MyDataModels recognized by Gartner as a “Sample Vendor” for Enterprise Information Management, Small Data in 2020

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By applying Machine Learning solutions to complex tasks, “Small Data techniques enable organizations to manage production models that are more resilient and able to adapt to major world events like the pandemic or future disruptions.” reports Gartner.

Who are we?

MyDataModels is a premier Machine Learning provider. As a Small Data Artificial Intelligence specialist, our product TADA™, brings a simple to use solution. It is well suited for any business manager dealing with data. No need for massive datasets as in traditional Machine Learning solutions. TADA™ starts to be very performant with 100 rows in a spreadsheet. Our expertise is coupled with a highly referenceable client list and numerous analyst references.

We are industry domain experts whose platforms are fine-tuned for our customers’ business needs. MyDataModels understands the complexity and uniqueness of various business operations, such as digital marketing, customer acquisitions, and other knowledge worker-focused areas. We know they require ad hoc strategies, perfect timing, and targeted effort to produce optimal business outcomes, better judgment, and decision-making. MyDataModels is a company building Small Data Machine Learning solutions that are easy to use, quick and efficient for business operation requirements.

“We’ve provided simple solutions to meet the operational exploration needs of various businesses. Our solutions are constantly evolving to solve your specific business challenges, reducing costs, increasing productivity,” says Alain Blancquart, MyDataModels Founder and CEO.

Gartner’s Recognition

We are pleased to announce our recognition by Gartner as a Sample Vendor in “Hype Cycle for Enterprise Information Management, 2020”, “Hype Cycle for Artificial Intelligence, 2020“, and “Hype Cycle for Emerging Technologies, 2020” , for Small Data Artificial Intelligence solutions.

Gartner reports, “These Small Data techniques are ideal for AI problems where there are no big datasets available. Using smaller amounts of data allows data scientists to use more classical machine learning algorithms that provide good accuracy without big data training sets. It can also speed up the business exploration and model prototyping for novel solutions, as this approach reduces the time, compute power, energy and costs to collect, prepare or label large datasets.”

Enterprise Information Management

Enterprise information management (EIM) applies to the optimization and processing of data generated and handled by an enterprise. Enterprise information management attempts to guarantee that data, as a business asset, is dealt with securely through its lifecycle and is available to the appropriate business methods.

Enterprise information management (EIM) means the optimal use of data inside a company. EIM supports improved performance, increased data quality, and integration of data. Enterprise information management, a comparatively new information management method, is frequently employed as a generic name for the processes, procedures, and software solutions adopted to control data across an extensive business throughout its everyday operations.

TADA™ and Enterprise Information Management

TADA™ can be used on any dataset, however small it might be, to improve and speed up the corporate and management decision process. Combined in a few clicks with the use of spreadsheets, it immediately analyzes the data at hand. It provides an estimation when used on past corporate income statements and asked to predict and explore future revenues. But moreover, it provides the critical elements in the income statement impacting the revenue. Indeed, it finds the four or five most important columns in the spreadsheet that affect the revenues. It might be the cost of salaries, but frequently there are surprises. TADA™ might show that an unexpected column in the data has a strong influence. It, in turn, provides excellent guidance for decision-making.

Disclaimer

Gartner does not endorse any vendor, product, or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of Gartner’s research organization’s opinions and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, concerning this research, including any warranties of merchantability or fitness for a particular purpose.

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