Big data

    Cloud and AI Technology Help USB Flash Drives Stay Relevant

    Cloud technology and AI are rapidly changing the state of our technological landscape. Many old forms of technology have started to become obsolete, as a growing number of new tools utilizing these new forms of technology are making things easier.

    However, the cloud and big data are also offering some benefits that help older forms of technology stay relevant. USB drives are an example. Despite the growing relevance of cloud technology, global customers still spend over $35 billion on USB devices and the market is growing over 9% a year. Ironically, newer technologies may be helping make USBs even more useful by making them easier to backup and making data recovery easier.

    How Does the Cloud and AI Help Make USB Drives Stay Relevant?

    USB drives may seem like they would not be as useful in a day where so much data can be easily stored on the cloud. However, they have some clear benefits.

    For one thing, they are portable and can accessed without an Internet connection. As versatile as cloud technology is, you still need to be able to connect to the Internet to access data stored on it. This is one of the reasons that cloud technology hasn’t eliminated the need for data recovery software.

    However, there are some downsides of USB devices. One of the biggest challenges is that data can be easily lost or corrupted.

    Fortunately, advances in AI and cloud technology have created the best of both worlds for USB users. They can use cloud integration tools from companies like Kingston to backup their USB data on the cloud.

    AI technology has also resolved some of the problems with USB technology. AI has helped restore lost data on various types of devices, including USB drives. Joanna Riley has talked about some of the benefits of using AI to improve data recovery efforts, such as predicting when data storage capacity will be exceeded or performing emergency restorations.

    One of the benefits of using AI for data recovery on USB drives is that the tools can better identify lost data and reconstruct it more fully. This is making it easier to restore data more easily.

    If you want to use AI to restore data from a USB, then you need to make sure that you use the right tools. Keep reading to learn more.

    What Steps Should You Take to Restore Lost Data from USB Drives?

    With an increased dependency on data storage devices, there is a significant increase in data loss situations. It can be due to an empty recycle bin, accidentally formatting devices, or a malware attack. So, today we bring a detailed but simple guide on how to recover permanently deleted photos from any storage devices.

    As we stated in the past, AI and big data are changing the state of the data recovery industry. You need to know how to use the right AI tools to recover lost data more easily.

    We’ll go through the detailed steps to get back deleted or lost data with professional data recovery software easily and effectively. Also, some alternative methods will be introduced, if you don’t want to install any software. We’ll also cover quick tips related to image recovery for beginners to eliminate data loss problems and some alternatives like using backup, CMD, etc. Starting recovering photos from USB Flash Drive with photo recovery software.

    Part 1: Steps to recover deleted photos from USB Flash Drive with Wondershare Recoverit?

    Wondershare Recoverit is the one-stop solution when it comes to how to recover lost data. Whether it is due to the random use of the “Shift + Delete” command or an empty recycle bin, virus attack or system crash, this tool can precisely handle it and save your data from any kinds of disaster. In addition, this data recovery software can recover more than 1000 file formats from almost all storage devices, be like USB flash drive, SD card, digital camera, etc.

    The quick steps to use Wondershare Recoverit for recovering photos are:

    1. Start by launching Recoverit Photo Recovery on your PC or Mac.
    2. Select the location: Select the hard disk for recovering the deleted photos. Click on the USB flash drive option. Press the “Start” button to initiate the hard disk scanning.
    1. Scanning the location: the software will start a thorough scan of the selected location to trace the deleted or deeply hidden or lost photos. It also allows you to preview the scanning results in this process.
    1. Recovering the lost photos: Preview the recovered files and click on the “Recover” option to get the photos back.

    Why choose Wondershare Recoverit?

    Out of all the possible options available in the photo recovery software, Wondershare Recoverit is the preferred choice of millions users globally due to the following reasons:

    1. Specialized in restoring lost photos, audio, videos, etc., from USB flash drives, SD cards, cameras, SSD, HDD, etc.
    2. Performs comprehensive scanning to find deleted photos, videos, and audio files from external and internal devices.
    3. Awarded 35 patents for innovative data recovery methods.
    4. Support restoring data on crashed computers by creating a bootable USB drive.
    5. Saves time by offering a quick preview of the files before recovery.

    Part 2: How to recover deleted photos from USB Flash Drive without any software?

    Hence, it is a simple and straightforward process to use Wondershare Recoverit for restoring photos in different storage devices. Readers looking for some quick alternatives to how to get deleted pictures back without software can go through the following two options:

    Option1: Recovering from a backup

    If you back up your system data regularly, it is easy to restore deleted photos from the system backup. Let us go through the quick steps to use the in-built backup and recovery tool for Windows:

    • Connect the storage media having system backup.
    • Open the Windows “Start” button and then press “Control Panel” > “System and Maintenance” > “Backup and Restore.”
    • Select from the “Restore my files” or “Restore all users’ files” option.
    • Search the lost photos using the “Browse for files” or “Browse for folders” option. Select “Restore” to recover the selected file.

    Option 2: Recover deleted photos from CMD

    After using the Windows in-built data backup and recovery tool, it is easy to recover photos from CMD. The command prompt feature works precisely for the data loss situations of hidden photos or corrupted photos. The quick steps for the same are:

    • Go to the “Start” menu and type “cmd” in the search bar.
    • Select the “Run as administrator” option.
    Graphical user interface, application

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    • Type “chkdsk *:/f” in the command prompt window. You must replace * with the hard drive letter in the command and press enter.
    • Now type “ATTRIB –H –R –S /S /D D:*.*. You must replace D with the hard drive letter and press enter.
    • It will immediately start the file restoration process.

    Part 3: Tips to avoid deleted photos on your USB Flash Drive

    After going through the detailed steps of how to recover photos from USB flash drives using different methods, it all comes down to some quick tips. So, below are some of the easiest tips on the permanently deleted photo recovery process:

    1. Start by taking a backup of your photos on different devices before moving ahead with an external storage device. It is easy to connect the external USB device to your computer, take a quick backup, and use your system as the backup device.
    2. It is recommended not to delete any photo from the photos storage location during backup to ensure a complete backup of images.
    3. It is advised to save the recovered photos at a different place from the one where it gets deleted. It eliminates the repeat case of data loss.
    4. Download image recovery software like Wondershare Recoverit at a safe location on the system. Further, it is recommended to go for a tool supporting multiple file formats.

    AI and the Cloud Make USB Devices Even More Promising

    There are a lot of reasons that cloud technology and AI are helping the USB market. These new technologies make it easier to backup and recover data from USBs.

    Different reasons, like malware attacks, accidental use of the “Shift + Delete” command, or an empty recycle bin, may mark the need for an image recovery tool. Hence, different data loss scenarios can be quickly managed with the leading tool like Wondershare Recoverit, which can effectively recover deleted photos, audio, videos, etc. Further, it is easy to understand the different steps of how to recover permanently deleted photos from a USB flash drive.

    This tool lets users quickly go through the detailed steps for photo recovery. No need to worry about your data when it is easy to recover the deleted photos from the backup and CMD on your system. Not to miss are the quick tips on image recovery.

    The post Cloud and AI Technology Help USB Flash Drives Stay Relevant appeared first on SmartData Collective.

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    Habib Bank manages data at scale with Cloudera Data Platform

    As the leading financial institution of Pakistan, Habib Bank Limited (HBL) is at the forefront of all development initiatives which includes growth of priority sectors and targeting the unbanked population in the country. HBL remains committed to its objective of client centric innovation and financial inclusion for all segments of society. 

    HBL was the first Pakistani commercial bank to be established in Pakistan in 1947. Serving over 32 million clients worldwide, over the years, HBL has grown its branch network and maintained its position as the largest private sector bank in Pakistan with 1700+ branches*, 2200+ ATMs*, 61,000+ Konnect* by HBL agents (branchless banking platform), and 52,000+ QR locations. HBL is currently the largest Domestic Bank with a presence across major trade zones in the world. 

    HBL has re-envisioned itself as a ‘Technology company with a banking license’, as it transforms into the bank of tomorrow – one which empowers its customers through digital enablement. In 2021, HBLs customers digitally carried out over 330 Mn financial transactions valued at PKR 7 Tn) in payments, a growth of 30% over 2020. HBL aims to double its banked customers by 2025.

    We needed a solution to manage our data at scale, to provide greater experiences to our customers. With Cloudera Data Platform, we aim to unlock value faster and offer consistent data security and governance to meet this goal.Aqeel Ahmed Jatoi,  Lead – Architecture, Governance and Control, Habib Bank Limited

    The Solution: CDP Private Cloud brings a next-generation hybrid architecture with cloud-native benefits to HBL’s data platform.

    HBL started their data journey in 2019 when data lake initiative was started to consolidate complex data sources and enable the bank to use single version of truth for decision making. The architecture of data lake was designed keeping in mind reliability, security, high performance and robust data structures which can fulfill current and future business needs. Blutech Consulting was selected both by HBL and Cloudera as the implementation partner based on their in-depth technical expertise in the field of data. 

    In 2020, Cloudera professional services were engaged to perform technical audit of the ongoing data lake implementation and to understand if there are any gaps and to align with best practices. Cloudera professional services audited the entire implementation and architecture and found the entire setup extremely satisfactory and further provided areas for improvements.

    While Cloudera CDH was already a success story at HBL, in 2022, HBL identified the need to move its customer data centre environment from Cloudera’s CDH to Cloudera Data Platform (CDP) Private Cloud to accommodate growing volumes of data. CDP Private Cloud’s new approach to data management and analytics would allow HBL to access powerful self-service analytics. The platform’s capabilities in security, metadata, and governance will provide robust support to HBL’s focus on compliance and keeping data clean and safe in an increasingly complex regulatory and threat environment.

    With CDP, HBL will manage data at scale through a centralized data lake, serving Pakistan, Sri Lanka, Singapore and other international territories. The bank will be able to secure, manage, and analyse huge volumes of structured and unstructured data, with the analytic tool of their choice. 

    Smooth, hassle-free deployment in just six weeks

    Leading data analytics company Blutech Consulting and a very reliable partner of Cloudera was selected as the implementation partner, working with Cloudera Professional services to oversee the upgrade activities. The implementation took just over six weeks, and involved end-to-end planning, stakeholder communications, pre-requisites and actual upgradation. 

    Farrukh Cheema, Client Engagement Partner, Blutech Consulting said, “Our goal for Habib Bank was a modern unified data platform that is built for growth and the analytics demands of tomorrow. Cloudera’s CDP is the only solution that can address the system, hosting, integration and security, enabling us to deploy quickly and easily with minimal impact to operations.”

    Prior to the upgrade, HBL’s 27 node cluster ran on CDH 6.1 and primarily served regulatory reporting and internal analytics requirements. All prerequisites had to be completed to ensure smooth deployment to ensure minimal impact on daily BAU activities. Subsequently, the upgrade activities took place on a long weekend to ensure zero to limited downtime for HBL teams. 

    Future-proof data capabilities

    Aqueel Jatoi, Lead – Architecture, Governance and Control shared, “The implementation has been quick and seamless, and our teams are already reaping the benefits of CDP. We are confident the upgrade and continuing partnership with Cloudera will form an important foundation to our customer-centric approach, significantly improving our ability to respond to customers.”

    Following the successful upgrade, HBL will further future-proof their data architecture leveraging Cloudera’s solutions powered by analytics and machine learning to address anti-money laundering (AML), fraud and cybersecurity.

    See other customers’ success here 

    The post Habib Bank manages data at scale with Cloudera Data Platform appeared first on Cloudera Blog.

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    5 Strategies For Stopping Bad Data In It’s Tracks

    For data teams, bad data, broken data pipelines, stale dashboards, and 5 a.m. fire drills are par for the course, particularly as data workflows ingest more and more data from disparate sources. Drawing inspiration from software development, we call this phenomenon data downtime– but how can data teams proactively prevent bad data from striking in the first place?

    In this article, I share three key strategies some of the best data organizations in the industry are leveraging to restore trust in their data.

    The rise of data downtime

    Recently, a customer posed this question: “How do you prevent data downtime?”

    As a data leader for a global logistics company, his team was responsible for serving terabytes of data to hundreds of stakeholders per day. Given the scale and speed at which they were moving, poor data quality was an all-too-common occurrence. We call this data downtime-periods of time when data is fully or partially missing, erroneous, or otherwise inaccurate.

    Time and again, someone in marketing (or operations or sales or any other business function that uses data) noticed the metrics in their Tableau dashboard looked off, reached out to alert him, and then his team stopped whatever they were doing to troubleshoot what happened to their data pipeline. In the process, his stakeholder lost trust in the data, and valuable time and resources were diverted from actually building data pipelines to firefight this incident.

    Perhaps you can relate?

    The idea of preventing bad data and data downtime is standard practice across many industries that rely on functioning systems to run their business, from preventative maintenance in manufacturing to error monitoring in software engineering (queue the dreaded 404 page…).

    Yet, many of the same companies that tout their data-driven credentials aren't investing in data pipeline monitoring to detect bad data before it moves downstream. Instead of being proactive about data downtime, they're reactive, playing whack-a-mole with bad data instead of focusing on preventing it in the first place.

    Fortunately, there's hope. Some of the most forward-thinking data teams have developed best practices for preventing data downtime and stopping broken pipelines and inaccurate dashboards in their tracks, before your CEO has a chance to ask the dreaded question: “what happened here?!”

    Below, I share five key strategies you can take to preventing bad data from corrupting your otherwise good pipelines:

    Ensure your data pipeline monitoring covers unknown unknowns

    Data testing-whether hardcoded, dbt tests, or other types of unit tests-has been the primary mechanism to improve data quality for many data teams.

    The problem is that you can't write a test anticipating every single way data can break, and even if you could, that can't scale across every pipeline your data team supports. I've seen teams with more than a hundred tests on a single data pipeline throw their hands up in frustration as bad data still finds a way in.

    Monitor broadly across your production tables and end-to-end across your data stack

    Data pipeline monitoring must be powered by machine learning metamonitors that can understand the way your data pipelines typically behave, and then send alerts when anomalies in the data freshness, volume (row count), or schema occur. This should happen automatically and broadly across all of your tables the minute they are created.

    It should also be paired with machine learning monitors that can understand when anomalies occur in the data itself-things like NULL rates, percent uniques, or value distribution.

    Supplement your data pipeline monitoring with data testing

    For most data teams, testing is the first line of defense against bad data. Courtesy of Arnold Francisca on Unsplash.For most data teams, testing is the first line of defense against bad data. Courtesy of Arnold Francisca on Unsplash.

    Data testing is table stakes (no pun intendend).

    In the same way that software engineers unit test their code, data teams should validate their data across every stage of the pipeline through end-to-end testing. At its core, data testing helps you measure whether your data and code are performing as you assume it should.

    Schema tests and custom-fixed data tests are both common methods, and can help confirm your data pipelines are working correctly in expected scenarios. These tests look for warning signs like null values and referential integrity, and allows you to set manual thresholds and identify outliers that may indicate a problem. When applied programmatically across every stage of your pipeline, data testing can help you detect and identify issues before they become data disasters.

    Data testing supplements data pipeline monitoring in two key ways. The first is by setting more granular thresholds or data SLAs. If data is loaded into your data warehouse a few minutes late that might not be anomalous, but it may be critical to the executive who accesses their dashboard at 8:00 am every day.

    The second is by stopping bad data in its tracks before it ever enters the data warehouse in the first place. This can be done through data circuit breakers using the Airflow ShortCircuitOperator, but caveat emptor, with great power comes great responsibility. You want to reserve this capability for the most well defined tests on the most high value operations, otherwise it may add rather than remove your data downtime.

    Understand data lineage and downstream impacts

    Field and table-level lineage can help data engineers and analysts understand which teams are using data assets affected by data incidents upstream. Image courtesy of Barr Moses.Field and table-level lineage can help data engineers and analysts understand which teams are using data assets affected by data incidents upstream. Image courtesy of Barr Moses.

    Often, bad data is the unintended consequence of an innocent change, far upstream from an end consumer relying on a data asset that no member of the data team was even aware of. This is a direct result of having your data pipeline monitoring solution separated from data lineage – I've called it the “You're Using THAT Table?!” problem.

    Data lineage, simply put, is the end-to-end mapping of upstream and downstream dependencies of your data, from ingestion to analytics. Data lineage empowers data teams to understand every dependency, including which reports and dashboards rely on which data sources, and what specific transformations and modeling take place at every stage.

    When data lineage is incorporated into your data pipeline monitoring strategy, especially at the field and table level, all potential impacts of any changes can be forecasted and communicated to users at every stage of the data lifecycle to offset any unexpected impacts.

    While downstream lineage and its associated business use cases are important, don't neglect understanding which data scientists or engineers are accessing data at the warehouse and lake levels, too. Pushing a change without their knowledge could disrupt time-intensive modeling projects or infrastructure development.

    Make metadata a priority, and treat it like one

    When applied to a specific use case, metadata can be a powerful tool for bad data incident resolution.When applied to a specific data pipeline monitoring use case, metadata can be a powerful tool for data incident resolution. Image courtesy of Barr Moses.

    Lineage and metadata go hand-in-hand when it comes to data pipeline monitoring and preventing data downtime. Tagging data as part of your lineage practice allows you to specify how the data is being used and by whom, reducing the likelihood of misapplied or broken data.

    Until all too recently, however, metadata was treated like those empty Amazon boxes you SWEAR you're going to use one day – hoarded and soon forgotten.

    As companies invest in more data solutions like data observability, more and more organizations are realizing that metadata serves as a seamless connection point throughout your increasingly complex tech stack, ensuring your data is reliable and up-to-date across every solution and stage of the pipeline. Metadata is specifically crucial to not just understanding which consumers are affected by data downtime, but also informing how data assets are connected so data engineers can more collaboratively and quickly resolve incidents should they occur.

    When metadata is applied according to business applications, you unlock a powerful understanding of how your data drives insights and decision making for the rest of your company.

    The future of bad data and data downtime

    End-to-end lineage powered by metadata gives you the necessary information to not just troubleshoot bad data and broken pipelines, but also understand the business applications of your data at every stage in its life cycle. Image courtesy of Barr Moses.End-to-end lineage powered by metadata gives you the necessary information to not just troubleshoot bad data and broken pipelines, but also understand the business applications of your data at every stage in its life cycle. Image courtesy of Barr Moses.

    So, where does this leave us when it comes to realizing our dream of a world of data pipeline monitoring that ends data downtime?

    Well, like death and taxes, data errors are unavoidable. But when metadata is prioritized, lineage is understood, and both are mapped to testing and data pipeline monitoring, the negative impacts on your business – the true cost of bad data and data downtime – is largely preventable.

    I'm predicting that the future of broken data pipelines and data downtime is dark. And that's a good thing. The more we can prevent data downtime from causing headaches and fire drills, the more our data teams can focus on projects that drive results and move the business forward with trusted, reliable, and powerful data.

    The post 5 Strategies For Stopping Bad Data In It’s Tracks appeared first on Datafloq.

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    How to Use Pre-Labeled Data for AI Algorithms With High-Quality Requirements

    In machine learning, data labeling is the process of identifying objects or events on raw data (images, text files, videos, etc.) and adding one or more meaningful and informative labels to provide context so that a machine learning model can learn from it. For example, labels might indicate whether a photo contains a bird or car, which words were uttered in an audio recording, or if an x-ray contains a tumor. Data labeling is required for a variety of use cases, including computer vision, natural language processing, and speech recognition.

    Successful machine learning models are built on the shoulders of large volumes of high-quality annotated training data. But, the process of obtaining such high-quality data can be expensive, complicated, and time-consuming, which is why sometimes companies look for ways to automate the data annotation process. While the automation may appear to be cost-effective, as we will see later on, it also may contain some pitfalls, hidden expenses, cause you to incur extra costs to reach the needed annotation quality level as well as put your project timing at risk.

    In this article, we take a closer look at the hidden risks and complexities of using pre-labeled data which can be encountered along the way of automating the labeling process and how it can be optimized. Let's start by getting an overview of what pre-labeled data is.

    What is Pre-Labeled Data?

    Pre-labeled data is the result of an automated object detection and labeling process where a special AI model generates annotations for the data. Firstly the model is trained on a subset of ground truth data that has been labeled by humans. Where the labeling model has high confidence in its results based on what it has learned so far, it automatically applies labels to the raw data with good quality. Often the quality of pre-labeled data may appear to be not good enough for projects with high accuracy requirements. This includes all types of projects where AI algorithms may affect directly or indirectly the health and lives of humans.

    In quite many cases, after the pre-labeled data is generated, there are doubts and concerns about its accuracy. When the labeling model has not sufficient confidence in its results, it will generate labels and annotations of the quality, which isn't enough to train well-performing AI/ML algorithms. This creates bottlenecks and headaches for AI/ML teams and forces them to add extra iterations in the data labeling process to meet high quality requirements of the project. A good solution here will be to pass the automatically labeled data to specialists to validate the quality of annotations manually. This is why the step of validation becomes really important since it can remove the bottlenecks and give the AI/ML team peace of mind that a sufficient data quality level was achieved.

    As we can see, there are some challenges companies face with pre-labeled data when the ML model was not properly trained on a particular subject matter or if the nature of raw data makes it difficult or even impossible to detect and label all edge cases automatically. Now let's take a closer look at the potential issues companies need to be ready for if they choose to use pre-labeled data.

    Pre-Labeled Data May Not Be as Cost-Effective as You Think

    One of the main reasons companies choose to use pre-labeled data is the higher cost of manual annotation. While from first look it can seem like automation would lead to huge cost savings, in fact, it might not. Different types of data and various scenarios require the development and adjustments of different AI models for pre-labeling, which can be costly. Therefore, for the development of such AI models to pay off, the array of data for which it is created must be large enough to make the process of developing the model cost-effective.

    For example, to develop ADAS and AV technologies, you need to consider a lot of different scenarios that include many variables and then list those factors. All of this creates a large number of combinations, each of which may require a separate pre-annotation algorithm. If you are relying on pre-labeled data to train the AI system, you will need to constantly develop and adjust algorithms that can label all of the data. It results in a significant increase in costs. The price tag of generating high-quality pre-annotations can grow exponentially depending on the variety of data used in the project, which would erase any cost savings you may obtain from hiring a dedicated annotation team. However, if the data array is really large, then the path of pre-labeling data will be fully justified, but the quality risks of these annotations still must be taken into account, and in most cases, the manual quality validation step will be necessary.

    You Will Incur Data Validation Costs

    In the previous section, we talked that an ML system has limited ability to learn all of the possible scenarios to label a dataset properly, which means that AI/ML teams will need a quality validation step to ensure that the data labeling was done correctly and the needed accuracy level was reached. Algorithms for data pre-annotation have a hard time understanding complex projects with a large number of components: the geometry of object detection, labeling accuracy, recognition of different object attributes, etc. The more complex the taxonomy and the requirements of the project the more likely it is to produce predictions of lower quality.

    Based on the experience of our work with clients, no matter how well their AI/ML team developed the pre-annotation algorithms for cases with inconsistent data and complex guidelines, their quality is still nowhere near the quality level requirement, which usually is a minimum of 95% and can be as high as 99%. Therefore, the company will need to spend additional resources on manual data validation to maintain the high-quality data supply to ensure the ML system meets the needed accuracy requirements.

    A good solution in this case will be to plan ahead the quality validation step and the resources not to put the project quality and deadline in risk, but to have the needed data available in time. Also the bottleneck can be easily eliminated by finding a reliable experienced partner who can support your team with annotation quality tasks to release the product without delays and ensure faster time-to-market.

    Some Types of Data Annotations Can Only Be Done by Humans

    Certain annotation methodologies are difficult to reproduce via the pre-labeling method. In general, for projects where the model may carry risks to life, health, and safety of people, it would be a mistake to rely on auto-labeled data alone. For example, if we take something relatively simple, like object detection with the help of 2D boxes, common scenarios of the automotive industry can be synthesized with sufficiently high quality. However, the segmentation of complex objects, with large unevenness of object boundaries, will usually have a rather low quality with automated annotation.

    In addition to this, often there is a need for critical thinking when annotating and categorizing certain objects, as well as scenarios. For example, landmarking of human skeletons can be synthesized, and the quality of pre-annotations can be satisfactory over the course of training and refinement of the algorithm. However, if the project includes data with a large number of different poses, as well as occluded objects with a need to anticipate key points for labeling, for such annotation, critical thinking will be necessary to achieve a high-quality level. Even the most advanced algorithms today and in the near future will not have critical thinking, so such a process is possible only through manual annotation.

    The post How to Use Pre-Labeled Data for AI Algorithms With High-Quality Requirements appeared first on Datafloq.

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    Challenges to Successful AI Implementation in Healthcare

    “Al will not replace doctors but instead will augment them, enabling physicians to practice better medicine with greater accuracy and increased efficiency.” – By Benjamin Bell (Scottish Scientific Surgeon)

    Artificial intelligence (AI) and machine learning (ML) have received widespread interest in recent years due to their potential to set new paradigms in healthcare delivery. It is being said that machine learning will transform many aspects of healthcare delivery, and radiology & pathology are among the specialties set to be among the first to take advantage of this technology.

    Medical imaging professionals in the coming years will be able to use a rapidly expanding AI-enabled diagnostic toolkit for detecting, classifying, segmenting, and extracting quantitative imaging features. It will eventually lead to accurate medical data interpretation, enhanced diagnostic processes, and improved clinical outcomes. Advancements in deep learning (DL) and other AI methodologies have exhibited efficacy in supporting clinical practice for enhanced precision and productivity.

    Hurdles to AI Integration into Healthcare

    Though AI can empower healthcare and diagnostic processes with automation integration, some challenges exist. The lack of annotated data makes it difficult to train deep-learning algorithms. Moreover, the black-box nature leads to the opacity of the results of deep learning algorithms. Clinical practice faces critical challenges when incorporating AI into healthcare workflows.

    The key challenges to successful AI implementation in the healthcare practice are as follows:

    1. Ethical & Legal Issues for Data Sharing
    2. Training Healthcare Practitioners and Patients to Operate Complex AI Models
    3. Managing Strategic Change to Put AI Innovations into Practice

    1- Ethical & legal Issues Hindering Access to High-Quality Datasets for AI Developers

    Whether integrating artificial intelligence in medical imaging or employing deep learning technology to maneuver clinical diagnostic procedures, high-quality healthcare datasets are the key to success. As we tend to figure out the critical roadblocks to developing AI models for healthcare, it's been found that ethical and legal issues have so far been the biggest hurdle to developing AI-powered machine learning models.

    Since patients' health information is protected by law as private and confidential, healthcare providers must comply with strict privacy and data security policies. However, it keeps healthcare practitioners under the ethical & legal obligation not to provide their data to any third party. Consequently, it hinders AI developers from accessing high-quality datasets to develop AI training data for healthcare machine learning models.

    In addition to ambiguities in existing laws and challenges associated with sharing data between organizations, healthcare leaders also identified external conditions and circumstances as challenges. As a result of these challenges, uncertainties arose concerning responsibilities concerning the design and implementation of AI systems and what is permissible, resulting in legal and ethical concerns.

    2- Training Healthcare Practitioners and Patients to Use Complex AI Models

    Incorporating AI systems could improve healthcare efficiency without compromising quality, and this way, patients could receive better and more personalized care. Investigations, assessments, and treatments can be simplified and improved by using AI systems that are smart and efficient. However, implementing AI in healthcare is challenging because it needs to be user-friendly and procure value for patients and healthcare professionals.

    AI systems are expected to be easy to use and user-friendly, self-instructing, and not require extensive prior knowledge or training. Besides being simple to use, AI systems should also be time-saving and never demand different digital operative systems to function. For healthcare practitioners to efficiently operate AI-powered machines and applications, AI models must be simple in terms of their features and functionality.

    3- Managing Strategic Change to Put AI Innovations into Practice

    The healthcare experts noted that implementing AI systems in the county council will be difficult due to the healthcare system's internal capacity for strategic change management. For the promotion of capabilities to work with implementation strategies of AI systems at the regional level, experts highlighted the need for infrastructure and joint ventures with familiar structures and processes. Organizational goals, objectives, and missions needed to be achieved through this action to obtain lasting improvement throughout the organization.

    Healthcare professionals only partially determine how an organization implements change since change is a complex process. In Consolidated Framework for Implementation Research (CFIR), we need to focus on organizational capabilities, climates, cultures, and leadership, which all play a role in the “inner context.” Maintaining a functioning organization and delivery system is part of the capacity to put innovations into healthcare practice.

    Enhancing Healthcare by Integrating Artificial Intelligence in Medical Imaging through Data Annotation

    An imaging technique that allows us to see inside the body without having to open the body up surgically is known as a medical imaging technique (MIT). The use of AI in clinical diagnostics has demonstrated some of its most promising applications, including X-ray radiography, computed tomography, magnetic resonance imaging, and ultrasound imaging.

    Machine learning will improve the radiology patient experience at every step. Much of the initial focus for the application of machine learning in medical imaging has been on image analysis and developing tools to make radiologists more efficient and productive. The same tools will often enable more precise diagnosis and treatment planning or help reduce missed diagnoses, thus leading to improved patient outcomes.

    AI & machine learning have a much broader role in radiology beyond clinical decision-making and can help improve the patient experience throughout the imaging process – all the way from the initial scheduling of the imaging examination to the end of diagnosis and follow-up.

    Taking a look at the trends around the healthcare system, we can see machine learning has applications that go beyond diagnostic and medical imaging. It can enhance the data acquisition process to ensure the highest quality image for each examination and assist imaging departments in maximizing operational performance efficiently.


    Since the medical industry is at the dawn of a new wave of AI-fueled tech innovation, it is time for health providers to establish a roadmap for incorporating AI into their clinical practice. As the global population continues to grow, healthcare practitioners must invest in technologies that can improve patient care and transform clinical workflows. The application of artificial intelligence to healthcare delivery is unquestionably at the top among technologies that can revolutionize clinical processes.

    this post is originally published at click here

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    Data Swamps and The Tragedy of The Commons

    A silent alarm rings in my head whenever I hear someone utter the phrase, “data is everyone's responsibility.”

    You can just as easily translate this to mean that “data is no one's responsibility,” too. This, readers, is what I call the “data tragedy of the commons.”

    Here's why.

    The term tragedy of the commons comes from economic science and refers to situations where a common set of resources is accessed without any strong regulations or guardrails to curtail abuse.

    As companies ingest more data and expand access to it without clear data lineage, your beautifully curated warehouse (or lakehouse, we don't need to discriminate) can slowly turn into a data swamp as a result of this unfortunate reality of human behavior.

    To use another adage… having too many cooks in the kitchen spoils the broth. Having too many admins in the Looker instance leads to deletions, duplications, and a whole host of other issues.

    So, how can we fix this data tragedy of the commons?

    In my opinion, the answer is to give data consumers, and even members of the data team when appropriate, guardrails or “controlled freedom.” And in order to do this, teams should consider four key strategies.

    Remove the gatekeeper but keep the gate

    Don't get me wrong: everyone should care about data quality, security, governance, and usability. And I believe, on a fundamental level, they do. But I also believe they have a different set of incentives.

    So the documentation they are supposed to add, the catalog they need to update, the unit test they should code, the naming convention they should use-those things all go out the window. They aren't being malicious, they just have a deadline.

    Still, the tradeoff of faster data access is often a poorly maintained data ecosystem. We never, of course, want to disincentivize the use of data. Data democratization and literacy are important. But the consequences of sprawl are painful, even if not immediately felt.

    All too often, data teams attempt to solve the “data democratization problem” by throwing technology at it. This rarely works. However, while technology can't always prevent the diffusion of responsibility, it can help align incentives. And the end user or data consumer is most incentivized when they need access to the resource.

    We don't want to impede access or slow organizational velocity down with centralized IT ticket systems or human gatekeepers, but it's reasonable to request they eat some vegetables before moving to dessert.

    For example, data contracts add a little bit of friction early in the process by asking the data consumer to define what they need before any new data gets piped into the warehouse (sometimes in collaboration with the data producer).

    Once this has been defined, the infrastructure and resources can be automatically provisioned as Andrew Jones with GoCardless has laid out in one of our previous articles. Convoy uses a similar process as their Head of Data Platform, Chad Sanderson, has detailed.

    An architecture that asks the data consumer to define their needs upfront in a data contract. Image courtesy of Chad Sanderson.

    The next challenge for these data contract systems will be with change management and expiration. Once the data consumer already has the resources, what is the incentive to deprovision them when they are no longer needed?

    Make it easy to do the right thing

    The semantic layer, the component of the modern data stack that defines and locks down aggregated metrics, is another type of gate that makes it easier for people to do the right thing than to do the wrong thing.

    For example, it's likely easier for an analytics engineer to leverage a pre-built model in the dbt Semantic Layer as part of their workflow instead of having to (re)build a similar model that inevitably takes a slightly different perspective toward the same metric. The same thing is true for analysts and LookML. Making the most efficient path the correct path takes advantage of human nature rather than trying to fight it.

    A vision of where the dbt Semantic Layer should sit within a modern data stack architecture via dbt.

    A vision of where the dbt Semantic Layer should sit within a modern data stack architecture via dbt.

    Another great way to do this? Integrating with established workflows.

    Data catalogs, whose adoption challenges I've covered in the past, are classic examples of what happens when the responsibility to keep documentation up-to-date is spread thin across the team. Most of these solutions are just too far out of people's daily workflows and require too much manual effort.

    More modern data catalog solutions are wisely trying to integrate their use cases with DataOps workflows (which Forrester has recently pointed out) to better establish clear ownership and accountability.

    To use a different example from the world of data, the human aversion to logging into yet another tool that marginally adds value to their task is one of the reasons reverse ETL solutions have been so wildly successful. Data moves from systems that intimidate users, such as marketers, to systems they are already leveraging like Marketo or Salesforce.

    “Privatize” the commons

    The most successful data governance programs I've come across have domain-driven processes with clear lines of ownership and manageable scopes.

    For example, Clearcover Senior Data Engineering Manager Braun Reyes described how his organization has been successful deploying this type of strategy.

    “We originally tried to make data governance more of a centralized function, but unfortunately this approach was not set up for success. We were unable to deliver value because each team within the wider data and analytics organization was responsible for different components and data assets with varying levels of complexity. A one-size-fits-all, centralized governance approach did not work and was not going to scale.

    We have had much more momentum with a federated approach as detailed in the data mesh principles. Each data domain has a data steward that contributes to the data governance journey.

    Now, the proper incentives are in place. It all boils down to ownership….Governance works best when each service that generates data is a domain with people who own the data and contract.”

    Sometimes, just let the machine do it

    Data quality has also traditionally suffered from the tragedy of the commons.

    Data engineering leads will kick off their weekly meetings and talk about the importance of adding unit and end-to-end tests within all production pipelines until they are blue in the face. Everyone on the Zoom call nods and tells themselves they will rededicate themselves to this effort.

    One of two things happen at this point. In most cases nothing happens for all the reasons we've iterated thus far.

    But some teams create tight enough processes that they successfully create and maintain hundreds even thousands of tests. Which creates the problem of…creating and maintaining hundreds even thousands of tests. This consumes nearly half of your team's engineering hours.

    This is why the best solution to a tragedy of the commons problem, when it's possible, is to automate the solution. Machine learning, for example, is better suited for catching data incidents at scale than human written tests (and of course they are best used in concert).

    The key for any automation is to have it take action by default wherever possible rather than requiring a human to kickstart the process (and thus bring you back to your original challenge of inaction through the diffusion of responsibility).

    Data commons can be beautiful…with guardrails

    The modern data stack has enabled teams to move faster and provide more value than ever before.

    As teams continue their path toward decentralization, self-service, and the removal of data gatekeepers, data leaders need to understand how the diffusion of responsibility may impact their team's ability to execute.

    Teams that align incentives, integrate with workflows, focus scope, automate, and operational ownership – in other words, achieve controlled freedom – will be much more successful than the teams that create bulky processes, rely on weekly admonishment, or succumb to the tragedy of the commons.

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    Assuring Data Quality: How to Build a Serverless Data Quality Gate on AWS

    Data is a vital element in business decision-making. Modern technologies and algorithms allow for processing and storage of huge amounts of data, converting it into useful predictions and insights. But they also require high-quality data to ensure prediction accuracy and insight value.

    In today's world, the importance of data quality validation is hard to overestimate. For instance, the 2020 Gartner survey found that organizations estimate the average cost of poor data quality at $12.8 million per year, and this number will likely rise as business environments become increasingly complex.

    Assuring the quality of data is possible with modern data pipelines that should include data quality components by default. I have solid experience in the Data Quality Assurance (Data QA) niche and understand how to achieve data quality in the best way possible. I will share some of my expertise in this article.

    Great Expectations – A Data QA Tool of Choice

    To begin with, let's talk about one of the best Data QA tools – Great Expectations (GX).

    Great Expectations is an open-source data quality tool based on Python. GX can help data teams to profile, test, and create reports for and on data. GX has a friendly command-line interface (CLI) that enables you to easily set up and create new tests, while quickly customizing available test reports. GX can be integrated with various extract, transform, and load (ETL) tools, such as Airflow, and also with many databases. (You can find the list of integrations here and official documentation here.)

    Most importantly, Great Expectations supports AWS.

    Reporting on Data with Allure

    Allure is the gold standard for reporting in QA. Allure enables managers and non-technical professionals to review test results and keep track of the testing process. That is why, we decided to use Allure as a demonstration tool, to display Data QA results and implement a self-written adapter that converts GX results to the Allure format.

    We suggest the following Data QA approach for automating test creation:

    1. Retrieve tested data from data sources using AWS Lambda
    2. Run AWS Lambda with Pandas Profiling and generate tests for GX
    3. Run GX Test Suite for each dataset, all run in parallel for each dataset
    4. Store/serve results for each data source as a static Amazon S3 website
    5. Convert GX results to the Allure report format using AWS Lambda
    6. Store results in Amazon S3
    7. Generate Allure reports from the Allure format; reports are stored and served in Amazon S3
    8. Send the reports to a Slack channel with AWS Lambda
    9. Push results to Amazon DynamoDB (or Amazon S3 to reduce costs)
    10. Crawl data from Amazon DynamoDB by using Amazon Athena
    11. Create a dashboard with Amazon Quicksight

    Building a Data Quality Gate

    We now have all the components needed to build an efficient data quality gate. To simplify their deployment to AWS, we created a Terraform module – Data Quality Gate – that enables you to assure the quality of your data in one click. This module allows you to quickly deploy the infrastructure for DQ and generate the first test suite for your data. Use this module as a standard Terraform module for AWS-based deployments.


    Data Quality is a fast-growing field, and many engineers are involved in this process daily. Data Quality Engineers should build a solid pipeline for testing data and presenting results to stakeholders. Today, leveraging the availability of open source tools to deploy solutions faster plays a crucial role in data processing.

    The post Assuring Data Quality: How to Build a Serverless Data Quality Gate on AWS appeared first on Datafloq.

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    Data Lineage is Broken – Here Are 5 Solutions To Fix It

    Data lineage isn't new, but automation has finally made it accessible and scalable-to a certain extent.

    In the old days (way back in the mid-2010s), lineage happened through a lot of manual work. This involved identifying data assets, tracking them to their ingestion sources, documenting those sources, mapping the path of data as it moved through various pipelines and stages of transformation, and pinpointing where the data was served up in dashboards and reports. This traditional method of documenting lineage was time-intensive and nearly impossible to maintain.

    Today, automation and machine learning have made it possible for vendors to begin offering data lineage solutions at scale. And data lineage should absolutely be a part of the modern data stack-but if lineage isn't done right, these new versions may be little more than eye candy.

    So it's time to dive deeper. Let's explore how the current conversation around data lineage is broken, and how companies looking for meaningful business value can fix it.

    What is data lineage? And why does it matter?

    First, a quick refresher. Data lineage is a type of metadata that traces relationships between upstream and downstream dependencies in your data pipelines. Lineage is all about mapping: where your data comes from, how it changes as it moves throughout your pipelines, and where it's surfaced to your end consumers.

    As data stacks grow more complex, mapping lineage becomes more challenging. But when done right, data lineage is incredibly useful. Data lineage solutions help data teams:

    • Understand how changes to specific assets will impact downstream dependencies, so they don't have to work blindly and risk unwelcome surprises for unknown stakeholders.
    • Troubleshoot the root cause of data issues faster when they do occur, by making it easy to see at-a-glance what upstream errors may have caused a report to break.
    • Communicate the impact of broken data to consumers who rely on downstream reports and tables-proactively keeping them in the loop when data may be inaccurate and notifying them when any issues have been resolved.
    • Better understand ownership and dependencies in decentralized data team structures like the data mesh.

    Unfortunately, some new approaches to data lineage focus more on attractive graphs than compiling a rich, useful map. Unlike the end-to-end lineage achieved through data observability, these surface-level approaches don't provide the robust functionality and comprehensive, field-level coverage required to deliver the full value that lineage can provide.

    Data lineage mapping represented as spaghetti

    Don't let your data lineage turn into a plate of spaghetti. Image courtesy of Immo Wegmann on Unsplash.

    Let's explore signals that indicate a lineage solution may be broken, and ways data teams can find a better approach.

    1. Focus on quality over quantity through lineage

    Modern companies are hungry to become data-driven, but collecting more data isn't always what's best for the business. Data that isn't relevant or useful for analytics can just become noise. Amassing the biggest troves of data doesn't automatically translate to more value-but it does guarantee higher storage and maintenance costs.

    That's why big data is getting smaller. Gartner predicts that 70% of organizations will shift their focus from big data to small and wide data over the next few years, adopting an approach that reduces dependencies while facilitating more powerful analytics and AI.

    Lineage should play a key role in these decisions. Rather than simply using automation to capture and produce surface-level graphs of data, lineage solutions should include pertinent information such as which assets are being used and by whom. With this fuller picture of data usage, teams can begin to get a better understanding of what data is most valuable to their organization. Outdated tables or assets that are no longer being used can be deprecated to avoid potential issues and confusion downstream, and help the business focus on data quality over quantity.

    2. Surface what matters through field-level data lineage

    Petr Janda recently published an article about how data teams need to treat lineage more like maps-specifically, like Google Maps. He argues that lineage solutions should be able to facilitate a query to find what you're looking for, rather than relying on complex visuals that are difficult to navigate through. For example, you should be able to look for a grocery store when you need a grocery store, without your view being cluttered by the surrounding coffee shops and gas stations that you don't actually care about. “In today's tools, data lineage potential is untapped,” Petr writes. “Except for a few filters, the lineage experiences are not designed to find things; they are designed to show things. That's a big difference.”

    We couldn't agree more. Data teams don't need to see everything about their data-they need to be able to find what matters to solve a problem or answer a question.

    This is why field-level lineage is essential. While table-level lineage has been the norm for several years, when data engineers want to understand exactly why or how their pipelines break, they need more granularity. Field-level lineage helps teams zero in on the impact of specific code, operational, and data changes on downstream fields and reports.

    When data breaks, field-level lineage can surface the most critical and widely used downstream reports that are impacted. And that same lineage reduces time-to-resolution by allowing data teams to quickly trace back to the root cause of data issues.

    3. Organize data lineage for clearer interpretation

    Data lineage can follow in the footsteps of Google Maps in another way: by making it easy and clear to interpret the structure and symbols used in lineage.

    Just as Google Maps uses consistent icons and colors to indicate types of businesses (like gas stations and grocery stores), data lineage solutions should apply clear naming conventions and colors for the data it's describing, down to the logos used for the different tools that make up our data pipelines.

    As data systems grow increasingly complex, organizing lineage for clear interpretation will help teams get the most value out of their lineage as quickly as possible.

    4. Include the right context in data lineage

    While amassing more data for data‘s sake may not help meet your business needs, collecting and organizing more metadata-with the right business context-is probably a good idea. Data lineage that includes rich, contextual metadata is incredibly useful because it helps teams troubleshoot faster and understand how potential schema changes will affect downstream reports and stakeholders.

    With the right metadata for a given data asset included in the lineage itself, you can get the answers you need to make informed decisions:

    • Who owns this data asset?
    • Where does this asset live?
    • What data does it contain?
    • Is it relevant and important to stakeholders?
    • Who is relying on this asset when I'm making a change to it?

    When this kind of contextual information about how data assets are used within your business is surfaced and searchable through robust data lineage, incident management becomes easier. You can resolve data downtime faster, and communicate the status of impacted data assets to the relevant stakeholders in your organization.

    5. Scale data lineage to meet the needs of the business

    Ultimately, data lineage has to be rich, useful, and scaleable in order to be valuable. Otherwise, it's just eye candy that looks nice in executive presentations but doesn't do much to actually help teams prevent data incidents or resolve them faster when they do occur.

    We mentioned earlier that lineage has become the hot new layer in the data stack because of automation. And it's true that automation solves half of this problem: it can help lineage scale to accommodate new data sources, new pipelines, and more complex transformations.

    The other half? Making lineage useful by integrating metadata about all your data assets and pipelines in one cohesive view.

    Again, consider maps. A map isn't useful if it only shows a portion of what exists in the real world. Without comprehensive coverage, you can't rely on a map to find everything you need or to navigate from point A to point B. The same is true for data lineage.

    Data lineage solutions must scale through automation without skimping on coverage. Every ingestor, every pipeline, every layer of the stack, and every report must be accounted for, down to the field level-while being rich and discoverable so teams can find exactly what they're looking for, with a clear organization that makes information easy to interpret, and the right contextual metadata to help teams make swift decisions.

    Like we said: lineage is challenging. But when done right, it's also incredibly powerful.

    Bottom line: if data lineage isn't useful, it doesn't matter

    Monte Carlo is an automated data lineage solution that surfaces context about data incidents in real time

    Monte Carlo's field-level lineage surfaces context about data incidents in real time, before they affect downstream systems.

    Even though it seems like data lineage is everywhere right now, keep in mind that we're also in the early days of automated lineage. Solutions will continue to be refined and improved, and as long as you're armed with the knowledge of what high-quality lineage should look like, it will be exciting to see where the industry is headed.

    Our hope? Lineage will become less about attractive graphs and more about powerful functionality, like the next Google Maps.

    Want to see the power of data lineage in action? Learn how the data engineering team at Resident uses lineage and observability to reduce data incidents by 90%.

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    Email Marketers Use Data Analytics for Optimal Customer Segmentation

    Email marketing is widespread, with 333.2 billion emails exchanged every day. How can you ensure Outlook recipients open your bulk emails with severe competition?

    Email marketing is the most acceptable way to give precise customer data, but you must guarantee your efforts aren’t wasted. Using data analytics help your email marketing strategies succeed.

    Data Analytics’ Importance in Email Marketing

    Like any other marketing strategy, you must measure email performance. According to McKinsey, email marketing is 40 times more successful than Facebook, Twitter, etc.

    Without continual review and analysis, a firm can’t show how its campaigns are doing or if they’re producing the expected results. 

    Types of data analytics

    There are four types of data analytics for various marketing reasons. They examine data insights to better email marketing efforts. 

    • Demographic data about your target audience includes geography, interests, age, gender, etc..
    • Preferences of customers include assessing product’s usefulness in consideration of price and consumer income.
    • Transactional data includes first and final purchases, products, number of purchases, date, statistics, typical order value, commodity purchase history, and total spending by a consumer.
    • Behavioral Information includes understanding the audience’s interest and interaction with your email.

    How do data influence your email marketing campaigns?

    Digital workers measure almost anything they choose.

    Most email marketers utilize behavior analysis. It’s likely because this data is easy to access. Most email marketers display this data on their dashboards. Most marketers assume behavior analytics are enough since they’re so valuable.

    Outcome analysismeasures the effectiveness of your campaigns. Showing the executive team the result numbers transforms how they see email marketing and its impact on the firm.

    Finally, experience analysis reveals why something is the way it is. Experience analysis helps us understand why our consumers do what they do. 

    The diverse use of data analytics in email marketing

    Incorporate the following four data analytics strategies into your email marketing campaigns for improved results: 

    1. Segmentation

    Email segmentation is an efficient approach for dividing email subscribers into distinct subgroups depending on various factors. Segmentation has the potential to deliver a 58% increase in income. According to market research, however, 42% of corporate marketers don’t use this strategy.

    Typically, segmentation is a customization technique to build and send subscribers relevant and personalized email newsletters. There are several methods and technologies available for segmenting client data.


    Strategies for email marketing segmentation

    Organizations may collect client information and separate their contact lists in various ways to reap the benefits of email segmentation. 

    Choose an email marketing platform

    Many small companies use various platforms for email marketing that serve as a database for all company relationships. The ability to collect and utilize data for list segmentation enables businesses to send customized emails while concentrating on improving deliverability rates and reporting on email marketing engagement.

    MailChimp, Constant Contact, ActiveCampaign, and Hubspot are some popular email marketing solutions.

    Let’s discuss the famous email marketing platform MailChimp. Since its inception in 2001, Mailchimp has had more than two decades of expertise in email marketing for millions of subscribers. Paid options include marketing automation, integrations, and 24/7 support. However, small firms are seeking economic MailChimp alternatives that address MailChimp’s problems and where small businesses may discover affordable features, pricing, and plan. 

    Use forms to gather contact information

    Using internet forms to collect demographic information about contacts is the simplest method.

    Teams may select which fields to include on certain forms to collect business-relevant information. The more information a company has about a prospect, the more tailored is the marketing.


    Send a greeting email

    When a prospect enters a database with minimal information, organizations frequently do not know which list to place them on. By sending a welcome email,marketing teams may utilize various links and content suggestions to direct visitors to potentially relevant material. This allows the user to choose which list they may best fit into. This email also directs the recipient to a form to fill out, allowing companies to collect information omitted from the first database entry and the consumer to offer a better notion of what emails to send.

    Track point of consumer entry

    Tracking when a person accesses a website and what material they interact with is essential for delivering relevant content and email marketing. Including a visitor converter, such as a giveaway or advertisement promoting a specific product line or service, on a website landing page encourages users to become customers and helps companies to determine what information and material a user is interested in.

    Using web analytics software like Google Analytics, businesses may monitor the traffic, clicks, and bounce rates of the customer and prospect-visited web pages.

    With this knowledge, marketers may tailor subsequent email marketing messages to clients’ indicated requirements and interests. For instance, if a prospect connects with a top-of-funnel product, organizations may divide users into a list of contacts who have downloaded that asset. Subsequent emails send information that helps to advance them further down the sales funnel. 

    Separate prospects and clients

    Despite the importance of reaching out to prospects for increasing revenue development, organizations should also pay equal attention to returning customers. It is necessary to create separate email lists for prospects and customers. Current customers are simpler to market, so creating tailored emails for this group is a fantastic method to enhance customer retention and lifetime value. Companies use this strategy to upsell or cross-sell items or services to already-engaged audiences since satisfied consumers frequently return for more purchases. 

    Segment by engagement

    Frequently, a corporation will operate many marketing efforts simultaneously. Email segmentation based on involvement helps marketing teams determine where to position individuals within a process or campaign stream.

    For instance, if the objective of a marketing campaign is to get a prospect to download white paper, companies might send a succession of emails until the user interacts with the material. After users interact with the information, firms may add them to a new list or marketing campaign stream to get the subsequent asset or offer. This advances a user along the sales funnel and increases engagement by sending timely, relevant information to each user. 

    Send emails depending on the abandonment of a shopping cart or form

    This combination strategy is suitable for both B2B and B2C firms.

    As per a survey performed by the Baymard Institute, 68% of shopping carts are abandoned before purchase. As a result, having a cart abandonment program enables emails to reignite the initial interest in acquiring the goods, typically with a discount offer.

    For B2B businesses, form abandonment is a reality. Perhaps the visitor was uninterested in the offer, but the opportunity to recover them with other offers is a similar tactic employed by e-commerce businesses seeking to convert prospects. 

    Send emails to referrals

    Some of a company’s most valuable clients suggest other businesses and prospects to the brand. By separating these contacts into their list, organizations launch email campaigns requesting recommendations or even their published case study. Maintaining an open and robust connection with this audience will increase brand equity and income from referral sources. 

    Lists are segmented by geographic area

    Companies that sell products or services on a national or international scale profit from segmenting their lists based on the location of their database members. Businesses adapt their offerings through promotional campaigns based on seasonal or geographical expertise.

    For instance, a company that sells landscaping equipment may be more inclined to promote new snowblowers in regions with snow than in countries with milder weather. By staggering transmissions at the optimal time in different time zones, time-based email messages are also advantageous for reaching inboxes when it makes the most sense.


    2. Automation

    You are less likely to build effective email marketing campaigns if you lack knowledge about your audience’s preferences and market trends. Through the use of the automation tool, data analytics has made it possible to reduce the workload of marketers. Segmentation relies heavily on automation as well.

    Email automation is scheduling emails with a specific message to be sent to customers at a predetermined time. A marketer utilizes this contact point to send a consumer a reminder email, for instance, if they have added an item to their online shopping basket but forgotten to continue. 

    3. Timelessness

    It is crucial to find the optimal time to send emails. Regardless of how targeted and automated your email campaigns are, they end up in the spam folder if not sent appropriately. As with segmentation, grouping your subscriber list may help determine the optimal time to send emails. 

    4. Data science


    Data science plays a vital role in the efficacy, quality, and other aspects of email marketing campaigns and their administration. Generally, some factors are crucial for each campaign, such as customer segmentation, data segmentation, personalization of emails, Campaign/Email Optimal Timings, Content Automations for reducing the campaigns turnaround time and optimization of the Email templates, as well as many other areas in which data science produces quantifiable results.


     It is not difficult to do data analytics for your email marketing efforts. Data analytics may offer helpful marketing information, such as consumer behavior, market trends, and ad clicks. Companies must create effective email marketing campaigns by evaluating data and making more informed choices.

    The post Email Marketers Use Data Analytics for Optimal Customer Segmentation appeared first on SmartData Collective.

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    How to Identify the Right Cloud Architecture for Your Business?

    It’s an excellent idea if you have decided to move your business operations to the cloud. Cloud computing solutions can help your business reduce operational costs, gain unlimited scalability and improve external and internal collaboration.

    As a matter of fact, worldwide spending on public cloud services is forecasted to reach around $494.7 billion by the end of 2022, growing at a rate of 20.4% in a year. Moreover, by 2023, the figure might reach nearly $600 billion.

    There are many great reasons that the cloud computing market is growing. As a result, the market is growing remarkably.

    Cloud computing is the ultimate powerhouse that drives every digital business today. Therefore, utilizing cloud consulting services to migrate your workload to the cloud is not a matter of “If”; it’s a matter of “when” and “what cloud architect solution” you should be using.

    This article guides you through your journey of choosing the exemplary cloud architecture for your business. Let’s begin with understanding how cloud architecture works.

    Components of Cloud Architecture You Need To Know

    Cloud Computing architecture is divided into two parts: frontend and backend.

    Both parts communicate via the internet or a network. Here is a diagrammatic representation of the cloud architecture:

    Key features of the frontend cloud architecture:

    • It is responsible for the interfaces and applications required for the cloud-based service.
    • Also known as the client infrastructure, the frontend consists of client-side applications in the form of web browsers like Google Chrome and Internet Explorer.
    • Cloud infrastructure is the only component in the frontend architecture of the cloud where software and hardware components, such as virtualization software, server, data storage, etc., function.
    • Frontend also offers a graphical user interface for the end users to perform multiple tasks.

    Key features of the backend cloud architecture:

    • The backend is responsible for monitoring all the programs that run the application on the front end.
    • It has a large number of servers and data storage systems and is an essential part of the entire cloud infrastructure. Some of these cloud data storage are especially great for manufacturers.

    What else is included in the backend?

    • The application can be used as software or a platform
    • Service, another essential part of the backend, is responsible for providing utility in the architecture.
    • Storage for storing and maintaining data over the network. Some of the popular storage services are Oracle cloud, Amazon, Microsoft Azure, etc.
    • Management to allot specific resources to specific tasks and coordinate the cloud resources.
    • Security, the most crucial aspect of the backend, to maintain management in the cloud server with virtual firewalls.

    Now that you are familiar with the essential components of cloud architecture let’s move to the significant cloud architecture types.

    Types of Cloud Architectures Available for Your Business

    You can base your cloud architecture development on one of the various architect types mentioned below:

    1.      Single server

    Single server is the least preferred cloud architecture due to multiple security risks. Here, only one server can be used, either physical or virtual, that is, a web server, database, and application. Single-server architecture is typically used for development projects since it helps developers create functionalities in a short time.

    2.      Single site

    Single-site cloud architecture still operates based on one server, where all the layers are split to create a three-tier architecture. There are two types of single-site cloud architecture:

    • Non-redundant three-tier architecture- You can utilize this architecture to manage operational costs and other resources; however, if a single failure occurs in any component, all traffic flow in the environment might get disturbed. An ideal time to consider this architecture is while building an ecosystem for testing and development.
    • Redundant three-tier architecture– Here, you will find another set of the same component added, which enhances the architecture complexity; however, it’s secured and designed for recovery protection.

    3.      Multi-cloud architecture

    You must consider multi-cloud architecture if you have a medium or large-scale business where you require a reliable and highly scalable application.

    Now, an essential question : how to identify the right cloud architecture?

    How To Choose The Right Cloud Architecture For Your Business?

    There is no systematic approach to choosing the right cloud architect except by strategizing your business requirements. You can start by partnering with an experienced cloud consulting service to guide you throughout the process.

    Apart from this, the below tips might support you in identifying the right cloud architecture:

    1.      Defining business IT needs

    Before signing up for any cloud computing plan, list down the stock of your businesses’ long-term and short-term IT needs.

    Your requirement list must contain the following:

    • Regulatory compliance needs
    • Data storage requirements
    • Estimated number of users
    • Remote or mobile capabilities
    • Level of performance or uptime, etc

    Moreover, document the existing infrastructure and resources to state your budget to your cloud development team.

    2.      Understand the cloud architecture solution

    Make an informed decision based on various cloud architect solutions, market research, and data statistics. Run through the features and applications of public, private, hybrid, and multi-cloud to understand pricing, capabilities, performance, and arrangement of your business needs.

    3.      Choose the option that meets your objectives

    Choose a public cloud service if your team needs to work on collaborative projects. It is easier to verify every project on the public cloud and deploy it later on the private cloud.

    Use a private cloud if your primary need is to store sensitive business data. Although keep in mind that every cloud architecture comes with different pricing models where choosing a private cloud might cost you more than the other available solutions.

    Choosing the right cloud architecture for your business can result in savings, productivity, efficiency, and security. So what are you waiting for? Get started by finding reliable cloud consulting companies.

    The post How to Identify the Right Cloud Architecture for Your Business? appeared first on SmartData Collective.

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