How Data Pipelines Have Evolved to Reduce Business Complexity
The rapid rise in the popularity of cloud computing in its various forms has massively increased the complexity of developing, maintaining, and operating efficient data pipelines. With data hosted in and transferred from a complex series of databases and third-party cloud services, it can be difficult for businesses to manually manage an integrated pool of reliable and observable data. Read on to learn the role of AI in managing this increasing complexity and how data leaders can close operational gaps by employing AI to automate data processes.
The Evolution of Data Pipelines in the Last 10 Years
The amount of operational and customer data that businesses generate, collect, and store has grown at an astounding rate in recent years. As the tools used to collect and store this data become more capable of organizing and analyzing this data, businesses will continue to scale their data collection practices further—forcing them to deal with a staggering amount of data. In short, an increased data capacity has brought increased complexity and challenges in how to actually organize and use the data.
Considering the amount of data that companies must manage and the increasing demand for data-based analytics, teams have had to work extremely hard to ensure that the data they collect and analyze is reliable and of high quality as well. However, challenges to keeping up with a rapidly evolving data environment remain. Data issues are still a barrier for employees going about their day-to-day activities for over a third of surveyed respondents.
The Challenges Created by Increased Reliance on Cloud and Hybrid Computing
Maintaining High Levels of Performance
When companies embrace hybrid computing, they must put in place data pipelines that allow them to easily transfer data that is kept on-premises to the cloud and vice versa. These processes, when handled manually, can be extremely time-consuming and prone to human error. Even when processes are automated using a simple rule-based system, teams must keep an eye open for replicated, incomplete, or incorrect data. These mistakes often occur when data has to be moved around regularly. Hybrid computing models rely on data being in transit regularly and so create greater performance challenges for the data teams that depend on them.
Keeping Costs at an Acceptable Level
As businesses deal with more data, the cost of collecting, storing, organizing, and analyzing information has to be considered. In a hybrid or cloud computing environment, costs and cloud space are endlessly scalable. This is often considered a positive cost-saving point in favor of cloud computing. However, the endless scalability of cloud and hybrid computing can also encourage businesses to collect and store more data than they might need, increasing costs to unacceptable levels.
The Role of AI in Managing Complex Data Processes
AI-Powered Data Cleansing Helps Maintain High Levels of Data Quality
The large amounts of data that businesses have to deal with today have encouraged them to go beyond manual processes to collect, store, organize, and analyze operational and customer data. AI helps go through extremely large amounts of data quickly and weed out data that is replicated, missing, or incorrect. Modern AI systems can go as far as automatically fixing these issues with minimal human intervention. This grants business leaders more confidence in the insights generated by their data teams.
Automation Can Simplify Complex Manual Data Management Tasks
Despite the importance of data management processes and tasks, business leaders must recognize that they can be highly time and resource-intensive. This can take time and resources away from tasks that could generate more value for the company, such as advanced data analytics. As repetitive tasks become more complex, a rule-based system for automating these processes becomes insufficient. However, a vast majority of data practitioners believe that repetitive tasks involving feature engineering and data wrangling can and should be automated. AI can help plug this gap by keeping up with the increasing complexity of such data tasks while being significantly less resource intensive than using manual processes to manage data.
Automatically Generated Insights Improve Reporting and Data Observability
It is often said that data is the lifeblood of a modern enterprise. However, data collected by a business can’t generate significant value until it’s organized and analyzed to reveal insight. This insight then has to be reported to business leaders who are in positions to enact changes within the business. If done manually, these insights come at intervals, and in a fast-paced business environment, this can lead to outdated information being used to make decisions. AI can help deliver these insights in real time and can present them in a way that highlights the most important information.
Ultimately, data pipelines and processes will continue to evolve in the coming years and the complexity and scale of data processes will only increase. Data teams must retain the agility and flexibility to keep pace with this change. Introducing AI into data processes brings this agility to critical business processes that deliver crucial insight to decision makers and operational leaders.
Author Bio:
Loretta Jones is VP growth at Acceldata.io with extensive experience marketing to SMBs, mid-market companies, and enterprise organizations. She is a self-proclaimed 'startup junkie’ and enjoys growing early-stage startups. She studied Psychology at Brown University and credits this major to successful marketing as well as navigating a career in Silicon Valley. She’s a nature lover and typically schedules her vacations around the migratory patterns of whales and large ocean creatures.
