Deciphering the Data Lifecycle
From sourcing to ingestion to disposal, understand how to consume and manage capital market data
Data lies at the heart of the financial markets, providing the fuel for efficient and successful operations. Investors and financial firms with access to the best quality data – in terms of its completeness, timeliness, and accuracy – have an advantage over those less well informed.
In 2024, however, the concept of ‘data as an asset’ is well understood by financial services organisations, with the general principle being that the asymmetry of information as well as the asymmetry of insight offers a competitive advantage, which is why it is imperative to work with information that is complete, curated, readily available and monetisable.
Broadly speaking, the financial services industry’s business need for software solutions that can process data falls under three categories, with all areas of the data cycle, from sourcing to disposal, needing to be optimised for effective use:
1. Sourcing
Sourcing is the process of identifying, obtaining, and managing data from various internal and external sources for use in analysis, decision-making and other business processes, characterised by the following key activities:
● Identification of Sources – Determining the relevant data sources, which could include databases, data warehouses, data lakes, APIs, Web scraping, third-party vendors and public data sets;
● Acquisition of Data – Collecting data from the identified sources. This involves extracting data in various formats, such as structured, semi-structured and unstructured data;
● Data Integration – Combining data from different sources into a unified view. This often involves transforming and cleaning the data to ensure consistency and compatibility;
● Data Validation & Quality Assurance – Ensuring the accuracy, completeness and reliability of the data. This step includes error checking, validation and data cleansing activities;
● Metadata Management – Documenting the sources of the data, the methods of collection and any transformations applied. Metadata provides context and helps ensure data traceability and governance;
● Compliance & Security – Ensuring that data sourcing practices comply with legal, regulatory and organisational policies. This includes managing permissions, protecting sensitive data and ensuring data privacy; and
● Data Refresh & Maintenance – Establishing processes for updating and maintaining the data to ensure it remains current and relevant.
2. Ingestion
Ingestion is the process of collecting and importing data from various sources into a centralised repository for storage, processing and analysis. This process involves several steps and can handle a variety of data types, including structured, semi-structured and unstructured data. The key activities and components of data ingestion include:
● Data Collection – Gathering data from diverse sources such as databases, files, streaming data, APIs, sensors, and external systems;
● Data Parsing – Interpreting and converting incoming data into a format that can be understood and processed by the destination system. This may involve handling different data formats like .JSON, .XML or .CSV, for example;
● Data Transformation – Applying necessary transformations to the data to ensure it meets the required format and structure of the target system. This might include data normalisation, cleaning, enrichment and other pre-processing steps;
● Data Loading – Importing the transformed data into the target repository, which could be a data warehouse, data lake, database or other storage systems;
● Batch & Real-time Processing – Data ingestion can occur in batch mode, where data is collected and processed in large chunks at scheduled intervals, or in real-time / streaming mode, where data is ingested and processed continuously as it arrives;
● Error Handling & Data Quality – Implementing mechanisms to detect, log and handle errors that occur during the ingestion process. Ensuring the quality and integrity of the ingested data through validation and verification steps;
● Scalability & Performance Optimisation – Designing the data ingestion process to handle large volumes of data efficiently and to scale with increasing data loads; and
● Security & Compliance – Ensuring that the data ingestion process adheres to security protocols and regulatory requirements, protecting sensitive data during transfer and storage.
3. Handling Across Multi-vendor Market Data Feeds
This refers to the processes and techniques used to manage, manipulate and utilise data throughout its lifecycle, ensuring its quality, security and accessibility. It encompasses a wide range of activities and practices aimed at maintaining the integrity and usability of data. Key components of data handling include:
● Data Collection & Acquisition – Gathering data from various sources through methods like data entry, data scraping, APIs, sensors and automated data feeds;
● Data Storage – Storing data in appropriate formats and locations such as databases, data warehouses, data lakes, Cloud storage or file systems. This involves selecting suitable storage solutions based on data types, access patterns and performance requirements;
● Data Organization & Structuring – Arranging data in a structured manner to facilitate easy access and analysis. This can involve creating data models, schemas and indexes;
● Data Processing – Performing operations on data to convert it into a useful format. This includes data cleaning, transformation and enrichment;
● Data Analysis & Interpretation – Applying analytical techniques to extract meaningful insights from data. This can involve statistical analysis, data mining, machine learning and other data science methods;
● Data Access & Retrieval – Providing mechanisms for users and applications to efficiently query and retrieve data. This includes implementing APIs, query languages (for example, SQL) and user interfaces for data exploration;
● Data Security & Privacy – Ensuring the protection of data from unauthorised access, breaches and other security threats. This involves implementing encryption, access controls, anonymisation and compliance with data protection regulations;
● Data Backup & Recovery – Creating copies of data to prevent loss and ensure recoverability in case of failures or disasters. This includes implementing back-up strategies and disaster recovery plans;
● Data Governance & Compliance – Establishing policies and procedures for data management to ensure data quality, consistency and compliance with legal and regulatory requirements. This includes defining data ownership, stewardship and accountability; and
● Data Archiving & Disposal – Managing the long-term storage of data that is no longer actively used but must be retained for historical, legal or regulatory purposes. This also involves securely disposing of data that is no longer needed.
Understanding different stages of the data lifecycle, from initial consumption to disposal, can help firms to optimise their data strategies and generate long term value to the organisation.
For further information, please do not hesitate to contact us at london@greyspark.com with any questions or comments you may have. We are always happy to elaborate on the wider implications of these headlines from our unique capital markets consultative perspective.