This is the first of three articles on Continuous Data Modelling. This describes the complexity challenge that affects all capital market firms – large and small. A specific and practical data modelling solution is outlined, and the benefits of the solution explored. The author has purposefully removed all reference to specific tooling and named technology products. For further information, please contact Norton Edge.
The last few decades of technology advancement have been transformational, increasing real capabilities, speed and scale but also increasing operational and technical complexity. Many times, mismanagement of technology and misunderstanding of the business will have compounded problems and created organisational friction. In our analysis, this complexity causes three core challenges (1) the Production Support Challenge – where many firms are at a tipping point, and infrequent technical outages and issues are exceptionally hard to troubleshoot. And (2) the Quality Assurance and Testing Challenge – where testing strategy and frameworks are often detached from the “too difficult to replicate” live environment, and can be relegated to a crude checkbox culture. And finally, (3) the Sustainable Compliance Challenge – where partially documented Policies and Procedures (P&P) are out of date, impractical, and burdensome. In each of these three specific challenges, the solution requires an ongoing commitment to understand the data and operating model. Particularly, in today’s climate of increased cost pressure, it’s all too easy for firms to ignore these complexity challenges. However, by maintaining focus on the data model and also by embedding the right data model tooling, these challenges can be readily solved.This article will briefly introduce Data Modelling, and then outline the first of these complexity challenges – the Production Support Challenge
Data Modelling: to begin with, Data Modelling always appears difficult. Management want to obtain insight, but don’t want to get “too into the weeds”. Junior staff struggle to understand the business context of specific activities & infrastructure, or struggle to articulate it in business-friendly language. An impasse occurs, and widens over time. Applications are endlessly adjusted and integrated, responding to fluid business & regulatory requirements. A perpetual cycle of migration, enhancement and retirement is set in motion. At no time, is there a pause, where the business can stop and take stock of the situation, in order to identify common ongoing problems and work out how they can be solved. Can the analysis be done in parallel or as part of BAU?
Continuous Data Modelling: if the business cannot pause to review, then it needs to be done continuously. Data modelling is the mapping of process across technical architectures, business silos and shared services. Done correctly, it measures change over time, providing auditability, and can drive standardisation. A by-product includes data dictionaries and business glossaries, ensuring all staff benefit from using a common taxonomy. Data modelling can provide the building block for true digital transformation, identifying common process and candidates for microservices, aiding the command & control analytics that business owners need. In short, you need to know your business, to change your business. There is no logical impediment preventing this being done continuously. At a basic level, it already exists in restrictive change control (depending on how much this is a checkbox exercise or abused for a dilution of responsibility), but ideally it should be embedded into the day to day business activities to provide further value.
The Production Support Challenge
Background: capital market businesses struggle to manage the proliferation of complex technology, and Production Support are at the coal face. Today, many production issues tend to be in edge cases, or in the myriad interactions between systems and processes. Many issues also tend to cluster around older, legacy technology although, microservices aim to reduce reliance on and insulate the firm from this legacy tech. The separation of development and support teams makes troubleshooting harder. Often, vendor provided and/or hosted software requires engagement of yet another support team, rules of engagements and SLAs (even in the a-a-S models). This all makes it harder to gain actionable insight from the less frequent issues that actually occur.
Solution: this is straightforward, as all firms can integrate the living, breathing Data Model (including a glossary or data dictionary) directly or via referenced links (like tinyURL) into their own support tools. The Data Model could be brought into (a) support workflow tools (b) defect tracking and project management systems and (c) internal and external messaging apps (not precluding vendor portals). In terms of activities, it could be used in incident troubleshooting (minor, major, severe), helping measure SLA adherence and performance, used in formal review (problem management, post mortems, periodic Vendor or Dev/Support reviews), and last but not least, for real business oversight and MIS.
Benefit: these are multiple, covering (1) improved support (in resolution speed) (2) improved incident review (using quantifiable data, with greater understanding and precision by reference to a common taxonomy) and (3) improved handover and interaction between teams (between the Support Team and the second or third lines of Support, Development Teams, Testing and QA Teams, Users, Business and Product Heads, as well as 3rd Parties and Vendors) and finally, (4) genuine improvement to the ubiquitous “on the job training”.
In summary, reasons abound for why firms should focus more on data modelling, and do this as a continuous ongoing exercise. Many institutions both large and small will appreciate that complexity has been a side effect of the recent, rapid technical advances. A specific scenario is the challenge providing Production Support. Complexity is inherent in most of the Support Team’s day to day responsibilities, and despite problems being less likely, they are far harder to troubleshoot. By integrating the firm’s data model into the support process tools (workflows, defect and project tracking, and messaging apps) this can provide extensive benefits (primarily better, faster support, and constructive post incident review) as well as better managing the handovers and interactions with third parties, and gaining true business insight. Reiterate the mantra – know your business to change your business.
More information on continuous data modelling and how it applies to your organisation is available upon request.
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