Gartner's 5 Big Data Myths: Fact or Fiction?

When Gartner speaks, the industry typically listens. But is Gartner always right -- particularly when it comes to market forecasts and industry reality checks? For the latest reality check: Here's Gartner's take on the Big Data market -- along with After Nines Inc.'s spin on Gartner's spin.

In a Gartner Myth blog, the company pointed out...

1. Myth: Everyone is ahead of us in adopting big data.

Gartner says 73 percent of organizations are investing in -- or plan to invest in -- big data technologies. But take a closer look, and Gartner says only 13 percent of companies have actually deployed Big Data solutions.

Our spin: That explains why "hot" big data companies like Cloudera and Hortonworks are relatively tiny (though growing fast) compared to established cloud giants like Salesforce.com. Amid all the hype, Hortonworks is still ringing up big annual losses and roughly 20 percent of the company's revenues come from one customer -- Microsoft. That suggests most of the world has yet to adopt Hortonworks and other big data tools.

2. Myth: We have so much data, we don't need to worry about every little data flaw.

Many IT leaders believe huge data volumes offset concerns about small amounts of "bad" data in the system. In reality, Gartner says bad data is a bigger problem than ever -- because so much data comes from outside the organization (through purchased lists, social networks, etc.).

Our spin: We agree completely. As we consider our own data strategy here at After Nines Inc., we're being very careful to weed out bad data sets that may undermine our overall data quality.

3. Myth: Big data technology eliminates the need for data integration.

Many big data pundits think the "schema on read" approach will solve the big data integration challenge. But Gartner thinks "schema on write" scenarios will stick around, meaning that the need for data integration will continue.

Our spin: Hard to argue with Gartner on this one.

4. Myth: It's pointless to use a data warehouse for advanced analytics.

Many data chiefs think it's pointless to build a data warehouse because its time consuming, and new analytics tools use new types of data beyond the warehouse. Gartner says data warehouses will remain important for many advanced analytics projects.

Our spin: Careful with this one. Gartner's clientele includes data warehouse vendors that want to cash in on Big Data. I'm not saying Gartner is pandering to those data warehouse companies. I am saying you should carefully study the pros and cons of building a data warehouse before opening your wallet.

5. Myth: Data lakes will replace the data warehouse.

Gartner notes that some vendors make "data lakes" as "enterprisewide data management platforms for analyzing disparate sources of data in their native formats." But Gartner is quick to warn that data lake foundation technologies lack the maturity and breadth of features found in existing data warehouse technologies.

Our spin: We're on the same page, generally speaking, here.

Our closing confession: Frankly, all this big data talk has given me a headache. Time to unplug for a bit. I'll be back later with more blogs.

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