Is Scraping of LinkedIn Data Legal?

The scraping of data may be both, legal and illegal. The legality depends on the type of data and the way of using the data.

Its finest example is a legal battle started in 2017 that is still going on between LinkedIn and hiQ. A US federal judge allowed scraping personal data from public profiles. The hiQ Labs did so. It is the business that used publicly available data on LinkedIn. Its representative confirmed that the data under the tag –PUBLIC can be used for any purpose. The US Supreme Court sent this case back to the 9th Circuit of Appeals.

The federal circuit ruling found that hiQ has not violated the Computer Fraud and Abuse Act (CFAA). It is because the companies like LinkedIn have freely allowed the use of public user data.

Certainly, it was a right decision because giving companies like LinkedIn a free right over who to use public user data is risky. It can harm privacy and the public interest.

Vital Points for Data Scraping Companies

What this battle shows that the full scope of getting benefits from the data scraping is not legally permitted. This is why the data users, like marketers and entrepreneurs should extract data from LinkedIn responsibly.

• Following all guidelines under GDPR and privacy policies is mandatory.
• Determine if the data are public or private before using.
• Always seek advice from the legal counsel or other qualified legal advisors while clearing all doubts regarding legal aspects of web data scraping.
• If the data are not protected, which happens when the user voluntarily contributes his data, the user shall retain ownership over their profiles.
• To take ownership or using it, the scraping company has to set the public interest a PRIORITY. An access to any third party without permission is a crime as per GDPR.

How does web data extraction work?

The extraction of user records is a harsh decision. But simply put, it’s an automatic process of getting information from any website or URL over the internet.
Smart programmers write specific codes to scrape whatever they want to capture. They start with sending a request to the website for accessing data. Some smart cookies save time and efforts for scrapers, which get the permission automatically.

Later, they convert it into a usable form like spreadsheet so that it can be understood easily. The analysts, thereafter, use it to collect insights in no time and help to prepare the next plan of action. These actions can be business strategies or intelligence to improve workflows in no time.

Years ago, it was hardly possible.

Is it legal to scrape data from LinkedIn?

Partially, it is legal. One should know that the public interest should not be compromised. You can collect personal profiles for deriving business benefits from web scraping, which are:

• Crafting business offers and services for customers on the basis of what you learn from the collected data
• Understanding what people feel about your brand or products or services
• Getting contacts, which can be used for lead generation & coversion
• Getting details on a product and why people prefer or dislike it, what’s the change in trend, is it a money-making deal and a lot more things
• Quickly collecting valid email ids from authentic resources
• Acquiring competitors’ information to analysis their USPs and KPIs etc…
• Gathering business reviews for the online reputation management of the brand
• Making comparisons with other website data to discover whose web content is living up the customer’s expectation
• Having a pool of fresh and useful data to improve the scope of growth and opportunities.

This is to notice that the above mentioned benefits can shape into a legal battle, as in between LinkedIn and hiQ Labs. Various companies and smart analysts can misuse the unprotected data for creating algorithms. The best part of these algorithms is that one could easily predict what the target audience might do in the next step. And, the possibility of that happening is maximised because the data always tell the fact.

Unfortunately, these predictions can prove a big weapon for cyber criminals, who oftentimes predict users’ moves, feelings and emotions to play with later. They draw their interest out of it. For instance, the opinion polls can end up in predictive analytics. This could be dangerous because the drive insights can be used in the proactive campaigns for changing the mindset of people, which disallows natural thoughts. The artificial ideas are put into your mind to think the way the campaigner like.

This kind of big data analytics use could be disastrous.

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Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

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This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt

Applications Of Data Science On 3D Imagery Data

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

• Industrial metrology for quality assurance.
• 3d object detection and its volumetric analysis.

This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data

What Is ETLT? Merging the Best of ETL and ELT Into a Single ETLT Data Integration Strategy

Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:

• ETL is valuable when it comes to data quality, data security, and data compliance. It can also save money on data warehousing costs. However, ETL is slow when ingesting unstructured data, and it can lack flexibility.
• ELT is fast when ingesting large amounts of raw, unstructured data. It also brings flexibility to your data integration and data analytics strategies. However, ELT sacrifices data quality, security, and compliance in many cases.

Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.

#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data