#data governance company?

[elsa16744]

Building a Robust Data Governance Framework: Best Practices and Key Considerations  

A good framework for data governance compliance assists organizations in ensuring data security. As a result, they can combat cybersecurity risks and fulfill regulatory requirements for responsible enterprise data operations. This post will focus on building a relevant, outcome-oriented data governance framework and strategy. 

A comprehensive strategy will help protect an organization’s digital assets from different data breaches. Global brands want to avoid reputational damage and regulatory penalties by picking up governance initiatives. However, they seek reliable compliance approaches to maximize the value of data. That is why they want to know how to foster data etiquette for accuracy, timely access to insights, and relevance to business decisions. 

Best Practices and Key Considerations for Building an Effective Data Governance Framework 

1. Clearly Communicating Process Objectives and Data Ownership 

Before a data governance framework is implemented, the objectives need to be clearly defined. What is the organization aiming to achieve by employing a data governance company? Some objectives may include the following. 

-          better quality of information, 

-          regulatory compliance, 

-          data-driven decision-making (DDDM). 

These goals will determine the governance framework’s structure. 

It also becomes very important to define data asset ownership. The data governance team, led by a data governance officer (DGO), must be held responsible for this. Those data professionals will develop data access and usage policies. They will also monitor data quality and address data-related issues across the entire organization. 

2. Drafting Comprehensive Data Protection and Anti-Espionage Policies 

A good data governance framework is based on well-articulated policies and standards. Those documents will guide data solutions and management practices, helping the firms and their suppliers. Such policies should define the most important compliance areas. 

Considerations must include unbiased data classification and 24/7 security incident tracking. Moreover, privacy assurance assessments may be conducted. A broader data lifecycle management (DLM) vision can further streamline governance compliance efforts. Companies must also establish user permission standards. They will help allow access to data only if authorized personnel submit requests to their respective superiors. Similar access controls help prevent corporate espionage actors from entering IT systems and compromising sensitive business intelligence. 

The policies developed should be flexible. After all, you will need to modify business governance frameworks. Otherwise, you cannot keep up with the regulators’ amendments to applicable laws. 

3. Focus on Data Quality and Integrity 

Ensuring data quality and integrity is very fundamental to data governance. Remember, inaccurate data leads to poor decisions. It inevitably results in misaligned strategies, causing inefficiencies in business activities. Therefore, data validation rules must be implemented. You want to encourage regular integrity audits based on those rules. Furthermore, adequate data cleansing practices will help ensure businesses’ dataset accuracy and reliability. 

Consider data stewardship programs. It involves a competent individual or a team assuming responsibility for the quality of specific data assets. 

4. Leverage Technology for Scalability and Automation 

Data management can be enhanced by using modern technologies. Newer data governance platforms, artificial intelligence, and automation tools will aid you in improving governance compliance. These technologies will, therefore, make it easier to automate the tracking of data changes. Many tools also enforce governance policies. So, users can bypass the manual work of optimizing data protection measures based on the organization’s growth. 

Conclusion 

Building a strong data governance framework will demand definite objectives. Leaders will want to develop cross-functional collaborative environments to promote data ethics and integrity policies. 

Accordingly, the best compliance assurance practices involve defining data ownership, ensuring data quality, and using novel technology. These measures would help organizations protect their data assets. Their superior compliance levels also make them attractive to more investors. 

A robust data governance framework and compliance strategy does not just mitigate risk. Rather, it would deliver strategic success while respecting regulatory and consumer values concerning privacy. 

[elbiotipo]

The most absurd thing about all this bullshit is that all my data is going towards selling me things I don't want or I can't buy. Tech companies have built the world's most sophisticated and complex surveillance system, effectively trapping billions of people into a panopticon and consuming wasteful amounts of resources, technology and human work and talent, all just to sell ads. It's incredibly absurd.

[frogeyedape]

I am so unbelievably pissed off. FUCK HOAs

Oh, my trash/recycling bin can't be visible except on pickup day? Ok whatever fine I hate you but I can deal with this

Weekly inspections?????? FU FU FU FU FU

SECOND NOTICE ALSO WE'RE CHARGING YOU MONEY TO SEND YOU CERTIFIED MAIL OF THIS TOTALLY LEGIT TOTALLY SECOND NOTICE OF WHAT IS ACTUALLY A VIOLATION cue me: checks notes. Hmm. My recycling bin was. on the curb. on recycling pickup day. You know. The day it has to be out. The day it is motherfucking ALLOWED TO BE FUCKING OUT AND VISIBLE.

so. 1) not a violation

I have sent them the trash AND recycling pickup schedules, which are DIFFERENT, btw

I have disputed the fact of the violation

I have disputed the linking of this "violation" to a previous violation MONTHS AGO--their "first notice" in this case was a "Courtesy Notice" LITERALLY 5 MONTHS AGO and they've done so many inspections since then and my bin CLEARLY WASN'T OUT IN THOSE INTERVENING MONTHS so WTMFH

So I am posting like a crazy person here instead of sending the absolutely deranged email I almost sent (I did send a slightly less deranged version with the disputes, and requesting a hearing)

OMG. It has been. Less than one hour since I learned this fun fun news. My bin was out YESTERDAY, y'all. YESTERDAY. I am going to blow a gasket

[orcelito]

Being in a data governance class makes it so weird to watch the shit that tech giants are continuously pulling. I see them mass data scraping for AI training and I'm like. But Wait. What about GDPR??? Hello??? User privacy and right to access/correct/delete??? I know that's an EU thing but other places have their own data protection acts too!!! And they're pulling these moves that communicate such flagrant disrespect and irresponsibility for the handling of user data.

In the IT courses I've taken, they've taught us something that's really stuck with me: "Just because we can doesn't mean we should."

My data governance professor keeps emphasizing that as IT personnel, we will need to have a much higher sense of integrity than most other fields bc we have access to SO MUCH SHIT. IT and tech industries are so under-regulated bc of how relatively new it all is and how slow the law moves. But people in charge of handling user data should keep in mind that this is data about Real People, and capitalizing off of it is a violation of privacy.

But tech giants don't care. They'll keep poking and prodding, trying to capitalize off of users' data in order to earn as much money as they can. Data regulations like GDPR exist, but they don't cover everyone, and tech giants are more of "Do First, ask forgiveness later". Or in this case, pay the fines later. Bc if they violate GDPR, they Will be paying fines. And maybe they'll even change the way they handle the data... for EU citizens.

The rest of us are fucked tho. Tech giants really don't care. They just view us as assets to profit off of.

[nando161mando]

The FTC's latest investigation found giant tech companies collect way more data than users expect and share it widely without oversight. Federal privacy legislation is long overdue.

https://www.eff.org/deeplinks/2024/09/ftc-report-confirms-commercial-surveillance-out-control

[srldesigns6277]

Why is it that whenever I wish to scream, I am at work?

[st5lker]

soooo sad because thats an artist whose art i really like! but they have shit idiot brain fungus.

wait are you talking abt the ai person its hard to tell when u sent this sorry 😭 if you are then yeah i feel like generally sensible people often fall into this like really dumb fearmongering when it comes to ai and its just like i get it to an extent im not like an ai defender but people just shut their brains off and dont want to hear any nuance on the topic at all its kind of wild

[friendrat]

But I don't wanna live in a dystopian world!!!

[aizenat]

Anyway, glad our government got something right and is working to ban tik tok.

[vague-humanoid]

At the California Institute of the Arts, it all started with a videoconference between the registrar’s office and a nonprofit.

One of the nonprofit’s representatives had enabled an AI note-taking tool from Read AI. At the end of the meeting, it emailed a summary to all attendees, said Allan Chen, the institute’s chief technology officer. They could have a copy of the notes, if they wanted — they just needed to create their own account.

Next thing Chen knew, Read AI’s bot had popped up inabout a dozen of his meetings over a one-week span. It was in one-on-one check-ins. Project meetings. “Everything.”

The spread “was very aggressive,” recalled Chen, who also serves as vice president for institute technology. And it “took us by surprise.”

The scenariounderscores a growing challenge for colleges: Tech adoption and experimentation among students, faculty, and staff — especially as it pertains to AI — are outpacing institutions’ governance of these technologies and may even violate their data-privacy and security policies.

That has been the case with note-taking tools from companies including Read AI, Otter.ai, and Fireflies.ai.They can integrate with platforms like Zoom, Google Meet, and Microsoft Teamsto provide live transcriptions, meeting summaries, audio and video recordings, and other services.

Higher-ed interest in these products isn’t surprising.For those bogged down with virtual rendezvouses, a tool that can ingest long, winding conversations and spit outkey takeaways and action items is alluring. These services can also aid people with disabilities, including those who are deaf.

But the tools can quickly propagate unchecked across a university. They can auto-join any virtual meetings on a user’s calendar — even if that person is not in attendance. And that’s a concern, administrators say, if it means third-party productsthat an institution hasn’t reviewedmay be capturing and analyzing personal information, proprietary material, or confidential communications.

“What keeps me up at night is the ability for individual users to do things that are very powerful, but they don’t realize what they’re doing,” Chen said. “You may not realize you’re opening a can of worms.“

The Chronicle documented both individual and universitywide instances of this trend. At Tidewater Community College, in Virginia, Heather Brown, an instructional designer, unwittingly gave Otter.ai’s tool access to her calendar, and it joined a Faculty Senate meeting she didn’t end up attending. “One of our [associate vice presidents] reached out to inform me,” she wrote in a message. “I was mortified!”

[evitcani-writes]

“It seems like almost all of those people don’t have HIV,” said Jennifer Kates, HIV policy director at KFF, a health-research nonprofit. “If they did, that would be substandard care at a pretty severe level,” she said.

Ya’ll. United Health just got accused of $17 billion in medicare fraud.

Basically they made up diagnosis which are improbable or impossible, “forgot” to remove ones which had been cured, and overall allegedly stole billions from taxpayers.

The government pays insurers a base rate for each Medicare Advantage member. The insurers are entitled to extra money when their patients are diagnosed with certain conditions that are costly to treat.

… About 18,000 Medicare Advantage recipients had insurer-driven diagnoses of HIV, the virus that causes AIDS, but weren’t receiving treatment for the virus from doctors, between 2018 and 2021, the data showed. Each HIV diagnosis generates about $3,000 a year in added payments to insurers.

… He said internal company data for 2022 showed a treatment rate for patients UnitedHealth diagnosed with HIV of more than triple what the Journal found. He said the pandemic disrupted care, lowering treatment rates during the period analyzed by the Journal, and that the analysis failed to account for patients who started treatments in future years.

The Medicare data, however, show UnitedHealth’s patients with insurer-driven HIV diagnoses were on the antiretrovirals at low rates even before the pandemic, and hardly any started the drugs in the years after UnitedHealth diagnosed them.

Source: https://www.wsj.com/health/healthcare/medicare-health-insurance-diagnosis-payments-b4d99a5d

I bet United Health really wishes it was a different week right now.

UPDATE/EDIT: Article is from July. I didn’t notice myself since it came up in my news feed. Don’t always trust the internet to be time accurate. 😎My guess is it is getting promoted due to current events. However, there are some updates concerning actions taken based on the report which you can look into by checking the authors’ other articles.

[jcmarchi]

Data Monocultures in AI: Threats to Diversity and Innovation

New Post has been published on https://thedigitalinsider.com/data-monocultures-in-ai-threats-to-diversity-and-innovation/

Data Monocultures in AI: Threats to Diversity and Innovation

AI is reshaping the world, from transforming healthcare to reforming education. It’s tackling long-standing challenges and opening possibilities we never thought possible. Data is at the centre of this revolution—the fuel that powers every AI model. It’s what enables these systems to make predictions, find patterns, and deliver solutions that impact our everyday lives.

But, while this abundance of data is driving innovation, the dominance of uniform datasets—often referred to as data monocultures—poses significant risks to diversity and creativity in AI development. This is like farming monoculture, where planting the same crop across large fields leaves the ecosystem fragile and vulnerable to pests and disease. In AI, relying on uniform datasets creates rigid, biased, and often unreliable models.

This article dives into the concept of data monocultures, examining what they are, why they persist, the risks they bring, and the steps we can take to build AI systems that are smarter, fairer, and more inclusive.

Understanding Data Monocultures

A data monoculture occurs when a single dataset or a narrow set of data sources dominates the training of AI systems. Facial recognition is a well-documented example of data monoculture in AI. Studies from MIT Media Lab found that models trained chiefly on images of lighter-skinned individuals struggled with darker-skinned faces. Error rates for darker-skinned women reached 34.7%, compared to just 0.8% for lighter-skinned men. These results highlight the impact of training data that didn’t include enough diversity in skin tones.

Similar issues arise in other fields. For example, large language models (LLMs) such as OpenAI’s GPT and Google’s Bard are trained on datasets that heavily rely on English-language content predominantly sourced from Western contexts. This lack of diversity makes them less accurate in understanding language and cultural nuances from other parts of the world. Countries like India are developing LLMs that better reflect local languages and cultural values.

This issue can be critical, especially in fields like healthcare. For example, a medical diagnostic tool trained chiefly on data from European populations may perform poorly in regions with different genetic and environmental factors.

Where Data Monocultures Come From

Data monocultures in AI occur for a variety of reasons. Popular datasets like ImageNet and COCO are massive, easily accessible, and widely used. But they often reflect a narrow, Western-centric view. Collecting diverse data isn’t cheap, so many smaller organizations rely on these existing datasets. This reliance reinforces the lack of variety.

Standardization is also a key factor. Researchers often use widely recognized datasets to compare their results, unintentionally discouraging the exploration of alternative sources. This trend creates a feedback loop where everyone optimizes for the same benchmarks instead of solving real-world problems.

Sometimes, these issues occur due to oversight. Dataset creators might unintentionally leave out certain groups, languages, or regions. For instance, early versions of voice assistants like Siri didn’t handle non-Western accents well. The reason was that the developers didn’t include enough data from those regions. These oversights create tools that fail to meet the needs of a global audience.

Why It Matters

As AI takes on more prominent roles in decision-making, data monocultures can have real-world consequences. AI models can reinforce discrimination when they inherit biases from their training data. A hiring algorithm trained on data from male-dominated industries might unintentionally favour male candidates, excluding qualified women from consideration.

Cultural representation is another challenge. Recommendation systems like Netflix and Spotify have often favoured Western preferences, sidelining content from other cultures. This discrimination limits user experience and curbs innovation by keeping ideas narrow and repetitive.

AI systems can also become fragile when trained on limited data. During the COVID-19 pandemic, medical models trained on pre-pandemic data failed to adapt to the complexities of a global health crisis. This rigidity can make AI systems less useful when faced with unexpected situations.

Data monoculture can lead to ethical and legal issues as well. Companies like Twitter and Apple have faced public backlash for biased algorithms. Twitter’s image-cropping tool was accused of racial bias, while Apple Card’s credit algorithm allegedly offered lower limits to women. These controversies damage trust in products and raise questions about accountability in AI development.

How to Fix Data Monocultures

Solving the problem of data monocultures demands broadening the range of data used to train AI systems. This task requires developing tools and technologies that make collecting data from diverse sources easier. Projects like Mozilla’s Common Voice, for instance, gather voice samples from people worldwide, creating a richer dataset with various accents and languages—similarly, initiatives like UNESCO’s Data for AI focus on including underrepresented communities.

Establishing ethical guidelines is another crucial step. Frameworks like the Toronto Declaration promote transparency and inclusivity to ensure that AI systems are fair by design. Strong data governance policies inspired by GDPR regulations can also make a big difference. They require clear documentation of data sources and hold organizations accountable for ensuring diversity.

Open-source platforms can also make a difference. For example, hugging Face’s Datasets Repository allows researchers to access and share diverse data. This collaborative model promotes the AI ecosystem, reducing reliance on narrow datasets. Transparency also plays a significant role. Using explainable AI systems and implementing regular checks can help identify and correct biases. This explanation is vital to keep the models both fair and adaptable.

Building diverse teams might be the most impactful and straightforward step. Teams with varied backgrounds are better at spotting blind spots in data and designing systems that work for a broader range of users. Inclusive teams lead to better outcomes, making AI brighter and fairer.

The Bottom Line

AI has incredible potential, but its effectiveness depends on its data quality. Data monocultures limit this potential, producing biased, inflexible systems disconnected from real-world needs. To overcome these challenges, developers, governments, and communities must collaborate to diversify datasets, implement ethical practices, and foster inclusive teams. By tackling these issues directly, we can create more intelligent and equitable AI, reflecting the diversity of the world it aims to serve.

[spaghettioverdose]

Hello usamerican tumblr liberal. Why is it scarier when a Chinese company has your data? What is the Chinese government going to do with your data if you don't live there? Tell me what the fuck are they gonna do? Why are you more scared of a foreign government that has no power over you than your own government that does have power over you, and you know for a fact they will do something about it. Facebook has literally snitched to the cops over people getting abortions in the US. Why is that less scary than when Chinese companies collect your data?

[orcelito]

Worst thing about having taken business classes is that I see people complain about bullshit companies are pulling and a part of my brain goes "Oh, that's an effective tactic for cost reduction" or something around those lines. And then the part of my brain that is Not a wannabe businessman is just like "Bro."

[trainsinanime]

I've seen a number of people worried and concerned about this language on Ao3s current "agree to these terms of service" page. The short version is:

Don't worry. This isn't anything bad. Checking that box just means you forgive them for being US American.

Long version: This text makes perfect sense if you're familiar with the issues around GDPR and in particular the uncertainty about Privacy Shield and SCCs after Schrems II. But I suspect most people aren't, so let's get into it, with the caveat that this is a Eurocentric (and in particular EU centric) view of this.

The basic outline is that Europeans in the EU have a right to privacy under the EU's General Data Protection Regulation (GDPR), an EU directive (let's simplify things and call it an EU law) that regulates how various entities, including companies and the government, may acquire, store and process data about you.

The list of what counts as data about you is enormous. It includes things like your name and birthday, but also your email address, your computers IP address, user names, whatever. If an advertiser could want it, it's on the list.

The general rule is that they can't, unless you give explicit permission, or it's for one of a number of enumerated reasons (not all of which are as clear as would be desirable, but that's another topic). You have a right to request a copy of the data, you have a right to force them to delete their data and so on. It's not quite on the level of constitutional rights, but it is a pretty big deal.

In contrast, the US, home of most of the world's internet companies, has no such right at a federal level. If someone has your data, it is fundamentally theirs. American police, FBI, CIA and so on also have far more rights to request your data than the ones in Europe.

So how can an American website provide services to persons in the EU? Well… Honestly, there's an argument to be made that they can't.

US websites can promise in their terms and conditions that they will keep your data as safe as a European site would. In fact, they have to, unless they start specifically excluding Europeans. The EU even provides Standard Contract Clauses (SCCs) that they can use for this.

However, e.g. Facebook's T&Cs can't bind the US government. Facebook can't promise that it'll keep your data as secure as it is in the EU even if they wanted to (which they absolutely don't), because the US government can get to it easily, and EU citizens can't even sue the US government over it.

Despite the importance that US companies have in Europe, this is not a theoretical concern at all. There have been two successive international agreements between the US and the EU about this, and both were struck down by the EU court as being in violation of EU law, in the Schrems I and Schrems II decisions (named after Max Schrems, an Austrian privacy activist who sued in both cases).

A third international agreement is currently being prepared, and in the meantime the previous agreement (known as "Privacy Shield") remains tentatively in place. The problem is that the US government does not want to offer EU citizens equivalent protection as they have under EU law; they don't even want to offer US citizens these protections. They just love spying on foreigners too much. The previous agreements tried to hide that under flowery language, but couldn't actually solve it. It's unclear and in my opinion unlikely that they'll manage to get a version that survives judicial review this time. Max Schrems is waiting.

So what is a site like Ao3 to do? They're arguably not part of the problem, Max Schrems keeps suing Meta, not the OTW, but they are subject to the rules because they process stuff like your email address.

Their solution is this checkbox. You agree that they can process your data even though they're in the US, and they can't guarantee you that the US government won't spy on you in ways that would be illegal for the government of e.g. Belgium. Is that legal under EU law? …probably as legal as fan fiction in general, I suppose, which is to say let's hope nobody sues to try and find out.

But what's important is that nothing changed, just the language. Ao3 has always stored your user name and email address on servers in the US, subject to whatever the FBI, CIA, NSA and FRA may want to do it. They're just making it more clear now.

[hari-100]

Transform Your Business with Pixid.ai Data Engineering Services in Australia

Transform Your Business with Pixid.ai Data Engineering Services in Australia

Data engineering is essential for transforming raw data into actionable insights, and Pixid.ai stands out as a leading provider of these services in Australia. Here’s a comprehensive look at what they offer, incorporating key services and terms relevant to the industry

Data Collection and Storage

Pixid.ai excels in big data engineering services in Australia and New zealand  collecting data from various sources like databases, APIs, and IoT devices. They ensure secure storage on cloud platforms or on premises servers, offering flexible cloud data engineering services in Australia tailored to client needs.

Data Processing

Their data processing includes cleaning and organizing raw data to ensure it’s accurate and reliable. This is crucial for effective ETL services in New zealand and Australia (Extract, Transform, Load), which convert raw data into a usable format for analysis.

Data Analysis and Visualization

Pixid.ai employs complex analytical algorithms to detect trends and patterns in data. Their big data analytics company in Australia and New Zealand provides intelligent research and generates visual representations like charts and dashboards to make difficult data easier to grasp. They also provide sentiment analysis services in New zealand and Australia, helping businesses gauge public opinion and customer satisfaction through data.

Business Intelligence and Predictive Analytics

Their robust data analytics consulting services in New zealand and Australia include business intelligence tools for real time performance tracking and predictive analytics to forecast future trends. These services help businesses stay proactive and make data-driven decisions.

Data Governance and Management

Pixid.ai ensures data quality and security through strong data governance frameworks. As data governance service providers in Australia and New zealand they implement policies to comply with regulations, maintain data integrity, and manage data throughout its lifecycle.

Developing a Data Strategy and Roadmap

They collaborate with businesses to develop a comprehensive data strategy aligned with overall business goals. This strategy includes creating a roadmap that outlines steps, timelines, and resources required for successful data initiatives.

Specialized Consulting Services

Pixid.ai offers specialized consulting services in various big data technologies:

Apache Spark consulting services in Australia: Leveraging Spark for fast and scalable data processing.

Data Bricks consulting services in Australia: Utilizing Databricks for unified analytics and AI solutions.

Big data consulting services in Australia: Providing expert guidance on big data solutions and technologies.

Why Choose Pixid.ai?

Pixid.ai’s expertise ensures businesses can leverage their data effectively, providing a competitive edge. Their services span from data collection to advanced analytics, making them a top choice for big data engineering services in Australia and new zealand They utilize technologies like Hadoop and cloud platforms to process data efficiently and derive accurate insights.

Partnering with Pixid.ai means accessing comprehensive data solutions, from cloud data engineering services in Australia to detailed data governance and management. Their specialized consulting services, including Apache Spark consulting services in Australia and Data Bricks consulting services in Australia and new zealand ensure that businesses have the expert guidance needed to maximize their data’s value.

Conclusion

In the competitive landscape of data driven business, Pixid.ai provides essential services to transform raw data into valuable insights. Whether it’s through big data consulting services in Australia and new zealand or data analytics consulting services in new Zealand and Australia, Pixid.ai helps businesses thrive. Their commitment to excellence in data engineering and governance makes them a trusted partner for any business looking to harness the power of their data.

For more information please contact.www.pixid.ai