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5 key success factors of a modern data platform

Written by Olivia Klayman, Marketing & Corporate Communications Analyst at Systech

“Data management” and “data framework” are some of the phrases that are thrown around more frequently in casual conversations these days. As a leader in a major corporation, perhaps you are also curious why many of your competitors have already begun migrating from on-premises to a cloud infrastructure, equipped with an end-to-end data management system. Here are some of the key factors that make a modern data platform virtually unbeatable…

1. Self-Service. Self-service analytics allow business persona to consolidate data sources, generate visualizations, and deduce business insights. It helps you spend less time building the reports and more time analyzing them. Don’t wait to draw actionable takeaways from your findings.

2. Visual Data Engineering. Sorting and vetting data can be a thankless and arduous task… but not anymore. Visual Data Engineer capabilities allows you to automate data pipelines and integrate various data sources almost immediately. It can be used to not only build analytic models, but also be a tool in maintaining the consistency, integrity, and quality of your data.

3. MLOps and Auto MLOps. ML/AI can be instrumental in the creation and integration of automated processes and elevated analytic capabilities (SiriusEdge). It can be used to identify patterns or hazards within your data set, and even regulate quality errors and common keys within your data infrastructure (SiriusEdge). ML/AI take on the heavy lifting of “data” so that users can focus on tasks that require a human touch: problem solving and growth initiatives, among other things.

Dopplr, a platform created by Systech Solutions, employs the use of ML/AI technique so that their customers can either utilize a pre-configured auto ML model from the app library, or build their own according to their analytic and business processes (Dopplr). It allows for the seamless integration of existing products and applications, streaming analytic insights directly into existing software applications. This is extremely powerful, considering that AI/ML projects are becoming a mainstream business need in order to solve everyday problems.

It is currently extremely challenging to translate data science to business value. This can be attributed to a gap in skills, lack of proper native governance ML structures, relevant management systems, and more.

Currently, Dopplr has an MLOps model under development. This solution could help bridge this disconnect through the introduction of DevOps into AI/ML practices (CI/CD). The goal would be to create a seamless, continuous delivery flow of ML intensive applications. This technology would allow users to remain production-ready, automating a great amount of the packaging and tasks throughout the user’s data journey (Dopplr). The lofty objective of Dopplr is to one day drastically reduce the demand for data scientists. They would accomplish this through Auto MLOps to drive impact, as well as through the leveraging of Kubernetes to orchestrate deep learning AI/ML models at scale. Kubernetes abstract some of the infrastructure layer, allowing ML workloads to take advantage of containerized GPUs (Graphical Processing Unit), and standardized data source ingestion and ensures workloads run smoothly across all micro-services (Dopplr). This technology would theoretically allow for a more high-quality model from a data set, as well as a turbocharged set of analytic and business processes (i.e., reduced time to market, skill, busy work, etc.) to produce the same quality of ML models.

4. The Cloud. Cloud technology is a scalable and cost-effective alternative to on-premises that allows you to pay only for the storage or resources that you use. With a dynamic ability to scale based on usage, there is also the added benefit that network connections are less likely to lag. Cloud technology saves you money and can be accessed 100% remotely from any place and time in the world.

5. Security & Governance. An end-to-end data management system allows you to stay protected even when you’re not on guard. It can provide a continuous stream of security updates, and alert you of any suspicious activity.

Big Picture. The data analytic landscape is changing faster than ever before. To stay in the game and thrive in this competitive landscape, businesses need to improve and better their processes each day. End-to-end data management systems are highly appealing due to their ability to identify, define, develop and operationalize the entire lifecycle from data to insights and save more time compared to traditional BI solutions. and save more time compared to traditional BI solutions.

Watch out for more information in the coming weeks to learn how the future of technology is headed towards “App-I-fying” ongoing solutions so that it is entirely encapsulated in a MLOps or Auto MLOps model. The Systech Solutions, Inc. Blog Series is designed to showcase ongoing innovations in the data and analytics space. If you have any suggestions for an upcoming article, or would like to volunteer to be interviewed, please contact Olivia Klayman at

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