Many businesses face the challenge of getting the right data to the right people at the right time.

Business systems obtain large volumes of data from various sources constantly. They need to mine key quality data to support critical decision-making. This embodies data management challenges ranging from procurement to high volume storage and transactions to deriving insights while making the process time cost-efficient.

The DataOps paradigm helps gain business agility by transferring and pushing data from various sources into a centralized platform.

Challenges addressed by DataOps

Bad Data Quality

Low-quality data loses credibility in the entire analytics setup. Diverse data formats, data types, and schemas can cause integration complexities and data errors. These include duplicate entries, schema changes, and feed failures that can be difficult to trace and manage. Also, constant updates in the data pipeline need continuous validation, which is time-consuming.

What can Organizations achieve with DataOps?

DataOps helps organizations become more data-driven with emphasis on quality. Business Owners can make informed decisions using validated data, while data Scientists can develop models based on a catalog of information rather than investing in time-consuming Data Mining activities.

Important Considerations

If your teams are working on a hybrid data ecosystem, here are some requirements met by DataOps.

DataOps automates a subset of Operations in the Data Management Process.

A standard DataOps process consists of the following stages and aspects.

At Radiant Digital, we help you overcome them and discover the full potential of your organization’s data. Connect with us to learn more.

Learn more about the Principles, Processes, Framework, and the Transformation Journey for DevOps in our next blog.

by Sri Arepally, Radiant Digital
Practice Director, Big Data

All rights reserved. © 2020 Radiant Digital Solutions

Methodical software development processes can unlock hidden value in your digital transformation projects. A well-implemented Agile framework that incorporates adaptive, evidence-based planning, iterative delivery, and automation can reduce risk, shorten release cycles, and diminish workforce disruption. Effective test automation, including strategies such as Test-Driven Development, Behavior-Driven Development, and Acceptance Test-Driven Development, can increase predictability but also add their own unique challenges.

We live in exciting times where the digital innovations materialized by EDGE Computing, SDN, and IoT applications translate into significant business use cases. By 2025, the Edge Computing market is expected to be worth $3.24 billion, with 26 billion smart devices generating at least 500 ZB of data a year.

Everything is being transformed by these technologies ‘ high-powered computing and networking capabilities, from the administrative processes in factories to the power grids in Smart Cities.

Once the business case for Robotic Process Automation (RPA) implementation is approved and a decision is made to move forward, the next step is to understand RPA platforms available and find the right partner who can assist in the transformation. As discussed in the first article in this series, the combination of technological and business factors will help select the RPA platform.

The following are key features to keep in mind:

The continuous digital revolution calls upon enterprises to abandon the crumbling, almost ready-to-expire legacy systems and replace them with more agile modular applications and database management systems.

New-world programming is an enterprise norm today that delivers cost-efficiency and faster time-to-value. Modern-day applications provide better support for multi-device compatibility, resource reusability, cross-platform functionality, and seamless integration with other business systems.

With the increasing complexity of modeling frameworks and their relevant computational needs, organizations find it harder to meet the evolving needs of Machine Learning.

MLOps and DataOps help data scientists embrace collaborative practices between various technology, engineering, and operational paradigms.

MLOps is a set of practices that infuses Machine Learning, DevOps, and Data Engineering practices for a reliable and data-centric approach to Machine Learning systems during production.

In our previous blog, “Enterprise BPM Transformation – Embrace the Change,” we discussed the transformational capacity of BPM and how it helps organizations gain better visibility that translates into higher productivity. Many companies invest in tools like Lucidchart, Visio, Modelio, Pega BPM, ServiceNow BPM, etc., based on their diverse and specific business needs. While each of them has benefits and limitations, we at Radiant highly recommend jBPM.

In our previous blog, The Fundamentals Of MLOps – The Enabler Of Quality Outcomes In Production Environments, we introduced you to MLOps and its significance in an intelligence-driven DevOps ecosystem. MLOps is gaining popularity since it helps standardize and streamline the ML modeling lifecycle.

Data is a crucial digital asset for any individual or organization in their decision-making journey. According to IDC, by 2025, global data will grow to 175 zettabytes. This explosion in data from multiple sources like connected devices requires deriving valuable insights to make smarter data-driven decisions. Data Science helps enterprises understand data better and optimize its utilization for time-consuming and expensive processes.

In the UX design world, the contributions of designers and developers are equally essential to shaping its success. Good design is not always about the product but the process leading to it. As experts say, when developers and designers work together, magic happens. But this magic can manifest into great designs only if the existing collaborative gaps between these two groups of experts are closed sooner than later. Designers should not limit themselves to sharing specifications, assets, interactions, mockups, etc., with developers.

Ways to Improve communication

Experts recommend the following tips to improve designer-developer communication.

Implementing collaboration tools 

Developers often wait on the design team to deliver sample designs and then proceed with the development. This method is simply not practical. Tools like Zeplin and InVision improve collaboration efforts with better transparency and direction. In addition, collaboration tools can be used in different phases of design development. Here are some excellent tools both designers and developers can use.