Explore our Services in Big Data Management
and Big Data Analytics
We’ve heard the “data is the new oil” phrase too many times already. But as businesses enter the new “software 2.0” era, the value of data is no longer just about the potential to gain insights to power data-driven decisions.
Data is both the foundation and the main ingredient for building successful AI solutions – you just can’t apply Machine Learning to a problem unless you have data of sufficient quantity and quality to train your models. There are no two identical datasets, each having unique statistical properties, data quality challenges, and potential value and use cases for a particular business. Even if they are closely related, the right solution may be different, and developing AI solutions with publicly available datasets for training ML models is not good enough.
With storage costs plummeting, there is no longer any valid reason to decide what data to store long term and what data to throw away – all data is potentially valuable in the era of AI/ML, and all data can be cost-effectively stored (the right solutions can store terabytes at negligible storage costs and make multi-petabyte datasets storage feasible). But at the same time, one can’t just store vast amounts of data with yet unknown uses in production databases, with catastrophic application performance consequences. Big Data technologies and processes need to be adopted to successfully manage large and ever-growing datasets that need to be cataloged for future data science use cases and satisfy compliance requirements.
From data science to
While a data science team can provide insight, knowledge, and predictions for the business teams like sales and product development to base their decisions on, this is only the first step.
Most data science processes can be automated and delivered as data products that BI/BI/sales/marketing teams can use autonomously to understand data and generate predictions.
Data science – from data to business
valuable insights, and beyond
Traditional data science is about deriving insights and knowledge from data. At Tremend, our data science experience was gathered in realistic scenarios while analyzing client datasets, with the purpose of developing a multitude of AI/ML solutions, from churn prediction models, and content and product recommender systems to anomaly and fraud detection.
This puts us in a unique position to tackle the next-generation data science tasks, moving from understanding present data patterns to predicting future trends, enabling our customers to anticipate market development trends, and ultimately empowering them to stay one step ahead of the competition.
Big Data management – effective
storage & cataloging of large amounts of data
Big Data may be overused as a marketing buzz-word, but it’s also a reality: as businesses store more and more data, their datasets quickly grow beyond the sizes that can be processed on a single machine, forcing the adoption of “big data technologies” like data-processing frameworks made to run on clusters of machines.
This changes the context of data science tasks – analyses and model development needs to be done using different technologies like big data frameworks and services. In particular, big data mining needs to be employed to extract smaller datasets suitable for traditional data science and model development.
Analytics is a broad term encompassing both continuous gatherings of data concerning user behavior and systems operation and the continuous and automated analysis of this data.
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Tremend has experience in the underlying technologies used to build enterprise-grade analytics pipelines, store and perform information extraction on a single data point and time-series related data, apply enhancement techniques on the data, and apply unsupervised AI/ML techniques for implementing anomaly detection and fraud detection.
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TN Office 2 building, 4th floor,
District 3, Bucharest, Romania, 030134