Data pipelines are outgrowing our ability to manage them — and that’s bad for business. By using AI to simplify and automate performance improvements, applications will become more reliable, productive, and cost-efficient, both on-prem and in the cloud.
We’ve come a long way since Excel Spreadsheets first introduced us to the benefits of data organization, visualization, and processing about 20 years ago today, every enterprise is a data-driven business.
Modern data applications, including machine learning, business intelligence (BI), and the internet of things (IoT), are responsible for a fundamental change and shift in value, and companies employ this data to create new products and improve operations. Moreover, organizations are increasingly leveraging real-time streaming data to support business-critical applications in industries like healthcare, finance, tech, and manufacturing. Common apps that leverage real-time data include fraud detection, social media sentiment analysis, recommendation engines, and predictive maintenance.
Unfortunately, because of the complexity of these sophisticated apps, many organizations are unable to manage them successfully. The modern, distributed app architecture includes many moving parts, which ingest massive volumes of data. When data applications slow down or fail, it’s extremely difficult to figure out which component is the root cause of the problem. When something goes wrong, a data expert has to dig through raw log files, multiple tools, and organizational team silos to identify issues, and then go through a long process of trial and error to fix it and finger-pointing to fix it. This process can take several weeks for a single app issue.
Although many things have changed over the past decade, one issue remains the same: we are still trying to solve the same fundamental problems we aimed to address with spreadsheets, except now we have a web of specialized systems and intricate networks of data pipelines that connect them all together. So, what’s next?
What Role Does the Cloud Play Here?
Organizations are re-architecting enterprise infrastructures to focus on long-term data growth, flexibility, and cost savings as a result of the rapid increase in data volume, sources, variety and most importantly, Value. Current, on-premises systems have proved to be too complicated, inflexible, and are not returning the expected value, and instead, organizations are looking to cloud services to accommodate modern capacity requirements and elasticity (like Azure, AWS, and Google Cloud).
Driven by use cases such as video streaming, modern data workflows are expected to be flexible, and process higher and lower traffic loads synchronously pending user needs. Cloud deployments are optimal for these resilient use cases due to its agility and efficient scalability. For example, an e-commerce app that has surging usage around the holidays or an accounting app that is mainly used at the end of a quarter. You can quickly spin up extra compute resources to support these apps when you need them then de-provision those resources when you’re done.
Unfortunately, the persistent cost and complexity issues remain. Organizations are challenged by unexpected costs of running their apps in the cloud, especially when a ‘lift and shift’ approach has been applied. A lack of data and insights about the dependencies and requirements of your app workloads can mean the difference between success and failure of cloud migration projects. Organizations need to leverage the right technology that provides clarity into how they’re using data in the cloud, explains how they can select candidate workloads, identify specific cloud instance types and configure their cloud deployments to optimize usage, performance, and future capacity needs.
Why is AI the Answer?
Enterprises realize that manually managing these widely deployed data apps is not practical as the complexity, scale, and dynamic behavior of modern applications is outpacing the ability of data teams to effectively monitor, tune, troubleshoot, and remediate problems. Fortunately, AI is maturing just in time as compute scales up and out – and becomes more complex (ever more layers of software), more abstracted (even more layers of virtualization), and more data-intense (often incorporating highly scaled out big data stacks).
AI allows organizations to quickly pinpoint which component of a complex data app isn’t working correctly and provides automated recommendations on how to remedy errors. As a result of these predictive capabilities, AI eliminates human error and offers intelligent guidance and automation to all aspects of IT operations, software development, and BI/Analytics teams. This not only helps improve the performance and reliability of modern data applications, but it also is useful for deciding which apps are the best fit to migrate to the cloud.
Up until recently, some of the largest public corporations in the US were still experimenting with data projects as they moved towards digital transformation. Now, as these organizations have moved from proof-of-concept deployments to full productions, the industry is more focused on optimizing these data-driven deployments to drive maximal ROI and create new revenue streams.
AI is changing how companies manage and analyze their data workloads by not only cutting the manual, time-consuming trial-and-error processes but also reducing the time lag of diagnosing and solving problems, resulting in significant cost-savings and business agility. Modern data apps serve a mission-critical role in enterprises across every industry. In the next few years, AI expertise will be a key qualification in the job market.