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In our fast-paced world filled with bulks of data and information, it’s easy to feel lost in the volume. But fear not because data parsing is here to save the day!

Data parsing can be your trusty guide that helps make sense of all that overwhelming data clutter. In this blog, we’re diving into the world of data parsing – what is data parsing, how it works, and why it’s like a genie for understanding the meanings hidden within raw data.

So, buckle up and scroll down to learn everything about data parsing!

Defining Data Parsing

Let’s start by understanding what data parsing truly entails. Data parsing definition calls it a process of breaking down complex and unstructured data into smaller, more manageable parts for analysis. 

In the digital setup, data parsing involves extracting relevant information from various sources, such as text documents, websites, or databases, and transforming it into a structured format that can be quickly processed and interpreted.

The Essence of Data Parsing

When you are working, you might be dealing with vast spreadsheets containing a mix of names, dates, and quantities, all jumbled together. Data parsing would involve systematically segregating each type of information into distinct columns, allowing you to organize, sort, and analyze the data more effectively.

By breaking down the information into its constituent parts, data parsing helps transform a bulk of undifferentiated data into a coherent and understandable format.

Applications of Data Parsing: A Multifaceted Tool

Data parsing finds its applications across various domains, each harnessing its power to achieve specific goals. Here are a few notable applications:

Text Analysis

Data parsing plays a pivotal role in extracting insights from textual data. It can identify and categorize specific keywords, phrases, or patterns within a large text corpus. This is particularly valuable for sentiment analysis, content categorization, and trend identification.

Web Scraping

Extracting data from websites is a prime example of data parsing in action. By parsing the HTML structure of a webpage, relevant information such as product details, prices, or news articles can be extracted and utilized for various purposes.

Log Analysis

In IT and cybersecurity, log files contain a wealth of information about system activities. Data parsing can help decipher these logs, identifying anomalies, patterns, and security breaches.

Financial Data Processing

Parsing financial data, such as stock prices, transaction records, and market trends, allows you to make informed decisions in finance and investment.

Natural Language Processing (NLP)

NLP techniques often involve data parsing to break down sentences into grammatical components, enabling machines to understand and respond to human language more effectively.

The Mechanism of Data Parsing: How Does It Work?

Didn’t you love to do puzzles when you were a kid? You can take data parsing as a skillful puzzle solver that assembles scattered pieces into a coherent picture. 

Let’s delve deeper into the mechanics of data parsing, step by step, and shed light on how the introduction of Smart Proxy magnifies its significance by ensuring robust data security and safeguarding data privacy.

Step 1: Tokenization – Breaking It Down

At the beginning of data parsing lies the first step: tokenization. It’s like breaking down a complex problem into smaller, manageable parts. 

Similarly, tokenization involves dissecting raw data into smaller units called tokens. Depending on the data type, these tokens can be words, characters, or even symbols. Tokenization serves as the foundation for further analysis and understanding.

Step 2: Pattern Recognition 

With tokens in hand, the process moves to pattern recognition. Like identifying recurring themes in a story, pattern recognition algorithms detect consistent structures within the tokens. 

For instance, in textual data, patterns could be sentences or paragraphs. In web data, patterns might include HTML tags or attributes. This step is about recognizing the framework that gives the data its meaning.

Step 3: Extraction 

Once patterns are recognized, data extraction comes into play. This is like mining for valuable gems within a vast mine. Algorithms sift through the recognized patterns and extract specific information. 

For instance, extraction could involve identifying dates, names, or monetary figures from a text document. It’s all about retrieving the valuable nuggets hidden within the data.

Step 4: Transformation 

Following extraction, the transformation phase commences. You can take it as a process of refining rough diamonds into sparkling jewels. 

Similarly, data transformation involves refining the extracted data to ensure consistency and accuracy. This might involve converting data into a standardized format, eliminating redundancies, or applying necessary formatting. The goal is to prepare the data for meaningful analysis.

Step 5: Synthesis – Putting It All Together

The parsed data is synthesized and organized into a coherent structure in the synthesis phase. The parsed data is arranged logically, allowing for easy interpretation and analysis. This step involves categorizing data, grouping related elements, and arranging them systematically.

Step 6: Analysis and Insights – Discovering the Story

The culmination of data parsing lies in the analysis and gaining actionable insights. This phase provides the opportunity to conclude, make informed decisions, and gain a deeper understanding of the underlying story that the data tells.

Smart Proxy: Elevating Data Parsing with Security and Privacy

Now, let’s introduce the concept of Smart Proxy to this narrative. Smart Proxy goes beyond enhancing data security and privacy; it acts as a vigilant guardian throughout the data parsing journey.

Smart Proxy ensures secure data exchange, safeguards sensitive information, and adds an extra layer of assurance to the entire process. By prioritizing data security and privacy, Smart Proxy contributes to the accuracy, reliability, and integrity of the parsed data, fostering trust and confidence in the insights derived from the information.

In Conclusion: The Art of Data Parsing Unveiled

As we conclude our exploration of data parsing, we’ve delved into the intricate steps that lead to transforming raw data into actionable insights. Each phase of data parsing plays a crucial role in deciphering the language of data and unveiling its underlying narrative. 

With Smart Proxy as a steadfast companion, the data parsing journey becomes even more robust, secure, and reliable. This powerful combination empowers you and your organization to confidently extract valuable insights while ensuring the utmost protection of sensitive information. 

So, as you embark on your own data parsing endeavors, remember that this process is not just about unraveling data; it’s about deciphering the stories that data holds, ultimately guiding you toward informed decisions and meaningful discoveries.

Featured image by Claudio Schwarz on Unsplash

The post What is Data Parsing, and How It Is Used? appeared first on noupe.

A data pipeline is a set of steps that are used to process data. The data is ingested at the beginning of the pipeline if it is not currently loaded on the data platform. Each step delivers an output that serves as an input to the next step in a series of steps. This process continues until the pipeline has been completed. Parallel execution of independent steps is possible in some cases.

The data pipeline transports raw data from software-as-a-service platforms and databases to data warehouses for analysis and business intelligence (BI). The developer can construct pipelines by writing code and manually interacting with source databases – or they can avoid reinventing the wheel using a SaaS data pipeline.

Data pipelines-as-a-service represents a revolutionary concept, yet how much work goes into assembling an old-school data pipeline? Let’s review the principal components and stages of data pipelines, as well as the most commonly used pipeline technologies.

What Is a Data Pipeline?

Data pipelines consist of a series of steps for processing data. A pipeline begins with data ingesting if it has not yet been loaded into the data platform. Then, there are a series of steps, each producing an output that is the input for the next step. This process continues until the pipeline has been completed. It is possible to run independent steps in parallel in some cases.

A data pipeline has three key components: a source, a processing step or steps, and a destination. Data pipelines may have a sink as their destination. It’s easy to move data across an application and a data warehouse, connecting a data lake to an analytics database with a data pipeline. It is also possible for data pipelines to have the same source and sink so that the pipeline is purely concerned with changing the data. There is a data pipeline when data is processed between points A and B.

Data pipelines play a critical role in the planning and development of organizations as they develop applications with small code bases that serve a specific purpose. A source system or application may generate data that feeds multiple data pipelines, which may feed multiple other pipelines or applications.

Let’s take a look at a single comment on social media. Data from this event could feed a real-time social media mentions report, a sentiment analysis application that shows positive, negative, and neutral results, or a world map application that displays each mention. The data in all these applications come from the same source; however, each application requires its own set of data pipelines that must be completed smoothly before the end user can see the results.

The Benefits of Data Pipeline

Data is likely to be a significant part of your organization’s operations. You require a single view of all of that data in order to analyze it. For in-depth analysis, data from multiple systems and services must be combined in ways that make sense. The actual data flow itself can be unreliable: corruption or bottlenecks may occur at numerous points during the transfer from one system to another. As data’s breadth and scope expand, the magnitude and impact of these problems will only increase.

This is the reason why data pipelines are so important. This automates the process and eliminates the majority of manual steps. You can make faster, data-driven decisions with the help of real-time analytics. Your organization needs them if:

• Analyzes data in real-time
• Cloud-based data storage
• Contains data from multiple sources

Data pipeline components

Next, we will discuss some of the basic components of a data pipeline that you should know about if you plan to work with one.

• Origin. A data pipeline begins at the origin, where data is entered. A company’s reporting and analytical data ecosystem can include data sources (transaction processing applications, IoT devices, social media, or APIs) and storage platforms (data warehouse, data lake, or data lakehouse).
• Destination. A destination is a point at which data is transferred. The destination will depend on the use case: For example, data can be used to fuel data visualization and analytics tools, or it can be stored in a data lake or warehouse.
• Dataflow. This refers to the movement of data from point A to point B, including the modifications it experiences along the process, as well as the data stores it passes through.
• Storage. In storage systems, data is preserved at different stages as it passes through the pipeline. There are many factors that affect data storage decisions, such as the volume and frequency of data queries to a storage system, the use of data, etc.
• Processing. Data processing involves ingesting data from the source, storing it, transforming it, and delivering it. Data processing is related to dataflow, but it focuses on how to implement this movement. Ingesting data can be achieved by retrieving it from existing systems, copying it from one database to another (database replication), or streaming it. There are more options than the three we mention.
• Workflow. In a data pipeline, a workflow defines the sequence of processes (tasks) and their dependency on one another. Here, it would be beneficial for you to have a good understanding of several concepts – jobs, upstream, and downstream. A job is a section of work that performs a specific task – in this case, data processing. Data enters a pipeline from an upstream source while it exits at a downstream destination. The data pipeline is like a river that flows downhill. Additionally, upstream jobs must be successfully completed before downstream jobs can begin.
• Monitoring. Monitors check that the data pipeline and its stages are working effectively: whether they maintain efficiency as data volumes grow, whether data remains accurate and consistent during processing stages, and whether the information is not lost along the way.

Data pipeline architecture

ETL data pipeline

A data pipeline architecture based on ETL has been a standard for decades. Typically, it extracts data from various sources, formats it, and loads it into an enterprise data warehouse or data mart.

ETL pipelines are typically used for

• Migrating data from legacy systems to a data warehouse,
• Utilizing multiple touchpoints to gather all customer information in one place (usually the CRM system),
• Providing a holistic view of business operations by consolidating large volumes of data from various internal and external sources
• Integrating disparate datasets to enable deeper analysis.

The critical disadvantage of ETL architecture is that you have to recreate it whenever your business rules (and data formats) change. 

ELT data pipeline

ELT varies from ETL in the flow of steps: loading occurs before the transformation. The ELT architecture is useful when

• It is unknown what you will do with data and how exactly you will transform it;
• Ingestion speed plays an important role; and
• There are large amounts of data involved.

ELT, however, is still a less mature technology than ETL, which causes problems in terms of tools and talent pools. Data pipelines can be built using either ETL or ELT architecture or a combination.

Batch pipeline 

Data pipelines using batch processing collect data over a period of time and process it on a regular basis. A traditional data analysis workflow involves asking questions of previously collected data – you are likely to think of batch analysis when you imagine a traditional data analysis workflow. Throughout the decades, batch processing has been a critical component of analytics and business intelligence.

The batch processing of data is an established method of working with large datasets in non-time-sensitive projects. However, if you require real-time insights, you should choose architectures that support streaming analytics.

Streaming data pipeline 

Real-time or streaming analytics are based on real-time data processing, also known as event streaming. Essentially, it is a way of processing data continuously as it is collected in a matter of seconds or milliseconds. A real-time system responds quickly to new information when it is based on an event-based architecture. While real-time data pipelines can be utilized for analytics, such pipelines are vital for systems that need rapid processing of data.

The use of real-time analytics enables businesses to get up-to-date information about operations and react accordingly without delay, as well as provide solutions for monitoring the performance of infrastructure in a smart and efficient manner. Companies that cannot afford to experience any delays in processing data, such as fleet management companies operating telematics systems, should opt for streaming architecture over batch processing.

Big Data pipeline

Big Data pipelines carry out the same tasks as smaller pipelines. Their ability to support Big Data analytics distinguishes them from one another.

As a Big Data pipeline, ELT seems to be the perfect solution for loading unlimited amounts of raw data and analyzing it live, extracting insights on the fly. However, batch processing and ETL are capable of handling large amounts of data as well, thanks to modern tools. Typically, organizations use a combination of ETL and ELT as well as several stores to analyze Big Data in both batch and real-time.

Conclusion 

A data pipeline is undoubtedly essential to modern data management and strategic planning. By using data pipelines, you can connect data between different organizations and stakeholders. Data engineers can gain valuable insights for better decision-making by supporting in-depth data analysis with effective data movements.

There are many design architectures and tools available for developing the pipeline, making it easier to achieve better analysis. However, before implementing data pipelines, it is essential to realize what data can do for your organization and how you can crawl data from the web.

Featured Image by Mudassar Iqbal from Pixabay 

The post Data Pipeline Architecture Explained appeared first on noupe.

Crawlers are such essential tools on the Internet today that imagining a world without them would make navigating the web a different experience. Web crawlers assist in the operation of search engines, serve as the brains behind web archives, assist content creators in finding out what content is copyrighted, and assist website owners in identifying which pages on their sites require attention.

You can accomplish a lot with web crawlers that would be difficult or impossible without them. If you need to collect data from the Internet, you might need to use web crawlers at some point as a marketer. However, choosing a suitable web crawler for your needs may be difficult. It is because, unlike web scrapers, you can find a lot of general-purpose scrapers; you will need to dig deeper to find web crawlers. The reason is that most popular web crawlers are usually specialized.

We’ve compiled the top 8 web crawler tools with their features and pricing for you in this article.

1. Crawlbase

Crawlbase provides crawling and scraping services to people who wish to crawl data at a large scale and maintain the most significant level of anonymity throughout the process. The Crawler allows you to crawl any website or platform on the Internet. You will be able to benefit from proxy support, captcha bypass, as well as the ability to crawl JavaScript pages with dynamic content.

The crawler is a pay-as-you-go model with no hidden fees, so you only pay for successful requests. The first 1,000 requests are free, and you will be informed of the exact cost based on how many requests you make. A monthly pricing calculator makes calculating your price relatively easy, as you only pay for successful requests, and if there are any unsuccessful requests, you will not be charged.

Features:

• The company provides a wide range of scraping services
• A headless browser is supported for rendering JavaScript
• They only charge you for successful crawling
• Geo-targeting supported by a lot of countries
• It has a pool of over one million IP addresses
• Smart rotation of IP address
• The number of successful requests determines the price
• 1000 Free requests for new users

2. Nokogiri

Nokogiri is an open-source software library for parsing HTML and XML in Ruby. Libxml2 and libxslt provide the functionality of the library.

Nokogiri provides a comprehensive API for reading, writing, editing, and querying documents. The tool simplifies the process of working with XML and HTML for Ruby developers. Nokogiri is based on two fundamental principles. As a first step, it automatically treats all documents as suspicious. Second, it does not attempt to correct the behavioral differences detected between parsers.

Features:

• DOM Parser for XML, HTML4, and HTML5
• SAX Parser for XML and HTML4
• A document search tool based on CSS3 selectors, with some jQuery-like extensions
• Validation of XSD Schemas
• XSLT transformation
• ” Builder” DSL for XML and HTML
• Push Parser for XML and HTML4
• Completely free.
• Good XML and HTML parser for Ruby.
• Superior security.

3. UiPath

UiPath is an end-to-end robotic process automation tool. It provides solutions to automate routine office activities to accelerate business change. 

UiPath has built-in capabilities for performing additional crawls. It is particularly effective when dealing with complex user interfaces. It can easily extract data in tabular or pattern form from multiple different web pages. The screen scraping tool can extract individual text components, groups of text, blocks of text, and data in a table format.

Features:

• By streamlining processes, identifying efficiencies, and providing insights, we can achieve fast digital transformation at reduced costs.
• A UiPath robot follows your exact requirements to ensure compliance. Using Reporting, you can view your robot’s documentation at any time.
• If you standardize your methods, your outcomes will be more effective and successful.
• Crawling of web and desktop data with intelligent automation.
• It is not necessary to have any programming knowledge in order to create web agents.
• It is capable of handling both individual and group text elements.
• Easily manages complex user interfaces.

4. Webharvy

The Webharvy tool includes a point-and-click interface for scraping web pages. It is designed for people who aren’t programmers. Using WebHarvy, you can automatically scrape text, images, URLs, and emails from websites. You can access target websites via proxy servers or a VPN.

Features:

• Pattern Detection.
• You can save it to a file or a database.
• Keyword submission.
• Handle pagination.
• It is easy to use.
• Keyword-based extraction.
• VPN support is included.
• The crawling scheduler is impressive.

5. Import.io

Import.io is a platform that facilitates the conversion of semi-structured web pages into structured data, which can be used for a variety of purposes, ranging from business decision-making to integration with apps.

They provide real-time data retrieval through their JSON REST-based and streaming APIs and support integration with a variety of common programming languages and data analysis tools. 

It is great for businesses and marketing research that wants organized data. There are multiple programming languages that can be used with the software. The crawler’s point-and-click interface makes it easy to use.

Features:

• Point-and-click training
• Automate web interaction and workflows
• Easy Schedule data extraction
• Support almost every system
• The integration of multiple languages is seamless.
• Pricing flexibility.

6. Zyte 

Zyte is another web crawler designed for developers who are proficient in coding. The tool offers several features that enable users to quickly extract information from websites across the Internet.

Crawlera, a sophisticated proxy rotator utilized by Zyte, allows users to crawl large sites and bot-protected pages without worrying about bot countermeasures. Users can crawl from multiple IP addresses and locales through a simple HTTP API without maintaining proxy servers.

Features:

• Content Planning
• Keyword tracking
• Website accessibility testing
• Content auditing
• Automatically build sitemaps.

7. Open Search Server

The OpenSearchServer software is based on Lucene and is a powerful, enterprise-class search engine solution. You can easily and quickly integrate full-text search capabilities into your application by utilizing the web user interface, crawlers, and JSON web services.

It is a good tool for crawling websites and building search indexes. Additionally, it provides text extracts and auto-completion features that can be used to create search pages. Depending on your needs, the software will allow you to select from six different scripts to download.

Features:

• Crawlers can index everything.
• The classifications are made automatically.
• This is a free, open-source tool.
• There is a wide range of search functions available.

8. Dexi.io

The Dexi.io web scraping tool allows businesses to extract and transform data from any web source through advanced automation and intelligent mining technologies. 

You can scrape or interact with data from any website using Dexi.io. You can use three types of robots: Extractors, Crawlers, and Pipes. An advanced feature set and APIs enable you to combine and transform data into robust datasets.

Features:

• Automatic Data Capture.
• Location-based analytics.
• Category Analytics.
• Highly customizable.
• you can create your own agents
• The data is automatically deduplicated before it is sent to your systems.

Conclusion

We discussed some of the best Crawlers available in marketing with their top features to help you crawl available online data according to your own needs. Let us know which crawler tool worked the best for you in the comments below. 

The post 8 Best Web Crawlers To Get Better Data appeared first on noupe.

Shadow data is, first and foremost, a concern for the security and compliance teams. What can they do to ensure the security and compliance of data stores they are unaware of or have little or no control over? 

There is an inherent tension between DevOps and security teams amplified by regulatory frameworks, such as GDPR and CCPA, that set and enforce high privacy standards for data.

In this blog post, we will discuss what shadow data is, why you should not ignore it, and how to secure it effectively.

What Does Data Shadow Mean?

The term shadow data refers to your organization’s data that is likely copied, backed up, or located in a data store that is neither governed, under the same security structure or updated on a regular basis. The following are examples of shadow data:

• Customer data that has been copied from production to development for testing purposes
• Data stores that contain sensitive information for an application that is no longer in use
• An application’s byproducts, such as log files, may collect sensitive information
• Applications that use hard-to-find local databases
• The data generated by shadow IT 
• A siloed set of data that is only available to a specific line of business

As a matter of fact, shadow data is primarily a problem for security and compliance professionals. If they are not aware of or have little or no control over data stores, how can they be responsible for their security and compliance? Regulations, such as GDPR and CCPA, that establish and enforce high data privacy standards have amplified the tension between DevOps and security teams.

Shadow data also affects operations teams since unmanaged data sprawl can increase infrastructure costs. Cloud budgets are exceeded, with little or no visibility into how the overspend was incurred or how it can be contained. Here are some real-life examples of shadow data

• Public, unmanaged databases: The developer implemented an SQLite database for storing sensitive data input by web application users. What is the problem? A standard web server was used to deploy the database, which was, by definition, publicly accessible. The company’s security and compliance guardrails were violated, exposing sensitive data to threats without the security team’s knowledge.
• The data generated by backend applications: Backup files, log files and debug dumps serve the needs of DevOps engineers but are typically not monitored by security professionals. Shadow data, however, may contain sensitive information. 
• Unmanaged cloud resources: Developers may create an S3 bucket in restricted geolocation as part of internal testing procedures that are not audited. These testing resources unnecessarily add to infrastructure costs if they are not properly decommissioned, posing security and compliance risks.

It’s time to stop ignoring shadow data

It is now challenging to ignore shadow data due to the prevalence of hybrid and multi-cloud environments. According to a recent report, 92% of enterprises today have a multi-cloud strategy, of which 82% have adopted a hybrid approach. A lack of visibility in these environments makes it difficult to monitor them effectively, and shadow data is likely to accumulate as a result.

Shadow data is also increasing due to the adoption of cloud-based continuous integration and delivery methods. In today’s market, developers have more freedom to introduce new products and features. Additionally, the self-service cloud model allows developers to provision data stores with just a few clicks, often without consideration of the organization’s governance or compliance policies. 

The proliferation of cloud-native applications based on micro-services, containers, and server-less functions has brought the issue of shadow data to the fore since decentralized workload-based data stores contribute significantly to data sprawl.

What is the difference between shadow data and dark data?

The term dark data refers to all the data within an organization that is unused, untapped, and unknown as the result of users’ daily interactions online with countless devices and systems – everything from machine data to server log files to unstructured data derived from social media interaction.

The data may be considered outdated, incomplete, redundant, or inaccessible due to a format that can’t be accessed using available tools. They don’t even know it exists most of the time.

However, it is essential to note that dark data may be one of an organization’s most valuable untapped resources. Data has become a major organizational asset, and competitive organizations must capitalize on its full potential. Furthermore, more stringent data regulations may require organizations to manage their data entirely.

Shadow data differs from dark data in that it is created within an organization’s IT infrastructure during routine business operations, serves no other purpose, and becomes unaccounted for over time. Shadow data can be viewed as a subset of dark data. A dark data set is a collection of sensitive information that was once used for legacy applications or irrelevant information generated by an application. 

Shadow data, on the other hand, is created in two main ways: by shadow IT, which is intentionally developed outside an organization’s IT infrastructure to leverage cloud-managed and SaaS applications that DevOps teams, DBAs, and others would not be able to access otherwise; or by over-sharing within an organization. Shadow data is unaccounted-for data that poses the same security risks in either case.

A three-step process for securing shadow data

1. Visibility: It is essential that your security teams identify every cloud-managed environment and SaaS application in which your organization may have sensitive data. There is no way to apply security controls to data that is stored in repositories that you cannot access.
2. Discovery and classification of data: Data in all of your repositories must be identified and classified so that security controls can be applied. There is a need to extend discovery and classification capabilities beyond traditional structured data; semi-structured and unstructured data must also be able to be classified. You can quickly detect anomalous behavior by consolidating your data repositories into a single source and allowing dashboard access to see what is happening across all data sources.
3. Control the privileges of data access: Shadow data can only be mitigated by preventing insiders from creating it inadvertently. When it comes to rooting out malicious user behavior, a rigorous analysis of anomalous behavior is very effective. It is possible to baseline typical access for privileged users and send alerts if it deviates from that. It is also possible to use machine learning analytics to determine what data is business-critical and if it can be accessed by privileged users.

Minimizing the risks associated with shadow data

Data management best practices can mitigate shadow data risks in several ways, including:

• Understand your data: Maintain a catalog of data assets that are categorized according to their sensitivity and criticality as you scan your workloads continuously. It is important that the data catalog is comprehensive, accessible to all stakeholders, and searchable according to a variety of parameters, such as owner, sensitivity, used by, version, and so on.  
• Follow your data: A next-generation data catalog should also incorporate visualizations that highlight relationships, flows, and dependencies among data stores in a typical mid-sized to large organization. The ability to build outflows and identify who is interacting with what data will allow you to identify shadow data that is not being used. It is not only a waste of storage resources to store this data, but it can also pose a threat to cybersecurity, such as data exfiltration, due to its unused nature.
• Clean your data: A key component of IT hygiene is keeping your data assets lean and mean. In general, each time a developer replicates a data store for testing or a database is mirrored before an upgrade, the developer or operation person should delete it when the testing environment is decommissioned, or a successful upgrade has been completed. Your environment, however, is full of redundant, incomplete, or low-value data. Ensure that shadow data is regularly disposed of.
• Protect your data: A risk-based data protection policy and process must include shadow data. Risk assessments, which should be as automated as possible, will take into account the type of shadow data and its location, as well as any compliance requirements related to its sensitivity. After establishing the appropriate guardrails, you should include access controls, minimal privileges, monitoring for anomalous behavior, alerting to threats, and resolving misconfigurations.

Conclusion

By eliminating shadow data and including new data that can be useful, businesses and organizations can significantly improve their analytics, reporting, machine learning, and AI. It consequently leads to faster, and more intelligent decision-making across the enterprise.

The best way to combat shadow data is to begin by educating your entire firm’s staff about the issue. 

Educate people in your vicinity about shadow data and why they shouldn’t ignore it. Engage experts who have demonstrated their ability to successfully manage data-intensive operations if you want to gain a greater understanding of the shadow data.

The post Tips to Secure Shadow Data appeared first on noupe.

Website engagement hacks help a great deal in engaging users and increasing conversions. The strategies to improve online engagement include many different activities that can have a positive result on the overall user experience of your website.

Metrics To Track User Engagement

There are multiple metrics that can help to measure engagement. Here are few of the most important ones:

1. Bounce Rate:

This metric defines how many users leave the website without interacting with it. The lower the bounce rate, the more engaging your site is. If you have a very high bounce rate maybe people cannot find what they were looking for on your website or they might find better information somewhere else so they leave immediately. 

2. Time On Website:

This metric shows how long users stay on your website. It’s crucial because if people spend lots of time on your site, it means there are relevant contents and things to learn from it. Tracking this metric will help you engage visitors better by understanding what kinds of content they appreciate most.

3. Returning Visitors:

This metric represents people who come back to your website and browse it again. Website engagement strategies include tools that help you to better engage returning visitors and that encourage them even more for next visits.

What is website engagement?

Website engagement usually refers to the process of encouraging users’ engagement with contents on a web page, such as by adding social media buttons or displaying comments sections on blog posts. 

Website engagement can also refer to encouraging user interaction with other parts of the website through features like quizzes or surveys. 

Top Website Engagement Hacks You Should Know

So, let us explore 10 Website Engagement Hacks to use today:

1. Add Social Media Buttons To Your Website

Social media buttons are the easiest way for users to share your content with their network or follow you on social media platforms. Once you have noticed an increase in traffic from these shares, consider offering something special only to those who followed through the links provided by you. 

This could be a discount code, free premium access, or a simple invitation to give feedback about the site’s features. Remember that sharing is not going to influence anything unless users feel rewarded for it.

2. Website Popups

Website popups are one of the most known ways to improve your site’s conversions. Website popup example that evokes curiosity, can be used in re-engagement efforts or help with boosting sales by offering alternative payment options (for instance customers who did not pay using PayPal could get the option to pay using a credit card). 

Website popups should be subtle and short enough to leave visitors no other choice but to interact if they want to exit the page. Website popup examples like this one work because you don’t need more than 10 seconds’ worth of attention span from users while providing useful information at the same time (such as coupons, marketing campaigns, etc.).

3. Make Your Website Responsive

Having a responsive website can help you to engage more customers. Website responsiveness is especially important for e-commerce websites since users are expecting the same experience everywhere they are browsing or shopping online. 

Make sure your site responds nicely on different devices so avoid building one website version for every device, instead focus on providing tablets and mobile users with simplified interface options that will improve their browsing experience. 

Website responsiveness examples like this one include offering a single version of content that is easily readable on all devices. A responsive website makes it accessible to a larger audience. 

4. Website Countdown Timers

Website countdown timers have shown to be effective at increasing conversions during sale events by encouraging visitors to buy before time runs out. 

Website countdown timers can also be used as re-engagement efforts – for instance, if there is some promotion running only for the next 48 hours you could include a counter on your website with this information in order to encourage users to act before it’s too late.

Website countdown timers offer serious motivation in taking action fast in order to get an attractive discount or benefit upfront.

5. Website Scrolling Mechanic

Website scrolling mechanics are helpful for showing additional content based on the user’s behavior and preferences, these usually appear right after first landing on a certain web page and can be used as re-engagement efforts (for instance showcasing free premium content if the first 3 articles were read by visitors). 

Website scrolling provides further insight into customers’ browsing behavior and allows marketers to identify what exactly needs to be improved so that more customers would convert.

6. Website Reviews 

Website reviews are one of the most popular ways for marketers to understand their targets’ needs and interests, website reviews can be either product or service related, but they are definitely one of the best re-engagement efforts these days – especially if they are coming from people who have shown some interest in your brand online. 

Website review shows trustworthiness to website visitors by showing targeted testimonials straight from people who were actually using services provided by you.

7. Include Video Tutorials On Your Website

As video content is very engaging (users love videos nowadays) it would only make sense to include them on your website in order to engage more users and increase conversions at the same time. 

User-friendly video tutorials are especially important for website usability since there is no better way of guiding your customers to use your website or services. 

Website videos help visitors a visual guide to using the site, by breaking down things into easier steps and it’s always better than written instructions that are hard to follow.

8. Website Navigation & Flow Improvement 

Website navigation improvement is all about customer-focused experience which can significantly increase conversions if handled carefully. Website navigation is an often overlooked part of every website design process that needs constant adjustments in order to match business objectives with customer expectations. 

Website navigation example with color coding helps users understand where they are on the web page, what other options are available and how to return back easily if needed. 

Website flow improvement is all about creating a smooth browsing experience for your visitors and it’s especially important when trying to increase conversions.

Website flow improvement (including quick and easy registration and checkout process) can help you make the website buying process as fast as possible which increases conversion rates without actually annoying users with troublesome sign-up procedures.

9. Product Screenshots 

Product screenshots are helpful in providing additional information to certain web pages. They usually appear on the home page, feature pages, or product detail pages. 

Product screenshots offer clear insight into a company’s credibility by showing real-world numbers that definitely improve website trustworthiness and show true popularity of certain brands online.

10. Include FAQs 

Website FAQ (Frequently Asked Questions) is another way of engaging users on a website. Website FAQs are helpful for improving website navigation and user experience since they provide answers to the question that visitors might have about a specific web page or product.

The FAQ’s section helps customers understand what you offer without forcing them to go through the entire web page every time they visit your site.

What is Your Favourite Hack?

We have explained some of the best hacks to improve website engagement that can give you better metrics and business revenues.

Now it’s your turn to implement these tips on your website or online store to grow your business. Let us know in the comments below how these hacks have helped in meeting your goals.

The post How to engage users on your website? 10 Hacks To Use Today appeared first on noupe.