DataOps – Levelling Up your Business Operations and AI Journey

New Island Technologies
September 13, 2023
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This is a story about DataOps and AI in Business.

Innovation often happens in the dark corners of your business.

These days, everybody wants to tinker in the AI & ML pot…the hype is purportedly over and now reality will kick in. The latest Gartner Hype Cycle tells the story…or at least part of it.

Source: What’s new in Artificial Intelligence from the 2023 Gartner Hype Cycle

There was great excitement over the past few months with everybody imagining the new superpowers donned unto data. Innovation was triggered and ideas were quickly formulated into business value propositions.

OpenAI’s ChatGPT was largely credited for this sudden explosion of interest in AI. I myself jumped onto this and started to experiment and use it to varying degrees of success.

Google Bard, OpenAI ChatGPT, Speechify, Microsoft Designer, OpenAI DALL-E2 and others started to form part of my experiment and toolbox.

This is not intended as a review of these tools, but a few things stand out for me after using it for a bit. (Go ahead and ask ChatGPT and it will tell you the same – PS this is my personal view)

Data can be biased by the system and the machine learning processes applied
Data cannot be easily validated or corroborated with supporting studies and facts
No material references are directly available
Data can be factually incorrect
You can tell the system it is wrong and suddenly get an opposing answer
The image generators cannot do words
If you use it as a tool and not blindly trust the results, it can be of huge value
The open access and availability of AI and ML models to business users create both risk and opportunity for businesses.

You do not want to stifle innovation within the business but still need to maintain control to protect the business.

Data is all over the business in different pockets and not always unified.

Source: Forrester, Enterprise Data Fabric Enables DataOps, August 2, 2021

This is where good DataOps practices and BMC’s Control-M come in…

Let’s first understand, what is DataOps? According to Gartner, “DataOps is a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and data consumers across an organisation.”

If you refer to the DataOps Manifesto, you will find that many of the practices described in the manifesto can be delivered or enhanced by the powerful integration, automation and orchestration capabilities of BMC’s Control-M.

Control-M in relation to the DataOps Manifesto…

The DataOps Manifesto outlines 18 key principles. Control-M has a vital role to play in each of these principles. (And don’t make the mistake of thinking it is only applicable to the “Orchestration” Principle)

Continually satisfy your customer: Control-M can be used to automate the delivery of analytics to customers, ensuring that they always have access to the latest insights.
Value working analytics: Control-M can be used to track the performance of analytics and identify any problems that need to be addressed.
Embrace change: Control-M is a flexible platform that can be adapted to changing business needs.
It’s a team sport: Control-M can be used to facilitate collaboration between data scientists, data engineers, and other stakeholders.
Daily interactions: Control-M can be used to automate the communication and collaboration between team members, ensuring that everyone is always up-to-date on the latest developments.
Self-organise: Control-M, through its self-service capability, can be used to empower the team to make decisions and take action without having to wait for approval from a higher authority.
Reduce heroism: Control-M can be used to distribute the responsibility for the success of the team among all members.
Reflect: Control-M can be used to track the performance of the team and identify any areas where improvements can be made.
Analytics is code: Using the Automation API and the Python Client through its jobs-as-code approach, Control-M can be used to treat analytics as code, which makes it easier to manage and maintain.
Orchestrate: Orchestration is obviously the mainstay of Control-M. Control-M can be used to automate the data pipeline, freeing up the team to focus on more creative and strategic work. Control-M does not stop at the data pipeline, it covers the end-to-end orchestration of all your data, application and business process flows.
Make it reproducible: Control-M can be used to track the lineage of data, making it easier to reproduce the results of analysis. Using inherent capabilities to set policies and standards, Control-M supports reproducible artifacts and reuse of existing data and code assets.
Disposable environments: Control-M can be used to create disposable environments for development and testing, which helps to prevent contamination of production data.
Simplicity: Control-M is a simple platform to use, which makes it easy to get started with for technical and business users alike. It offers a unified platform for integration and automation of jobs across many different mainframe, distributed and cloud technologies.
Analytics is manufacturing: Control-M can be used to treat analytics as a manufacturing process, which helps to ensure that it is delivered in a consistent and reliable way.
Quality is paramount: Control-M can be used to automate the quality checks of data, ensuring that it is clean and accurate before it is analysed.
Monitor quality and performance: Control-M can be used to monitor the quality and performance of the data pipeline, helping to identify and fix problems early on. This includes SLA monitoring of critical business processes with an ability to forecast completions times, thus keeping the business informed all the time.
Reuse: Control-M can be used to reuse as much code and data as possible, which helps to save time and resources. Not only does it help make the data providers more accessible, but it also helps adhere to good SDLC practices to store and reuse automation job and integration artifacts within the Control-M platform.
Improve cycle times: Control-M can be used to automate the data pipeline, which helps to improve the speed and efficiency of delivering insights.

As you can see, BMC’s Control-M offers a wealth of capability to enable and evolve your DataOps practices and data-driven business goals.

Overall, DataOps can be a valuable tool for improving the success of AI and ML initiatives. It can help to improve the quality of data, automate the data pipeline, improve collaboration, and improve the reproducibility and monitoring of results.

If you would like to explore the benefits of using BMC’s Control-M in your organisation, you can contact André Esterhuysen at New Island Technologies.