Big data marketing is no longer just a concept to many businesses, but a here-and-now reality. It involves managing huge volumes of unstructured and structured data and using it to gain insights that drive business value. Big data marketing can identify trends, find patterns that might otherwise be missed, and in general make sense of some of the masses of data available to businesses today.
But big data analytics should be planned and executed by people who know what they’re doing, because some of the more advanced tools aren’t ready for prime time yet. Businesses that embrace big data marketing, however, have the opportunity to understand their customers much more deeply, so they can design sales and marketing strategies that are the most effective. Here are 4 trends in big data marketing analytics to keep an eye on in 2015.
1. Data Lakes
If you were around in the early days of databases, the protocol was to design the database before entering information into it. A data lake, on the other hand, doesn’t work that way. With a data lake for big data marketing, you take multiple data sources and dump them into a Hadoop repository. There’s a high-level definition of what’s in the data lake, but tools are built organically, as people analyze the data and gain new insights into it. Since the data contained in a data lake may include things like clickstream data as well as more traditional data, analysts have to be highly skilled. But many companies are creating and “democratizing” tools for making sense of what’s in data lakes so that businesses can use them effectively for big data marketing.
2. Big Data in the Cloud
The Hadoop framework – tools for processing huge data sets – was designed to work on collections of physical machines. That made big data marketing impractical or impossible for businesses without deep pockets. But now, more tools are becoming available for big data processing in the cloud, which puts Hadoop and all its promise in the hands of smaller companies as well. For example, Amazon Redshift is a cloud-hosted data warehouse that offers extensive reporting features for structured data. Big data marketing is a major undertaking, and moving it to the cloud makes it easier and more cost-effective to scale analytics operations up or down on an as-needed basis.
3. More Powerful Predictive Analytics
Big data marketing means working with more data and having tools that extract meaning from data records with multiple attributes. The era of cheap computational power means that marketing professionals can explore new types of data and can explore existing data in new ways. Traditional machine learning can now be turned loose on huge numbers of data records, allowing people to formulate queries and problems in new and innovative ways. The challenge with the Hadoop tools used in big data analysis is speed, since Hadoop can take longer to get answers than traditional query technologies like SQL. Fortunately, data scientists are developing ways to apply SQL query tools in the Hadoop framework, greatly improving performance.
4. SQL on Hadoop
The Hadoop Distributed File System (HDFS) uses the power of parallel processing on big data. But in business applications like big data marketing, it works better with SQL-style querying. Early Hadoop tools required specialized skills for creating queries, and then the queries took a while to run and weren’t interactive. Now, however, tools like the Spark analytical engine are bridging the gap between Hadoop and business queries. The ultimate goal of these new tools is to allow businesses to use parallel Hadoop applications as part of their existing workflows, bringing big data marketing tools down to earth and making them much more usable to non-data scientists.
Big data marketing tools are in the process of maturing, but the big data environment is becoming friendlier for business analytics due to improving platforms (like the cloud) and emergence of big data best practices as analysts learn more. One of the most critical determinants of success with big data marketing is data quality.
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