With data changing and growing so rapidly, the need to get value out of your data is even more urgent. Here's some advice about how to approach data lakes
"Although still a burgeoning term, data lakes have recently gained more recognition among IT teams as data increasingly becomes a foundation of modern business. Conceived as a solution to reduce data sprawl and data siloes, data lakes emerged from the industry of data warehousing, which targeted the frustrations IT encountered when trying to create an organized repository of strategic datasets on which to make key business decisions. This use can range from data analytics to better understand customer needs to artificial intelligence to solve for real-time challenges..."
You can't get insights using big data techniques without the data. That much is clear. But where, exactly, is the data you need?
"Where does it reside, and how can you get access to it? The answers to those questions are not always directly obvious. But with the help of data catalogs, organizations are discovering that data doesn't have to be so hard to find after all.
More than a decade into the 'big data' era, we're finally figuring out that Hadoop isn't the answer to all of our data problems. Instead of centralizing data in giant HDFS clusters meant to serve the data needs of entire companies or departments, organizations are once again building one-off systems to handle specific data storage, processing, and analytic tasks..."
Data analytics have long been seen as a valuable way for businesses to refine their marketing and improve their communication
"However, as we learn more about the ways that data can be applied to a business, we're better understanding the many ways that it can improve business management.
Data plays an important role in both the public and private sectors. With the ever-evolving collection and analytics tools available, agencies and businesses can use data to streamline workflows, help identify fraud, and much more..."
Why do so many companies still struggle to build a smooth-running pipeline from data to insights?
"They invest in heavily hyped machine-learning algorithms to analyze data and make business predictions. Then, inevitably, they realize that algorithms aren't magic; if they're fed junk data, their insights won't be stellar. So they employ data scientists that spend 90% of their time washing and folding in a data-cleaning laundromat, leaving just 10% of their time to do the job for which they were hired..."
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