As per Gartner, by 2024, 75% of organizations will operationalize AI. It may lead to a 5x increase in streaming data and analytics infrastructures. The advancement in modern technologies and accelerating the time to insights are the fundamental driving forces for implementing automated data pipelines.
Data pipeline â The need
Modern enterprises use different apps to manage their business functions. HR functions may be using Workday, and Officevibe for payroll and engagement, the sales may rely on Salesforce and MongoDB, Marketing on HubSpot, and Marketo for automation. All these systems operate in silos. If you want to identify the best customer group and how to serve them, you need to filter all of them.
Data pipelines ensure that the data remains consolidated into a common destination from all these disparate data sources. The data pipeline also enables data with consistent quality. Most importantly, you can accelerate your time to insights for quick decision-making.
Data Pipelines â The common architectures
Batch data pipelines
If you want to move data at a specific time, batch data pipelines are the best choice. They trigger significant amounts of data as per a predefined threshold or react to a particular data flow behavior. ETL processing leverages these pipelines and is primarily used for standard BI reports.
Streaming data pipelines
Mainly used for real-time data processing, these pipelines move data once it starts creating. In other words, they are used in data lakes as part of data warehousing integration. Also, preferred for real-time ML use cases like recommendation engines to integrate, transform and feed into the real-time ML algorithms to generate the best product recommendations.
Change data capture pipelines
Do you want to refresh your datasets after the last sync? You can refresh big data sets to maintain consistency across many systems with change data capture pipelines. These pipelines come in handy in cloud migration projects.
Common challenges for building a data pipeline
Most organizations often face the dilemma to build their data pipelines independently. But it comes with a few challenges:
Integration with multiple data sources
Businesses constantly need to integrate with new data sources. Integration into data pipelines is mandatory too. Integrations often become cumbersome due to different protocols or API documentation. And these APIs also need constant monitoring for any changes and require more time and resources. Integration and maintenance of data sources constantly involve a lot of costs and effort and delay the insights process.
Latency
Data extraction in real-time is not as easy as it looks. Data pipelines should be capable of transferring data faster for accurate business insights. The entire data ecosystem may not be competent to handle real-time data processing all the time.
Flexibility
Data pipelines should be able to handle the changes at a swift pace. Any change in the APIs or data sync-up issues may lead to inaccurate insights. Also, there might be a significant lag while accommodating the changes. Data pipelines need to be flexible to handle these changes.
Operational efficiencies
A central IT team usually manages the data pipelines. Also, the data processing is centralized. Collectively, this may lead to inaccuracies and a lot of effort. Establishing a team for data pipelines comes with a price tag. Instead, if different can leverage pre-built data pipelines according to their needs, organizations can witness significant improvement in operational efficiency.
The solution
Automated data pipelines provide the needed solution for all these challenges. You can leverage the easy-to-use interface with automated data pipelines to transform the data in minutes.
Why do you need automated data pipelines?
Accelerated insights
Many companies struggle to generate the needed BI and analytics reports for days. With pre-built data pipelines, it becomes easier to extract, transform and load the required data for insights in no time. Also, with automated data pipelines, you can enhance data processing, improve workflows, and generate BI reports for real-time decision-making.
Enhanced data utilization
As organizations started to leverage all the data accumulated in their processes, interactions, and communication channels, the emphasis on real-time data processing also increased. Older architectures and systems caused a lot of integration challenges and delays.
Actionable intelligence from neglected data
Businesses always have dark data stored but neglected for analytics and revenue opportunities. Advanced analytics and ML techniques have evolved rapidly to act quickly and provide better insights for more opportunities. Utilizing all the data from multiple streams for quality insights can expand the scope for decision-makers.
Improved data mobility
Do you think that a data pipeline is a vital component of infrastructure? As the needs of every business function â sales, marketing, and HR keep on changing rapidly, it is crucial to have the infrastructure required to deliver the KPIs for these functions. An automated data pipeline serves the need by moving data rapidly across functions, processes, and systems and providing real-time strategic insights.
Holistic customer insights
Organizations can garner better insights about consumers if they have access to quality data. Another critical factor is the ability to process the data in real time. Advanced analytics techniques may be of no value if you cannot access accurate and timely data. Business users do not require an exception data engineering team now. You can leverage simple, low-code-based automated data pipelines. Respective teams can pull the data from any data source and load it into the required BI or analytics tools for effective data analysis and visualizations.
Garnering a data-driven culture
Data is essential everywhere, from understanding the products/services to offering targeted promotions, and understanding process efficiencies. As data drives business operations, it is crucial to access quality data at an accelerated pace and timely manner. Hence automated data pipelines play a vital role in a data-driven culture.
How InsightBox help?
Focused on efficiency, InsightBox is an end-to-end platform to accelerate your data journey from data engineering to BI and ML-based insights. It is equipped with prebuilt connectors, pre-built data pipelines, and pre-defined data cleansing routines to accelerate your data preparation for insights. Moreover, it also has pre-built dashboards and industry-specific AI models for users to easily generate insights in no time.
Are you interested to know more about InsightBox? Schedule a demo now!
