and individuals be notified when they need to perform a task? Manual communication or automated notifications from tools are options.

Data

The most important thing to ensure in data projects is that the data is high quality, particularly the “base” data set. If the base data is incorrect, everything built on top of it will be bad. Of course, the correctness of intermediate and user-facing data is also just as important, but the validation of the base data is critical to achieving the correct data all over.

  • Ensure that table counts, field counts and row counts of key data are correct.
  • Does the data warehouse data match the source data?
  • Test detailed, low level records with small samples of data
  • Test to ensure that the data and the values conform to what is expected. For example, ensuring that there is no data older than 3 years old, or ensuring that there are no account values outside a certain range. The Data Governance Team may become involved in these activities across all projects.

Next in line is the “intermediate” data such as derived metrics, aggregates, specialized subsets, and more. These will also need to be verified.

  • Are the calculated values correct?
  • Are the aggregates correct? Test aggregate data with small, medium and large sets of data
  • Verify metric calculations

Then the user-facing data or data prepared for self-service usage needs to be validated.

  • Does the data on the dashboard match the data in the database?
  • Are the KPIs correctly reflecting the status?

Test the full flow of the data. The validity of the data should be verified at each stage of the data flow – from the source, to the staging, to the final tables in the data warehouse, to aggregates or subsets, to the dashboard.

Take snapshots of key datasets or reports so you can compare results post data migration.

Some additional data prep might be needed in some cases.

  • These include making sure that you have sourced adequate data for testing. For example, if you need to test an annual trend, then it might be best to have at least a year’s worth of data, preferably two.
  • You may need to scramble or redact some data for testing. Often Test data is taken from the Production environment and then scrambled and/or redacted in order to not expose sensitive information.
  • You may need to temporarily load in data for testing. For various reasons, you may need to load some Production data into the QA environment just to test the solution or a particular feature and then remove the data after the testing is complete. While this can be time consuming, sometimes it’s necessary, and it’s good to be aware of the need early and make plans accordingly.

Aesthetics & Representation of Data

Presentation matters. Although the most critical thing is data correctness, how the data is presented is also very important. Good presentation helps with understanding, usability, and adoption. A few things to consider include:

  • Does the application, such as dashboard, look good?  Does it look right? 
  • Are the components laid out properly so that there is no overcrowding?
  • Are the logos, colors and fonts in line with company expectations?
  • Are proper chart options used to display the various types of data and metrics?
  • Is the information provided in a way that users can digest?

Usage

The data application or solution should be user friendly, preferably intuitive or at least have good documentation. The data must be useful to the intended audience, in that, it should help them to understand the information and make good decisions or take sensible actions based on it.

The application should present data in a manner that is effective – easy to access, and easy to understand.

The presentation should satisfy the analytic workflows of the various users. Users should be able to logically step through the application to find information at the appropriate level of detail that they need based on their role.

A few things that affect usability include:

  • Prompts – ensure that all the proper prompts or selections are available to users to slice and filter the data as necessary. And of course, verify that they work.
  • Drill downs and drill throughs – validate that users can drill-down and across data to find the information they need in a simple, logical manner.
  • Easy interrogation of the data – if the application is ad-hoc in nature, validate that users can navigate it or at least verify that the documentation is comprehensive enough for users to follow.

Security

Securing the application and its data so that only authorized users have access to it is critical.

Application security comprises of “authentication”– access to the application, and “authorization” – what a user is authorized to do when he or she accesses the application.

Authorization (what a user is authorized to do within the application) can be broken into “object security” – what objects or features a user has access to, and “data security” – what data elements a user has access to within the various objects or features.

For example, a user has access to an application (authenticated / can log in), and within the application the user has access to (authorized to see and use) 3 of 10 reports (object-level security). The user is not authorized to see the other 7 reports (object-level security) and, therefore, will not have access to them. Now, within the 3 reports that the user has access to, he or she can only see data related to 1 of 5 departments (data-level security).

All object-level and data-level security needs to be validated. This includes negative testing. Not only test to make sure that users have the access they need, but testing should also ensure that users do not have access that they should not have.

  • Data for testing should be scrambled or redacted as appropriate to protect it.
  • Some extremely sensitive data may need to be filtered out entirely.
  • Can all the appropriate users access the application?
  • Are non-authorized users blocked from accessing the application?
  • Can user see the data they should be able to see to perform their jobs?

Performance

Performance of the data solution is important to user efficiency and user adoption. If users cannot get the results they need in a timely manner, they will look elsewhere to get what they need. Even if they have no choice, a poorly performing application will result in wasted time and dollars.

A few things to consider for ensuring quality around performance:

  • Application usage – is the performance acceptable? Do the results get returned in an acceptable time?
  • Data Integration – is the load performance acceptable?
  • Data processing – can the application perform all the processing it needs to do in a reasonable amount of time?
  • Stress Testing – how is performance with many users? How is it with a lot data?
  • How is performance with various selections or with no selections at all?
  • Is ad-hoc usage setup to be flexible but avoid rogue analyses that may cripple the system?
  • Is real-time analysis needed and is the application quick enough?

These items need to be validated and any issues need to be reported to the appropriate teams for performance tuning before the application is released for general usage.

Methodology

Each organization, and even each team within an organization, will have a preferred methodology for application development and change management, including how they perform QA activities.

Some things to consider include:

  • Get QA resources involved in projects early so that they gain an early understanding of the requirements and the solutions to assess and plan how best to test.
  • When appropriate, do not wait until all testing is complete before notifying development teams of issue discovered. By notifying them early, this could make the difference between your project being on-time or late.
  • Create a test plan and test scripts – even if they are high-level.
  • Where possible, execute tasks in an agile, iterative manner.
  • Each environment will have unique rules and guidelines that need to be validated. For example, your application may have a special naming convention, color & font guidelines, special metadata items, and more. You need to validate that these rules and guidelines are followed.
  • Use a checklist to ensure that you validate with consistency from deliverable to deliverable
  • When the solution being developed is replacing an existing system or dataset, use the new and old solutions in parallel to validate the new against the old.
  • Document test results. All testing participants should document what has been tested and the results. This may be as simple as a checkmark or a “Done” status, but may also include things like data entered, screenshots, results, errors, and more.
  • Update the appropriate tracking tools (such as your SDLC or Change Management tools) to document changes and validation. These tools will vary from company to company, but it is best to have a trail of the development, testing, and release to production.
  • For each company and application, there will a specific, unique set of things that will need to be done. It is best if you have a standard test plan or test checklist to help you confirm that you have tested all important aspects and scenarios of the application.

This is not an all-encompassing coverage of Quality Assurance for data solutions, but I hope the article gives you enough information to get started or tips for improving what you currently have in place. You can share your questions, thoughts and input via comments to this post. Thanks for reading!

Analytics, Business Intelligence, Data Analysis, Data Integration, Data Integration (ETL), Data Science, Data Security, Data Strategy, Data Warehousing, Reporting

Creating a Business Intelligence (BI) & Analytics Strategy and Roadmap

This post provides some of my thoughts on how to go about creating a Business Intelligence (BI) & Analytics Strategy and Roadmap for your client or company.  Please comment with your suggestions from your experience for improving this information.

 

When creating or updating the BI & Analytics Strategy and Roadmap for a company, one of the first things to understand is:

Who are all the critical stakeholders that need to be involved?

Understanding who needs and uses the BI & Analytics systems is critical for starting the process of understanding and documenting the “who needs what, why, and when”.

These are some of the roles that are typically important stakeholders:

  • High-level business executives that are paying for the projects
  • Business directors involved in the usage of the systems
  • IT directors involved in the developing and support of the systems
  • Business Subject Matter Experts (SME’s) & Business Analysts
  • BI/Analytics/Data/System Architects
  • BI/Analytics/Data/System Developers and Administrators

 

Then, you need to ask all these stakeholders, especially those from the business:

What are the drivers for BI & Analytics? And what is the level of importance for each of these drivers?

This will help you to understand and document what business needs are creating the need for new or modified BI & Analytics solutions. You should then go deeper to understand … what are the business objectives and goals that are driving these business needs.  This will help you to understand and document the bigger picture so that a more comprehensive strategy and roadmap can be created.

The questions and discussions surrounding the above will require deep and broad business involvement. Getting the perspective of a wide range of users from all business areas that are using the BI & Analytics Systems is critical.  The business should be involved throughout the process of creating the strategy and roadmap, and all decisions should tie back to support for business objectives and goals. And the trail leading to all these decisions must be documented.

Some examples of business drivers include:

  • Gain more insight into who our best customers are and how best to acquire them.
  • Understand how weather affects our sales/revenue.
  • Determine how we can sell more to our existing customers.
  • Understand what causes employee turnover.
  • Gain insight into how we can improve staffing schedules.

 

And examples of business objectives and goals may include things like:

  • Increase corporate revenues by 10%
  • Grow our base of recurring customers
  • Stabilize corporate revenues over all seasons
  • Create an environment where employees love to work
  • Reduce payroll costs without a reduction in staff, for example, reduce turnover.

 

Then, turn to understanding and documenting the current scenario (if not already known). Identify what systems (including data sources) are in place, who are using them (and why and how), what capabilities do they offer, what are the must-haves, and what are the pain points and positive highlights.

Also, you will need to determine the current workload (and future workload if it can be determined) of the primary team members involved in developing, testing, and implementing BI & Analytics solutions.

This will help you understand a few things:

  • Some of the highest priority needs of the users
  • Gaps in capabilities and data between what is needed and what is currently in place (including an understanding of what is liked and disliked about the current systems)
  • Current user base knowledge and engagement
  • IT knowledge and skills
  • Resource availability – when are people available to work on new initiatives

 

What are the options and limitations?

  • Can existing systems be customized to meet the requirements?
  • Can they be upgraded to a new version that has the needed functionality?
  • Do we need to consider adding a new platform or replacing one or more of the existing systems with a new platform?
  • Can we migrate from/integrate one system to/with another system that we already have up and running?
  • Are any of our current systems losing vendor support or require an upgrade for other reasons? Has the pricing changed for any of our software applications?
  • What options does our budget permit us to explore?
  • What options do our knowledge and skills permit us to explore?

 

Once you have identified these items …

  • Identify and engage stakeholders, and document these roles and the people
  • Identify and document business drivers, objectives and goals
  • Understand and document the current landscape – needs (including must-haves), technology, gaps, users, IT staff, resource availability, and more
  • Identify and document options – based on current landscape, technology, budget, staff resources, etc.

… you can develop a “living” Strategy and Roadmap for BI & Analytics. And when I say “living”, I mean it will not be a static document, but will be fine-tuned over time as new information emerge and as changes arise in business needs, technology, and staff resources.

 

Your Strategy and Roadmap for BI & Analytics should include, but is not limited to:

  • BI & Analytics that will be used to satisfy business drivers, objectives and goals
  • Data acquisition and storage plan for meeting the analytics needs
  • Technology platforms that will be used to process and store data, and deliver the analytics
  • Information about any new technologies that needs to be acquired or implemented, and schedules
  • Roles and Responsibilities for all stakeholders involved in BI & Analytics projects
  • Planned staffing allocations and schedules
  • Planned staffing changes and schedules
  • User training (business users) and Delivery team training (technical implementers & developers for example)
  • List dependencies for each item or set of items
Business Intelligence, Data Strategy, Data Warehousing, Reporting

Terminology Primer – Goals, Objectives, Measures, Metrics, Key Performance Indicators (KPIs), Meters

The essential purpose of business intelligence is to provide information that will allow people throughout an organization to make informed decisions relevant to their business processes and responsibilities.  This post is a simple terminology primer that describes the meaning of a few terms commonly used in business intelligence, and explain how they relate to each other, and their relevance to supporting the overall goals of an organization.

Goals are a business’ desired outcomes. They are typically around growth, cost savings, innovation, improvement in efficiency, the company’s workforce, and the competition, but may include a lot of other things.
Goals help a company to stay focused by providing team members within a company with an aim to work towards.

Objectives are specific strategies and steps that a business needs to take to achieve the goals that have set. These objectives are usually specific and measurable.  Success toward achieving objectives usually indicates progress toward achieving goals.
Objectives are sometimes referred to as Critical Success Factors because they are critical to the success of achieving the goals.

Measures are numeric representations of various transactions that occur through various business processes. For example, when the company makes a sale (during the sales process), some measures that are generated in that transaction are:  sales amount, discount amount, number of items sold, and number of items discounted.  Then from this, other measures can be determined, such as total sales for all customers, total number of items sold, total number of sales for each customer, and so on.  Measures are numeric and therefore can have mathematical calculations performed on them – such as sum, avg., min, max, etc. – to generate metrics.

Metrics are calculations derived from one or more measures. For example, as mentioned in an example above, you may have the measures “Discount Amount” and “Number of Items Discounted”, and you may use these measures to calculate a metric of “Average Discount Amount per Discounted Item” or “Average Discount Amount per Sale”.  As another example, you may simply add up all the Discount Amounts over a specific time period, such as month, to get a “Total Discount Amount by Month” metric.

Key Performance Indicators (KPIs) are metrics that measure how well a company is doing toward their objectives. Companies will have hundreds or thousands of metrics, but there will be a few key ones that the executive team wants to keep a close eye on for the overall company or divisions, and other managers will want to keep an eye on KPIs relevant to their respective departments.  Those key metrics are the KPIs. All KPIs are metrics, but not all metrics are KPIs.

Meters are a group of metrics that collectively provide a broader, overall view of a subject area.
For example, you may have individual metrics for Sales to Date, Sales in Pipeline, Number/Value of Expiring Contracts, Avg. Time to Close Sales, etc. Putting these all together in a Meter presents the user with a lot of related information that provides a broad, overall picture of sales (and loss of sales) which would allow for analysis such as determining the chances of meeting sales targets. The Meter in this example could be called “Sales Forecast” for example.

Thanks for reading!

Business Intelligence, Business Intelligence Platform, Data Development, Data Governance, Data Quality Assurance, Data Security, Data Strategy

How to generate detailed Oracle BI (OBIEE) Repository Documentation

In this post, I will show the steps for using the OBIEE “Repository Documentation” utility to generate repository (RPD) lineage information.  I will also provide a couple example of how this documentation (output file) can be used.

To access and run the Repository Documentation utility,  from the BI Admin Tool menu, select Tools -> Utilities.

biadmintool_menu_tools_utilities

From the Utilities dialog, select “Repository Documentation”, and click “Execute…”

utilitiesdialog

In the “Save As” dialog, select the destination and enter the name you would like for the output file.

saverepositorydocumentationdialog

When it finishes, it will generate the output csv file.  Note  – this will likely be a large file.  It will contain all your repository objects.

obieerepositoryoutputfile

The RPD documentation file will contain the following columns:
Subject Area, Presentation Table, Presentation Column, Description – Presentation Column, Business Model, Derived logical table, Derived logical column, Description – Derived Logical Column, Expression, Logical Table, Logical Column, Description – Logical Column, Logical Table Source, Expression, Initialization Block, Variable, Database, Physical Catalog, Physical Schema, Physical Table, Alias, Physical Column, Description – Physical Column

You can use this file to quickly track lineage from physical sources to the logical columns to the presentation columns and identify all the logic and variables in between.
You can also use it to identify where and how much a specified table, column, variable, etc. is used which will help you to identify dependencies and know the effect of making changes or deleting elements.

Development, Data Governance, and Quality Assurance teams may find this information useful in this format.