To compete in modern digital environments, machine learning, deep learning and AI are increasingly accessible. By using machine learning and AI, companies can use dark data to acquire more competitive business insights.
Dark data consists millions of unstructured data points that businesses accrue and store in multiformat data lakes. Until recently, there have been few tools available to mine these massive volumes, but that's changing.
Explore different approaches to process dark data and how organizations can harness that information to strengthen machine learning results.
Define dark data
Dark data is different in each industry. It's primarily unstructured, untagged and untapped information that flows through every organization. "Classic" dark data, while captured and stored, is never analyzed. It comprises everything from log files, company documents and emails to social media sentiment, webpages, tables, figures and images. Increasingly, companies are deploying sophisticated technologies to process this data to gain valuable business insights and drive systems automation with deep learning algorithms.
Companies apply the three components that comprise machine learning: models, training data and hardware. Models have become a commodity due to the availability of user-friendly frameworks, including TensorFlow, PyTorch and Keras. Developers can easily install the latest natural language processing (NLP) models, deploy them and begin to see results.
Even with standardized models and hardware, technicians still must supply the training data -- and engineers must structure it. The information is often noisy and imprecise, but finding the connections between unrelated pieces of information is key to uncover dark data's potential.
The manual processes to label and manage dark data are inefficient and consume valuable time and resources. Dark data analysis tools, such as DeepDive, Snorkel and DarkVision, streamline categorization and help computers understand human-generated documents.
Approaches to harness dark data
Machine learning relies on the adoption of AI to accelerate learning and enable systems to make decisions and take certain actions automatically. This acquisition process uses data pattern recognition and specific teaching methodologies, such as supervised, unsupervised and reinforcement learning.
By relying on decision-making rules and human intervention to resolve exceptions, machine learning systems internalize reactions and use repetition to respond correctly to new events. By combining pattern analysis with deep learning, machines incrementally acquire higher-level capabilities to produce the right responses as choices grow more complex.
To successfully undertake machine learning initiatives, organizations must prioritize and invest in learning how to analyze their dark data. Then, it's up to individual companies to develop treatment strategies and prepare their unstructured information for processing.
First, technicians ensure the targeted data is trustworthy and can deliver useful insights. For example, noncompliant or inaccurate data isn't useful to an organization under strict regulatory requirements, even if it exists. Along with automated processes to audit dark data, technicians should apply metadata labels to support future machine learning projects and provide an orderly structure going forward. The goal is to automate the transformation of unstructured data into comprehensible, readable assets.
Cloud services gather and store comprehensive information, which simplifies access to dark data. Cloud services are critical to capture real-time data and to service edge data centers, remote assets and IoT endpoints.
Technicians can also use NoSQL data storage to apply a schema to the information. NoSQL ensures greater analytic flexibility once organizations learn how to classify dark data. Then, business and IT leaders need a clear, unified vision on how to use the results.
NLP is another valuable tool to help make sense of dark data, as well as accelerate machine learning preparation. NLP visualizes syntactical connections between language blocks and enables machines to quickly process and analyze terabytes of information. Combined with AI to accelerate data preparation, NLP helps IT admins understand the vast array of documents and records generated within their organization.
Inherent dangers of dark data
As machine learning models access massive data lakes to ingest and process information, they become potential vectors for data leaks or targets for attacks. Security deficiencies around data access models enable attackers to gain operational insights or infer document structures within organizations.
If a business lacks adequate data inventory or knowledge about storage content, they risk audits, regulatory fines or brand damage if they use the data.
Information integrity is essential. Businesses that don't trace their data to an established, credible source shouldn't use that content in search of insights. And business and IT leaders must restrict who can access certain data, reinforce usage guidelines and implement encryption and security protections.
Cognitive technologies and evolutions of analytic techniques are opening up dark data for large-scale, cost-effective and automated analysis. These techniques minimize the number of resources contributed to working with dark data. And with the right strategies in place, business and IT leaders can expedite data preparation and define the information's value, or use, in the future.