The challenge of handling data is a priority for enterprises as they determine how to effectively store the flood of information streaming in from cloud solutions and Internet of Things (IoT) devices. Weighing the costs and benefits of different types of cold data storage is necessary to choose the right solution. What many enterprises ignore in their search for data solutions is the other side of big data.
Not only is big data the flow of information coming into the company from customer orders, inventory and security logs for the network, but it is also the vast wealth of data that can help shape business strategy. It is information related to opportunities and target markets that companies need to have the infrastructure to handle.
While enterprises are designing the necessary structures to handle the data that results from their business transactions, they should simultaneously be creating the structures that handle big data that drives their growth strategies. These generally fall into the following categories:
Geospatial: Nearly every industry has a need for big data relative to geospatial analytics. From healthcare centers that want to find the most concentrated populations in a service area to an oil company identifying the most competitive placements for new gas stations, there are geospatial aspects to nearly every company. A geospatial path or vector into big data is a critical step for deriving information for growth.
Product: Whether it’s from customer service and sales or data pulled from social media comments about products in your industry, a product-driven brand relies heavily on the big data. For anything from comparing sales reports for an existing line to identifying pain points that a new product could solve, product data requires foundational infrastructure to fuel growth.
Research: Managing big data related to research can come in a variety of forms. From visitor behaviors on a website to the types of words that should be included in an email newsletter subject line, there are countless ways that companies may need to gather research. Algorithms are often used to gather the right types of information and make it useful for business strategy.
It’s important that neither big data related to company processes or coming from geospatial, product or research are handled in isolation. Structures for data should be built with both types of big data in mind. In addition, enterprises should create policies for checking data against assumptions and refining the data infrastructure with periodic reviews.
To discuss big data and its implications for business strategy in more detail, contact us at Enterprise Visions. We can help you build the systems you need to handle big data and make the most of it for your growth objectives.