/ Big Data Concepts for Companies that Can’t Afford Big Data

These days, we keep hearing buzzwords floating around like “big data” and “the cloud”, but those grandiose concepts seem daunting when you’re working with a simple list of a few hundred leads and customer addresses. Reading about the sizable investments in architecture and software involved for some of these big data projects leads many entrepreneurs to put off utilizing the precious data already at their fingertips.

If that’s where you currently find yourself, stop focusing on the enormity of large-scale data deployments. Start with smaller chunks to begin building up your knowledge repository for those leads and existing customers and make yourself a more efficient marketer by initiating more intimate one-to-one conversations with them. If you have a spreadsheet filled with clients and prospects, their addresses, and how you’ve interacted with them previously, you’ve already set yourself on the right path.

The primary concepts of big data are helpful to keep in mind when you start thinking about expanding your knowledge base. Those concepts are volume, variety, velocity, and veracity. The discussion begins with the tenet of volume, which is simply the quantity of data being stored and generated in your database or spreadsheet and depends on your business needs. While the size of your data set helps determine its overall marketing value and the potential insights it holds, that doesn’t mean that a small data set lacks value, only that there are opportunities available to enhance it by comparing it to larger databases that may give you deeper insights.

The variety of information in a big data set are typically stored nuggets of information about your customers and leads that may come from any number of sources. This can include details of your interactions and sales, customer demographic information, or even harvested data from any number of external content sources like social media interactions or mentions. These are the pearls of wisdom that will help you more effectively communicate with clients and find other leads like your existing customers.

How quickly data is being generated, processed, and added to meet the needs of business is a measure of the frequency of data generation. Most big data must be available to analysts as it is generated for it to be meaningful which can lead to huge architecture and infrastructure costs. That is a component of the frequency of data handling. These frequency components make up the velocity of big data. How often will you need to access this data, and when will it need to be refreshed? From a marketing perspective, the answers can range from real-time to annually or longer depending on what your contact schedule with your audience looks like.

Lastly, let’s touch on veracity, or the overall value and quality of the data being stored, and this can vary wildly. We’ve all heard the phrase “garbage in, garbage out”, and that is the key component of veracity. In big data solutions, each piece of stored data needs to be properly optimized, coded, and stored in separate tables so velocity isn’t hampered for those who need real-time access for analytics or for machine learning through pattern detection. In these data sets any stored “garbage” may derail or delay the project. We can scale this concept down to any data project. For instance, if your existing lead data has a business name and address but doesn’t include the names of decision makers for that business, your marketing messages may not make it through as intended.

An ideal way to start creating a more robust data set is by ensuring the veracity of your own data. Performing a cleanup of addresses in your existing customer and lead data sets will help ensure a more successful build scenario. 1Vision can pass your existing addresses against the US Postal Service’s address standardization database to find out if those addresses are valid or if they need some sort of cleansing to correct them. This cleansing will give you information on whether an address is a valid delivery point or not and makes suggested corrections to those addresses if the process finds any minor flaws.

Once your addresses have been cleansed, an opportunity opens to broaden familiarity with your customer base by adding more variety. That one step allows you to pass your data against many other existing databases containing robust intelligence about your clientele. You’ll be able to retain more of your own data and start generating a meaningful repository to help you make better business decisions. For more on utilizing intelligence from other databases, see our post on segmentation.

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