Why your business strategy needs strong Data engineering

Digital isn’t new anymore. It’s the default. Data has shifted from being just a by-product to becoming the fuel for decisions, customer engagement, strategy, and innovation. The data every business sits on can completely reshape how it operates and grows. 

That’s why so many organizations invest in data science, BI, and analytics. But there’s a catch: without solid data engineering as the foundation, those investments rarely reach their full potential.

Data engineering is about designing, building, and maintaining the systems that collect, store, and prepare data for use. It lays the foundation for analytics and AI, helping businesses make smarter, data-driven decisions.

No more boring conversations in a meeting room, hovering around data engineering. Data engineering is now a priority in the digital-first economy. It’s the cornerstone of digital transformation, and your agility, competitiveness, and resilience all depend on how well you manage data behind the scenes.

Why should business leaders care about Data engineering?

While we say data engineering is necessary to make informed decisions, it’s critical to understand what parameters data can add to your decision-making process.

Speed to insight

A robust data engineering practice is one that’s fast and offers on-demand access to the required data. It reduces the time taken to answer a question. Decision makers can now quickly access data-backed insights, allowing them to invest more time in curating business strategies that lead to informed choices.

Taking an example of a new product or service launch, business leaders, at some point in time, would like to understand how well it is being received by customers. Data engineering practices can enable business leaders to have near-real-time customer feedback and sentiment analysis on the launch of a new product or service supported via real-time data ingestion, transformation, integration, and analysis.

Trustworthy data = Better decisions

Business leaders need to have trust in the data they are going to base their decisions on. Taking a business decision or building a strategy on incomplete or noisy data is detrimental to the growth of the organization. Data talks and has lots of stories to tell; it is up to the businesses on how much time and effort they invest in hearing those stories and using them to devise strategies for growth.

A quality data engineering practice makes sure they build and retain this trust by making sure data is cleansed, deduplicated, and standardized, reducing the risk of conflicting reports and inaccurate KPIs.

Data engineering practice enables setting up and maintaining centralized data pipelines and a single source of truth, aligning cross-functional decision-making across different departments like marketing, finance, and operations, making sure they are all on the same page.

Scalability and growth

Organizations grow both vertically by expanding existing capabilities of the products and services offered and horizontally by introducing new products and services, and reaching new customers and regions. Any kind of growth brings in the responsibility of not only adding new systems, but also making sure that existing systems work seamlessly, and the integration is smooth.

The growth of data goes hand in hand with and is directly proportional to the growth of business. What’s a good data engineering practice? It includes infrastructure scaling, preventing bottlenecks caused by increased volume, variety, velocity, and veracity of the data. 

Operational efficiency

A business is driven and supported via hundreds of thousands of operations based on the scale at which it operates. A lot of engineering hours and technical rigour go into building and maintaining these operations, which are often backed by data. 

A mature data engineering practice focuses on the implementation of automated data pipelines, reducing the manual work and allowing analysts and engineers to focus on innovative tasks while also reducing the likelihood of human error, thus improving operational resilience.

AI and advanced analytics readiness

We're experiencing what some would call an AI boom. Every organization, every business is looking back at its offerings and asking itself, “How do we use AI to make it better?" It feels like an AI race, where every organization is trying to modernize its offerings by leveraging the capabilities of AI before others, and most importantly, before it becomes the norm.

However, what most organizations fail to realize is that their goal of incorporating AI to improve their offerings can only be realized by leveraging the value the data holds.

You can’t run before you walk. AI and machine learning models require access to high-quality and well-structured data to be effective. A robust data engineering practice is of utmost importance to make any organization ready to leverage the capabilities of advanced AI models. 

Raw data needs to be cleaned, processed, enriched, reconciled, and aggregated across various dimensions to make it ready for advanced analytics and AI models, which is achieved via data engineering.

Business leaders often find themselves asking questions like, “How can we become more data-driven?” and “How can we become AI-ready?” The answer does not begin with setting data science teams to build dashboards, training, and deploying machine learning models; it just shifts the problem downstream. 

According to estimates, data scientists still have to spend up to 80% of their time cleaning and preparing their data for modeling, which ideally should come under the domain of data engineering. A mature data engineering practice shifts these responsibilities upstream, allowing data scientists to focus on model building and insights generation.

Taking an example of an e-commerce company, the majority of them have a goal of personalizing customer experiences by recommending products they would like. Without a data engineering team integrating clickstream data, inventory, and customer profiles, personalization would remain a pipe dream. On the contrary, with the right pipelines and infrastructure, you can use that data to power real-time recommendations, increase conversion rates, and boost customer satisfaction. This results in an improvement in the core growth parameters.

Similarly, businesses in logistics want to optimize their delivery routes. It reduces their delivery timelines and the incurred costs. To make this happen, they need data engineering to gather fragmented data from their GPS trackers, weather APIs, and customer systems. Centralizing this data into their data repository powers advanced analytics, helping these organizations move towards their goals while saving time and money in the process.

What a robust data engineering backbone looks like

A robust backbone is not just about tools or cloud platforms. It involves:

• A modern toolset including Apache Spark, Airflow, and cloud-native solutions such as AWS Glue or Snowflake
• A warehouse architecture to ingest and serve data reliably
• Monitoring and observability to detect failures and data quality issues in real time
• Data governance and access control, ensuring compliance and security
• Modular, scalable design adaptable to evolving business needs