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Last month I addressed how data quality is perceived by different specialists inside the organisation. This month, I turn the spotlight onto what makes modern data quality management different from traditional approaches. Edwin Walker’s Data Science Central article ‘Difference between modern and traditional data quality’ provides an excellent starting point.
The technical skills required to be a data analytics professional form the cornerstone of training approaches. If these individuals are not proficient in things like data wrangling, analytical approaches, modelling techniques, data exploration, or data visualisation, then they will likely not qualify. But that is just one part of what the job requires. I believe …
Data science has been gaining attention over the last few years, but as with most new trends or topics, some organisations have been slow to actually adopt and implement it. Although data science, by its nature, is quite an unstructured discipline, you may just need a more formal strategy to implement such a program in …
In a previous blog, I discussed how to make self-service BI more appealing to the users, given that it allows an organisation to access and work with corporate information without the IT department’s involvement. So following on, I now examine the aspects that need to be considered in order to implement self-service BI effectively. Getting …
Since my initial post on data visualisation (http://www.martinsights.com/?p=492), I have noticed a steady increasing interest in the topic. This is especially noticeable in the business intelligence (BI) field, as more and more organisations are realising the value of using visualisation to positively affect their day-to-day interactions with data. In this post I focus on the …
Social media is fast becoming a mechanism of choice for the dissemination of and collaboration around the information delivered by BI – to the extent that it is fast becoming a key input channel of behavioural data as well.
