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Pick The Right Architect for Your Project

The increasing complexity of enterprise data architecture deployments makes it more important than ever to have an experienced professional who can act as an architect on every project. Modern data projects now require someone who can be the main person everyone else can come to for solutions and insights. The main reason why things are getting so complicated is that modern cloud deployments are making data deployments more difficult. With the cloud, data has to be more distributed, and you require more complex software to work with it. It makes having that central figure more important to our project’s success.

That central figure is crucial, but which is the right one for your specific project? Let’s break down for different types of architects that you can potentially have on your project.

The Traditional Data Architect

These traditional data architects are usually the ones who define how data can be collected, stored, and used. They essentially create the main architecture that will drive your data project. They determine the direction the business goes in when dealing with data projects. They will also be the ones who control access to that data. Data permissions is an even more significant part of the puzzle now that we have massive corporations and distributed cloud environments. They deal with the governance, as laws are now affecting the way we use and consume data today.

As you can see above, the traditional data architect is the central figure in almost everything an organization does concerning data projects.

MLOps Architect

The overwhelming complexity of machine learning architecture design has now reached a place where MLOps makes a lot of sense for most organizations. When you have such a project, a machine learning architect might be the right person to tackle the complexities your deployment has to deal with. Since machine learning projects are cyclical, the architect has to be flexible enough to ensure that the right strategy is being chosen at every phase. They also have to be able to communicate with the various teams within the machine learning project. That is because most machine learning projects are done by data scientists and machine learning engineers along with the various stakeholders and executives in the company. They also have to Institute a data engineering architecture that will scale to wherever the company needs it to go.

Enterprise Architect

The enterprise architect is responsible for laying down a great foundation to manage information inside what is possible for a corporation’s data needs. These architects are usually working hard to ensure that the corporation is compliant with various privacy laws and regulations.

They are also the ones that set the tone that the workers will have to follow. For example, they will have clear policies on how various people in the organization can use and access data. The enterprise architect is responsible for choosing the best architecture possible for the project. They are also there to ensure that the overall project steers clear of anything that could hinder progress.

The Architect Specializing with the Cloud

Cloud computing has made infrastructure a specialized domain within the data science world. You now need an architect that specializes in managing various aspects of whatever cloud you are working on. For example, Amazon has its own specific services and virtual machine instances that you can work with. You need an architect who understands the back-end architecture of whatever cloud platform the organization has decided to use.

The right data management architecture can be the difference between a smooth sailing project or one that stalls completely. These architects are also responsible for monitoring changes in cloud services and ensuring that these services stay up over 99% of the time. While this position isn’t purely about data engineering, it has a massive effect on whether your project will be completed successfully or not.

Data Driven Decision Making: Going On the Offense Is…

With the data being touted as the new oil, it is more important than ever for the chief data officers inside companies to create a data management strategy that is offensive instead of defense. What that means is that the data should be used to drive marketing and sales, which are the lifeblood of every business. Instead of focusing on defensive things like regulations, compliance, and management, companies can leverage data to improve profits and cut costs.

The role of the Chief Data Officer is now to facilitate the transition from defense to offense inside their organization. However, many people don’t understand this new reality, and they continue to languish behind organizations that just get it. Data driven decisions are the answer to the majority of their business problems, but brave CDOs would have to take the risk to make that transition.

How the CDO’s Role Has Changed

The CDO can be thought of as the overarching authority that executes the data strategy for an organization. They are usually responsible for everything that goes into a data driven organization. That has traditionally meant going through the process of managing, warehousing, and ensuring that the data is clean and reliable. They also focus on infrastructure, but this was a lot more difficult back then because the cloud did not exist. Each individual organization had to have its own data warehousing infrastructure and toolkits.

The ability to perform complex calculations in place did not exist, and you could not have data driven decision making. The way that organizations used data was purely as a commodity that could be a secondary facilitator of growth inside the organization. It wasn’t the main thing, and it is why the changes being made today are so sorely needed.

As the chart shows, things will need to change dramatically, as data becomes a more central part of how organizations can make decisions.

The Traditional Defensive Strategy

Data has gotten more important because of the cloud and the ability for almost anyone to get access to almost unlimited computational power. When that is coupled with the massive amounts of data available today, it makes for an amazing strategy in determining how companies can shape their future by harnessing the power that data provides. It makes it a lot easier for companies to create a data management strategy that is both robust and forward-facing.

However, the traditional model focused exclusively on the bad side of data. Instead of seeing data as a crucial resource, they saw it as a resource that needed to be guarded and cared for. You can think of it as data being gold in a vault that requires a lot of security to protect. This defensive strategy was not conducive to success, which is why that strategy is changing so dramatically. The CDO no longer has to focus only on infrastructure, regulation, and compliance. They no longer have to focus on data management and how it is warehoused.

What It Means to Go On the Offense

The modern Chief Data Officer sees data as a valuable resource that can be used to improve businesses relatively quickly. The focus is now on using data to make better decisions that translate into a better and bigger bottom line. It means using machine learning and AI solutions to drive growth and innovation inside an organization.

While managing data is less important in this new paradigm, it is still a critical component. Offensive strategies focus more on taking advantage of real-time opportunities that pop up. It means being flexible and using data as a critical weapon in your arsenal to generate massive amounts of income.

Massively Scaling AI Projects for Long Term Success in…

Many organizations realize that they need to integrate AI into their entire workflow to be successful. This enterprise AI scaling will have a massive effect on whether they are successful or not. However, there is a right way to scale AI projects, and these companies will have to identify and do those things. AI has taken off in recent years, which has led to many issues for many people in the industry. There is a lot of fragmentation caused by companies not understanding how crucial AI is to their success. Many organizations think that it is enough for them to slap AI code onto a single project and have that be the major growth factor they need to succeed in this industry. That could be further from the truth, and it is something they need to work on for the future.

Scaling AI Needs to Make Sense

The major issue with scaling AI is that many organizations see it as a fad they want to jump on. They don’t do the cost-benefit analysis that many companies need to make smart decisions. They see it as something that they should do because everyone else is doing it. They want to be able to say that they have this sweet feature integrated into their products. However, before you get started with AI in business, you need to decide whether it is worth your time and effort. Enterprise AI must make sense for your specific business needs, or you will be in a world of hurt when implementing it. You will spend a lot of time and money doing something that brings you no benefits. What value are you getting from your AI integration? That is the question that should drive you for machine learning in business to make sense.

Testing to Make Sure You Get Great Results

Did your AI and machine learning tests deliver in the way you expected? It is also a big question that businesses need to start asking when working with AI projects. You need to understand that it is something that should aid your bottom line. It is not enough for your project to be fancy or something else. It will have to make sense for your specific business needs, or you will not profit from using AI in this way. Can you trust the people in your organization to grow your business quickly using the power of these algorithms? It is also something you need to ask, and the answer can have a massive impact on where your business goes in this industry.

Get Workers to Work Together

The final piece of the puzzle when scaling AI is how well your teams can collaborate. Collaboration is key, as you cannot have one team building massively scalable AI systems inside an organization. If you are to integrate AI into all aspects of your business, you will need everyone on board with that decision. They will have to understand how collaborative AI works, and they will need to understand the value of machine learning in business. These things must come together to form a cohesive unit that can get you the results you want. However, these things are just a start, and you will have to do many more things to be successful in the long run.