Data Tips #25 – Handling re-runs
Today I want to look at a topic that happens to all teams running a data platform. How do we handle re-runs in the best manner? First of all, I want to look at the purpose of the data platform, generally it is to provide the right data at the right time to the right […]
Data Tips #24 – Data Retention & Data Deletion
In this article we are looking at Data Retention and Data Deletion inside the data platform. There are two typical scenarios that we need to cater for when it comes to deletion: In order to be fully compliant we need to delete the data from all parts of our data platform. That includes the archive, […]
Data Tips #23 – Organisation 3. Platform and facilitating roles
This article is a continuation on the organisation track. Please se previous articles: 1. Roles in a data team and 2. Surrounding roles Today we are discussing the Platform and facilitating roles. The sole purpose of these roles is to make sure that the Data Teams and the roles in the teams have an efficient […]
Data Tips #22 – Organisation 2. Surrounding roles
Today I am following up the last organisation article with an article describing some of the roles surrounding the data team. Roles that have a daily interaction with the team. 2. Surrounding roles – Roles directly interacting with the data team 3. Platform and facilitating roles – Roles making sure that the data teams have […]
Data Tips #21 – Putting effort where it matters
Today I want to adress something that is very important. That is putting effort where it is effectful and really matters to the organisation. There are three main areas I think are very important to prioritize within. Those areas are: It is really easy to say: “We need to improve data quality” or “We need […]
Data Tips #20 – Organisation 1. Roles in a data team
Starting off a series of organisation articles to discuss different roles that you may need in your organisation to become datadriven. As always, what roles you need is dependent on your company and what you want to achieve. Lets start out with the roles for the data team. In general I think that the data […]
Data Tips #19 – Data Quality 4. Practical examples of the impact of data quality
We have discussed data quality in some different dimensions and angles in previous articles. It does get a bit abstract at times so today we will look at some practical examples of when data quality matters. So, to start off. Let us take an easy case that has a very direct impact. Example 1: Old […]
Data Tips #18 – Data Quality 3. Data Quality in the Data Platform
We have in the two previous articles discussed what are different dimensions that can be measured and the holistic view to take. We also looked at where it makes most sense to handle data quality issues (as early as possible). In a perfect world, data quality is handled in the source or close to the […]
Data Tips #17 – Data Quality 2. The holistic view
In the last article we discussed the different dimensions to measure data quality in. There are no right and wrong measures to choose, the measures should fit the use case. In this article we are looking at the holistic view of Data Quality. In a simplified world there are four important steps to the data: […]
Data Tips #16 – Data Quality series: 1. The basics
First off we have to talk about what Data quality is and why it is important. Data quality is a series of measurements that describes how well your data conforms to different dimensions. That is a tricky way of saying that Data Quality is not one measurement, it is many measurements. The purpose of the […]