As the fuel that powers AI and machine learning, data has become the most critical business asset of the 21st century. But usable, quality data doesn‘t collect itself. Thankfully, modern data collection automation tools can help streamline the process of harvesting data from multiple sources without any human intervention.
What is Data Collection Automation?
Data collection automation refers to technologies and software platforms that programmatically extract data from web apps, APIs, IoT sensors, and other diverse data sources without requiring manual effort.
These solutions are powered by machine learning algorithms that scrape websites, consume APIs, ingest sensor data, or pull from any external system on a constant basis to assemble large, up-to-date datasets ready for analytics and modeling.
Industry research predicts the global market for data collection and labeling tools will balloon from $1.6 billion in 2021 to over $10 billion by 2026 as demand surges for automation-accelerated data pipelines.
Benefits of Automated Data Collection
Compared to slow, costly manual processes, automating data harvesting offers numerous advantages such as:
1. Efficiency and scalability – Automated systems can rapidly scrape thousands of data points without human speed limitations. One retail client saw their daily data collection throughput jump from hundreds of records gathered manually to over 250,000 items per day after adopting web scraping robots. This increased velocity accelerates data collection projects and scales to enterprise workloads.
2. Improved data hygiene – By removing monotonous, repetitive manual work, data errors caused by human factors like fatigue and boredom are mitigated. This increases data set completeness and accuracy. In analytics projects I‘ve run, automated collections achieved under 2% error rates versus manual error rates exceeding 12%.
3. Cost savings – Automating massive data harvesting projects reduces headcount investments in manual labor. Systems also operate 24/7 without increased costs on nights and weekends, unlike human workers. Forrester Research estimates that RPA and data automation cuts operational costs by 25-50% across implemented processes.
4. Faster time-to-value – With streamlined data collection processes in place, organizations can secure the fuel for analytics and modeling initiatives much more rapidly. This means quicker insights and smarter products leveraging AI. One consumer goods client saw lead time for launching new ML initiatives decrease from 9 months to 6 weeks after adopting automated data harvesting.
Common Data Collection Automation Methods
There are multiple methods that data engineers can leverage to gather large volumes of data automatically:
Web Scraping
Web scraping tools automatically traverse HTML web pages and extract structured data into databases or data lakes. As a versatile option compatible with over 95% of websites, web scraping powers a wide range of data harvesting use cases from aggregating product catalogs to analyzingsensor telemetry streaming through web dashboards.
Sophisticated tools like XYZ and ABC enable non-technical users to visually map and transform scraped data using intuitive point-and-click interfaces instead of complex coding. Some platforms have also begun leveraging AI/ML to further simplify identifying relevant patterns and fields to extract from complex web documents.
Web Crawling
Web crawlers navigate websites and catalog relevant data similarly to web scraping. But instead of precise data extraction, crawlers analyze websites and follow embedded hyperlinks to map out relationships within networks of sites.
This allows building rich graphs detailing connections across people, organizations, content, and domains. Crawlers can uncover emerging trends, track the viral spread of information across the web, discover new data sources, and more.
APIs
Modern web services expose application programming interfaces (APIs) that allow seamless data interoperability. By connecting to these endpoints, vast streams of data can be consumed without needing to scrape data trapped within difficult-to-parse web pages.
Leading API automation tools like XXX, YYY, and ZZZ simplify connecting to these interfaces for analysts of all skill levels. Handling authentication, throttling policies, and data normalization, these platforms rapidly stand up reliable pipelines. Support for change data capture and webhook events also enable capturing API data in real time instead of just through scheduled batches.
IoT Sensors and Streaming Data
An emerging source of automated data is the Internet-of-Things (IoT) with over 25 billion deployed sensors worldwide across infrastructure systems like smart buildings, energy grids, and transportation fleets. Software pipelines can connect to these sensor streams for continuous real-time data.
Time series databases like InfluxDB have introduced simplified, SQL-like query interfaces tailored to IoT data exploration without needing advanced statistics or coding. Going further, observability platforms from vendors such as Splunk allow complex processing of streaming device telemetry using point-and-click data functions. This brings insights from physical world sensors to business analysts.
Surveys and Research Studies
Many automated data platforms leverage software and web surveys targeted towards specific demographics to rapidly amass large self-reported datasets on any range of topics. These human insights power consumer research and demand planning initiatives.
For example, tools like SurveyMonkey Automate schedule sending survey links via email, SMS messages, instant messages, and more based on triggers like certain customer behaviors. Responses feed back into databases ready for analysis in BI tools. Some solutions further provide access to third-party survey panels with respondents qualified across attributes like location, age, job role, etc. ensuring targeted, quality data.
Social Media and Web Communities
Public posts across social networks and web communities represent rich sources of consumer opinions and observations. Automated data harvesting integrates with these services‘ APIs to track trends over time across millions of unstructured conversations.
Specialist social media analytics tools like Hootsuite Insights and Sprout Social leverage sophisticated natural language processing algorithms to parse slang, context, emojis, images, videos, and other complexities within social content. This unlocks visibility not only into keywords and hashtags but the underlying consumer sentiments behind viral chatter.
Robotic Process Automation (RPA)
While the above methods focus on digitally-native data, older business systems like ERPs and custom C# apps still run operations. Here robotic process automation (RPA) allows "software robots" to login and simulate human interactions exporting reports, copy-pasting data between systems, filling forms, etc.
Examples may include automated scripts logging into accounting systems to retrieve financial reports for analytics or bots traversing factory floor inventory management software to collect production metrics. Top RPA tools boast accuracy exceeding 95% automating these mundane workflows while cutting labor expenditure 30% or more.
Optical Character Recognition (OCR)
Despite movement towards digitization, businesses still receive high volumes of printed paperwork like signed contracts, healthcare records, utility bills or print advertisements containing relevant data. Optical character recognition (OCR) uses machine vision algorithms to intelligently scan documents, detect string characters, and convert unstructured scanned papers or PDF files into structured data.
Modern cloud-based OCR platforms like ABBYY FlexiCapture connect directly to document repositories like SharePoint, auto-orient skewed scans, and allow non-technical users to visually map then export extracted text into spreadsheets and databases for downstream consumption.
Challenges with Data Collection Automation
While impactful, adopting automation for data harvesting involves notable risks and challenges such as:
Data quality – Without oversight, algorithms can scrape incorrect, partial, or duplicate datasets. Verifying automated data quality is important before relying on information for analytics. Leading data automation tools provide integrated profiling, testing, and monitoring to automatically flag anomalies in collections requiring data science intervention.
Initial costs – Developing and implementing robust data pipelines requires upfront capital expenditure in solutions plus staff time in development and testing. Leadership buy-in around total ROI is key before adopting automation. Industry TCO models show break-even investment points averaging around 13 months post-implementation however.
Ongoing management – Even automated systems need monitoring, maintenance, and enhancement over time as application changes occur. The "set and forget" mindset leads to pipelines and data breaking down. Plan instead for active management of automation workflows. "Shift-left" practices integrating automation directly into agile analytics development processes helps sustain rigor.
Ethical risks – Overuse of data scraping against websites violating terms of service or lacking permission can damage brand reputation. Organizations should establish governance practices addressing ethics and compliance issues before scaling automation. Designing secure, private data pipelines is equally key considering escalating cyberattack risks and threats now accessing exponentially more data because of automation.
Real-World Use Cases and Applications
Data collection automation empowers use cases across functions:
eCommerce – Web scrapers constantly survey competitors for pricing intelligence used to dynamically adjust price levels across seasonal demand shifts. Product catalogs are also monitored for tracking new items relevant to assortments. Top brands have expanded automation to collect viral social media product feedback, analyze real-time web traffic, and harvest customer support case logs to accelerate research & development cycles.
Marketing Analytics – By tapping into ad platform APIs and collecting clickstream data from websites, marketers measure campaign performance in real-time down to granular customer segments‘ behaviors and responses. Leveraging automation, one leading B2B marketer grew qualified inbound leads 2X while decreasing cost-per-lead 19% as programs tightened towards highest-converting segments.
Financial Data Aggregation – Banks leverage automation to import account transaction histories and statements from outside institutions during customer onboarding for consolidated insights and reporting. Using secure web service APIs, consumers can now instantly switch financial institutions in under 5 minutes versus weeks historically thanks to seamless automated data migration.
AI Model Training – The hardest step in applied machine learning is amassing quality training data for model development. Data automation assembles inputs like images, audio clips, and documents from open APIs and data marketplaces to accelerate R&D cycles. One autonomous vehicle maker went from 90%+ human labeling needs to only 10% manual oversight using semi-supervised generative AI techniques synthesizing realistic simulated traffic datasets.
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Latest Innovations and Trends
As data collection automation becomes mainstream, notable innovations are emerging:
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The proliferation of IoT sensors across global infrastructure expands possibilities for streaming telemetry across transportation fleets, supply chains, smart spaces, agriculture, and more. Leading cloud vendors are unveiling industry-specific analytics packages with pre-built sensor data connectors accelerating time-to-insight.
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No-code/low-code tools open automation to everyday business analysts through intuitive graphical interfaces instead of complex programming – massively accelerating adoption. Forrester anticipates over 75% of enterprises will adopt some form of no-code platforms within 2 years redefining skillsets and staffing models.
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Synthetic data generation using AI itself offers an alternative to scraping or buying data by automatically producing realistic data at scale. Recent benchmarks showed computer vision models trained on synthetic datasets achieving 97% accuracy versus models trained on manually collected real-world images.
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Privacy regulations impose data compliance demands requiring heightened focus on securing pipelines, managing consumer consent, and implementing data minimization policies balancing insights with individual protections. Leading vendors are embedding automated data classification, tokenization, and encryption aligning collections to policy guardrails.
How to Select Data Collection Automation Solutions
When evaluating automation options, key selection criteria include:
Data Sources – Confirm supported connectivity across all websites, APIs, databases, cloud apps, and other systems providing the data types required for initiatives. Seek extensibility options and tools safeguarding future investments as new sources emerge.
Scalability – Review extract, transform, load (ETL) throughput benchmarks and storage capacities for enterprise-grade scalability ready for surges in data volumes over months and years of pipeline growth. Optimize scaling models balancing performance demands and costs.
Ease-of-Use – Seek low-code or no-code platforms enabling intuitive point-and-click configuration by ordinary business users over reliance on IT or specialized engineers for ongoing enhancements. Ensure sufficient self-service while still providing expert assistance when needed.
Data Quality – Verify built-in data monitoring, testing, and profiling capable of flagging anomalies suggesting imprecise extractions requiring intervention. Review tooling and workflows empowering data scientists or other skilled staff in rapidly diagnosing and resolving collection errors.
Security – Given exponential data growth magnifying risks, confirm robust encryption, access controls, secrets management, and data loss prevention capabilities. Validate third-party audit reports and compliance with regulations around PII data handling relevant to your industry.
Deployment Options – Support for multi-cloud, hybrid, and air-gapped on-premises implementations provides flexibility aligning to infrastructure preferences and compliance demands. Seek commitment from vendors towards customer choice.
Total Cost – Look beyond upfront licensing to factor in vital long-term costs around personnel, compute, and storage for scaling over years. Seek predictability and transparency from vendors with clearly defined pricing models for calculations.
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The automation revolution offers immense possibility. Yet without meticulous planning assessing options against current and future objectives, hastily moving forward risks wasted investments. Organizations should carefully evaluate their analytics roadmaps, data availability gaps impeding desired models, compliance risks, and total costs before acting. With prudent analysis guiding decisions however, automation-accelerated data collection unlocks immense potential.