Data Source Handbook: A Guide to Public Data
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It also includes relevant events in the data space. Anyone can add events to it. When creating an event for the entire Data Team, it might be helpful to consult the Time Blackout sheet. The team honors Meeting Tuesday. We aim to consolidate all of our meetings into Tuesday, since most team members identify more strongly with the Maker's Schedule over the Manager's Schedule. We Data. We do this by maintaining a data warehouse where information from all business systems are stored and managed for analysis.
The Data Team at GitLab is working to establish a world-class analytics function by utilizing the tools of DevOps in combination with the core values of GitLab. We believe that data teams have much to learn from DevOps. We will work to model good software development best practices and integrate them into our data management and analytics. A typical data team has members who fall along a spectrum of skills and focus. Analysts are divided into being part of the Central Data Function and specializing in different functions in the company.
Data Engineers are essentially software engineers who have a particular focus on data movement and orchestration. The transition to DevOps is typically easier for them because much of their work is done using the command line and scripting languages such as bash and python.
One challenge in particular are data pipelines. Most pipelines are not well tested, data movement is not typically idempotent, and auditability of history is challenging.
In the past, data queries and transformations may have been done by custom tooling or software written by other companies. These tools and approaches share similar traits in that they're likely not version controlled, there are probably few tests around them, and they are difficult to maintain at scale.
Data Scientists are probably furthest from integrating DevOps practices into their work. Much of their work is done in tools like Jupyter Notebooks or R Studio. Those who do machine learning create models that are not typically version controlled.
Providing Unprecedented Access to Data
Data management and accessibility is also a concern. We will work closely with the data and analytics communities to find solutions to these challenges. Some of the solutions may be cultural in nature, and we aim to be a model for other organizations of how a world-class Data and Analytics team can utilize the best of DevOps for all Data Operations. The Data Team operates in a hub and spoke model, where some analysts or engineers are part of the central data team hub while others are embedded spoke or distributed spoke throughout the organization.
Central - those in this role report to and have their priorities set by the Data team.
They currently support those in the Distributed role, cover ad-hoc requests, and support all functional groups business units. Embedded - those in this role report to the data team but their priorities are set by their functional groups business units. Distributed - those in this role report to and have their priorities set by their functional groups business units.
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However, they work closely with those in the Central role to align on data initiatives and for assistance on the technology stack. All roles mentioned above have their MRs and dashboards reviews by members in the Data team. Both Embedded and Distributed data analyst or data engineer tend to be subject matter experts SME for a particular business unit. Analysis usually begins with a question. A stakeholder will ask a question of the data team by creating an issue in the Data Team project using the appropriate template.
- The Science of Economics - The Economic Teachings Of Leon Maclaren;
- What is Open Data?;
The analyst assigned to the project may schedule a discussion with the stakeholder s to further understand the needs of the analysis, though the preference is always for async communication. This meeting will allow for analysts to understand the overall goals of the analysis, not just the singular question being asked, and should be recorded. All findings should be documented in the issue. Analysts looking for some place to start the discussion can start by asking:.
Detailed courses and expert interviews for all workflow levels
An analyst will then update the issue to reflect their understanding of the project at hand. This may mean turning an existing issue into a meta issue or an epic. Stakeholders are encouraged to engage on the appropriate issues. The issue then becomes the SSOT for the status of the project, indicating the milestone to which it has been assigned and the analyst working on it, among other things.
The issue should always contain information on the project's status, including any blockers that can help explain its prioritization. When satisfied, the analyst will close the issue. The data team's priorities come from our OKRs. We do our best to service as many of the requests from the organization as possible. You know that work has started on a request when it has been assigned to a milestone. Please communicate in the issue about any pressing priorities or timelines that may affect the data team's prioritization decisions.
Please do not DM a member of the data team asking for an update on your request. Please keep the communication in the issue. The data team, like the rest of GitLab, works hard to document as much as possible. We believe this framework for types of documentation from Divio is quite valuable. For the most part, what's captured in the handbook are tutorials, how-to guides, and explanations, while reference documentation lives within in the primary analytics project.
We have aspirations to tag our documentation with the appropriate function as well as clearly articulate the assumed audiences for each piece of documentation. At the beginning of a FQ, the team will outline all actions that are required to succeed with our KRs and in helping other teams measure the success of their KRs. The best way to do that is via a team brain dump session in which everyone lays out all the steps they anticipate for each of the relevant actions. This is a great time for the team to raise any blockers or concerns they foresee. These should be recorded for future reference.
Specialty data analysts who have the title "Data Analyst, Specialty" should have a similar break down of planned work to responsive work, but their priorities are set by their specialty manager. The data team currently works in two-week intervals, called milestones. Milestones start on Tuesdays and end on Mondays.
Benefits of Open Data
This discourages last-minute merging on Fridays and allows the team to have milestone planning meetings at the top of the milestone. Milestones may be three weeks long if they cover a major holiday or if the majority of the team is on vacation or at Contribute. As work is assigned to a person and a milestone, it gets a weight assigned to it. The short-term goal of this process is to improve our ability to plan and estimate work through better understanding of our velocity.
During the milestone planning process, we point issues. Then we pull into the milestone the issues expected to be completed in the timeframe.
Points are a good measure of consistency, as milestone over milestone should share an average. Then issues are prioritized according to these categories. Issues are not assigned to individual members of the team, except where necessary, until someone is ready to work on it. Work is not assigned and then managed into a milestone. Every person works on the top priority issue for their job type.
As that issue is completed, they can pick up the next highest priority issue. Opening up government data has many benefits, such as allowing for the creation of new businesses or simply promoting transparency. This handbook originally aims at EU organisations, but can also be used as a general source on how to best find and prepare data inside your organisation to make them publicly available.
In this article, we give you a quick overview of the handbook and a summary of the key points. With a slim 62 pages, the handbook describes the way for organisations on how to bring their data into the EU Open Data Portal and make it publicly available. The first chapter briefly describes what opendata is and why it is such an important topic not just for the EU. From chapter two on it gets more practical, focusing on a six step workflow to liberate your data. The six steps are, what makes this book so interesting for everyone involved in opendata.