Five pillars of knowledge management for software engineering
Knowledge management is a bit of a sticking point across the financial services industry.
Despite modern management solutions, it has remained a major challenge for decades. This is because of:
Banks’ reliance on large, legacy technology estates and application portfolios, plus inconsistent knowledge capture
Staff churn and the consequent loss of knowledge
Complex integration across a multitude of in-house systems, third-party products and hybrid solutions
Unreliable capture of the incremental knowledge gained through testing, user acceptance, production, warranty and maintenance
Knowledge management practices generally mature over a period of time:
So, what are the best practices for knowledge management? Here are five points to consider:
1: Knowledge capture needs to be prioritized and extensible
Gone are the days when software development began at inception for every product or application. Today, many products are extensions of previous products, or they’re built in partnership with third-party products, or leverage off-the-shelf coding solutions and so on.
A prime requirement is to establish a robust knowledge transition process to make sure that teams have access to this historical knowledge before they attempt the future building of a product.
This knowledge transition process needs to ensure that:
Knowledge capture is prioritized. Not all historical knowledge needs to be captured at the time of transition and can be prioritized as follows:
Visible book of work for a 6-month period
Analysis of most frequent areas of software change over the last 6 months
Analysis of most frequent areas of support in the last 6 months if already in production
Knowledge capture is extensible to augment the knowledge base in the future as required. This can be achieved by:
Building a clear inventory of knowledge sources as metadata (documents, Wiki pages, development management tools like JIRA, source-code libraries, application logs, support ticketing systems, etc.)
Mapping knowledge sources to relevant functional and technical modules of the product
2: Known knowns make up only half of the required knowledge
The other half is probably locked in the minds of previous team members or implemented systems on the ground.
Knowledge falls into one of three types:
Explicit: Visible and clearly available in known sources
Implicit: Knowledge gained from practical application of explicit know-how
Tacit: Knowledge that is understood, assumed or accumulated over time
Capturing implicit knowledge:
Practical assessment: Study of implemented systems. For instance, code reverse-engineering tools can be used to develop documentation from implemented code
Shadow incumbent experts: Observe or work alongside to gain insights
Capturing tacit knowledge:
Analyze historical chat transcripts, emails, application logs, development and support ticket history and so on, based on relevance and reflecting real-time behaviors exhibited in history. You can use Natural Language Processing solutions to analyze unstructured knowledge sources and derive meaningful insights
3: Knowledge management needs quantification
Team knowledge levels are the most proactive indicators for expected quality of software and product. So, you need to develop an objective view of team knowledge, and a solid plan to aid its gradual improvement. “Knowledge index (KI)” is the primary metric.
Measuring the KI:
Proficiency-based KI: The most objective approach is the Dreyfus model’s five levels of proficiency (novice to expert)
Objective KI: Certifications, internal assessments, deliverable reviews, etc.
Team or squad KI: Granular level measurement — specific to teams
Organizational baselines: For common areas of knowledge e.g., specific domains or technologies
Target versus actual: Baseline current KI, establish target KI and have an objective plan to bridge the gap between actual and target levels
Other objective metrics, such as software quality, efficiency and productivity, can provide a retrospective view of knowledge also. An ongoing corelation analysis between these metrics will produce the most accurate quantitative knowledge levels of the teams.
4: Knowledge is revised continuously
Knowledge management is a continuous journey. Fresh knowledge might not be captured in full due to time and cost pressures — you need to capture this knowledge before it becomes history.
Minimize the cycle by:
Incentivizing fresh knowledge capture:
Convert collaboration tools like Confluence and Miro into knowledge repositories
Deploy knowledge automation tools such as code reverse-engineering to generate documentation from code
Simplifying incremental knowledge capture: A lot of knowledge is gained post-development of code and testing onwards. Use defect management and ticket management tools to record fresh knowledge whenever gained
5: Knowledge extends beyond people
Although knowledge means people and people mean knowledge, we still need to decouple the two.
Key person dependencies can cause stress which impacts work-life balance and affects team morale. You must download this knowledge to an offline repository and make it available for upskilling.
Offline knowledge repositories:
Recorded sessions: Incentivize experts to make their knowledge available as offline recorded audio and video presentations
Automated knowledge repositories: Use code reverse-engineering tools and natural language processing solutions on implemented code
Collaboration tools: Like Confluence and Miro
Organize these offline knowledge repositories into a structured, on-demand library with a “course curriculum”. The team can assimilate this knowledge at their own pace, charting a career path for themselves based on their expanded knowledge.
If properly executed, knowledge management can become one of the greatest assets for banks, ensuring the successful delivery of products and services to their clients.
DXC Luxoft help clients leverage our leading-edge knowledge-management practices to mitigate risk and grow their businesses.
Balaji is a senior director with DXC Luxoft India, and heads the Digital Delivery Strategy and Solutions for BCM APAC. He has over 21 years’ experience in the IT industry. Balaji has driven large-scale technology solutions and transformation initiatives, directly, in Silicon Valley technology companies as well as in services partnership with global financial clients. He specializes in large-scale knowledge transitions, transformations, Agile, DevOps, big data and analytics, cloud and program management.