#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

[bright-and-burning]

i just got several emails for a job with the federal gov/cdc saying like oh we've received your application oh youre eligible for this series/grade combination oh you've been referred to the hiring manager in DC for these positions which is like. great. cool. only i stopped applying to jobs in uh november. of 2023. i've been at this job since january... like. hello????? did you JUST get around to this??????

[theygender]

I may be considering the crime of... Getting a business degree 🤢

[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.

[justonefeather]

The paranoid piece of my brain wants to delete all my social media apps and all the data i can because i hate having a presence on the internet that is like. Trackable by advertisers and potential bad actors. I don't want facebook to know what i do and don't like. I don't want insta to suggest ads based on who they think i am. I don't want snap to have my location or my face in their ai. I want to scream. The way that our internet/communication systems and our apps and our operating systems work is just not secure enough and it drives me insane like how is this ok there should not be a back door into your phone "for cops" guess who else will use it?? There should not be location data from Grindr to be sold to companies that want to target gay men. This is absurd. And it's because of capitalism and authoritarianism that it stays this way !! Hate hate hate hate all this shit

[awkward-teabag]

It's old but here's a video from 8 years ago about one factor of it.

Some numbers from it:

The internet used 2% of the world's electricity in 2011

The internet used 91 billion kilowatt-hours in 2013 just in the US.

The internet data transfer rate was 28,000 gigabytes a second in 2013.

And those are numbers from over 10 years ago. I couldn't find any exact number but I see online advertising specifically uses the same amount of electricity as a small country these days.

And here's a study from 2018 about it if you want something long and crunchy.

From the highlight:

Online advertising consumed between 20.38 to 282.75 TWh of energy and 11.53 – 159.93 million tons of CO2e was emitted to produce the electricity consumed.

Of course there's no exact number as it's not recorded and, if it is, it's guarded (hence the wide range) but it's safe to say it uses a lot of electricity.

At least with TV commercials, they gave actors, directors, and screen writers a way into the film and TV industry and also employed a crew. There were even dedicated crews/houses for specific types of ads. They also had to be interesting or entertaining enough to draw attention given how many people used commercial breaks as bathroom/snack breaks and stopped paying attention to ads.

Sure they were trying to sell you something but at least there were others who were getting a paycheque and building up a resume from them. The more successful or viral an ad went (and going viral before the internet was a big thing), the better it was for the cast and crew and more than a few celebs got their start from a commercial or three.

Not so much now, especially when the whole ad is just CGI. They aren't interesting or entertaining and the production value is low to keep the costs down on that end. If an ad is more than lukewarm, odds are it was designed for TV first because internet advertising is all about quantity over quality.

honestly advertising is so fukcing wasteful not even just in the convincing you to buy shit you never actually wanted but like

how much electricity is wasted displaying ads that could've gone to keeping houses warm. How much paper is produced just to be turned into pamphlets and ads that will just be thrown away. how much internet bandwith is wasted just on the amount of ads that are on the internet nowadays. How many hours worth of labor went into producing ads that are going directly into my adblocker or my waste bin that could've been time spent doing literally ANYTHING useful?

[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."

[jcmarchi]

How AI-Led Platforms Are Transforming Business Intelligence and Decision-Making

New Post has been published on https://thedigitalinsider.com/how-ai-led-platforms-are-transforming-business-intelligence-and-decision-making/

How AI-Led Platforms Are Transforming Business Intelligence and Decision-Making

Imagine a retail company anticipating a surge in demand for specific products weeks before a seasonal shopping event. Or consider a healthcare provider accurately predicting patient influx during peak flu season, enabling them to allocate resources efficiently and improve care. These scenarios are not hypothetical—they are becoming the norm in organizations leveraging artificial intelligence (AI) for real-time, actionable insights.

AI is revolutionizing the way businesses strategize, make decisions, and maintain a competitive edge. As Deloitte’s “State of AI in the Enterprise” report reveals, 94% of business leaders view AI as essential for achieving success in the next five years. AI is no longer just a tool; it is a strategic enabler that high-performing organizations are leveraging to enter new markets, enhance products, and drive significant revenue growth.

This is where AI-led platforms come into play. Moving beyond traditional data processing, these platforms continuously analyze and interpret data from diverse sources, transforming it into intelligence that guides strategic actions in real-time. By integrating AI at the core of decision support, these platforms empower businesses to anticipate market shifts, adjust strategies, and respond swiftly to evolving conditions.

From Static Data to Real-Time Strategic Agility

AI-led platforms are a leap forward from static reporting and periodic insights. Today’s organizations need intelligence that continuously adapts to market shifts and consumer behaviors. According to McKinsey, by 2030, many companies will be approaching “data ubiquity,” where data is not only accessible but also embedded in every system, process, and decision point. This embedded data will drive automated, insight-driven actions with sufficient human oversight, allowing businesses to react to changes instantly and improve operational effectiveness.

For instance, healthcare organizations rely on AI-led platforms to predict patient needs with remarkable accuracy. These platforms analyze vast, real-time datasets from patient records, treatment histories, and diagnostic trends, enabling providers to optimize care delivery. By predicting patient inflow and aligning resources accordingly, healthcare institutions can improve outcomes and increase operational efficiency. This kind of agility is not just a benefit; it addresses the urgent demands of an industry that frequently operates under resource constraints, making healthcare delivery more adaptable and responsive.

Speeding Up Decision Cycles with AI-Driven Responsiveness

A core advantage of AI-led platforms is their ability to dramatically accelerate decision cycles, enabling organizations to respond to changes in real-time. Traditional business intelligence processes often involve time-consuming data collection, analysis, and interpretation, limiting an organization’s ability to act swiftly. In contrast, AI-led platforms provide continuous analysis, equipping leaders with data-backed insights that empower rapid, confident decision-making.

In retail, where customer preferences shift quickly, and demand can fluctuate hourly, AI-led platforms are invaluable. By continuously analyzing live data from sales, inventory, and customer interactions, these platforms allow retailers to dynamically adjust stock levels and adapt pricing strategies. According to a Deloitte report, by 2025, 20% of top global retailers are expected to achieve holistic results by using distributed AI systems. Additionally, 91% of executives identified AI as the most game-changing technology for retail in the next three years.

This responsiveness helps retailers minimize waste, avoid stockouts, and ensure products are available exactly when and where customers expect them. Such agility does not just meet immediate needs—it transforms retailers from reactive to proactive, allowing them to deliver exceptional customer experiences and operational efficiency in a competitive market.

Building Compounding AI Value Through Learning Systems

AI-led platforms do not merely provide static insights; they are self-learning systems that improve with each interaction. This ability to “learn” from past data and refine recommendations makes AI platforms more adept at predicting future outcomes, creating an ongoing cycle of improvement that helps organizations build resilience and foresight. By building compounding AI value, these platforms allow every successful decision to enhance future outcomes across interconnected areas of the business.

For financial services providers, this compounding value is transformative. Predictive models within AI-led platforms enable banks, investment firms, and insurers to identify and mitigate risks proactively. By recognizing emerging patterns in market data, these platforms help financial institutions adjust their strategies, make informed investment choices, and comply with regulatory requirements. This proactive approach safeguards their operations and enhances customer trust—a critical advantage in a sector where stability and trust are paramount. Over time, this cumulative learning leads to a stronger, more resilient organization equipped to navigate evolving financial landscapes with confidence.

Elevating Customer Engagement with Hyper-Personalized Intelligence

AI-led platforms are reshaping customer engagement by enabling unprecedented levels of personalization. Traditional customer segmentation methods are limited in scope, often categorizing customers into broad groups. AI, on the other hand, can deliver hyper-personalization by analyzing individual behaviors, preferences, and purchasing patterns. This enables businesses to provide experiences tailored to each customer’s unique needs, fostering stronger connections and driving loyalty.

Retailers, for example, are already harnessing the power of AI-led platforms to understand customer behavior in real-time. By analyzing data on previous purchases, browsing habits, and even location data, retailers can provide tailored product recommendations, exclusive promotions, and personalized reminders at optimal times. This level of engagement boosts immediate sales and builds lasting customer loyalty and brand affinity. In the competitive retail landscape, where customer expectations for personalization are constantly rising, such capabilities are becoming essential for long-term success.

Engineering Excellence and Optimizing for Scalability

To fully realize the potential of AI-led platforms, tech leaders must prioritize several strategic and operational imperatives. These include a commitment to engineering excellence, adaptability, scalability, and ethical transparency:

Precision in Model Development AI models are only as effective as the data and design behind them. Developing models that provide reliable, accurate insights demands rigorous attention to data quality, model training, and validation processes. Effective deployment also means ensuring that AI models can perform well in a wide range of real-world scenarios and adapt as new data comes in.

Modular and Adaptive Architectures Organizations benefit significantly from modular architectures that support rapid deployment and adapt to evolving needs. This flexibility enables tech teams to adjust components or integrate new capabilities without disrupting the entire platform. As market conditions change, this adaptive architecture becomes invaluable for maintaining relevance and responsiveness.

Optimizing for Scalability Beyond the Pilot Phase Many organizations struggle to move AI initiatives beyond the pilot stage. To truly capture AI’s value, it is essential to develop platforms that are scalable, robust, and consistent. Successful scaling requires platforms that can handle increased data volumes and user demands without compromising performance. Scalable solutions maximize the reach and impact of AI across the organization, ensuring predictable ROI and seamless transitions from experimentation to enterprise-wide deployment.

Deterministic Outcomes for Stability and Reliability As organizations rely on AI-led platforms to make critical, data-driven decisions, ensuring deterministic outcomes—consistent, predictable, and reliable results—becomes essential. Deterministic AI systems reduce the risk of unexpected behaviors or “hallucinations,” delivering accuracy and stability even as data volumes increase and environments shift. This predictability allows organizations to maintain confidence in AI-driven insights, crucial for supporting innovation without compromising operational stability.

Security and Ethical Transparency As AI systems gain access to sensitive data, particularly in sectors like healthcare and finance, security and ethical considerations become predominant. AI-led platforms must incorporate rigorous data governance, privacy measures, and ethical safeguards to operate transparently and responsibly. Building trust through transparent practices and a commitment to ethical standards is crucial for the successful adoption of AI-led systems in high-stakes industries.

Setting a New Standard for Decision Support and Competitive Foresight

The power of AI-led platforms lies not in doing things better, but in reshaping how businesses operate and compete. Future leaders will leverage AI for incremental gains and seize strategic opportunities others overlook, creating positions unique to AI-enabled enterprises.

These platforms allow businesses to build models that grow stronger with each decision, balancing human expertise with AI capabilities to deliver lasting value. By anticipating and proactively meeting customer needs, they foster loyalty and drive exponential growth.

For today’s leaders, the question is not how AI can improve decisions, but how it can redefine the game. Those who embrace AI as a foundation for sustainable growth will set the benchmarks for tomorrow—using platforms that continually innovate, adapt, and add value, positioning their organizations to lead in the future of intelligent business.

[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?

[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