AI Architecture: Modern Data Management

Avatar photo Team Zaloni December 16th, 2019

Data management for a modern AI architecture

96% of companies are still using legacy systems. Take a second and let that sink in. You’re probably in that group. What do your legacy systems consist of? A strictly on-premise data warehouse? A single-system application instead of micro-services? Whatever you might be using, think of the new use cases and possibilities updating your data environment to a modern AI architecture would provide.

In a recent survey by our friends at New Vantage Partners, they interviewed senior executives from Fortune 1000 organizations and built a report detailing the state of big data and artificial intelligence (AI) for the year moving forward. Their results highlight a growing divide between executives that want a data-driven culture and the employees that need to adopt it. You can get a free copy of the report from us and below are our takeaways.

  1. Lack of adoption for modern data architectures
  2. Data-driven culture challenges
  3. What does the CDO do?
  4. Offensive vs. defensive investing
  5. Slow production of AI

Takeaway 1 – Lack of adoption for modern data architectures

Only 6% of companies are leveraging a modern data architecture in their big data environments. This means the vast majority aren’t utilizing the power that the cloud provides nor will they be able to scale well as the business grows. One is often required to effectively use next-generation applications and cloud environments.

Take a look back at the first stat we shared – 96% of companies are still using legacy systems. This ties in directly with the lack of adoption of a modern data architecture.

Takeaway 2 – Data-driven culture challenges

The idea of a data-driven culture has usually been a supply-driven approach. As in, if the data is provided then the organization will automatically be data-driven. The problem lies with the people and process challenges.

Try drinking out of a firehose and see what happens. That’s the exact metaphor that some data analysts are dealing with when trying to gain insights from the volume of data they’re presented with. On top of that, there are often skills-gaps that need to be filled and many people are unwilling or don’t have the bandwidth to learn new skills.

Takeaway 3 – What does the CDO do?

As a response to the ever-growing demand of being data-driven, many companies hired a chief data officer (CDO). Some with great success, most without. In fact, only 29% of respondents reported that the CDO role was successful. Why is this the case?

Often, deciding who reports to who, which departments own which data, lack of data visibility, and just lack of accountability can be insurmountable obstacles for these hires to overcome. It’s not that they’re unqualified, it could just be the system is against them from the start.

Takeaway 4 – Offensive vs. defensive investing

Putting money towards big data initiatives and projects is almost a universal given in the companies of 2020. The shift has been in what that money is going towards. Over half (54%) are now focusing their data efforts on projects that are revenue-generating and business transformative. The rest are investing in regulatory and compliance-meeting projects.

The interesting part of this data point is that all of the companies surveyed are part of a regulated industry. This means that generally, a larger focus is being placed on creating new revenue rather than compliance.

Takeaway 5 – Slow production of AI architecture

Companies are investing more and more into emerging technologies as over 91% have put some portion of their budget towards artificial intelligence-backed projects. However, only 14% have widespread deployment of AI architecture. Why such a drastic difference?

We should expect to see the widespread number increase in the coming years since over half of the survey respondents (51%) have AI deployed in limited production. My guess is they’re testing the waters and determining whether or not to continue investing. Once the use case gets proven and AI is fully embraced, more companies will be expanding their deployments.

What now?

If you haven’t yet, download your copy of the report. You’ve seen where the industry is headed. What companies are investing in. Does this align with your own goals? Are you on track to meet them this year? Are you ready to meet the difficulties you might face?

Luckily, Zaloni understands the challenges and we believe in a stay-and-play approach to data management. You can keep all your current tools (legacy and otherwise, we know you have them) and catalog and control your data wherever it resides without having to create the elusive and often difficult modern ai architecture. You don’t have to move your data to a central location for storage and processing.

With an extensible, flexible, and scalable data platform like Zaloni’s Arena, you can leave your data where it is and actually use it for better analytics while making sure it’s protected, secure, and industry-compliant. Zaloni’s platform provides a self-service catalog where users can easily find and use relevant data helping to improve enterprise adoption and empowering a data-driven culture. We help you meet the skills gaps that are keeping companies from fully embracing their data and invest in emerging technologies for our platform, so you don’t have to.

AI Architecture

Interested in seeing how Zaloni can help you meet your goals and help you become fully data-driven? Schedule your custom demo today!

about the author

This team of authors from Team Zaloni provide their expertise, best practices, tips and tricks and use cases across varied topics incuding: data governance, data catalog, dataops, observability, and so much more.