Unstructured, Structured and Standardized Data

Have you ever accidentally put an object in the wrong place and get frustrated when you can’t find the object later on? The function of data repositories and information systems, are to store large quantities of data in the right place, so it can be easily accessed at a later date. The aim is to make the formatting consistent across systems so that different types of data are compatible with each other. Usually, systems will have data models in place that lay out the structure, manipulation and other aspects of the stored data in order to define and format the data to make it more accessible. These types of data are usually separated into three categories: structured, unstructured and standardized.

Structured Data

Structured data is the easiest type to capture and organize in a data model. This is data that lives in a fixed field within a file, like information you would find in a spreadsheet. In order to store structured data, you have to define which fields of data you are planning to store and organize it into rows and columns. An example of this is keeping a database of clients along with names, dates, addresses, currency and so forth. It also may involve restricting the data input in certain ways, such as number of characters or drop-down menus.

The advantage of structured data is that it can be easily entered, stored, searched and analyzed. Data like this is usually managed using a Structured Query Language (SQL). When data isn’t properly structured, it can mean that you’re missing out on some big marketing and advertising opportunities – which we’ll get to in a minute.

Unstructured Data

Unstructured data refers to the information that doesn’t live in straightforward rows and columns. This information is a little more complex and can’t usually be stored in tidy fields. It includes things like email messages, text documents, social posts, photos, videos, web pages and other kinds of documents. The information within these files might be organized, but the data still doesn’t fit into a database in a logical way. Studies have shown that between 80 to 90 percent of data in any company is unstructured – and this number is continuing to grow!

Because so much of every company’s data tends to be unstructured, it makes sense that businesses want to find ways to use this information to make better decisions. The issue is that it’s hard to analyze unstructured data. That’s why a number of different software solutions have been developed in order to try to sort through unstructured data in order to find important information that businesses can use in order to stay competitive.

Standardized Data

Standardized data is data that has been received in various formats and then transformed to a common format that makes it easier to compare the two. This helps make many types of data crisp, clear and easily accessible. For instance, if you have a field for street names, and some people write North Main Street while others write N. Main St., you could standardize the fields to be N. or S. and Street or St. It’s a way of making the data fit more neatly into fields so that search queries can be run more effectively.

Why Are They Important?

Businesses in every industry are scrambling to compile and measure more and more data every day. Structured data can offer vital information about customer interactions and consumer behavior that can help translate into increased sales if dealt with in the right way. Processing unstructured data is, arguably, even more important, but it does present some challenges. Text-based information sources like email, social media interactions and mobile data can often go completely ignored because it’s difficult to compile and analyze. However, this unstructured data can tell us a lot about the customer journey and give us powerful insights into buying habits.

How to Analyze Unstructured Data

There are a number of different, non-traditional ways to analyze unstructured data. Companies can review conversations on social media and analyze customer interactions in call centers or in-house, and then find ways to record them in a structured format. You can also set up automated processes to capture data, such as tools that monitor social media sites or RSS feeds. Figure out which areas are of interest to your customers, and organize this information into relevant categories. In this way, you can work toward standardizing the data to make it easy to compare it with structured data to identify trends and patterns.

What Does This Mean for Marketing?

Social data is powerful for driving your content marketing strategy, as it helps you understand what content to create and how much weight to give each type of content within your plan. You can use metrics from Facebook Insights, for example, to see how well different types of posts are performing. Other tools, like Optimal Social, can offer structured data on what types of content your readers are engaging with.

Another important use of structured data is to boost your search engine rankings. Web crawlers are now trolling sites to look for specific items and keywords, and also to verify that you have high-quality content on your site and are a company that deserves to pop up in user searches. This kind of rich data markup helps Google understand what your content means, not just what it is saying. Google analytics and other site analytics can help you optimize your site and your online content so that Google picks it up first.

About Diggen, Inc.

Diggen is a data marketplace to help marketers become and manage data driven enterprises. Data driven marketing initiatives accelerate growth, since it improves key performance indicator (KPI) metrics and increases revenue over 19%. However, marketers have monumental technical challenges accessing data assets, sourcing data providers, and integrating into their marketing technology stack.

 

Our platform uniquely combines a data marketplace to source all marketing data with middleware to integrate into all marketing technology stacks. For example use our intuitive web interface to append gender, age, location to email addresses and integrate into an email service provider. Therefore, marketers segment their audience for newsletter emails, which creates relevancy, more engagement, and increases conversions.

 

Visit Diggen at http://www.diggen.com.

 

Sources:

  1. http://www.innovatingstuff.com/2012/12/03/structured-and-standardized-data-sources/

  2. http://acumenmd.com/blog/human-condition-structured-unstructured-data/

  3. http://www.webopedia.com/TERM/S/structured_data.html

  4. http://www.webopedia.com/TERM/U/unstructured_data.html

  5. http://digitalmarketingmagazine.co.uk/digital-marketing-data/how-understanding-unstructured-data-is-useful-for-customer-insight/2198

http://www.npws.net/blog/how-structured-data-can-enhance-your-online-marketing

Differences in Data

Have you ever been pre-judged by someone before they had a chance to really get to know you? When I look at where data-sets are today, that’s kind of how I feel. We are taking our best guest at personifying individuals based on a series of various data sets that semi-fit together. In our last blog we talked about the importance of content personalization. If we are going to get there, data interpretation is just as important as the data collected about a person.

Different Types of Data

Qualitative vs. Quantitative

The biggest distinction in reading quantitative vs. qualitative data, is whether something can be easily categorized or not. Quantitative data is data that can be categorized numerically. Your shoe size, your height, your income, your zipcode etc. etc. In other words, it’s the demographic information that can be easily collected.. Qualitative data, however, cannot be categorized numerically. In the words of Isaac Newton, “every action causes a reaction”. This reaction, or emotional response can be classified as qualitative data.  

Target, for example, takes to the twitter-world to see the emotional reactions to new product launches. They collect this qualitative data about the individual responses to better understand their customers and move one step closer towards content personalization.

These two data sets go hand-in-hand because you can infer many correlations such as, location eludes towards cultural responses, affluence levels indicate certain buyer behavior, and so on.

First Party Data

Finders keepers! Anything you collect about your customer, is yours to keep. This means your brand gets the first glimpse into your customer’s interaction with your brand. First Party Data is one of the most valuable data sets because you can deploy any data aggregation strategy to understand the exact relationship between you and your customer during the buying journey.

For example, if you want insight into how your customers interact with your website, deploying a heat-mapping strategy to collect data on what images individuals click on might be the best route for gathering this first-hand intel.

Second Party Data

Second party data is like the ultimate tease. A customer may be in a data relationship with someone else, but you’re still benefitting from that relationship. For example, a customer releases the rights for Google AdWords to track their search history but you’re still benefitting from that same relationship with AdWords. It’s common for brands to strategically partner in a data sharing strategy to obtain information that otherwise might be too costly to collect on their own. This is why second party data becomes valuable, and knowing what data you’d need to further complete the personalization puzzle will help define the strategic partnerships you can create.

Third Party Data

This data is the most widely adopted data collection strategy. Marketers depend on data collectors to aggregate intel on customers that they can use to develop a variation of marketing strategies. Unfortunately for third-party data, it’s becoming less common in strategy development as marketers want more first-hand insight aka. first party data.

Knowing what type of data you are collection, can help you figure out what pieces of data are missing that will help you complete the puzzle towards content personalization.

References:

http://www.huffingtonpost.com/advertising-week/turning-intentions-into-c_b_8137128.html

http://www.getelastic.com/beyond-product-recommendations-big-datas-role-in-personalization/

http://www.b2bmarketinginsider.com/strategy/are-you-using-first-party-data-to-drive-personalized-customer-experience

http://www.emarketer.com/Article/Marketers-Put-First-Party-Data-First/1012663

http://marketingland.com/can-marketers-find-best-customer-data-noses-139308

http://marketingland.com/second-party-data-digital-marketers-128254

http://www.signal.co/blog/data-sharing-second-party-data/