analytics-failure

When Big Data Means Bad Analytics

When Big Data Means Bad Analytics

When analytics delivers disappointing results, it is often because there is not enough analytic expertise, and/or lack of understanding of a business objectives for using Big Data in the first place. To avoid failure, insist on high standards.

Now that data scientist has been named one the sexiest job of the 21 century, a lot more people are claiming that they “do analytics,” and a lot more companies are claiming the same as well. The rise of Big Data means there’s more demand than ever for these types of businesses.

Unfortunately, few of these “analytics arriviste” companies have the experience necessary to follow best practices in terms of analytic processes.  And more to the point, many of these companies haven’t done the important-but-unglamorous work of really understanding the data that drives both the decisions and the building of analytic models.

When analytics delivers disappointing results, it is often because of one or both of these reasons:  1) there is not enough, or the right, analytic expertise; and 2) there is a lack of understanding of a business’s objectives for utilizing Big Data in the first place. That’s why this may be a year of bad analytics.

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Exploring huge amounts of data with advanced analytic tools can be fun, but it can also be a huge waste of time and resources if the results do not translate into something that solves real-world business problems.  And while today’s analytic tools are becoming more robust, that doesn’t mean there is less of a need for human expertise.  Analytic expertise informed by deep domain knowledge is essential for building effective predictive and decisioning models.

Today’s shortage of analytic talent puts more pressure on organizations to ensure they engage with well-trained data scientists, either their own in-house experts or vendors with whom they choose to work.  A McKinsey study predicts that data science jobs in the US will exceed 490,000 by 2018, but there will be fewer than 200,000 data scientists.  The fact that there is a talent shortage only makes the issue all the more urgent to address.

But don’t be discouraged by those numbers; there is some good news.;

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