The volume of data generated by businesses is enormous. As per Gartner, unstructured data is about 80% of this volume, and it is growing at 3X faster than structured data. Research from IDC says that 90% of unstructured data is not analyzed. As per a business leaders survey, 95% of businesses need solutions to manage unstructured data, and a few of them intend to do it frequently.
How to tap the value of the unstructured data for every business? Let us look at a few more details about the intricacies involved with unstructured data in 2022 and beyond.
What is Unstructured Data?
Unstructured data is not easy to store in simple columnar databases like Excel. Hence it poses challenges to search and analyze the information quickly. Examples include e-mail conversations, text conversations, social media posts, blogs, video, audio, call logs, and questionnaires/feedback forms.
Why is Unstructured Data vital in decision-making?
Big data is not new in any industry, but unstructured data intelligence is emerging for the complete visualization of all the data that organizations store. Do you think that unstructured data intelligence is not valuable?
Unstructured data intelligence is about:
- Deriving holistic insights utilizing all the data stored in the organization’s repository
- Contextualize information from all the available data
- Analysis and transformation with process-specific data
- Consumer feedback, sentiment, behavior, purchase patterns, risk analysis, and engagement with multi-channel interactions and data
- Enhancing predictive analytics to include more comprehensive data
Unstructured Data Trends in the Data Ecosystem
Unstructured data analytics will be paramount in 2022 and beyond with the evolving data management tools and technologies, cloud data warehouses, and machine learning techniques. Organizations should be ready to focus on developing and experimenting with new unstructured data analytics capabilities and new approaches in 2022.
Data Management – New Frontiers with Unstructured Data
Organizations now collect, store, and manage data related to customer interactions, products, e-mail, processes, supply chain, and more. Various analytics use cases like customer analytics, customer sentiment, customer 360-degree analysis, etc., now leverage this unstructured data alongside personal information and structured data. But, categorization and bringing structure and context to this vast amount of unstructured data is still a challenge even after using centralized data lakes and big data approaches.
Elastic compute platforms and data management from edge to cloud that can simplify unstructured data ingestion will soon see more investment from organizations. Indexing large volumes of unstructured data is still a huge effort. Data management solutions that can easily enable the search for the required data sets across the organizational data and stream this data for unstructured data analytics may also see increased demand.
Rapid Search for Effective Unstructured Data Analytics
Data silos are still the biggest challenge to leverage structured and unstructured data analytics. It is vital to have solutions that can search, classify, and visualize data irrespective of the location and technology with unstructured data.
IT infra teams now have to look for tools and technologies that can help in rapid search and data segmentation to feed into the unstructured data analytics pipelines.
Comprehensive Unstructured Data Analytics Tools
Traditionally, businesses leveraged historical data to draw insights about the business. AI and ML models now help organizations unleash unstructured data’s potential value. But there are multiple applications of AI from data management, data pipelines, data ingestion, and predictive models.
Though most of us know about the common use cases like customer analytics, personalization, and sentiment score analysis, AI can add value to each process in unstructured data processing and insights.
Synthetic Data and Unstructured Data – Solution for Data Growth
As the data collection process became increasingly complex, synthetic data generated from different algorithms solve the data scarcity and privacy issues. Though synthetic data reduces the footprint on real-time customer data, it is still not enormous as unstructured data.
Synthetic data and unstructured data together address the data growth needed for the good performance of ML models.
How does AI add value to Unstructured Data Analytics?
Although organizations generate vast volumes of unstructured data, utilizing that for insights is not simple. Due to the complexity of unstructured data, it is not easy to identify high-quality data for further insights. Machine Learning and AI models eased the processing and analysis of unstructured data to make it valuable. Let us look at a few areas where AI can add value to unstructured data analytics.
- Better data quality and data management
Unstructured data is pooled from multiple sources, and it does not come in a specific format across all the business applications. Even data scientists may not have unified standards to analyze this data.
Unstructured data also adds to the data validation vows as the differentiation between data points is unclear. Moreover, unstructured data comes with many inconsistencies like personal information, emails, audio, videos, etc. It is easy for the human eye to differentiate these, but such information noise impacts the machine-readable data quality. Identifying outliers, anomalies, and patterns in this data also compromises data quality. What is the solution for this?
AI can play a vital role in data cleansing and preparing unstructured data. Machine Learning models can be leveraged to validate the data, integrate from multiple sources, and maintain the data for critical organizational insights.
- Unstructured data to structured data conversion
Organizations may not convert all unstructured data to structured formats for further analysis. By leveraging NLP and machine learning capabilities, organizations can create their intelligent document processing solution to convert documents like invoices, contracts, agreements, customer onboarding documents, etc. into a structured format.
By extracting the required information and doing some text mining, organizations can reduce manual work while also making analytics like consumer sentiment easier. Document processing is just a simple use case; audio/video to text conversion, image to text, and cognitive capabilities in the latest virtual agents helps organizations access unstructured data analytics rapidly and efficiently.
- Unstructured data analytics and analysis with AI tools
The main challenge associated with unstructured data is not its availability but the lack of tools to extract relevant insights. AI tools and solutions like NLP, text mining and extraction, computer vision algorithms, chatbots, speech-to-text analyzer, and a few more aid in showing the value of underutilized unstructured data analytics.
To realize the full potential of unstructured data, organizations need to reduce their data silos and move beyond data lakes. Highly scalable and massively parallel processing will help rapidly search and identify the necessary data for unstructured data analytics. Are you looking for ways to leverage unstructured data for your organizational insights? Get in touch with our experts for the best RoI.