Putting the Data Monetization Architecture to Work

People who spend money on things instead of just talking about them are said to be putting their money where their mouth is. Data monetization extracts money from where your customers’ mouths are. That is, it allows you to learn everything you can from every interaction your company has with the customer and enhance your company or product as a result.

Data monetization projects using big data collected from customer interactions can create new lines of revenue and cut extraneous expenses. Enterprises can use data extracted from customer interactions to create new products that your customers are asking for, or enhance products you already have to increase customer demand. You can also identify products that are not performing well that need to be fixed or even discontinued. You can identify which customer touch points are adding to your business and which are hurting. You can also identify which advertising channels are working, as well as which messages are working.

Hadoop is now over 10 years old and is specifically designed for data monetization projects involving big, unstructured data. It analyzes data from multiple sources and detects trends that are helpful in making business decisions. For example, every time customers interact with your website or ordering process, you can give them the opportunity to enter free text. This could be something as simple as “special instructions for this order.” Most of the time, customers will leave this field blank. Sometimes, they will fill out exactly what was requested, such as “Please put a red bow on top.” Sometimes, however, they may enter data that expresses their feelings about the ordering process. An individual order that asks for a red bow or expresses dissatisfaction with the process might not mean anything. But if a significant portion of your orders always ask for a red bow, it’s pretty easy to see that your product should offer a red bow as a standard choice.

You can also analyze trends found in the words that repeatedly show up in this field that express customer satisfaction or dissatisfaction. You can analyze changes in satisfaction and tie those changes back to changes in the product, again letting you know when you did the right thing or the wrong thing.

It is easy to identify customer interactions on the phone or in email and tie each of those back to a certain product or a certain representative. Are customers normally happier or less happy after they talk to Steve Smith in Accounting? Are they normally happier or less happy after they order product X? These trends can easily identify both strong and weak parts of your company’s process and portfolio.

Where Hadoop’s design has all sorts of unstructured data in mind, Splunk’s specific design has machine data in mind. Automatically generated data, such as data created during the manufacturing or delivery processes, can be extremely valuable when looked at from a big data perspective. Are there times of the day when your employees  are making your product more or less productive? If you use multiple shippers to get your product to your customers, you will get various results based on shipper, day of the week, shipping source, and shipping destination. Identifying trends like this can help you to increase the quality of your service or product.

One challenge for products like Hadoop or Splunk, though, is finding a reliable place to store the millions of objects required for a big data project. The ability to recall each object by an ID or name (instead of a server name, directory path, and filename), or search with metadata makes object storage ideal for big data projects. It’s very simple with an object storage system for a big data project to request all of the shipment records for each shipper that you use, or all of the orders, emails, phone calls, and customer support interactions for a particular product. Retrieving huge volumes of data by using simple queries is exactly what object storage is designed for and what makes it perfect for big data projects.

StorageSwiss Take

Data monetization projects are different from industry to industry, but one thing is common to them all: identifying trends found in unstructured data can bring tremendous value to every company. The other common thing that is at the core of each data monetization project is the data. Storing it, protecting it, and easily accessing it are all keys to having a successful data monetization project, and object storage is a great tool for doing just that.

Sponsored by Caringo

W. Curtis Preston (aka Mr. Backup) is an expert in backup & recovery systems; a space he has been working in since 1993. He has written three books on the subject, Backup & Recovery, Using SANs and NAS, and Unix Backup & Recovery. Mr. Preston is a writer and has spoken at hundreds of seminars and conferences around the world. Preston’s mission is to arm today’s IT managers with truly unbiased information about today’s storage industry and its products.

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