How do you manage data requests from partners?

Different departments/teams require different KPIs. Understanding what's important to THEM is the #1 rule. Ask questions and a lot of them! Over time, you won't have to ask as many but it's great to set a good example and truly understand the "why" behind their request. The more you know, the better analyst you are!

Working in a big company this process is easy because we already know some legal aspects of data and the process comes from global area available. The second point of understanding the questions we need to align with respective area what is it for and the outcome they want to have and use.

This can sometimes take longer than any other part of the process. You actually need to get access to the data and see if it's possible to extract the data to begin work or accept a project. This is a crucial step, but can take upwards of a few weeks just to get access.

The consent subject is a point of attention, since we work with data and legal aspects. The company area needs to support this part before start managing all customer data. After that some tools integration make some correlation and give us an idea of witch data can be connected with other. Some improvement can be done as you start undrstanding the possibilities of outcomes and necessities.

Make sure to include copyright notices, licenses, and contract terms in your deliverables. Data is an extremely valuable commodity, and you want to ensure that it will be used within the right set of bounds.

Though it's tempting to call your delivery of an analysis a final step it's unlikely you'll completely walk away from the analysis. Often the delivery is the start of another conversation about where the end user would like the analysis to go or what she'd like to add having seen what was available.

ADLI is a popular approach where we need to decide the Approach then Deploy the approach. After deployment, check, control and Learn and finally Integrate and communicate the results.

Context is everything when presenting any kind of insight. You need to build a shared understanding when distinguishing between what's important and what's noise with your audience. Before you can spot anomalies or unusual patterns, then you will often need to understand and articulate what the normal rhythms and patterns of your data look like.

Managing the relationship is often overlooked but is a crucial component regarding all aspects of data analysis, especially data requests. This is because often you might be dealing with access-restricted proprietary data. So, it is essential to ensure not only proper channels of communication but dissemination as well. Also, it pays to be aware of established cadences and data quality requirements. So, being proactive and respectful will do wonders for stakeholder relations and help you gain a solid footing in a conflict.

A structured intake process is key. Whether managing routine or ad hoc data requests, establishing a structured intake form designed to delineate the client need is paramount. The process flow for client request should be structured such that using service level agreements based on report complexity, clients are directed to a landing page, where required fields and features of reports are specified and clarifications made to determine if the report is a new report or has been requested in the past. In the course of the request intake, clients are directed to standard reports and dashboards from where they could resolve their own data requests, hence embedding self-serve and improving the use and adoption of currently available reports.