Different Types of Data

Matt Lewis

Data Consultant: 5 years

In this video, Matt explores the critical role of data quality in finance, highlighting its impact on decision-making, risk assessment, and AI outcomes. He delves into various data types used in finance, key data management practices, and essential preprocessing steps for accurate analysis. He also discusses how big data and visualisations can drive real-time insights while addressing associated regulatory challenges, providing valuable knowledge for finance professionals navigating the data-driven landscape.

Finance

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Different Types of Data

12 mins 41 secs

Key learning objectives:

Understand the importance of data quality and types in finance and how inaccuracies impact data science and AI outcomes
Outline key data management and governance practices, including storage solutions and compliance standards
Understand data preprocessing steps, cleaning, transformation, normalisation, and feature selection, for accurate analysis
Recognise the role of big data and visualisations in enabling real-time insights and automation, while addressing regulatory challenges

Skills:

Technology-driven Advancement

Impacts:

Revenue Opportunity

Overview:

Data quality is vital in finance, where errors can lead to severe financial impacts. For example, Equifax’s 2022 coding error caused loan rejections and higher interest rates due to inaccurate credit scores. Quality data must be complete, timely, consistent, and accurate, directly affecting data science and AI results. Financial data includes structured types like numeric data and unstructured types like text, each requiring tailored management. Effective management involves databases for structured data and data lakes for unstructured data, with cloud solutions offering scalability. Key preprocessing steps; cleaning, transformation, normalisation, and feature selection, ensure data readiness for analysis. Big data enables real-time analysis and automation but also poses regulatory and stability challenges. Visualisations are essential for translating data insights into actionable decisions, while proper handling maximises data’s value and mitigates risks.

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Summary

Why is data quality critical in finance, and what are the potential impacts of poor-quality data?

Data quality is essential in finance as it directly influences decision-making, risk assessment, and customer experiences. Poor-quality data can lead to errors, such as inaccurate credit scores, which may result in higher interest rates, rejected loans, or lost customer trust. High-quality data, assessed by metrics like completeness, timeliness, and accuracy, ensures that financial models and AI systems produce reliable outcomes, reducing risks of financial misreporting and preserving both consumer opportunities and institutional stability.

What types of data are commonly used in finance, and how should each type be managed?

Finance relies on both structured and unstructured data. Structured data, like numerical values or dates, is organised in tables, facilitating analysis and reporting. Unstructured data, such as text or images, requires conversion into structured forms for processing and analysis. Proper data management includes storing structured data in relational databases and unstructured data in data lakes, often in cloud environments for scalability. Choosing the correct storage and management approach based on data type is critical for data accessibility, cost efficiency, and compliance.

What are the key steps in preparing data for analysis in data science and AI?

Data preparation for analysis involves several preprocessing steps: cleaning, transformation, normalisation, and feature selection. Cleaning ensures accuracy by removing duplicates and formatting errors, while transformation adjusts data for better interpretability. Normalisation aligns data scales for comparability, and feature selection isolates the most relevant fields for analysis. These steps improve data quality, reduce biases, and help models produce accurate, valuable outputs. Proper preprocessing is essential in financial analysis to minimise errors and ensure data integrity for decision-making.

How can big data and visualisations be used effectively in finance, and what challenges might arise?

Big data allows finance professionals to conduct real-time analysis and automate complex processes, supporting more informed, timely decisions. Effective use of data visualisation transforms raw data into accessible insights, enabling stakeholders to grasp patterns and trends quickly. However, integrating big data with traditional finance systems introduces challenges, such as increased regulatory scrutiny and risks to data security and system stability. Overcoming these challenges requires robust governance, regulatory compliance, and selecting appropriate visualisation methods to ensure clarity and actionable insights.

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Matt Lewis

Matt Lewis works as a consultant in the data space for 5 years, after he moved from the academic world to the world of data. He has helped clients in several domains, and with varying degrees of digital and data maturity, get to grips with their data and find ways to extract business value from it. Currently, as a program lead at Sand Technologies, he focuses on guiding a UK-based client in the water/wastewater sector, developing a value proposition for prospective clients, and mentoring junior managers in product development. His work involves collaborating with stakeholders up to the Director level to enhance business processes through data-driven solutions. He also holds a doctoral degree in Zoology and Animal Biology from the University of Cape Town.

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