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How data engineering can fuel multiple business use cases?

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By Raghvendra Kushwah, Co-Founder, Eucloid

Today’s world runs on Data. It is valuable and we save it, store it, move it around in various formats, and use it for multiple purposes. Now, with the advent of Big Data and rapidly increasing digitization, data is pouring in like never before – and while we still collect it in structured and unstructured formats, the issues of storing, managing, and analyzing it are becoming more complex with each passing day.

According to IDC’s Global DataSphere report, approximately 175 ZB of data will be created by 2025.
So, what to do with all this data? How do we store it? How do we manage it? How do we use it? This is where Data Engineering comes into focus.

Data is the new oil

Among multiple things that COVID-19 has altered, one was the realization that ‘big’ data – if analyzed properly – can open a world of possibilities for enterprises. As clichéd as it might sound, data has become the new oil, which is why data engineering and analytics can fuel unprecedented growth for new-age businesses. To facilitate analytics, enterprises need to design database architecture that facilitates data collection, storage, and analysis. Data engineers do all of these, enabling enterprises to collect raw data from multiple sources in multiple formats, and helping businesses analyze it to drive actionable insights.

Data, like oil, is valuable but cannot be used unless refined. Enterprises track, store, and analyze data across their customers’ journeys to make well-informed decisions, unlock new business opportunities, improve customer experience, innovate, and grow businesses. However, to make sense of that data, enterprises need to make it accessible to the right people at the right time.

Data Engineering makes this data accessible and consumable. Data engineering acts as a bridge between data owners and consumers to:

1. Transport and enrich data while integrating analytical and operational systems
2. Parse and transform unstructured data from multiple sources into clean data
3. Apply DataOps
4. Deploy models and other artifacts for data analysis

By enabling secure, compliant data utilization and democratization across the enterprise, data engineering facilitates analytics that fuels decision-making processes with insights like:
• Cost of acquisition of new customers and their lifetime value
• Ways to increase conversion ratios across stages
• Trend and sensitivity analysis

Insights like the above can fuel multiple use cases across domains like:
1. FMCG: customer 360 profiles, achieve personalization, upsell and cross-sell, sharpen product marketing etc.
2. BFSI: More stringent fraud detection, enhanced risk management, stronger compliance, payment surveillance etc.
3. Retail: More accurate and timely demand forecasting, improved product and category performance, better inventory management etc.
4. Healthcare: Personalized medication and care recommendations, more efficient document management, enhanced gene analytics, and editing, more accurate occupancy forecasting, drug delivery optimization etc.

How can enterprises take advantage of data engineering?

To facilitate the use cases mentioned above and thrive in the post-COVID world, enterprises can leverage data engineering to do the following:

Streamlining data flow: Use DataOps to maintain data integrity, accuracy, and reduce time-to-analytics. DataOps can help you improve communication, integrate and automate data flow, provide consumption-ready pipelines, manage data quality, catch errors rapidly, and build comprehensive data governance programs.

Democratizing data: Enable barrier-free data access to fuel robust analytical initiatives, drive self-service insights, and unlock the benefits of data engineering capabilities.

Securing data: Data engineering enables better data protection with encryption and security tools, thereby preventing unauthorized access of sensitive data.

Integrating data with the cloud: Data and cloud engineering are not mutually exclusive. With the surge in data privacy and cyberattacks, it is advisable to build/ migrate and optimize mission-critical databases in the cloud to facilitate large-scale ingestion and analytics, take advantage of the cloud’s scalability and flexibility, and ensure security and compliance.

Reducing cost: By reducing the need of manual data entry and processing, and improving data quality, enterprises can save costs associated with manual processing and bad data.

Data engineering can fuel unprecedented growth for your enterprise. To get the most out of your data, you need to eliminate siloes and create a unified data platform to enable users across the organization to make quick, data-driven decisions.

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