• Unravelling the Complexities of Data Transformation Challenges and Reducing Data Tech Debt

    Navigating the challenges of data transformation and mitigating data technology debt are crucial for technological and strategic advancement in sectors like banking, financial services, insurance, and the public sector. This post explores the reasons behind the ineffectiveness of data transformation initiatives and strategies for reducing data tech debt, offering insights to CTOs, CXOs, and leaders in these domains.


    Why data transformation sometimes fails


    1. Identifying the road blocks

    Data transformation initiatives often stumble due to a blend of technical and strategic challenges. These include issues with data quality, integration complexities, and the difficulties of dealing with legacy systems. Additionally, the scalability of data transformation processes often poses significant hurdles.


    Technical and tactical struggles

    Tactical challenges like the complexity of mapping and program based transformation, testing efficacy for large data sets, and limitations in transferring data to destination systems are common. On a strategic level, aligning business and technology priorities, developing sustainable data pipelines, and predicting future data needs are areas where many organisations falter​​.


    Strategic insights

    A key to overcoming these challenges lies in understanding the business requirements and aligning ETL tools accordingly. Developing a flexible and sustainable data pipeline and making informed decisions about data predictions and transportation needs are crucial for long term success​​.


    2. Strategies for reducing data tech debt


    Understanding data tech debt

    Data technology debt accumulates when temporary or quick fix solutions are implemented, leading to more significant challenges in the future. This can include outdated data models, inefficient data pipelines, and legacy systems that no longer meet the organisation's evolving needs.


    Effective strategies

    To reduce this tech debt, organisations need to focus on modernising their data architecture, implementing robust data governance policies, and ensuring data security. Adopting AI and machine learning for automating tasks like data profiling, cleansing, and transformation mapping can significantly enhance efficiency and scalability. Continuously monitoring and retraining models is also key to maintaining data quality and relevance​​​​​​.


    Practical Steps

    • Embrace modern, cloud native technologies for better integration and data management.
    • Implement comprehensive data quality assurance methodologies, using tools for data profiling, cleansing, and automated testing.
    • Foster a data driven culture within the organisation to enhance the understanding and use of data across all levels.



    For leaders in banking, financial services, insurance, and the public sector, tackling the complexities of data transformation and effectively reducing data tech debt are essential for driving innovation and maintaining a competitive edge. By addressing these challenges head on and implementing strategic solutions, organisations can unlock the full potential of their data assets and pave the way for a more data driven and efficient future.

  • Navigating the nuances of Data Transformation and Data Processing

    Data is pivotal for decision making, understanding the intricacies of data transformation and processing is crucial for leaders in banking, financial services, insurance, and the public sector. This post delves into how data transformation can alter data structures and the essence of data processing, blending insights from industry research with practical advice.

    Data Transformation: The art of structuring data

    1. The Concept and Importance

    Data transformation involves converting data from its raw form into a format more suitable for analysis. It's a process similar to translating a language, ensuring that the end result—be it in banking or public services—is both comprehensible and actionable.

    Changing data structures

    The transformation process can significantly alter the structure of data. It involves procedures like normalisation, where data is brought to a common scale, or aggregation, where data from various sources is compiled into a summarised format. This restructuring is vital for making sense of vast datasets, especially in sectors like financial services, where data driven insights are essential.

    Real world application

    Consider a bank analysing customer transactions. Raw data might be scattered and inconsistent, but through data transformation, it becomes a streamlined dataset, ready for analytics to identify trends or fraud.

    Challenges and solutions

    Organisations often grapple with choosing the right tools and ensuring data quality during transformation. The key is to select tools that align with the data's volume and variety, and to iteratively test throughout the transformation process to maintain data integrity​​​​.

    2. Understanding data processing

    Defining data processing

    Data processing is a broader term encompassing the collection, manipulation, and management of data. It's a crucial step that precedes data transformation, involving data entry, conversion, and editing.

    The role in organisations

    In sectors like insurance, where data comes in various forms; claims forms, customer interactions, and policy details data processing is essential to standardise this information before it can be analysed for insights or decision making.

    Challenges in data processing

    A major challenge is managing the quality of data. As data is collected and processed, ensuring its accuracy and relevance is paramount. Organisations must adopt robust data quality tools and methodologies, such as Six Sigma principles or Agile methods, to ensure the data's integrity throughout the process​​​​.


    The journey of data from its raw, unstructured form to a processed, transformed state is complex yet critical. For leaders in banking, financial services, insurance, and the public sector, understanding and implementing effective data transformation and processing strategies are keys to unlocking the true potential of their data assets. By focusing on these aspects, organizations can turn data into a strategic asset that drives decision making and innovation.

  • MITC's Data Transformation strategy

    In today's data-driven world, effective data transformation is essential for organisations to succeed. By leveraging the power of data transformation, organisations can gain deeper insights, improve decision-making, and drive greater business value.

    Data transformation refers to the process of converting data from one format, structure, or type to another to make it more accessible, accurate, and useful. Organisations need data transformation because they generate large amounts of data in different formats, from various sources, and for different purposes.

    Some of the key challenges faced by organisations undertaking data transformation include:

    - Legacy systems not supporting modern data formats and analytical tools.

    - Different systems within the organisation are not ‘talking to each other’ and restricting effective team collaboration.

    - Data quality and data security breaches are caused due to unclear data governance policies and procedures.

    - Struggling to integrate data from multiple sources into a single, usable format.


    To save you time, I’ve summarised the points below to consider when thinking about making transformational change in your organisation relating to data, and what you would need to achieve it (in no particular order).

    1.      Data Governance: Establishing and enforcing policies, standards, and procedures that define how data is collected, stored, processed, and shared across the organisation. The key resources required for this area include:

    a.      A data governance specialist to establish and implement policies, standards, and procedures.

    b.      A data governance framework and associated tools to support compliance monitoring and enforcement. Tools: IBM cloud Pac for data, or Informatica.

    c.      Training and awareness programs to educate employees and stakeholders about the importance of data governance.


    2.      Data Architecture: Designing and maintaining a scalable and adaptable data architecture that supports the diverse needs of the organisation. The key resources required for this area include:

    a.      A data architect to design and implement an enterprise-wide data architecture that aligns with business requirements and objectives.

    b.      Data modelling and database design tools to support data architecture design and maintenance. Tools: Tableau, DbScheme, Lucidchart.

    c.      A data integration platform to enable seamless integration of data across the organisation.


    3.      Master Data Management: Implementing a master data management (MDM) strategy is critical for ensuring that your data is consistent and accurate across all systems and applications.

    a.      A Chief data officer to create a master data model, define data ownership and stewardship and implement data quality controls.

    b.      MDM platform to leverage AI and automation for identifying, matching, and merging data across the different systems that hold it, and clean the data with the applications, systems, and analytics that need it. Tools: IBM MDM, Azure MDM, AWS MDM.


    4.      Data Analytics: Developing and deploying analytical tools and techniques to gain insights and improve decision-making across the organisation. The key resources required for this area include:

    a.      A data analyst or data scientist to develop and deploy analytical models and algorithms.

    b.      Data analytics platforms and tools to support data exploration, visualisation, and analysis. Tools: PowerBi, Tableau, Zoho.

    c.      Training and awareness programs to educate employees and stakeholders about the benefits of data analytics.


    5.      Data Security: Ensuring data confidentiality, integrity, and availability of data across the organisation. The key resources required for this area include:

    a.      A cybersecurity specialist to develop and implement data security policies and procedures.

    b.      Security technologies such as firewalls, intrusion detection and prevention systems, and data encryption solutions to protect data from unauthorised access, theft, and misuse.

    c.      Training and awareness programs to educate employees and stakeholders about the importance of data security.


    6.      Data Quality: Ensuring the accuracy, completeness, and consistency of data across the organisation. The key resources required for this area include:

    a.      A data quality specialist to develop and implement data quality policies and procedures.

    b.      Data quality tools to support data profiling, cleansing, and standardisation. Tools: IBM MDM or Informatica MDM (Data quality is supported within MDM).

    c.      Training and awareness programs to educate employees and stakeholders about the importance of data quality.


    By focusing on these key areas, you can develop a comprehensive data transformation strategy that will enable you to leverage the full potential of your data.

    - To implement the above programme plan, you’ll require the following resources:

    - A budget allocation for personnel, technologies, and tools required for each key area.

    - A project management framework to ensure timely and effective delivery of programme objectives.

    - A governance structure to oversee the programme and ensure alignment with organisational goals, priorities, and third-party management.

    - Collaboration and engagement with stakeholders across the organisation to ensure their input and buy-in into the programme.


    We would be happy to discuss this further and provide additional guidance on how to implement these strategies within your organisation.


    About the author:

    Asad Ansari is the Founder and Managing Director at Mayfair IT Consultancy, a data transformation specialist organisation helping private and public sector clients champion data and digital transformation. In 2023 under Asad’s continuous leadership, Mayfair IT Consultancy as a SME-certified Ethnic Minority Business (EMB) partnered with IBM and Deloitte to strengthen their client offering.

    Some of the notable clients Asad has led programmes with are Deloitte, Barclays, Aviva, Nomura, Virgin Money (CYBG), the AIM stock market, UK IPO, DWP, and KBR.

    Throughout his career, Asad has launched start-ups in Education, and Risk Management with successful exits by working across the UK and overseas along with the Department for International Trade (DIT).

    Away from business, Asad is a Freeman for the City of London and the youngest Asian board member with the Royal Air Force Museums (RAFM) working on a regeneration programme at the Cosford site in West Midlands, a board member for the Ministry of Defense (MOD) interfaith and communications committee, a British Asian Trust supporter, and an Ambassador for Graham Layton Trust.