Data management and analytics capabilities toolkit
The #DMAC toolkit aims to help FSPs understand the different data sources available to them and how to use them effectively to transform their business.
Get your data ready for 2020
Welcome to 2020 everyone Kicking off by borrowing a look forward from our partner, Syncsort How to Modernize Your Traditional Database in 2020 If your data center is like many others, you have several important applications that rely on traditional database implementations. For example, it’s not unusual for companies to have business-critical applications that use…
Is it 2020 already?
A recent post on Information Week highlights 10 data and analytics trends that Gartner analyst, Rita Sallam, predicts will expand over the next 3 to 5 years. These trends fit into three main themes – intelligence, structures and scale – bringing together the potential of big data and machine learning. Augmented Analytics Sallam predicts that…
The hidden cost of not finding data
Today I overheard someone say, “Most business users give up trying to find or understand the data they need to do their jobs within 30 minutes of trying.” I gave up after 3 minutes of trying to find its source The digital business is dependent on data Self-service BI is dependent on data. Business goals,…
Data Quality Problems in the Cloud
Cloud computing offers new opportunities and challenges to enterprises grappling with the explosion of data sources, growing data storage needs, and the problem of ensuring data quality.
5 courses to improve your data literacy
What can you do to become data literate?
What is data literacy and why is it important
Everybody that works with data, and that makes decision based on data, needs to have an understanding of the underlying, fundamental data management competencies – such as data governance, data quality and data modelling – that allow them to assess whether data is fit for purpose.
Streaming analytics is key to leveraging value from M2M and IoT data
The constant and relentless nature of ‘always-on’ data generation also makes real time analytics even more important. Much of this data is only relevant now, in the moment, and once it is historical it no longer has worth. It must be analysed immediately and then deleted, otherwise no value can be gained.
