Over the years, business intelligence (BI) solutions have played a pivotal role in assisting businesses in identifying and interpreting patterns, trends, anomalies, and relationships within their in-house data, as well as in collaboration with external data sources. Employees have been generating individual reports, running queries, sharing insights, and collaborating more effectively than ever before.
This explains why Business Intelligence (BI) is all the rage across various industries. Be it retail, finance, CPG, or healthcare, business intelligence serves as an upper hand in gaining a competitive edge and making strategic decisions. At the heart of this transformation are data engineers, who play a vital role in building and maintaining the infrastructure and pipelines necessary for BI systems to access and process data efficiently.
A survey shows that 90% of CEOs consider making strategic decisions more important than ever. This emphasis on Business Intelligence aligns with organizations recognizing its vital role in guiding decisions with data-driven insights.
This blog emphasizes the vital role data engineers play in leveraging Business Intelligence for informed decision-making in today’s business landscape.
Business Intelligence in the modern business environment
In modern business environment, Business Intelligence means:
“Business intelligence (BI) refers to the use of technology, processes, and strategies to analyze and interpret data to support organizational decision-making.”
It involves gathering data from various sources within and outside the organization, transforming it into meaningful insights, and presenting it in such a manner that is easily understandable and actionable by decision-makers. Business Intelligence is a multi-step process encompassing data collection, analysis, and reporting, all aimed at improving organizational performance and gaining a competitive edge in the data-rich marketplace.
Not just this, but Business Intelligence transcends beyond data accumulation. In fact, it is helping businesses in several ways, such as:
- Enhancing customer experience
- Streamlining operations
- Predicting market trends
- Managing potential risks
Since the ultimate focus of BI revolves around supporting data-driven decision-making, it is important to ensure the data used for analysis is accurate, complete, and relevant to the specific decision at hand. This necessitates the need for data engineers to maintain the robust data infrastructure for converting insights into actionable intelligence.
Is your business looking to leverage big data for making informed decisions? Discover the best practices for leveraging big data effectively.
Exploring the relationship between Business Intelligence and data engineering
Given that Business Intelligence revolves around converting raw data into actionable insights, it requires an expert that lays the foundation for data collection, storage, and analysis. This is where data engineers or data engineering comes in.
According to Gartner, more than 50% of the effort in any data science or reporting project is consumed by data engineering. This means over half of data science or reporting projects involve data engineering tasks, like collecting, cleaning, and organizing data.
At its core,
“Data engineering focuses on building and maintaining the infrastructure necessary to handle large volumes of data efficiently and reliably.”
Data engineers design and maintain the infrastructure for storing and processing large volumes of data efficiently. This includes setting up databases, data warehouses, and data lakes and leveraging cloud technologies, i.e., AWS and Microsoft Azure, for scalable storage and computation.
Data engineers also build data pipelines that automate the process of ETL, i.e., extracting data from various sources, transforming it into a usable format, and loading it into BI systems. These pipelines ensure a continuous flow of data, enabling real-time or near-real-time analysis for timely decision-making.
Moreover, data engineers also focus on maintaining data quality and reliability to ensure that BI systems yield accurate insights. However, by meticulously ensuring data quality, data engineers empower businesses to extract maximum value from their diverse data sets.
Common challenges businesses face in the absence of data engineers
With the vast volumes of data companies handle, many already employ business intelligence tools for valuable insights. However, business intelligence when used in combination with data engineering can yield exceptional results, such as identifying new opportunities, enhancing organizational agility, improving business performance, and increasing profitability.
Let’s explore challenges businesses face without data engineers and the impact of advanced data engineering in overcoming them heads-on.
Challenge 1: Data silos
Data silos occur when different departments within an organization store and manage data independently, leading to redundant efforts and data fragmentation. This hinders the organization’s ability to derive insights and make informed decisions.
Solution:
The solution involves strategically approaching data engineering to build a robust infrastructure to support seamless data analysis. Furthermore, data engineers can help design and develop data pipelines to extract, transform, and load (ETL) and extract, load, and transform (ELT) data from disparate sources into centralized data warehouses or data lakes.
Additionally, data engineers can establish data governance frameworks and metadata management practices to ensure consistency and accessibility of data across the organization.
Challenge 2: Poor data quality
Data collected from various sources through inefficient methods results in disparate formats, leading to poor data quality with errors. Consequently, this undermines the reliability of insights derived from data analysis.
Solution:
Since in data engineering, managing accurate, up-to-date, and consistent data quality is important, data engineers implement various strategies, such as data cleansing, data validation, data duplication, and data governance frameworks (to some extent) to enhance data quality.
Challenge 3: Inefficient data processing
Another challenge businesses face in the absence of data engineers is inefficient data processing. This leads to slow and resource-intensive tasks that delay insight delivery. Additionally, manual data processing increases the risk of errors, and in worst case results in data breaches or compliance violations.
Solution:
Data engineers address inefficient data processing by optimizing data pipelines, employing parallel and distributed computing techniques that boost data processing speed. Moreover, automating data processing workflows and fine-tuning systems also accelerate data processing speeds.
Understanding these challenges isn’t just about avoiding them, rather businesses should consider hiring data engineers to avoid the consequences of these challenges.
