How do you streamline a data infused process?

by Neil Berry

02 Jul '21

How do you streamline a data infused process?

 

 

Say you have a data infused process currently driven by 400 people. Could you streamline it to 40 or even 4 people?

 

 

How could you automate every step, change every part in a controlled and governed manner ensuring that you can continue to scale and develop this over time as data, technology, usage, consumption and deployment needs evolve?

 

 

A discussion has been rumbling on for a while now that seeks to identify how to improve customer experiences and operational inefficiencies. In recent years the hardware capable of achieving these goals has become more affordable through commodity hardware and cloud service platforms and the application of Data, ML and AI Ops powered by Robotic Process Automation (RPA) tooling.

 

 

Technology like the growth of the open source movement, the associated Open stack, development and general business practices alongside architectural thinking mean these visions of a more streamlined and efficient process can become a reality.

 

 

The landscape now is one of an open stack with inter-operability and connectivity that leverages appropriate technologies for data workloads, scale, types, pace, variability and value of data assets that flow through a business value generating foundry.

 

 

The streamlining process will be achieved by designing and executing next generation analytics solutions using composable service platforms that tightly integrates into an Open stack that loosely couples with adjacent, complementary and potentially competitive technologies.

 

 

This is all part of a continuous process that will likely never actually complete; if anyone ever tells you that they have finished developing, then it will have been the wrong answer in the first place; it will have evolving and transient capabilities that mature and retire when the timing is right and your needs require a different approach; it will operate a progressive replacement / migration strategy to manage these transitions when the timing and capability mean that it is the right thing to do.

 

 

In all areas of life things you bought start to lose value over time. As they become slower and less powerful than the latest version they become obsolete or outdated for the needs and wants of the current day.

 

 

By working with data, analytics, cognitive, technical and services specialists in a Centre of Excellence across an end to end data pipeline starting with the data creation personas of customers, employees and machines who author and generate new content and media across a gamut of ingestion capabilities the imaginable is now possible.

 

 

Through the transformation, analysis and management/persistence of data at rest and in motion – and everything in between; at scale, at pace, of varied types and formats/availability, across geographies and regulatory/cultural boundaries without invoking penalties or legal proceedings for breaches of trust, confidence or security the imaginable is now possible.

 

 

All the way to deployment and integration with services and applications that use only the data that they need, in the availability windows that they require for the consumer or colleague to be satisfied with the data driven experience that they have chosen to invoke.

 

 

The analysis of this data will be focused on delivering business outcomes and goals to help create and / or generate value using descriptive, predictive, prescriptive and cognitive techniques. From these engagements, solution designs based on replicable patterns will see project teams turn what were once pipe dreams into reality; from individual algorithms to simple and complex ensemble models applied to unique and industry-wide specific problems.

 

 

This will be done in support of human processes that enable the often-maligned data management / analytics / data science team’s impacts to capture the value that has been wasted so many times in the past when they have been told their job was done after an algorithm was created, or a plan was put in place, but not executed through to deployment, operation and evolution. The approach ensures that the business can be ready for the next development both in technology or its needs.

 

 

It will require laser like focus and understanding on how these algorithms can be deployed into production to create the most value; ultimately producing algorithms that can be embedded into technologies, processes and applications alongside the most important assets in the process – the people.

 

 

The age of a single data target provided by a single vendor as the foundational hub for all workloads, data-types, processes and usage scenarios was never going to work.

 

 

It’s never been fit for purpose, it could not and will never be. The requirements are always too variable and ever-changing.

 

 

The age of true digital innovation is here and it is possible to see that the technology is finally maturing to make our ideas real and Rockborne’s consultants are ready to shape pioneer this change within the Data & Analytics industry.

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