Nowadays most people in organizations understand how visibility into data adds overall value and there is a general dedication to be and remain data driven and increase overall data literacy. At the same time, sometimes there are limitations to how much organizations want to invest or augment their investment in data projects. It's important to make sure that companies have support across departments to budget appropriately for their data needs. This list is not exhaustive but presents some of the aspects I have seen used as a way to gain broader support for data and analytics projects across departments. After all, data assets can be leveraged across business domains, making it easier to share costs and increase benefits. Instead of limiting data access or increasing the number of silos, a more cohesive evaluation of data challenges and the need to look more holistically at data access and analytics creates more visibility across the company.
Value of investing in data projects more holistically include:
- Reuse of data pipelines to save costs across cost centers
- Enable more visibility into operations leading to more opportunities and ways to collaborate across the organization
- Automation saves developer time and delivers faster time to insight
- Holistic data management practices lead to better overall data quality, increasing trust and the ability to meet regulatory, compliance, and privacy requirements
- Limit data silos by enhancing visibility into data flows and how business domains intersect across the organization to leverage data across the data ecosystem
- Enable autonomy and better overall operations by empowering employees with more access to information required to make informed decisions
- Align departmental analytics goals with broader data strategies and high level key performance indicators (KPIs)
Looking at department needs can limit the outcomes achieved through analytics investments. Departures need to have analytics delivered, but development cannot be thought of in terms of individual sets of requirements as data pipelines need to be developed more broadly to ensure the most effective data management practices are being followed.
Starting with these seven benefits of investing in data more broadly to create cross-functional projects can enable organizations to become more data driven and see more value in their data and analytics investments over time.