How to Successfully Collect and Make the Most of Unstructured Data
There is a wealth of information (data) that is less commonly used than traditional structured data to gain insights into business activities, trends, and potential improvement areas resulting in increased operational efficiency, elevated customer experience, a sharpened competitive edge, and so on. All of this, of course, ultimately culminates in a better bottom line. This untapped resource, unstructured data, is usually textual but may also take shape in the form of images, video, or audio.
Let’s first discuss unstructured data.
It would be difficult to list all unstructured data source types comprehensively. However, many common collection points include social media posts, email messages, instant messages, word-processing documents, invoices, collaboration software, and, as mentioned, audio and video, to name a few. Unstructured data combined with structured data can provide powerful insights from enterprise operational and customer data.
Organizations may leverage numerous analytics techniques and tools to obtain insights from unstructured data within vast data lakes.
Text analytics – a form of unstructured data analytics is used to obtain deeper insights, including identifying patterns or trends within text. Typical areas of text analytics include natural language processing (NLP), sentiment analysis, and optical character recognition (OCR). Unstructured data analytics also includes data mining, machine learning (ML), and predictive analytics.
Image and video analytics – Computer vision algorithms are often applied to images and videos for deriving operational insight. With the aid of deep learning, this form of artificial intelligence allows for automated solutions in areas such as security, transportation (including self-driving cars), marketing, and so on.
Audio analytics – Artificial intelligence in audio analytics also utilizes deep learning and machine learning and is commonly used for enterprise solutions, healthcare, smart cities, etc. NLP may be applied to teach computers how to understand and interpret human language.
Finally, interpreting the data.
What are you trying to achieve?
Be clear about the results you are trying to achieve. You may be looking for qualitative or quantitative results such as trends, anomalies, keywords, or capturing structured data points hidden in vast data lakes.
Collecting unstructured data
Unstructured data exists in numerous forms and resides in various places, from social media platforms to emails and instant messages to hard copy and PDF documents. Fortunately, today’s advanced technology, including AI, enables you to collect and sort through vast amounts of data. Web mining tools have been around for some time now, and advances in OCR technology have made it possible to extract text from documents, invoices, and receipts that previously required human intervention to collect.
Storing unstructured data
MongoDB is an excellent choice for storing unstructured data since this type of data requires a non-relational database, such as in an object storage system.
Cleaning unstructured data
Of course, data cleansing is a crucial part of data analytics and directly impacts the success and accuracy of your analysis. Much of the unstructured is repetitive, and the unnecessary data must be scrubbed, which may include signatures, ads, emojis, superfluous banter, etc. First, inspect and audit your data, often known as data profiling. Next, cleanse your data. This process may involve spell checks, abbreviation expansion, and identifying abnormalities. More advanced cleansing might include applying Python code.
Normalizing unstructured data
Essentially data normalization is reorganizing data within a database to optimize further queries and analysis. Normalization requires extracting all repeated fields into separate files and assigning appropriate keys to the fields.
Then, all non-key elements that are fully specified by something other than the complete key are placed in a separate table. Typically, these non-key elements are dependent on only a part of a compound key.
Finally, normalization eliminates redundant data elements and tables that are subsets of other tables.
Perform unstructured data analytics
The following are typical approaches for data analysis.
- Metadata – review the data that provides information about the data to find clues for the analysis.
- Natural language processing (NLP) – is a machine learning technique to help discern the meaning of unstructured textual data, mimicking human processing of the data.
- Image analysis – is the processing of unstructured information contained within images.
Visualize your data analytics
Visualization through graphs, charts, and dashboards democratizes data allowing everyone to understand the represented data better and derive valuable insights.
Harnessing the value of unstructured data can provide a competitive edge for companies by being better informed. The strategic value of the vast volumes of data that otherwise go readily untapped can help an organization leapfrog the competition, ultimately positively impacting the bottom line.
If you are interested in applying data science and AI to harness your unstructured data, email us at intellect2@intellectdata.com. Intellect Data, Inc. is a software solutions company incorporating data science and artificial intelligence into modern digital products with Intellect2TM. IntellectDataTM develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellectdata.com.