Using AI for customer service in your company is a definite method to save time and money. If you’re like most business owners, you’re constantly searching for fresh, creative ways to improve your enterprise. We’re here to inform you that improving AI customer service is a simple and rapid win.
If you have a team of software engineers and want to move them to Mac, you will need to consider a number of things before you do so.
How different are DevOps and DataOps? Are we splitting hairs or hairstyles? Both are aligned to Agile principles and have a number of processes in common, but they have some key differences, too. Both are also very complex and aim to solve mostly different sets of challenges while also working to improve team cross-functionality. So, let’s take a look to get a better understanding of both.
DevOps covers a tremendous amount of ground as it’s relevant to the entire Software Development Lifecycle (SDLC). As we’ve defined it previously,
DevOps entails Agile practices, automated tools, and the cultivation of a team mindset to synthesize software development, quality assurance, and operations into increasingly cross-functional teams to increase the speed, efficiency, and quality of software development relative to both team and software business objectives.
Complex, yes. If we were to simplify, “DevOps is a mindset to continuously improve everything about how everyone does software development.” The faster and better we can develop software, the more profitable our teams will be, as we discussed in How Will AI Transform DevOps?
Even Puppet, which produces the annual State of DevOps reports, acknowledges that DevOps is still somewhat nebulous. The case can also be made that DevOps has a vested interest in activities that take place even before the SDLC begins – where the hiring or outsourcing of developers is concerned with developer skill has a major impact on the SDLC. And, it can also be said that DevOps’ goal of cultivating cross-functional teams readily lends to DevOps being an implicit element of nearly every job and role.
Continuous Integration, Delivery, and Deployment are DevOps practices that significantly reflect upon the maturity of DevOps on a team and company level. Each extends the range of automation across the SDLC to reduce the need for human intervention (i.e. merge approvals). The more a process depends upon humans to do it, the more inefficient it is likely to be.
A relatively new practice, DataOps has emerged from a set of best practices to become a discipline in its own right beginning around 2014 but has really picked up steam since mid-2018. DataOps is very different from DevOps even though it has several things in common. We see the definition of DataOps as:
DataOps entails Agile practices, organizational structure, statistical process control, and automated tools to continuously improve the quality and speed of the entire data lifecycle to align with business goals, improve operational efficiency, and facilitate better decision-making.
Or, to simplify, DataOps is about continuously improving the value of the data we use. That value includes issues like quality (accuracy and completeness) and is roughly proportionate to how fast we can:
DevOps and DataOps share many of the same principles and processes, but also have some key differences. For starters, it’s nigh impossible to practice DataOps without automation. While far from optimal, you don’t necessarily need automation to practice DevOps. It’s far easier to build teamwork than it is for a team to manually process Big Data.
Plus, where DevOps testing focuses almost entirely upon code, DataOps needs to test code and data in equal proportion. Just as a single typo can throw your software out of whack, it can sabotage an entire data library leading to erroneous reports fueling bad decisions.
The simplicity of this process belies that DataOps can involve 18 or more distinct steps:
|
Define |
Ingest |
Storage |
Analyze |
Repair |
|
Clean |
Prepare |
Augment |
Explore |
Compare |
|
Predict |
Prescribe |
Model |
Validate |
Deploy |
|
Refine |
Observe |
Destroy |
||
Much of the additional complexity involved with DataOps is wrapped around two components:
Data Sandbox Management involves defining the data sources, tools, services, and vendors are to be used for a DataOps project. It’s similar to how developers set up their own isolated environments based upon project specifications for selecting software languages, libraries, tools, and services. But where developers tend to use a few dozen components, DataOps may need to synthesize hundreds of data sources using a wide variety of tools and services.
Data Orchestration is the automated process of bringing all of this data together from so many different sources and making it more useful so people can use it. This would be impossible without automation to address the challenges associated with the Voluminous V’s of Big Data (Volume, Velocity, Variety, et al).
These sources often involve third-party APIs, but could also entail a variety of platforms, periodicals, and reports using a very wide range of electronic file formats. Not all DataOps projects are so verbose – but data collection efforts tend to expand over time.
Have you checked out Apple TV+’s Foundation? It’s based on a series of stories by science fiction writer, Isaac Asimov. In it, Hari Seldon, a mathematician and the developer of psychohistory uses algorithmic science to predict the collapse of the Galactic Empire.
DataOps is even better than psychohistory, with sufficient data we can prevent the collapse of the Galactic Empire. Or. Well… Maybe we can use DataOps to prevent the Empire from forming in the first place.
