Your IT capabilities are out of sync with data governance goals. How will you bridge the gap?

Data Governance implementation is a big change management excercise as well. Assessing gaps is not just about the data process or toolset gaps but also about assessing gaps in people involved who will be part of the Governance. Assigning new responsibilities of data handling or letting go previous control on processes to facilitate proper Governance or resources is a difficult task and need to be implemented by proper planning and keeping everyone informed about the ultimate benefits this change will provide.

Bridging this gap with the obtainable IT capabilities is similar to doing a refurbishing action on an old house. First, you assess what’s broken (Assess Gaps), then you get to the coordinates of the building(Define Strategy). Eliminate the old and add new elements to the base(Make Queries), but understand that even the most beautiful house will be useless without the occupants – educate the house staff(Staff training). Observe how the alteration is progressing(Monitor Progress) and respect every party in the construction, from buttresses to homeowners(Monitor Stakeholder Involvement). In this manner, with every single detail, you are repeatedly improving a construction to be strong and mass efficient in the coming days.

To bridge the gap between IT capabilities and data governance goals, I will focus on integrating business context by defining clear business terms and rules. Additionally, implementing a robust data catalog platform will provide a unified view, ensuring both IT and business stakeholders are aligned and can effectively manage data assets. With this approach data governance goals can easily be implemented on IT.

Data, in any organization, needs to be organized and made available with optimum cleansing done, to support the modern day data analytics and Data Science requirements. Understanding the core needs of data science and updating skills related to data quality and data governance is the key to aligning oneself with the organization's needs. Also, having the working knowledge of the modern day cloud technologies for data processing and data storage will be an added advantage.

A successful data transformation strategy focuses on achieving these four high level goals: 1) Measure and maximize the balance sheet value of your company’s data assets (don't do this by division or you'll hinder innovation and reduce synergy) 2) Maximize P&L return on those assets 3) Reduce operational, reputational, regularatory, and compliance risks 4) Don’t spend a lot and what you do spend, spend wisely Below this level, few firms stick with a strategy unless they see how well you’re achieving the above. Go for a strategy that allows for incremental wins that eventually turn into disruptive innovation.

To bridge the gap between IT capabilities and data governance goals: -Assess Capabilities: Evaluate current IT systems and identify shortcomings in supporting data governance. -Define Objectives: Align data governance goals with business objectives and set short-term and long-term priorities. -Develop Strategy: Upgrade IT infrastructure, implement suitable data management software, and revise policies. -Foster Collaboration: Engage stakeholders and provide training to ensure effective tool use and policy adherence. -Monitor Progress: Track performance with metrics and adjust strategies as needed for continuous improvement. This approach ensures IT and data governance goals are aligned and effectively managed.

This requires careful planning but is worth it: Eliminate contentful code like your company’s future depends on it. Because if you don't, your employee count, operating costs, and risks will all be too high and you won't be competitive. Contentful code refers to business logic and data. If you can tell what business you're in from looking at the code, your code is contentful and that's bad. This approach forces you to structure how you view your business. With those details in your business metadata tables, when a new customer, product, vendor, market, data source, etc. comes by, you simply add a few rows of business metadata here and there and everything works.

The wishes and needs of employees must be taken into account. Not everyone feels comfortable having to learn and use new software over and over again. A balance must be found between innovation and consistency, which only very few people find overwhelming.

?? ???????????? ?????? ????????????????: Start by assessing the current IT team's skills against the data governance goals. Identify specific areas where there are gaps, such as data management, security, compliance, or data quality. ?? ????????-???????????????? ????????????????: Customize training for different roles within the IT team. For example, data engineers might need in-depth training on data integration and ETL processes, while security specialists might focus on data protection and privacy laws. ?? ???????????????????? ????????????????: Pair less experienced staff with mentors who have strong governance knowledge. This helps in transferring practical, on-the-job knowledge in daily IT operations.

Implement real-time dashboards to provide continuous insights into the status of data governance initiatives. Dashboards can visualize key metrics, such as compliance rates, data quality scores, and incident response times, offering immediate feedback to management. This real-time information allows for quicker adjustments and ensures that governance practices remain aligned with objectives, making it easier to stay on track and achieve goals.

Create a detailed communication plan to keep all stakeholders informed and engaged throughout the data governance journey. This plan should outline how and when updates will be provided, how feedback will be collected, and how concerns will be addressed. Regular communication builds transparency and trust, ensuring that everyone feels involved and committed to the success of the data governance initiatives.