The State of Data Engineering in the Future

The Evolution of Data Analytics in the 21st Century

The objective of data in the corporate world is to help people make better decisions. And, of course, we use analytics to do so. Business Intelligence (BI), which originally only permitted reporting on past transactions, has grown into a diagnostic and descriptive analytics discipline that can operate on vast amounts of data and at the pace of business thanks to the power of cloud computing. Machine learning and AI also allow predictive and prescriptive analytics to precisely estimate sales, understand and nurture their most valuable clients, and more. In other words, it can assist businesses in generating income and remaining competitive.

It's no surprise, however, that 95% of organizations questioned believe AI is vital in their digital transformation efforts, according to 451's new research, "Voice of the Enterprise: AI & Machine Learning Use Cases 2021." It's also not strange to me that reality falls short of expectations.

Only 21% of the almost 5000 organizations studied have used AI, many AI proof-of-concepts never make it to production, and up to 70% of companies claim little benefit from AI investments.

However, operationalization has the potential to change these figures.

Why Is Operationalization Important?

Let's start with the granddaddy of all Ops, DevOps, to show why operationalization is important. Agility, communication, collaboration, alignment, reliability, and breaking down silos are all things that DevOps means to a lot of individuals. These are some of the many advantages of operationalization.

ML models are frequently generated in silos without DevOps-style operationalization. Disconnects between the data engineering solutions developing the model and the IT team deploying it, as well as the business team dealing with the problem, might exist. Due to an inability to test and deploy revisions on a continuous basis, your data science team may be working on perfecting something without significant business feedback. And what happens if it's not perfect when it's put into production? You'll be unavailable for weeks if you're not operationalized.

MLOps formalizes this discipline and ensures that your model adapts to change without requiring you to stop, redo, and restart.

And, while operationalization provides reliability and agility, XOps is defined by the automation and monitoring that support operationalization. Automation and monitoring bridge the gap between what people know and when and how they know it, bringing a sense of harmony to the cacophony that exists between business, development, and operations.

Why DataOps Are Necessary for MLOps and XOps

While operationalizing your models is a good start, DataOps is a game-changer for machine learning and MLOps efficacy. It's the same for every other Ops discipline. DataOps is required by all of them, from CloudOps to SecOps to DevOps.

We can look at ML/AI once more to see why. The more data a machine learning algorithm has to work with, the more precise its outputs will be. However, the benefit of AI, machine learning, and analytics is only significant if the data on which they work is valid throughout the ML lifespan. Sample data is required for exploration, test and training data is required for experimentation, and production data is required for evaluation. Traditional data engineering service could perform data quality checks to guarantee that only the cleanest data entered the models, but the pipelines were fragile. This method is extremely dangerous due to the scope and complexity of today's dynamic data systems.

It's the same with all of the Ops disciplines, as they all require intelligent data pipelines. Smart data pipelines must be operational at all times, not only when they are being built. As a result, we arrive at the three XOps success principles.

How DataOps Contributes to XOps Success

Every Ops discipline demands continuous data, and DataOps is required to give that data. Continuous design, continuous operations, and continuous data observability are the three main characteristics that enable continuous data transmission.

Your data team will be able to simply start, develop, and collaborate on data pipelines on an ongoing basis thanks to continuous design. They accomplish this with a 10x reduction in wasted time and a 50x reduction in downtime. It's intent-driven, which allows data engineering solutions to concentrate on what they're doing rather than how they're doing it. Continuous design is componentized to allow as much reuse of pipeline fragments as possible. Finally, each design pattern has its own unique experience.

Continuous operations enable your data team to easily deal with outages, migrate to new cloud platforms, and adapt to changes, whether they are outages or business needs. It enables automated deployments with pipelines orchestrated across any mix of on-premises and cloud infrastructures and platforms. Most significantly, these data pipelines are decoupled to the greatest extent feasible – both within and across pipelines, as well as from origins, destinations, and external processes. The more decoupling you do, the easier it will be to alter.

Continuous data visibility aids the data team in comprehending the data's contents as well as adhering to governance and compliance regulations. With a single, always-on Mission Control panel, it eliminates blind spots. Understanding data is invaluable, as it is required for digital transformation and innovation.

Data's Future: It's About the Means, Not the Ends

All of the properties of data in the future will be emergent. And what I mean is that you will gain a macro grasp of your data by tracking emerging patterns in how people use it as it evolves. The "ends" on the business value aren't the product of a top-down approach in which a group of well-intentioned specialists gather to decipher the meaning of data and tell you how to set up data pipelines a priori. Instead, self-organizing patterns emerge as a result of collaboration among all of these autonomous micro players. Adherence to the same set of standards and principles is crucial to this collaboration.

And finally, if you're a data consumer, you should demand operationalization. Deliver operationalization if you're a data engineering services provider or data engineer. Data will only become the lifeblood of company if this happens.