"The better data you have, the better these results will be... the problems come up with the data."
"Another way to improve the model is to just use way, way, way more quality data."
"If you want to make the strategic decisions that affect a population, then we have to have relevant and good quality data about that population."
"Quality and quantity of data, this is really impressive, really important stuff for us."
"Humanity is only as good as the data that you put in."
"Some things, no matter how long you've been in the industry, don't change: garbage in, garbage out."
"You need good actionable data, like real solid facts."
"If you have garbage data, you have garbage analysis."
"If my data set is not good, it can lead to a lot of problems."
"Improving the data is not a pre-processing step that you do once before you then do the 'real work'... improving the data is a core part of the iterative process of model development."
"Data is important... real data is better than unsure information."
"There are still good researchers that are doing good data."
"API 3 removes the middleman and incentivizes high quality data providing."
"We're seeing both the generation of more data and the quality of data being made available."
"Garbage in, garbage out - high-quality data is essential."
"Tesla is the only company collecting the type and quality of data needed for autonomy."
"AI can only perform as well as the data and perform as well as the quality of data that you have."
"There's gonna be the companies that have that automation, have good quality data, have actions, and they're gonna be successful."
"I don't think Twitter is a good Focus test, you're really getting very bad data."
"If you make it cheap enough and quality enough and give it away for free, then you make it everybody's economic best interest to use the higher quality data sets."
"Better quality data means people get better faster."
"Having really high quality data makes a huge difference in the performance of the model on tasks you care about."
"You've got to put good data in to get good data."
"Integrity concerns the accuracy, completeness, consistency, and trustworthiness of data throughout its lifecycle."
"AI has amazing potential, but you've got to have really good data."
"If you don't feed good data into the LLM, you're going to get bad results."
"If that piece doesn't work, the opposite will happen to assuming value from gen-AI. You'll actually expose to many more people inside your organization (maybe your partners, your customers) that the data you started with is in fact bad."
"The Delta architecture focuses on improving the quality of data."
"Clean data is data that's complete, correct, and relevant to the problem you're trying to solve."
"Dirty data is incomplete, incorrect, or irrelevant to the problem you're trying to solve."
"Data quality is important. We need to make sure we're catching everything."
"Bad data can lead to lost revenue, erosion of trust, wasted resources, there's just so many things that bad data can lead towards."
"Data quality first, performance second."
"Don't get me wrong, the ITP is really indeed high quality data."
"Enhancing your CMDB starts with good data quality."
"Tools... it's a lot better than this really old screenshot of it but basically you want to make it easy for your content creators to make good data."
"This course has been designed to provide you with the ability to obtain data that is both good quality and accurate without any previous experience with lidar data."
"Garbage in and garbage out, right?"
"I think they should consider a data quality project when there's a business name so you need to look at your data and see what business problem are we trying to solve and say don't do data quality just for the sake of doing data quality."
"Coherence is a quality indicator of our data."
"The classic garbage in, garbage out is very true in migration. If you do not validate your data properly, you will suffer at the end."
"Quality is way more important than quantity when you want to be having fine-tuning data."
"It's not about the Quantum of data, it's about the quality of the data."
"You'll get the best data if you do all your experiments on the same day in the same session."
"Nothing beats good data. You should think about what data you need before you take it."
"The error rate is about 1 percent, so you get 99 percent accuracy out of any given read."
"With all that clean data, we'll be able to look at our data much better and find better insights."
"The usefulness of your data depends on how well it's structured."
"Data wrangling is a process of cleaning, structuring, and enriching raw data into a desired format for better decision making."
"Good training data is one of the most important things for doing any type of classification."
"If there are quirks in your data that you are not aware of, or if there are errors in your data, it doesn't matter how good your statistical model is; it's a garbage in, garbage out situation."
"Take care of your foundation data first."
"The greatness of data warehouses is that you have data in pristine quality in purely structured form."
"Garbage in, garbage out. Your data is just as important, if not more important, than the actual architecture of the network you're trying to build."
"Integrity refers to maintaining consistency and trustworthiness of data."
"We can do such a good job by honing the right data set, eliminating biases in the data as much as we can so that AI can and will do better than people."
"It also increases trust of data because the data sets that are added to this catalog are curated, similar to the artworks displayed in a museum."
"The quality of data now is reliable for decision making."
"Cleaning data is essentially the idea of trying to correct or fill in any missing values or remove those bits completely."
"Better data means better results for researchers, reporters, and readers."
"It's like what the data scientists say: garbage in, garbage out."
"Good data makes good models, not only is getting the data a lot of work, then annotating it is even more work."
"Your predictions are only as good as your data."
"The value that you get out of the AI is upper bounded by the business value of the problem that you're solving, the data quality that you have to solve that problem, and then lastly the predictive signal."
"Cleaner data means less clutter, more focus, and a more streamlined team."
"Knowing that we have noise, knowing that we don't have infinite resolution, we have deltas rather than days, it's very important."
"Get the best data you can afford; poor data equals low confidence in homework."
"You need to know what it is that you want to find out about, and to do that your data has to be clean."
"The quality of the dataset and the ground truth... must avoid noise in the features and the target labels."
"Quality values estimate whether the variant call at that site is due to sequencing error."
"The genotype quality takes a lot of different things into account, but it definitely is important when trying to decide if your variant is real or important to keep considering."
"Green is perfect sampling, that's exactly what you want."
"The Registries are becoming more active and there's a real need to verify the quality and accuracy of the information they contain."
"Great Expectations... you can really test any type of data assets."
"The best tool for the job in my opinion is a library called Great Expectations."
"We've come a long way; we've now cleaned up our data, and it looks pretty good at this point."
"We better make sure that our data is of good quality and our analytical models are of good quality as well."
"Ground control points are so valuable because they help you identify issues that you didn't actually know were existing in your data."
"Visualization is often the first line of defense against incomplete or bad data."
"The best place to improve the signal-to-noise ratio is in the field."
"ProRes RAW won't give you more dynamic range, but it will bring you more data, better quality data."
"Alteryx utilizes a super easy to understand traffic light based system to understand the quality of your data."
"The analysis of the data can never be better than the data one puts into the database."
"The accuracy of predictions can vary widely depending on the quality and completeness of the data being used."
"If the mean and the median and the mode are all very close together, then you have a good data set without outliers."
"Machine learning is only as good as the data we feed it... data quality and having good predictable high-quality data is what we need."
"The accuracy of the data and the magnitude of data is extremely important."
"A model is only as good as its input."
"One good check to evaluate the quality of your data is to see whether or not you've got any missing values within your data."
"It's not that experts gather more data, they gather better data."
"I tend to favor Prop Stream when it comes to the data quality."
"You're only as good as your ground truth."
"Make sure you have a good data set to train on."
"Garbage in, garbage out - we can't underestimate the importance of proper data acquisition."
"Creating sharing quality data is not just a service to the community; it's principally a service to yourself."
"Garbage in, garbage out. The better the information you put into the software, the better the reports you can pull out of it."
"The idea is that by using much more data, although noisy, you can do better than using a curated high-quality data set."
"Having data standards in your practice, having ways to review the quality of your data of these objects, one of the use cases for Tracer that we've seen really come out, people are using the tool to set up QA/QC dashboards for the Revit models."
"An AI is going to need great data and an action system to deliver those timely, relevant moments."
"Involving program stakeholders and community members in conducting an evaluation can improve the quality of data."
"You need to get your data in the best shape that you possibly can before bringing it into Illustrator."
"Data quality means the information you collect should be good enough to make analysis and the decisions that can be reliable."
"We have imputation accuracy of about 95% that gives me a pretty good confidence."
"We want to make sure that we're getting high quality data into our data Lake and making sure that nothing gets messed up."
"We cleaned our data really well, we have good data, we implemented a machine learning algorithm and we got exactly what we're looking for."
"When you've got good data, then you go to process it, and it looks... amazing."
"No matter what model you get, you can never do better than the irreducible error because that's just because your data is noisy."
"This lack of high-quality data makes it impossible to draw scientific conclusions on the nature of UAP."
"The machine learning algorithm is only as good as the data that you use to train it."
"The better data you give it on the input side, the better the output's going to be."
"Data quality is really a relative issue; it's what matters for your business."
"If SPSS says your data's a mess and you're going as mad as a hatter, that's when normality matters."
"This is really fabulous data, Ron did a great job in acquisition."
"It's more worthwhile to invest in exploring and fixing the data than trying to tinker with the models."
"There are no flawless data sets but striving to make them flawless is the key to success."
"What we want to be able to do is kind of get to that first horizon."
"We shouldn't underestimate the calibration of the instrument that affects the quality of the data."
"When operators have better data, they will have a safer plant."
"So, now if we have these data and calculate the all the properties right, then to reliable QSAR models right we need to ensure the data are of high quality."
"The model is only going to do as good as the data that you provided."
"For almost all other industries where you have fewer than a billion users, often data quality is the critical difference."
"So what would have been better from the get-go than age? Probably just something like date of birth, DOB."
"If you want to address important questions, you need high-quality data with well-calibrated instruments."
"The data is of such high quality and the protein is rigid enough that we can still get to a sub three angstrom reconstruction from just 20 microscope images."
"The uncertainty of measurement tells us about the quality of the data."
"We are building the best training set we can get."
"The more authentic and accurate the data, the more trustworthy the model will be."
"Do you think it's better to have fresh daily updated data, more accurate, where you have insights into the methodology, you have transparency associated with that data?"
"Structural issues in data include typos, spelling errors, inconsistent capitalization, and extra spaces."
"It's better not to give me data, instead of giving me bad data."
"Turbo Prep is trying to tell us which features seem better quality or less quality by scoring them according to four different quality criterias."
"The better quality of your KB, the better quality of the results as you continue to use it to clean data."
"Algorithms don't care where the data comes from; they pretty much care about one thing: if you give me good data, I'll give you good output."