Is your data issue a business intelligence problem… or a change management issue? Can you distinguish these perspectives, or is it differing views on the same thing
Learn more about the metaverse and analytics, digital transformation, and business intelligence with my keynote at BI Innovation & Tech Fest on 26th- 27th September in-person + live-streamed, in Johannesburg.
Resetting Default Filters in Looker. From the user perspective, they will want to land on the dashboard and see the data for the current month (for example). Here’s a tip on how to set up a default filter.
Is it ever possible to eradicate Excel from an organization? What is the place of Excel in a modern business?
I was all set to present this session at the European Collaboration Summit in November 2021, but the organizers, well, God only knows what happened
Azure ML can become a part of the data ecosystem in an organization, but this requires a mindshift from working with Business Intelligence to more advanced analytics. How can we can adopt a mindshift from Business Intelligence to advanced analytics using Azure ML? Although CRISP-DM is not perfect, the CRISP-DM framework offers a pathway for machine learning using AzureML.
We demystify the jargon to untangle what these terms really mean, how they are related, and how they can be used for data modelling.
Data-driven decision-making is a somewhat mysterious objective for many businesses. How can we leverage business data for smarter decisions?
Join me at ITWeb Business Intelligence Summit. I will be presenting the international keynote on building a data, analytics and AI culture.
As businesses try to survive or take advantage of changing markets amid COVID-19, what role does data play in mergers and acquisitions?
Data debt is like technical debt, only visible in bad data, poor data quality, and an underuse of data-oriented technology to help make better decisions. It is the overhang of projects, whether they went well or poorly.
Technical debt is the implied cost of additional rework caused by choosing an easy (limited) solution now instead of using a better approach that would take longer. It also applies to data debt; the cost of ignoring the data by choosing the quick or easy solution earlier at a previous point, which now takes longer to resolve.
At some point, however, we need to pay back data debt if we are to succeed in making better decisions.
Many people, including myself, have discussed CRISP-DM in detail. However, I didn’t feel totally comfortable with it, for a number of reasons which I list