Data silos. These are data stores that are kept separate and distinct from other systems and data stores in the company. Examples of data silos are the files you store in Office, the data stored in the spreadsheets in the finance department, and the data locked up in your marketing automation software. The key to making data analytics work is knocking down those silos, consolidating your data, and conducting analysis once it's all together and working in concert.
Data Silos are a Corporate Culture Issue
The issues of offloading data from various systems and integrating the data for a holistic picture of your customers and marketing endeavors are just the technical part of the battle. The most trouble might come from various teams and departments who feel ownership for the data, and don't want to lose control (or perhaps even to share). This requires a top-down, inside-out, upside-down rethinking of data ownership and responsibility.
Inevitably, it is the company that owns the data, not a department or team. You can overcome these issues by fostering trust and camaraderie across the organization in place of mistrust and possessiveness. You must be as willing to share your marketing automation data with R&D as they are to share their testing data with production, and so on.
Data Silos Make It Impossible to Utilize Big Data Analytics
To be successful with big data means gathering and conglomerating all the data. Data discovery is the first step in the process toward data analytics, and it requires that you identify all sources of data and determine how to get the data offloaded from its original source to your big data storage solution (either in the cloud or onsite).
Big data's value isn't just in the fact that there's a lot of it. The value of big data comes from its variety. This is easy to see when you consider marketing data. If you only have data on what products your customers bought, that's a very limited source of information. But combine that with other data from your marketing automation tool, such as how much the average customer spends, how they find your products, whether they redeem coupons, what content they consumed on the journey, and whether they open your emails -- now you have rich data for powerful analytics! More data = better data and more kinds of data = more potent analytics.
Data Integration Can Overcome the Data Silos
Now that you have the tools, resources, and culture to break down those data silos, you can feed your marketing automation solution with the rich, varied, and powerful data that leads to great analytics. All data analytics must start with a good plan. The best way is to determine what you most need to know from your data. Are you looking for ways to reach lookalike audiences? Perhaps you're trying to map your customer journey, or need better means by which to make product recommendations. Data analytics can do it all.
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.