How is artificial intelligence – and its prominent discipline, machine learning – helping deliver better business insights from big data? Let’s examine some ways – and peek at what’s next for AI and big data analysis.
DevOps data dilemma: How to overcome analytics challenges
Data analysis proves crucial to DevOps success, but organizations often stumble for three common reasons. Here’s what to watch for and address
DevOps is all about eliminating the risks associated with new software development: Data analysis identifies those risks. To continuously measure and improve upon the DevOps process, analytics should span across the entire pipeline, providing invaluable insights to management at all stages of the software development lifecycle.
From build and pipeline activities to continuous testing, analytics offer visibility into the roadblocks that hold teams back from accelerating delivery and ensuring quality (and visibility into what’s working well). Whether it’s how much time builds are taking, where tests are getting stuck, or what percentage of the build is broken, DevOps pros rely on these qualified insights to influence and drive the SDLC.
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Although many applications and processes can benefit from analytics, many organizations struggle to maintain, manage, and ultimately, capitalize on the vast pool of data they’re generating – especially in the face of tighter time constraints and a skills shortage. As a result, they don’t expand analytics beyond basic use cases.
Let’s examine the top three challenges that often hinder analytics growth in organizations – and how to push past them.
What are the top DevOps data challenges?
Challenge 1: We don’t have enough time to analyze all the incoming data.
With all the data being generated at any given time, organizations need to accept that they can’t analyze it all. There’s simply not enough time in the day – and unfortunately, robots aren’t quite sophisticated enough to do it all for us quite yet.
For that reason, it’s important to determine which data sets are most significant. In most cases, this is going to be different for every organization, so before diving in, determine key business objectives and goals. Typically, these goals revolve around customer needs – primarily the most valuable features that are most important to end users. For a retailer, for example, analyzing how traffic is interacting with the checkout page on the site and testing how it works in the back-end is at the top of the list.
Some quick tips to identify which data is most important to analyze:
- Make a chart: Determine the impact outages will have on your business, asking questions such as, “If X breaks, what effect will it have on other features?”
- Look at historical data: Identify where issues have arisen in the past and continue to analyze data from tests and builds to ensure it doesn’t happen again.
Challenge 2: Silos make it difficult to analyze and communicate about data.
Today, most organizations still operate with different teams and personas identifying their own goals and utilizing their own tools and technologies. Each team acts independently, disconnected from the pipeline and meeting with other teams only during the integration phase.
When it comes to looking at the bigger picture and identifying what is and isn’t working, the organization struggles to come to one solution – mostly because everyone is failing to share the overall data, making analysis impossible.
To overcome this issue, overhaul the flow of communication to ensure everyone is collaborating throughout the SDLC, not just during the integration process.
- First, make sure there’s strong synchronization on DevOps metrics from the get-go. All teams’ progress should be displayed in one single dashboard, utilizing the same key performance indicators (KPIs) to give management visibility into the entire process so they can collect all the necessary data to analyze what went wrong (or what succeeded).
- Beyond the initial metrics conversation, there should be constant communication via team meetings or digital channels like Slack.
Challenge 3: We’re short-staffed and don’t have enough manpower to focus on analytics.
When short-staffed, we need smarter tools that utilize deep learning to slot in the data we’re collecting and reach decisions quickly. After all, nobody has time to look at every single test execution (and for some big organizations, there can be about 75,000 in a given day). The trick is to eliminate the noise and find the right things to focus on.
- Develop scripts and tests to move and validate different pieces of data
- Report on quality based on previously learned behaviors
- Work in response to real-time changes.
While we’re still on the cusp of AI innovation in the DevOps industry as a whole, there are strides being made to overhaul how we approach analytics throughout the pipeline.
Where does DevOps data analysis go from here?
In the next year, we’re only going to see the amount of data we create today multiply. To grow with that data, the industry is increasingly finding itself in need of more mature analytics solutions. As such, expect to see things like AI and ML evolve at an even greater rate, making a bigger splash in the world of DevOps – with more organizations adopting the technologies to help decision-makers be more productive and get the insights their teams need.
Beyond this, DevOps teams will begin to place a greater focus on tools for smart storage, making it easier to store data in a “smart” way so that they can find the data they need for analysis on-demand, whenever they need it. With more visibility into the user experience, businesses will be able to customize more functionalities, generate larger audiences, and ultimately, increase their bottom line.
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