As the data streams expand continuously, we have evolving requirements from business intelligence reports. The ever-growing tool sets, cloud architectures, and new data management systems mount the complexities in data engineering. Organizations invest a lot of effort to make data ready for BI reports and analytics. As per Gartner, data engineering consumes 50% of the time in the process of extracting insights.
The main focus of data engineering is on cleansing, integration, pipelines, quality, and governance. All these affect the costs of any data-centric organization. Inaccurate data causes a direct impact on the bottom line of 88% of the companies, creating a 12% revenue loss. Hence the focus of organizations is shifting towards adopting modern data stacks early in the insights lifecycle.
The purpose of BI
The perception of BI has changed over the last decade. Traditionally, the BI tools integrated data from different sources, managed governance and derived insights with easy-to-use self-service data. But over the years, all these components individually evolved into various tools to accelerate the insights.
BI with modern data engineering capabilities should provide key business insights, automate processes and boost efficiency. It should provide:
Easy to consume insights – Effective visualizations provide insights to everyone irrespective of their technical acumen and business understanding. A simple KPI report may not suffice the insights-driven approach. A pool of self-service dashboards can improve the access of BI across different roles and business functions.
Deep-dive into possibilities – From the high-level insights overview to understanding the root cause analysis, BI should provide answers and drill down many opportunities. The new AI capabilities in BI offer more in-depth analysis of the KPIs and easily consumable insights.
Single source of truth – Establishing a single source of truth is critical to deriving trusted and quality insights. Modern data pipelines, cloud data warehouses, and new-age data preparation methodologies provide a holistic data engineering approach to accelerate the insights process in the new BI era.
BI and Modern Data Engineering – Challenges
BI tools are supposed to be great for first-hand insights into the business. But, there are significant challenges concerning speed, transparency, and technical dependency. Let us look into a few more details about the challenges:
- Data integration is the most common concern leading to data silos as different departments create analysis in Excel worksheets and applications. It usually takes a lot of effort and time to integrate any new data source. Maintenance of the APIs is also not simple once the new data source integrates with the BI ecosystem.
- As we spoke about sourcing data, the next hidden challenge is processing the data and ensuring centralized access for everyone. Data transformation, ETL, and providing the data in the right format consume a lot of time for BI engineers. All in all, the delay in data engineering hinders the speed of the overall insights.
- The transformation logic in ETL is visible to everyone, but most stakeholders have to rely on BI engineers to access real-time data. BI is supposed to make things easier. But it is complex in the current scenario with dependencies on data engineers and BI consultants.
- The exponential growth of data is unquestionable. Organizations now require more sophisticated tools to analyze unstructured and semi-structured data. Real-time data analytics also seem to be a necessity for most organizations. BI and modern data engineering tools should reduce the complexities of handling different data formats and volumes.
Modern Data Ecosystem for faster Insights
Over the last decade, organizations quickly moved toward new technologies like data lakes, customer analytics, personalized offers, and complex AI models. These complex data architectures and existing infrastructures pose a few challenges for rapidly scaling up AI and analytics projects. The modern data ecosystem now relies on the following changes to accelerate BI, analytics, and AI efforts for organizations of any scale.
Distributed cloud ecosystem – Cloud storage has now become the norm across industries. Serverless platforms enable businesses to build and manage data applications at scale without additional operational overload. At the same time, containerized data solutions are helping organizations save costs by decoupling storage and computing. In the new distributed cloud ecosystem, organizations can now manage everything from one computer. It seems an essential adoption as organizations rapidly need faster access to data and deeper insights.
Real-time data processing – The transactions in real-time are increasing enormously for every business. Real-time messaging and data-processing costs have come significantly in the recent past with the new technologies. Data consumers can now receive the information constantly to leverage for relevant insights or distribute it to the end-user.
Domain-based data architecture, data mesh – A centralized ownership of data causes dependence on the data team for simple tasks like data access and governance. With the new domain-based distributed data architecture, data ownership, and governance lie with domain teams. The data mesh architecture leverages domain-oriented decentralized data products, self-service infrastructure, and simplified governance. Data mesh also enables new data products and services to democratize data and accelerate the time to value for data consumers.
Flexible data schemas – Adding new data sources is not simple in the traditional normalized schemas. With fewer physical tables and flexible data schemas, data can be more accessible, improving agility and performance in deriving insights.
Accelerating BI with Modern Data Engineering
Data engineering forms the foundation for tools like Power BI to extract insights faster. As organizations scale up their BI, analytics, and AI projects, it is essential to involve modern data engineering practices like data connectors, automated data pipelines, and pre-defined data preparation methodologies. Let me walk you through a few modern data engineering practices to make BI easier.
Data integration
Power BI has the best data integration features, but analysts may not follow these owing to their understanding and complexities. At the same time, it is not easy to build connectors for individual data sources and maintain them. These complexities may lead to performance bottlenecks and delay in the dashboard design while also increasing costs. Companies may now choose the best pre-built data connectors available in the market according to the use case and are easy to maintain.
Data pipelines
After the data integration, building the data pipelines to organize the data in the data warehouse is another crucial step. Once the data source refreshes, the data pipelines should be robust to adapt to the changes rapidly. Also, the data pipelines should have capabilities to make the data flow faster for easy accessibility and faster insights. Automated data pipelines can bring in more efficiencies, and they can be tailor-fit according to the use case for accelerated insights.
Data preparation
Though we see simple visualization at the end, data preparation is time-consuming and tedious in the entire lifecycle. Raw data from different data sources require a lot of processing, cleansing, and transformation before creating the visualizations. Automating all of these processes may not be possible at scale, so leveraging the best practices and tools can improve trust and data quality. It also leads to trusted and deeper insights into the business processes.
InsightBox for Accelerated Insights
Our end-to-end platform, InsightBox, across the data lifecycle to generate insights equipped with pre-built connectors, pre-built data pipelines, and more can accelerate your time to generate insights by 50%. Also, it is easy to use for all the stakeholders with embedded low-code capabilities and pre-built dashboards and AI models.
