Big data can be quite confusing for most marketers today. While marketers are still trying to wrap their arms around the data they have, more and more data is being produced by the minute.

I was recently at a marketing conference and one of the attendees told me, “Whoever has the most data wins.” This is the mentality for most companies -- collect as much data as quickly as possible. After all, more is better, right? Well, not exactly. Really it’s about, “Whoever has the BEST data wins.”

Businesses are stockpiling more data than ever before. From machine-generated data, social sentiment, transactional data and emails, many companies feel as if they are drowning in data. Seven in 10 companies collect syndicated third party data such as weather information (72%) or government data (70%), while many gather staff data (66%) and location-based information (41%), according to the Economist.

However, when it comes to data, too much can be a bad thing. Companies must take action to identify and collect the meaningful data and avoid the irrelevant data that will just create clutter.

Here are three types of big data you should be collecting and using – but of course, not every piece of it.

1. Social Data

By now, most companies have some sort of social platform or presence in place. A Facebook page to share content, a LinkedIn group to network with prospects, or perhaps a Pinterest page to showcase products. Many marketers are hyper-focused on measuring the number of followers, retweets or shares their pages and content are getting. This is certainly a good thing to measure and engaging with your customers and prospects on social platforms is a must. However, there are huge opportunities within social media that can lead to even bigger wins.

Your customers and prospects are having all sorts of conversations on social media about products and services you offer, your competitor’s name, or other discussions that may indicate readiness to purchase. Each of these conversations would be impossible to monitor on an individual basis. However, using data-as-a-service, a company can find “just the right” social conversations to target new prospects or engage with current customers.

Take the example of a boat retailer. Through data-as-a-service, web scraping technology can be utilized to find prospects who are looking to buy boats. Any number of key words can be used to find ideal prospects. These prospects can then be delivered on a daily basis, in real-time, to a company’s channel systems, ad agency, or marketing automation systems.

2. On-Line IDs

Many of us are familiar with the concept of sending “The right message, at the right time, and through the right channel.” Data has transformed marketing. We can collect any type of data to personalize our messages and send offers through the channels our customers most prefer.

Many of these preferred channels are digital. Consumers are doing more research on-line, checking email on the go, and using any number of mobile devices. These digitally-connected consumers must also be sold to on-line, or at least convinced through on-line channels to visit your brick-and-mortar buildings.

Many companies have email addresses for a portion of their customers and prospects, use paid advertising, or engage with consumers on social media. However, digital becomes much more powerful when off-line data can be connected to on-line identities.

So perhaps you have database with names, addresses, and phone numbers. Through a data onboarding process, this off-line data can be matched to a consumer’s digital identity, such as a social media account, IP address or email address.

 

By collecting and maintaining a database of digitally addressable consumers, you can market in real-time based on any number of real-time triggers and events.

3. Hard-to-Find Data

Companies today are producing mass quantities of data as a natural byproduct of their day-to-day business transactions. Many companies are realizing the benefits of monetizing these data assets and making this data available to other companies. For example, retailers are selling point-of-sales data. Telecom communities are monetizing their customers’ mobile phone usage data by letting banks and retail outlets integrate the mobile usage data with their existing customer transaction databases.


Numerous, hard-to-find data assets such as these can be sourced from the big data universe through a data-as-a-service solutions provider. What’s powerful about using a DaaS provider is that that you don’t need to implement a big data system or hire data scientists to start accessing this data. The insights have already been mined and sourced, and can be integrated directly into your database.

When data is used as a service in this way, a company is freed from the pressure of collecting whatever data is available, as quickly as possible. Rather, a company can be highly selective about which data makes the most sense to collect and utilize for specific business objectives.

By really thinking through the specific business objectives you are trying to achieve with data, big data is brought down to a much more manageable size. Rather than a daunting task to avoid, big data becomes much more achievable. With the right solutions and technology, the big data universe can easily be transformed into the right data for your business.

 

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