In my last post, we took a look at how marketers could take a nibble of big data by examining internal resources and surfacing the right people to help manage and leverage analytics.
But solving the people problem alone won’t make data work for your organization. Now, it’s time to master the technology side of your analytics, which is one of the trickier parts of leveraging big data.
Here’s why: most companies have enough data to fill seven seas. That means the challenge isn’t about collecting more data. Instead, you need to understand which parts are the most valuable, and separate them from the deluge of other information. Here are three ways to sail the seas of data successfully.
1. Understand the integrity of your data. Since you likely have multiple data sources, there’s a good chance more than one system is reporting on one metric. For instance, you might find your website analytics program is delivering information about the behavior of visitors who end up on your site after clicking on an email. However, similar data is likely stored with your email service provider (ESP). In some cases, you could have three or four different data sources reporting on the same metric, which can lead to internal confusion.
In order to truly make great use of your data, it’s necessary to make decisions about not only the goals of your analytics projects, but also which data sources will create the single “truth” in your organization. Plus, some data sources will just be better than others for certain metrics. For a retailer, a content management system (CMS) or e-commerce platform will likely be the best data source for actual sales and transactional data, while a warehouse management system will likely be the best source for inventory or operational information.
But just as every business is different, so are its data sources. Review your disparate data systems, and then decide which one has the best and most accurate data. You want to be able to point to a certain system and say: “This is the metric the entire company is going to agree on using as we move forward.”
Your own data shouldn’t overwhelm the organization and drown the entire project. Think of it as snorkeling through the seas of your own data and spearing the biggest, most delicious fish. Those big fish are your most important data points. Find them.
2. Create a data warehouse. Now that you’ve drained the seas of analytics, and surfaced your most valuable and important data points to create a record of attributes for both customers and products, it’s time to create a data warehouse. Compiling all of those important pieces of data means there’s a business decision to make.
You have two choices: Are you going the technology route and creating a data warehouse to compile and manage those analytics? Or are you going to leverage your personnel resources by appointing a data scientist or analyst to query against rudimentary structures?
While there are vocal critics for and against either one of these strategies, it’s really not that cut-and-dried. Every organization needs to take an honest look at its capabilities to determine what it is poised to do with data based on current systems and staff.
An older business, for example, might have enough staff to put one person in charge of querying the data warehouse. A newer company might already have the technology needed to create a data warehouse.
No matter which route you choose, don’t underestimate the time and money that will need to be invested to get a data program off the ground. There’s no way any company can just jump into their sea of data and expect results. Rather, it takes expertise, planning, and time to sail those seas to data success.
3. Uncover your customer profile. The goal of combining all of your most important data is ultimately to say: “How much information can we compile about our customers that’s important to our business?” Typically, that comes down to understanding both your customer and your products.
So once you’ve emerged from the sea of your top data, fish in hand, it’s time to put that information to good use. Start a checklist, and build out a full profile of what your customer and products look like.
- Customers. How do customers behave on the website? What content are they spending the most time with? Which customer segments are watching videos? Are they bouncing from search pages? Do they open emails? Do they click on particular offers?
- Products. How often is this product purchased? Who buys it? What differentiates it from similar products? Which types of customers buy it most often? How effective are the sales and promos for this product?
Answering these questions with your data will create a long-tail list of attributes for both your customers and your products, as well as uncover new insights about buying/purchasing behavior.
Sea of Data image on home page via Shutterstock.
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