Why Use Continuous Intelligence in DevOps/DataOps Why Use Continuous Intelligence in DevOps/DataOps
If you’re considering DevOps as a strategy to adopt continuous innovation, your data strategy has to evolve, too. Traditional BI has... Why Use Continuous Intelligence in DevOps/DataOps

If you’re considering DevOps as a strategy to adopt continuous innovation, your data strategy has to evolve, too. Traditional BI has too many silos and too much human intervention to support your move to an agile system. Businesses that nail continuous innovation will stand out. Those who don’t will put out shoddy products. Your data strategy, therefore, has to be seamless, frictionless, and automated. In the past, that was difficult, if not impossible, but with elegant deep learning models, we can process our data and automate it for insights around the clock. It could be time for Continuous Intelligence. [Related Article: 5 Mistakes You’re Making With DataOps]

What is Continuous Intelligence?

So much of our data is unstructured, which has been difficult for humans to process. We’re creating a ton of data and businesses have access to it now, but setting up a system to glean insights is tricky. How do you label it consistently? How do you write programs and models that process the data without having to chase down every rabbit hole? How do you keep your team free enough to work through the meat of insights without leaving potentially valuable insights on the table? AI deep learning models are answering these questions now. They’re capable of continually combing data looking for patterns as the data updates. It’s not torturing data. Instead, the machine is learning with every new minuscule addition without human intervention. Your team has access to these insights to direct new inquiries and drive brainstorming, pivot during sprints, and reach a frictionless state in which data flows in and insights become the next iteration of a product—or a new product altogether. Data involves risk. Putting humans in charge of processing that data is a recipe for mistakes. We’re great at innovating. We excel at problem-solving. Our deftness with details, however, is a weakness. That makes it difficult when you’ve got thousands or millions or billions of bits of data coming in, structured and unstructured, images, text, and video. Continuous intelligence allows you to analyze this data accurately and in real time.

How it Informs DevOps and DataOps

Real-time, deep data analytics gives businesses a competitive edge because no matter how big you are, no matter where you are in the product cycle, you can pivot. You can’t wait a few years or months to change your product based on user response. You can’t even afford to wait a few weeks. With the prolific innovation of Silicon Valley and every other city and startup looking to dethrone the ultimate tech space, your customers will walk if you don’t respond. Brand loyalty isn’t a thing anymore. Very few companies have managed to maintain traditional brand loyalty, and even then, they do it with continuous innovation. Those next big ideas all come from accurate, frictionless insights from the data you have today. Continuous intelligence has one goal: fix problems fast, faster than your legacy systems, faster than your silos. It unifies analytics, monitoring, and reporting for a more transparent process and less downtime. You’re deploying faster with open feedback loops.

Where That Leaves Business

If you’re moving towards operationalization of your data, you’re already on a path to adopt Continuous Intelligence. Hiring the AI expert to build your models for deep learning that runs at cloud scale gets you part of the way there. The other piece could be letting go of data wrangling as you know it. Until you’ve deployed CI, wrangling remains a huge and functional part of your data management plan. Eventually, however, you may have to let it go. Businesses are going to need analytics that spans time to look for patterns from the past and apply predictive analytics in real time. Your product iterations flow directly from this real-time data, giving your team a direction for your innovation and a chance at lightning fast problem-solving. [Related Article: What are MLOps and Why Does it Matter?]

Moving to a CI Model

Data isn’t a single-use event. It’s integral to the health of your business decisions. Data capable organizations will refine data continuously, adapting to a swiftly changing reality with each new insight. Gartner identifies six defining features of CI. Keep these in mind as you shift your worldview:

  1. Fast: Real-time insight keeps up with the pace of change in the modern age.
  2. Smart: The platform is capable of processing the type of data you get, not type you wish you had.
  3. Automated: Human intervention is rife with mistakes and wastes your team’s time.
  4. Continuous: Real-time analytics requires a system that works around the clock.
  5. Embedded: It’s integral to your current system.
  6. Results focused: It should go without saying, but data means nothing without insight. Your program should deliver those insights. Don’t forget the results in the search for more data.

Once you let go of batch processing and silos, moving towards an agile framework is a reality with CI. These always on and always focused learning models give your organization the power behind continuous innovation. It’s time to shift your thinking to this model of business intelligence and let real-time data set the pace.

Elizabeth Wallace, ODSC

Elizabeth is a Nashville-based freelance writer with a soft spot for startups. She spent 13 years teaching language in higher ed and now helps startups and other organizations explain - clearly - what it is they do. Connect with her on LinkedIn here: https://www.linkedin.com/in/elizabethawallace/