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data cubes

Think Outside the Cube

These days, most SaaS providers embedding BI in their applications recognize they also need some kind of ETL. (Check out this infographic if you aren’t sure what ETL is). In response, many BI platforms are coming up with their own proprietary data preparation solutions. Some have chosen to use multidimensional…

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data quality audit

Using BI to Audit Data Quality

Data Talks, Episode 15 Data Talks is Exago’s podcast on all-things business intelligence, analytics, and application software. When most people think of BI, they think about extracting insights from their data, not about their data. This month, George Firican explains why BI can be a surprisingly effective tool in data…

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assessing data veracity

Assessing Data Veracity

There are different takes on what veracity refers to, but the overall consensus is that data veracity reflects the truthfulness of a data set and your level of confidence or trust in it. I’ll take this a step further and say that data veracity is your level of confidence/trust in…

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Data quality issue logging

18 Data Quality Issue Attributes You Should Be Logging

If you’ve already started or are planning to start a data governance program to support your data quality improvement goals, you need a structured way of tracking your data quality issues and their status. There are different ways of doing this, of course, with either the help of dedicated data quality…

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How to Not Get Overrun by Your Data Overrides

Large-scale enterprises rely heavily on market and consumer data sourced from outside vendors, but that information comes at a cost. In addition to intermittent issues with the data quality itself, consumers of vendor data can face timeliness problems, non-standard formats, coverage gaps, and lags in technical support. Despite these and other…

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ETL primes your data for reporting

Infographic: How ETL Primes Your Data For Reporting

There’s a lot your BI solution can do to make reporting off live data faster and more efficient; but when you’re looking for an even more substantial performance boost, it’s time to bring out the big guns. Any pre-processing you do to your data is work your system can skip when…

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Evolution, Not Revolution: Tips for Transitioning to a Federated Database System

As enterprises scale, they develop data inefficiencies. Sometimes these inefficiencies arise internally, between departments or branches, while others emerge as the parent company acquires smaller companies, each with their own databases and IT departments. In either case, communication issues ensue as siloed databases and IT redundancies begin costing the enterprise…

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The Emergence of the Chief Data Officer

Above: Popularity of “Chief Data Officer” searches (blue) compared to “Chief Technology Officer” searches (red) and “Chief Information Officer” searches (yellow) from Jan 2004 through present according to Google Trends. The period with the most searches is given a y value of 100, and all other periods are charted in comparison to that peak. What...

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Most Technology Projects Fail. What is Your Data Management Plan?

The failure rate for technology projects continues to be over 50%, and some estimate it’s even higher. While that statistic may or may not surprise you, what will surprise you is how much data management strategy (or lack thereof) contributes to that failure rate, directly or indirectly. People blame what they can see, and because...

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