Breaking Down Big Data: 4 Tips to Simplify Data Analysis

Big data has revolutionized practically every industry from banking to manufacturing to healthcare, but it has made perhaps the strongest impact in the field of marketing. Marketers were among the first to leverage big data for real and tangible benefits. They've used it to make product recommendations, get a handle on social marketing, and connect with customers on a deeper level via content and email marketing.

To date, most marketers have depended on data products to do their "big data analytics stuff". Software systems like CRM solutions, marketing automation tools, and email marketing systems do leverage data analytics, but it's done for you, most often in a cloud environment outside your own corporate database management software.

"Doing big data" on your own is a different story. But it's doable! Here are the steps to get from here to there.

1. The Process of Data Discovery

Data discovery is the process of identifying the data sets and data streams that can become a part of your big data initiative. Most often, organizations possess numerous data silos. Data silos are systems in which data is stored, locked away, in a sense, from other databases within the organization. Data silos can be anything from the spreadsheets used by finance to the legacy system used by production to website platforms and the marketing systems listed before.

Data can also be found in your mobile apps (both internal and customer-facing), the text documents and pdf files around the office, and all of your previous marketing materials and metrics. Any data in any form that could be useful to marketing should be included in data discovery.

2. Tackling Storage

Now that you've found it, you need a place to put it. There are numerous options when it comes to database management software, including a distributed file system (like Hadoop's HDFS), an external cloud solution (like Microsoft Azure or Amazon EBS), or an on-premises data warehouse or data lake (though this is, by far, the most expensive option). This is a discussion you need to have with your IT department.

3. Data Collection

Now that you've found the data and established a place to put it all, there's the matter of getting the data out of its host systems (or the enterprise data center) and into your database management software. This is called offloading. It's more tedious than you'd like to think, because most of the systems that collect and store data natively don't play nicely with each other. For example, you can't just plug in your cloud-based ERP system to your mainframe and dump away. This is another discussion to have with IT. This time bring gifts.

4. Data Analytics Software

database management software
Once you've assembled the data in your data store, it's time for the fun. There are several choices when it comes to big data analytics software, the most popular being Hadoop and Spark. Hadoop is used primarily for batch processing, while Spark is used for speedier processing, such as with streaming data. These solutions can be used together or separately, and are compatible with an entire ecosystem of other big data software solutions.

Once you have your big data analytics software in place, you'll want to make sure you're collecting quality data and maintaining your data hygiene, in turn, helping you target better leads and grow your revenue.

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ReachForce helps marketers increase revenue contribution by solving some of their toughest data management problems. We understand the challenges of results-driven marketers and provide solutions to make initiatives like marketing automation, personalization and predictive marketing better. Whether you have an acute pain to solve today or prefer to grow your capabilities over time, ReachForce can unify, clean and enrich prospect and customer lifecycle data in your business, and do it at your own pace.

To learn more about how ReachForce can help you optimize demand generation and your impact on revenue, get a free data assessment and demo today.

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