The DataRobot AI Platform is presented as a solution that accelerates and democratizes data science by automating the end-to-end journey from data to value and allows users to deploy AI applications at scale. DataRobot provides a centrally governed platform that gives users AI to drive business outcomes, that is available on the user's cloud platform-of-choice, on-premise, or as a fully-managed service. The solutions include tools providing data preparation enabling users to explore and…
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IBM Planning Analytics
Score 8.1 out of 10
N/A
IBM Planning Analytics, powered by IBM TM1®, is an integrated planning solution designed to promote collaboration across the organization and help keep pace with the speed of modern business. With its calculation engine, this enterprise performance management solution is designed to help users move beyond the limits of spreadsheets, automating the planning process to drive faster, more accurate results. Use it to unify data sources into one single repository, enabling users to build…
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.
IBM Planning Analytics is good for small to medium-size jobs or less complex projects. It can generate reasonably comfident results if input datasets are well prepared and cleaned. IBM Planning Analytics is not quite good when it comes to large-scale datasets, or datasets from various sources (for example data dumped from different databases.
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.
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.
IBM Planning Analytics was an upgrade from an older version of TM1 that is experiencing some growing pains, some functionality is harder to reach than it has been in the past
It is easy to learn as a surface user with created reports, but it does require some technical skills to make advanced calculations and reports if there is no reliable consultant available, much like Excel
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.
Since IBM Cognos Express is suitable only for medium data warehouse environment, we are not sure if this tool solves the long term need as the business keeps growing rapidly. So its a 50/50 ratio to renew Express license. But having said that, the components of IBM Cognos Express are also available in other Cognos BI suites like Cognos 10.x version. So we will probably upgrade our environment to IBM Cognos 10.x which comes with more new features.
For developers, admins and end users looking for flexibility, IBM Planning Analytics would rate very highly on usability. For example, a developer has access to a highly performant built-in ETL (Extract Translate Load) tool and scripting language called Turbo Integrator that can (among other things) bring in data via flat file or direct connection from many data sources, move data around Planning Analytics, perform batch calculations, export to files or other data stores. In the rare situation where limitations are encountered there is a well documented REST API. Admins and end users benefit from the intuitive PAW (Workspace) interface as well as the rich Excel integration through Planning Analytics for Excel (PAfE). Since flexibility inherently comes with a little more complexity, so an organization with simple and "cookie-cutter" requirements may rate Planning Analytics a little lower.
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.
Although I find the IBM Planning analytics documentation quite time consuming, their support with email and call is something i can term as very considerate and patient, I have had few calls about the features and how i would want to implement them within my projects, and the teams have been super helpful to resolve my issues
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.
Anaplan does not handle sparsity; this is very problematic for large volume data sets (many 0's). There also are limitations to the number of dimensions that can be used in a module. If more dimensions are required, then separate modules need to be built and intertwined. IBM PA does not have these limitations.
