Robotic Data Automation (RDA): Top 5 emerging opportunities for CXO/IT leaders

September 21st, 2021

As per Gartner, Hyper Automation is the top strategic technology trend for Enterprises. “The shift towards hyper automation will be a key factor enabling enterprises to achieve operational excellence, and subsequently cost savings, in a digital-first world,” said Cathy Tornbohm, Distinguished Research Vice President at Gartner.

Businesses want to enable employees to make better decisions in the most cost effective way.

Robotic data automation (RDA) is a modern data-driven automation technology that is specifically designed to bring data, analytics and automation technologies to have better Business impact, save costs, boost productivity and gain efficiencies. It helps the Enterprises better leverage on the petabytes of data (raw, structured, unstructured) collected and generated by today’s digital enterprises. RDA is a critical piece to accomplish Hyper Automation (end-to-end Automation). 

Here are the Top 5 strategic benefits that RDA can deliver that have both bottom line benefits as well as providing a competitive edge through data informed decision making capabilities.

1. Harness enterprise wide data to gain competitive advantage: Modern IT environments are characterized by hybrid environments, data explosion and a mix of modern and legacy tools. IT leaders are struggling to leverage these existing data and tools to bring into an analytics driven ecosystem for gaining real-time insights and driving smart operations. As IT environments grow, it becomes challenging to effectively integrate and consume data from all of these systems, which often have complex data models, data delivery schemes, APIs and archaic & non-standardized interfaces. With RDA, you can completely automate the data ingestion, preparation and integration activities and accelerate your journey towards analytics and AI driven IT operations or AIOps. This way RDA helps you maximize return on your existing data and tool investments. IDC predicts that “By 2023, 50% of Organizations Will Adopt a “Data Supermarket” Strategy to Unify Data Storage, Access, and Governance Capabilities to Deliver a Consistent Data Experience and Maximize Value of Data”

2. Bridge Data/ML skills gaps using Low Code Platform: Numerous studies have shown that skill set gap is one of the key barriers for successful adoption of AI/ML in enterprises. RDA is designed to bridge the skills gap and enable any IT enthusiast or practitioner to embark on an AI/ML journey without being a data scientist or programming expert. RDA makes it easy to consume and analyze any operational data and apply ML using a rich set of bots providing a consistent interface. RDA provides low code, simple natural language and bots based data pipelining to solve day to day ML and IT tasks activities. The following example of 3 lines of code with bots get past 30days incidents data from ServiceNow and logically group them together (i.e clustering) to identify problem areas. “While low-code application development is not new, a confluence of digital disruptions, hyperautomation and the rise of composable business has led to an influx of tools and rising demand,” said Fabrizio Biscotti, research vice president at Gartner.

3. Achieve End-to-End Automation (Hyper Automation): The holy grail for all IT leaders is to achieve intelligent and end-to-end automation. There are many automation systems that are addressing domain specific and task specific activities such as Ansible, Chef, Puppet, RPA tools etc. but there are two missing pieces with current tools and approaches. 1) Automated decision making using A/MLacross various IT functions and 2) Putting end-to-end workflows/pipelines to make quality data available for the AI/MLengines. Both can be accomplished using RDA to enable IT leaders to accomplish the dream of end-to-end automation. “Organizations are transitioning from a loosely coupled set of automation technologies to a more-connected automation strategy,” said Cathy Tornbohm, distinguished research vice president at Gartner.

4. Industry 4.0 readiness with Edge & IoT: With the advent of 5G and proliferation of IoT, the boundaries of IT are expanding. There is a need for analytics to be done closer to the edge data sources, and also compose distributed analytics from multiple regions and domains. RDA provides an easy way of doing edge analytics and composing distributed data analytics pipelines with its bot network and data fabric. RDA data fabric connects bots from multiple regions and sites seamlessly to make distributed analytics a reality. “Currently, around 10% of enterprise-generated data is created and processed outside a traditional centralized data center or cloud,” says Rao. “By 2025, Gartner predicts this figure will reach 75%.”

5. Accelerate AIOps & Observability:
AIOps and Observability are leading IT Ops analytics and monitoring technologies. But a lot of data plumbing and the high touch nature of these technologies are making it hard to realize time to value faster. RDA automates most of AIOps & Observability pipelines to make these technologies cheaper, better and faster. RDA has successfully reduced AIOps implementation times more than 50%. Complement your IT force with these RDA Bots, you augmented worker force! “Given the complexity of integration, data processing, and data dependency to get the right data to the AIOps to make it more efficient, unless this is fixed, AIOps is not going to produce dramatic results. DataOps and self-service automation tools are the keys to solving the AIOps data problem, Andy Thurai, Principal Analyst, FieldCTO”

Get ahead of the game,  RDA can help transform your enterprise to modern and data driven!

References:

Malay Verma

Malay Verma is the Co-founder of Adept Innovationz LLC. Over the last 3 decades, Malay has held various leadership positions with companies like Zensar, Wipro, FPT, IBM to name a few. Malay is on the Advisory Board of CloudFabrix. He is a thought leader and has written many blogs and articles on smart and connected devices, IoT and edge computing and innovative sourcing.

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