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Reinforcing Insights Through Data Engineering

Unifying the Data

Companies have huge amounts of data to sift through to answer questions for their business. This is challenging as there are diverse data sources, formats and systems. Data engineering can play a central role in supporting the process of combing through and unifying the data to discern insights for data scientists and other stakeholders. Data is then stored in data lakes or data warehouses.

Dremio, in its blog on data engineering, notes that data engineers perform many different tasks including:

  • Acquisition: Finding all the different data sets around the business.
  • Cleansing: Finding and cleaning any errors in the data.
  • Conversion: Giving all the data a common format.
  • Disambiguation: Interpreting data that could be interpreted in multiple ways.
  • Deduplication: Removing duplicate copies of data.

This can seem to be on the surface a rather dry function, but data engineering is an increasingly important task. Data needs attention and processing. Indeed, Dremio notes that, “Data engineers play a crucial role in designing, operating, and supporting the increasingly complex environments that power modern data analytics.” This end-to-end journey, known as the data pipeline, carries data through a transformation of sorts, from raw data to information that has value for the data science and insights team, and ultimately for the business as a whole.

Leading to Insights

Data needs attention. In All Things Insights’ “Powering Business Intelligence to Capture Insights,” we explored the world of business intelligence. It’s a broad term that encompasses the technical infrastructure that collects, stores and analyzes company data to generate valuable insights. This could include data mining, analysis, performance benchmarking and analytics. Ultimately, business intelligence parses data generated by the business and that leads to reports, performance measures, and trends that inform management decisions. These tools and software come in a variety of formats, often with visually-oriented presentations that make it easier to help managers access and explore data, with the end goal being to support the company’s insights efforts.

In “Unifying Data Analytics and Insights,” we delved further into the latest trend that is impacting the data science field, artificial intelligence. To be human or not to be human? That is a relevant question in today’s technology-driven society, and one we posed when thinking about unifying data analytics and insights. The intersection of technology and methodology is happening.

In “Revealing Top Market Research Themes from TMRE 2023,” All Things Insights looked at some of the key themes from the conference, and what trends are impacting the market research field. AI, gathering intelligence, and data storytelling all made the list. Many of the educational sessions at TMRE focused on data and analytics, but also stressed the importance of the storytelling nature of the insights function.

Empowering Decision-Making

Data engineering plays a crucial role in generating insights and supporting market research by providing a solid foundation for data processing, storage, and analysis. We asked ChatGPT to reveal ten key benefits of data engineering in the context of insights and market research:

  1. Data Integration: Benefit: Data engineering enables the integration of diverse data sources, such as customer data, market trends, and competitor information. This integrated data forms a comprehensive view that supports more accurate analysis.
  2. Data Quality Assurance: Benefit: Data engineering processes help ensure data accuracy, completeness, and consistency. Clean and high-quality data is essential for reliable insights and research findings.
  3. Scalability: Benefit: Data engineering allows for scalable infrastructure, enabling organizations to handle large volumes of data efficiently. This scalability is crucial for market research as data sets continue to grow.
  4. Real-time Data Processing: Benefit: Data engineering enables the processing of real-time data, providing timely insights into market trends, consumer behavior, and other dynamic factors affecting the business environment.
  5. Data Transformation: Benefit: Data engineering facilitates the transformation of raw data into a format suitable for analysis. This involves cleaning, enriching, and structuring data to extract meaningful insights.
  6. Automation of Data Pipelines: Benefit: Automated data pipelines streamline the flow of data from various sources to analytical systems. This reduces manual intervention, lowers the risk of errors, and accelerates the time-to-insights.
  7. Cost Efficiency: Benefit: Data engineering helps optimize data storage and processing costs by implementing efficient data architectures. This allows organizations to manage and analyze data without unnecessary expenses.
  8. Data Governance and Security: Benefit: Robust data engineering practices establish data governance policies and ensure data security. This is crucial for compliance, protecting sensitive information, and building trust with customers and stakeholders.
  9. Enhanced Analytics and Machine Learning: Benefit: Data engineering provides the necessary infrastructure for advanced analytics and machine learning applications. This enables organizations to derive deeper insights and make data-driven predictions for market trends.
  10. Customized Reporting and Dashboards: Benefit: Data engineering supports the creation of customized reporting structures and interactive dashboards. This empowers decision-makers to visualize and understand complex data, facilitating more informed and strategic decision-making.

An Evolving Field

In today’s fast-paced and evolving business world, data engineering has become a fundamental component of a successful insights and market research strategy, providing the infrastructure and processes necessary for extracting valuable information from diverse data sources. Data engineering can seem to be a complex task. Yet aligned with data governance best practices and a philosophy of data democratization permeating the organization, data engineering can help support the insights discipline, priming both for an exciting and centralized role in the future of business intelligence.

Video courtesy of Seattle Data Guy

Contributor

  • Matt Kramer

    Matthew Kramer is the Digital Editor for All Things Insights & All Things Innovation. He has over 20 years of experience working in publishing and media companies, on a variety of business-to-business publications, websites and trade shows.

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