DataRobot vs. GoodData.AI

DataRobot can be used for risk assessment, such as predicting the likelihood of loan default. It can handle both classification and regression tasks effectively. It relies on historical data for model training. If you have limited historical data or the data quality is poor, it may not be the best choice as it requires a sufficient amount of high-quality data for accurate model building.

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I think it works nicely for shops that want the analytical power and are ok to host their own infrastructure for the data and etl. For smaller operations with limited budgets but still high demand for analytical features the math may not work out.

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• DataRobot helps, with algorithms, to analyze and decipher numerous machine-learning techniques in order to provide models to assist in company-wide decision making.
• Our DataRobot program puts on an "even playing field" the strength of auto-machine learning and allows us to make decisions in an extremely timely manner. The speed is consistent without being offset by errors or false-negatives.
• It encompasses many desired techniques that help companies in general, to reconfigure in to artificial intelligence driven firms, with little to no inconvenience.

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• The source datasets are often complex, semi-structured and un-linked to key entities. With GoodData, all of these datasets are unified to serve as a central semantic data model layer, building into a galaxy schema with dimensions, bridge, and facts, which then forms the backbone that powers the [...] data intelligence cloud. Building insights and dashboards become a much easier task once the underlying data model is designed. GoodData enforces certain best practices as a BI tool, which must be adhered to get the true value of the raw data. For e.g. the source FDA dashboard may just show inspection data but the Site Profile dashboard built on GoodData goes beyond the standard information and shows more insight into site risk scores and can be drilled into details. There is blog written on this topic: [...].
• GoodData provides a rich collection of visualization options that help us create compelling story-telling via dashboards. Being well-prepared for FDA inspections is essential for maintaining product quality, regulatory compliance, and avoiding serious business setbacks. FDA inspections are critical events that can shape a company’s market access and reputation. The FDA itself offers the FDA Data Dashboard, but it doesn’t make every document available. There is a blog written on this topic: [...].
• Medical devices and technologies do not stop evolving after they receive regulatory approval. Once a product hits the market, it faces real-world usage, compliance challenges, and an array of regulatory scrutiny. Managing these postmarket dynamics is critical to a product’s long-term success and patient safety. However, many companies struggle to keep track of relevant events across a product’s markets, from adverse event reports to changing regulations. Postmarket Intelligence developed on GoodData platform enables us to solve that problem. It empowers MedTech companies to efficiently monitor, assess, and act on postmarket data—saving time, improving decision-making, and ensuring compliance with industry standards. Anyone who is used to trying to get the data they need from the various FDA, and other regulatory agency websites, knows that collecting, cleaning, and structuring that data takes hours. And that’s before any analysis can get done. We enable customers to free up time to focus only on the high-value analysis and subsequent recommendations to leadership, rather than wrangling the data.
• The data pipeline refresh that is provided by GoodData Platform is also quite useful from data engineering perspective. The Automated Data Distribution v2 or commonly called as ADD refresh follows a set pattern of identifying the analytical data model through output stage which helps abstract the complex table definition to simpler views that can help with quick rebuild at the data warehouse level while loading the data into GoodData's ADS storage layer. The import first way of loading data into GoodData's cloud storage, followed by querying for any aggregations or metrics on the GoodData analyzer, makes this simple and fast.
• GoodData's latest product i.e. Cloud also offers several good features like Analytics as Code which helps software engineering teams follow a code-first approach to analytics, where building insights, dashboards or even datasets can be done in YAML templates or serviced by REST APIs. This is particularly forward thinking in the modern technology stack and evolving industry requirements. These provide seamless integration options to front-end and backend code, embedded analytics with multiple choices from HTML to React based workloads. At [...], we are currently exploring most of these features while planning for a future migration from Platform to Cloud.

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• The platform itself is very complicated. It probably can't function well without being complicated, but there is a big training curve to get over before you can effectively use it. Even I'm not sure if I'm effectively using it now.
• The suggested model DataRobot deploys often not the best model for our purposes. We've had to do a lot of testing to make sure what model is the best. For regressive models, DataRobot does give you a MASE score but, for some reason, often doesn't suggest the best MASE score model.
• The software will give you errors if output files are not entered correctly but will not exactly tell you how to fix them. Perhaps that is complicated, but being able to download a template with your data for an output file in the correct format would be nice.

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• Good Data is already have certain customizable options. However, having more flexibility in customizing reports and dashboards & control over the visual aspects would enhance the overall user experience.
• To make Good Data even more powerful tool, improving the speed and responsiveness of the tool, especially during data-intensive tasks, would be a significantly helpful.
• For new users, the interface can be made more user friendly which would promote easy navigation through features of tool.

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DataRobot presents a machine-learning platform designed by data scientists from an array of backgrounds, to construct and develop precise predictive modeling in a fraction of the time previously taken. The tech invloved addresses the critical shortage of data scientists by changing the speed and economics of predictive analytics. DataRobot utilizes parallel processing to evaluate models in R, Python, Spark MLlib, H2O and other open source databases. It searches for possible permutations and algorithms, features, transformation, processes, steps and tuning to yield the best models for the dataset and predictive goal.

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Because gooddata really helps us in processing data to make reports or dashboards. So we are very satisfied when we use it. What we like is the flexible use of charts. We change at will the use of charts to display in reports or dashboards. Thank you Gooddata for helping companies like us who need flexibility in usage

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From a customer perspective it is incredibly usable. We have more users building their own reports that would normally need custom work from our support team. The back end can be daunting when trying to configure things like new data elements or push changes to a report to all existing customers.

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We are approximately one month since go-live. There has been one short outage.

I'm generally impressed with how fast it reflects so much data.

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As I am writing this report I am participating with Datarobot Engineers in an complex environment and we have their whole support. We are in Mexico and is not common to have this commitment from companies without expensive contract services. Installing is on premise and the client does not want us to take control and they, the client, is also limited because of internal IT regulations ,,, soo we are just doing magic and everybody is committed.

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Support team has been highly responsive and helpful from our first initial deployment to present day. They engage and work with us. know when to escalate for more challenging problems. They also follow up. Overall have had a very good experience with support

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Petr was a rock star - patient, knowledgeable, clear and easy to work with.

GoodData implementation team was professional and courteous

Implementations are hard and we had limited technical resources. We relied too heavily on GD care team. When we found technical gaps, they weren't simple to overcome

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I've done machine learning through python before, however having to code and test each model individually was very time consuming and required a lot of expertise. The data Robot approach, is an excellent way of getting to a well placed starting point. You can then pick up the model from there and fine tune further if you need.

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GoodData comparing to other platform is very easy to use, customer support and on-boarding support. Set of features, speed of integration in our platform. Also great benefit for us was very competetive pricing.

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We never had any issues with scalability, other than being limited by workspaces

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• We have been able to cut costs by not buying leads that we will not be able to sell on
• We have been able to deploy loan eligibility reporting which brought in new business
• We have been able to improve the performance of our credit providers and our partners which has helped to retain business

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• I personally pushed our management to get GoodData implemented, and there on we have been making much more profits than we ever did!
• Last month we made a decision using the platform and withing a short span of time our ROI gets 7X.
• We also made a marketing spend of $100,000 every quater using the insights and metrics, and we are really happy with the returs.

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Invited by the Vendor

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info