9 Ways to Derive Value from Your Unstructured Data

There was a time when organizations could get valuable insights from structured data – information collected, sorted, categorized, labeled, analyzed and synchronized on spreadsheets and databases.

Sales transactions, profit margins, market value, stock-keeping units, delivery times and click-through rates could be easily accessed, extracted, calculated and examined to determine purchasing behaviors, increase efficiencies, discover opportunities and make other strategic decisions based on what the data indicated.

But advances in technology such as cloud, mobile, social and devices related to the Internet of Things have changed all that.

Organizations now have access to an incredible amount of additional information – emails, presentations, audio and video, web searches, images, social media posts, etc. – at their disposal, which can provide context or reasoning behind what the structured data shows.

Interpreting or analyzing data that does not neatly fit into a database or spreadsheet can prove difficult for some organizations, while others have found ways to derive value from unstructured data.

Retailer Chico’s FAS has been able to integrate social media posts with customer data to offer targeted promotions to its customers. Seton Healthcare has been able to save money from readmitting patients by identifying readmission triggers, according to an Information Management article.

Here are nine ways to derive value from your unstructured data:

1. Determine Your Organization’s Goal

Knowing your organization’s goal is essential to determine how to analyze your unstructured data, as not all information will be useful. For example, positive or negative social media posts or comments can indicate sentiment toward an organization, its products or marketing efforts. If the goal is to determine whether a marketing initiative gets a negative response, focusing on social media hashtags related to that campaign can provide better insights instead of analyzing everything.

2. Select a Way to Analyze Information

After selecting goals, you can create a way to analyze the information. For social media sentiment, certain words and phrases within posts can be scored based on assigned values, creating numerical data that can be analyzed. A good word may get a “+1,” a bad “-1,” and neutral “0,” with a sentiment score calculated by the sum of these numerical values.

3. Identify All Data Sources

Use sources that are entirely relevant (nothing tangentially related to the topic), including information from online reviews and customer feedback forms, as well as information from mobile phones, tablets and computers.

4. Choose the Right Technology

Choose data storage and information retrieval architecture based on scalability, volume, variety and philosophy. Some big data tools are designed to manage and analyze unstructured information, such as those based on Hadoop, a software platform that can store and process huge files.

5. Get Real-Time Access

Real-time access requires tracking real-time activities and making predictions relevant to the business based on predictive analytics. For example, in e-commerce, real-time access allows companies to provide quotes. In a 24/7 business climate, real-time data collection is especially essential and whatever technology platform an organization uses needs to make sure no data is lost.

6. Use Data Lakes

Data warehouses may be good for storing structured information, but data lakes are better for unstructured data. Data lakes allow you to access information in its native format, preserving the metadata and anything else that may assist in analysis.

7. Make Sure the Data is Clean and Organized

Clean and organize data so that all valuable information is represented. Expand any text that is informal or written in shorthand or symbols.

8. Retrieve, Classify and Segment Data

After data is cleaned up, prioritize based on what you are looking for. You can create tags based on parts of speech to extract entities, such as “person,” “location,” or “organization.” Other methods such as Logistic Regression, Naïve Bayes, k-means and other supervised and unsupervised machine learning can find patterns in customer behavior, target customers for a campaign and classify documents.

9. Visualize Data Analysis

Use graphs and charts to visualize your analysis to make sure other parties can use the information and make recommendations based on the data.

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