To avoid being in a similar situation, always ask your enterprise software solutions vendor for a proof of concept – especially if your company is just testing a new technology or methodology.

So, what is a proof of concept in software development? Who needs it? And how to go through PoC implementation?

What does PoC in software development mean?

Proof of concept in software development is a verification methodology that helps you test the feasibility of your software idea on a smaller scale. It aims to prove that the solution can be built, actually work in real life, solve existing pain points, and yield financial gain.

PoC can take place at any stage of the software development life cycle. You can conduct it in the very beginning to test the viability of the entire idea, or you can resort to it halfway through the project to test a particular feature. For instance, you might want to add artificial intelligence capabilities to the solution under development. So, you continue with the original project as planned and conduct a separate PoC to test the new AI feature. You can find more information on this topic in our article on AI PoC.

Proof of concept deliverables in software development can take different forms, including a document, presentation, written code, etc.

After executing a PoC, you will have a better understanding of whether your software idea has merits. Additionally, you will have a more clear view on the following:

• Which challenges you can anticipate during the implementation
• What risks and limitations the product entails
• How it functions
• Which technology is best suited for the development
• Which other benefits, that you haven't initially considered, this solution can offe
• How much it will cost to build the final product
• How long it will take to finish the application

People tend to confuse a proof of concept with a prototype and a minimum viable product (MVP), but these are different concepts, each one resulting in its own unique deliverables. Let's see how these concepts differ from each other.

PoC vs prototype

While a proof of concept in software development aims to validate the idea behind an application, a prototype assumes that the idea is viable and aims to test a specific implementation of this idea. It shows how the final product will look, which functionality it will include, and how to use it. A prototype displays the general look and feel of the application and shows how to access the functionality, without necessarily having all the functionality already implemented.

A prototype can take different forms, such as wireframes, clickable mockups, etc. You can show the prototype to your prospective clients to get their feedback on the visuals. Therefore, UX designers are heavily involved during the prototyping stage, while a PoC can still serve its purpose with a poor user interface.

PoC vs MVP

A minimum viable product is the next step after a prototype. It's the simplest working, market-ready version of your product that covers all the essential functionality. You can release an MVP to the general public to buy and use.

Unlike a prototype, which might not be fully functioning, an MVP offers the basic functionality, which actually works and provides value to the end users. It's introduced to the market to see if people are willing to use the product and to gather feedback from early adopters for the next improvement iterations. This step helps you understand if the target audience is ready for your product before you invest even more resources in a full-fledged solution that no one will end up buying.

Benefits of PoC in software development

Research shows that only 14% of software development projects are completed successfully.

So, what can you do to improve your chances? First of all, it makes sense to validate whether your product idea is feasible from the technical and financial perspectives. This is what a PoC can tell you in a rather short amount of time. And here are other benefits of opting for a proof of concept in software development:

• Getting some sort of feasibility proof that you can show to potential investors
• Understanding the limitations of such a product
• Identifying potential risks at the early stage and finding a way to mitigate them
• Preparing a more accurate budget estimation
• Accelerating the final product release

When PoC is a must, and when you can move forward without it

Proof of concept in software development is not limited to a particular industry. And contrary to popular belief that PoC is only applicable to startups, enterprises of any size can benefit from this methodology to evaluate their ideas.

Does it mean that a proof of concept stage has to be a part of every software development project? Let's see.

When is PoC in software development an absolute must?

• If your project relies on an innovative idea that was not tested yet
• If you're not sure whether the idea will work
• If you want to test a new technology or methodology before implementing it on a large scale
• When time to market is of utmost importance
• When you need to convince investors to fund innovative technology
• To test the efficiency and viability of a solution that you want to patent

And when can you skip a PoC and go straight to an MVP or a full-fledged project?

• If the software you want to develop is rather standard and resembles common practices in the field, such as building yet another eCommerce website
• If your idea relies on a technology that your engineers and developers understand very well
• When making minor changes to existing software
• When working on a project with meticulously documented requirements

A step-by-step guide through PoC in software development

After learning what PoC in software development is and when to use it, let's go through the implementation process.

Proof of concept in software development is like any other project, with the difference that you can terminate it or pivot at any point when you discover that the idea behind it isn't feasible. And you can iterate on one step as many times as needed.

Below you will find the five steps that ITRex takes while working on PoC projects. Please note that the PoC team can go through these steps either for the whole PoC scope or for each feature independently.

To clarify what each PoC implementation step entails, we will use an artificial PoC example throughout this section.

Here is the description of this fictional project:

A US-based company operating in wholesale and retail has around 10,000 partners, which results in a high sales orders (SOs) and purchase orders (POs) processing load. The company's operations are geographically limited to the US, and it doesn't have its own delivery system. The firm receives a large number of paper-based SOs and POs daily. Some arrive as PDF files, some as fax, and sometimes orders are placed through a phone call. All POs and SOs are processed manually.

This company is looking to partially or fully automate order processing to take the load off its employees and reduce costs.

They want to conduct a PoC to verify if it's possible to automate PO and SO document processing to support order handling.

Let's go through the PoC steps together to see how the methodology works.

Step 1: Define the scope

When a client comes to ITRex with a PoC idea, we work on defining the scope to prevent it from endlessly expanding. We can do this using interview techniques, questionnaires, or even resort to on-site observations. During this step, we aim to understand and document the current state of affairs and the desired future situation.

In the wholesale company's proof of concept in software development example, the PoC team will try to understand the current state of affairs by asking questions, such as:

• What are the data transport and consumption pipeline(s)?
• In which formats do you currently receive your PO and SO documents?
• What is the ratio of different formats (carbon copy, fax, email, etc.) for the POs and SO?
• Should you import it directly into your ERP system?
• How much data (address, PO/SO number, UPC, etc.) one uses from a single PO or SO throughout the whole processing routine?
• What data may be dictionarised for further automation?
• How much time do you spend on manual order processing?

The PoC team will then work together with the company to determine what they want to achieve. They can come up with the following list of features:

Feature 1: Converting all paper-based documents into electronic form and storing them all in one location

Feature 2: Automatically processing the electronic documents with optical character recognition (OCR) to extract relevant data

Feature 3: Analyzing and manipulating the extracted data

Feature 4: Feeding the extracted order data into the company's ERP system

At this stage, we have a list of features, but we didn't specify how to implement them yet. Let's move to the next step to clarify this.

Step 2: Define the solution's acceptance and success criteria

During this step, we will get specific about what we want to achieve and how to evaluate it by writing down acceptance and success criteria.

• Acceptance criteria are conditions that the PoC application has to meet to be submitted to the client
• Success criteria refer to what makes the PoC project a success in terms of supporting the hypothesis

At ITRex, we make sure that all the criteria are specific, measurable, achievable, realistic, and timely (SMART). And of course, approved by the client.

Coming back to the wholesale PoC project, the client estimated that 62% of all POs and SOs arrive as PDF files, 29% are sent over fax, 5% are transmitted as images, and the remaining amount is coming through phone calls. Consequently, the company decided to focus on PDF and fax and ignore the rest for the time being.

The PoC team proposed implementing an AI solution to transcribe phone calls, but given that these calls constitute only a small percentage of the PO and SO bulk, and this solution would be rather expensive, the client decided against it. You can find more information on costs associated with AI implementation on our blog.

Here are a few examples of acceptance and success criteria for this project:

Acceptance criteria:

• POs and SOs arriving as printed PDFs are converted into electronic format upon successful recognition
• Unrecognized documents are stored in the designated location
• A notification mail is sent to the designated user on each unsuccessful document recognition case

Success criteria:

• 70% of the physical PO and SO documents can be converted into electronic format. This includes PDFs, fax, image files, etc.
• All electronic documents can be integrated with the company's ERP system
• The selected OCR algorithm hits an 85% precision rate in data extraction
• Order handling time is reduced from 30 minutes when done manually to 10 minutes after automating document processing

Step 3: Select the tech stack

When it comes to choosing the right technology, in a nutshell, consider three main factors – speed, budget, and reliability. This will help you decide whether to purchase an off-the-shelf product or build a custom solution.

There are other important aspects to consider, such as compliance for industries like pharma.

In our wholesale company example, the PoC team decides to use an open-source OCR solution to save time and money and rely on AWS cloud storage to maintain the electronic version of sales and purchase orders. And, they will deploy the ready-made eFax solution to receive faxes in electronic format.

Step 4: Build and test the PoC application

During this step, the actual PoC application is built based on the features and the acceptance criteria identified above. Keep in mind that during PoC in software development, you can stop at any time if you have enough evidence that the solution is not feasible. At ITRex, we use time and materials (T&M) contracts for PoCs, which gives you the freedom to terminate a PoC project whenever you want without overpaying.

If you decide to move towards PoC implementation, our team will create the assignment backlog, set deadlines, decide on the team's composition, and begin implementing the agreed-upon features. Next, we perform PoC application quality assurance to validate it against the acceptance criteria, detect any bugs, fix them if needed, or just document them for future reference if their presence doesn't interfere with PoC hypothesis verification. And finally, we will present a demo that you can evaluate and give your feedback.

If you are interested in performing user acceptance testing, we can assist you with that as well.

Coming back to the wholesale company, the PoC team will implement the four features highlighted in the Define the scope section and test the resulting application against the acceptance criteria.

Step 5: Evaluate the results and decide on the next step

You assess whether the success criteria are met and decide if they are still relevant. If the PoC solution did not meet the expectations, our team will prepare a hypothesis failure report explaining why the PoC idea in its current form is not viable.

If you are satisfied with the results, you can use our MVP development services or start preparing for the full-fledged project. And in the case when the PoC application's performance wasn't up to par but you aren't ready to give up on the idea just yet, we can make improvements, redefine success criteria, or put forward a new hypothesis, and iterate on the PoC implementation process.

Proof of concept in software development examples from ITRex portfolio

Here are three examples from our portfolio that highlight PoC benefits and show what can happen if a company decides to skip the proof of concept stage.

Crawling social media for sentiment analysis

The area of operations

Entertainment

Project description

The customer wanted to build an AI-powered analytics platform that musicians can use to gauge people's sentiment toward them. This solution would crawl social media platforms, gather data, and process it to extract sentiment. Musicians who decide to sign up with this platform will receive information on how people perceive them, and which social media behavior will attract the most attention.

As we started working on the proof of concept, we realized that due to restrictions enforced by Meta, it was impossible to extract the needed data from Facebook and Instagram to use for commercial purposes. And the client failed to provide their verified business account on Meta, which was a prerequisite for retrieving data via Graph API.

Benefits achieved through a PoC

The client only spent $5,000 on the proof of concept until it became clear this idea wasn't viable. If the customer would have decided to skip the PoC, they would have wasted $20,000 more on the discovery project.

How a client skipped the PoC and was left with an unusable solution

The area of operations

Gambling and advertisement

Project description

An entrepreneur wanted to build a mobile app that would play different vendors' ads and randomly display a button, prompting the audience to claim their reward. If you manage to click the button before it disappears, you will be entitled to a monetary amount.

The entrepreneur hired an outsourcing company that proceeded to develop the apps without testing the idea's feasibility first. When both Android and iOS solutions were ready, the client was horrified to discover that due to technical issues with ad streaming, users couldn't clearly view and press the button in time to claim their reward, rendering the whole setup unusable.

The result of skipping PoC

The client spent over $70,000 to end up with two apps (Android and iOS) that the target audience can't use. He wouldn't have lost all this money if the vendor had suggested starting the project with a proof of concept.

What we did

This client was devastated when he turned to ITRex. Our team conducted a PoC to experiment with different user flows. As a result, we came up with a flow that wasn't impacted by latency and poor connectivity, allowing users to view the ads and press the reward button within the needed time frame.

Automating post-clinical care and recovery

The area of operations

Healthcare

Project description

A company operating in the healthcare sector wanted to build a solution that automates post-clinical care and recovery processes. This product is supposed to automatically generate detailed recovery plans that patients can use in insurance claims. This solution would also support patients in scheduling follow-up appointments with the right healthcare provider and connect with EHR systems of different hospitals to distribute questionnaires to patients.

The firm was planning to sell this product to hospitals and patients but wanted to test the viability of this idea first.

For this proof of concept in software development, the client wanted to build something cheap but still workable. Initially, they suggested using a specific EMR solution, but after thorough research, we suggested a more cost-effective alternative. We also skipped the automation part and provided recovery plans manually, while questionnaires were sent to patients through emails. This was a rather inexpensive setup to prove that this idea can work.

Benefits achieved through the PoC

As a result, the client could verify the viability of their idea while spending less, as we suggested an alternative to their proposed EMR system. In general, the client didn't have to spend time researching the issue on their own. They just brought in the idea, and our team researched it further.

Tips for a successful PoC implementation

Here are some tips that will help you sail through PoC in software development:

• Keep the proof of concept simple so that you can finish it in a reasonable timeframe
• Clearly define what success means to you
• Make sure the technical staff members understand the success criteria
• If you are conducting a PoC to convince investors to fund your project, make sure the language you use is understandable for people with no coding experience
• Involve key stakeholders, even though this is just a hypothesis verification phase. Let them experiment with the solution and witness its benefits firsthand
• The client and the team should understand the strategic values behind this project
• Make sure the PoC team is diverse and not limited to developers. You may want to include a business analyst and a QA engineer
• Always trust your tech lead regarding infrastructure and implementation tools
• Nobody is to blame for the failed hypothesis. It's not the same as a failed project

To summarize

Proof of concept in software development will help you test the viability of your idea, understand product limitations and risks, calculate the budget with precision, and more.

PoC is not limited to startups. Large enterprises can also benefit from this methodology, especially if they want to experiment with innovative technologies like ML, IPA, and IoT.

At ITRex, we approach all PoC projects with efficiency and reusability in mind. As a result, our teams reuse approximately 40-45% of the PoC's architecture and code base. For the sake of context, the median reusability rate in the IT industry is around 10%. With our savvy approach, a PoC in software development will not only help you prove the viability of your idea, but will also get you started with building the final product. If our team encounters any feasibility-threatening issues, we immediately bring that to your attention and discuss potential solutions, or stop if you don't want to take this risk.

The post What Is Proof of Concept in Software Development appeared first on Datafloq.

In today's rapidly changing business environment, agile data management and enterprise transformation are not just buzzwords; they're crucial elements for organizations striving for sustainability and growth. However, when these two concepts intertwine, they form an exciting synergy, catalyzing business transformations that are nimble, efficient, and value driven. This post seeks to uncover the role of agile data management in the era of enterprise transformation and demonstrate how data, when managed judiciously, can become a game-changer.

Understanding Enterprise Transformation

Enterprise transformation is a significant, intentional shift in an organization's operation, often aimed at aligning strategies, processes, and culture with future objectives and market trends. These transformations may be induced by various drivers such as technological innovations, evolving customer expectations, or changing competitive landscapes.

Data, in these transformations, is the raw fuel powering the engines of change. It offers insights, supports decisions, and enhances customer experiences. However, harnessing this data is not always a smooth sail-it poses challenges in terms of volume, variety, velocity, and veracity. Thus, the need for effective data management becomes an essential prerequisite for successful enterprise transformation.

Agile Data Management – An Overview

Agile data management is an iterative, value-centric approach that emphasizes communication, collaboration, and flexibility. By implementing agile principles, organizations can ensure their data strategies align with business goals, change as per emerging needs, and deliver reliable outputs promptly.

Contrary to traditional, siloed data management, agile data management offers several benefits. It accelerates decision-making, optimizes processes, and fosters a culture of continuous improvement. When applied to enterprise transformation, agile data management helps organizations navigate the unpredictable currents of change, ensuring they stay resilient and responsive.

Key Components of Agile Data Management in Enterprise Transformation

To implement agile data management effectively during enterprise transformation, several components come into play. Agile methodologies for data quality assurance promote data integrity and reliability, enabling sound decision-making. Data democratization enhances collaboration and innovation by empowering stakeholders with access to information.

A crucial piece of the puzzle is data virtualization, providing a unified, real-time, and accessible view of data, irrespective of its source or format. This singular incorporation of the term drives home the transformative power of data virtualization. It mitigates the complexities of data management and supports flexible, agile operations.

Practical Application of Agile Data Management

Consider the example of a global e-commerce company that is yet to undergo enterprise transformation to keep pace with digital advancements. Agile data management can become their guiding star, paving the way for seamless integration of disparate systems and processes. The application of data virtualization can prove to be invaluable, eliminating data silos and promoting a unified, coherent view of their business-critical information.

Conclusion

Agile data management is not a mere facilitator but a potent catalyst in the journey of enterprise transformation. By adopting agile data practices, organizations can navigate the transformation journey with enhanced agility, reduced risk, and heightened value generation. While data virtualization and enterprise data migration represent only a slice of this extensive domain, their strategic implementation can create a significant difference. Therefore, as we usher in an era of continuous transformation, let's harness the power of agile data management to steer our organizations towards a prosperous and sustainable future.

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On the other hand, based on the present patterns and forecasts, some trends and predictions may be established. In the following paragraphs, I will discuss some of these tendencies and forecasts for the near future of employment.

The Advancement of Automation and AI

There will be significant changes to the workforce in the future due to the prevalence of automation and AI.

As artificial intelligence (AI) continues to improve, machines will soon be able to do more and more work that was once exclusively done by humans. The industrial, transportation, and retail sectors-all of which rely heavily on regular and repetitive tasks that may be easily automated-are predicted to be hit hardest by this trend.

Some professions will be lost to automation, but experts believe that data analysis, machine learning, and programming will flourish as a result. Businesses are increasingly interested in these fields as they seek to maximize the potential of automation and AI to increase efficiency and output.

Additionally, it is anticipated that the advent of automation and artificial intelligence (AI) would raise the need for those who possess skills that cannot be easily automated. Machines have a hard time replicating human abilities such as creativity, effective communication, and empathy. As a result, careers requiring these types of soft skills-in sectors like medicine, teaching, and social work-are expected to grow.

AI and robotics may enable new job types, such as the gig economy and contract jobs. The adaptability of AI and automation may assist these jobs the most. Employees may soon be able to work for many firms, perhaps sparking new business and creativity.

Telecommuting's Rise

Remote and flexible employment is nothing new, but the COVID-19 pandemic has pushed the trend significantly.

In order to keep operations running during the pandemic, several organizations adapted to remote work, reaping the benefits of increased production, reduced costs, and a more flexible schedule. This shift has brought attention to the prospect of telecommuting becoming an increasingly common feature of the modern workplace.

Studies have shown that working remotely increases productivity by removing potential distractions and giving workers more control over their work environment. Reducing or eliminating the need for conventional office space and transportation expenses can help businesses and their employees save money. As a result, remote workers are able to better juggle their personal and professional responsibilities.

As a result of these advantages, many businesses are likely to continue providing remote work options even after the pandemic has passed. This could result in huge changes in the way we work and live, as workers may no longer need to live close to their workplace and may be able to pick when and where they work.

However, there are certain disadvantages to working from home. Workers may feel socially isolated and have fewer options for collaboration and guidance. Furthermore, there may be difficulties in managing remote workers, such as ensuring that they are productive and engaged and that their task is completed to the needed standard.

Continuous Learning for Professional Success

Because technology changes at a rapid pace, people must constantly learn new skills and information in order to remain employable. As a result, continuing education and professional development are likely to grow. New technologies and job responsibilities will make learning new skills increasingly important in the workplace.

We must reinvent education and training to allow for lifelong learning and skill development. Early childhood education may not be sufficient in today's fast-changing society. Workers will instead be expected to engage in lifelong learning and upskilling.

This tendency will very certainly result in the spread of novel types of education and training, such as MOOCs, micro-credentials, and skills-based credentials. As a result of these revolutionary kinds of education and training, workers will be able to learn new knowledge and abilities on their own time and at their own pace.

Online learning platforms offer a variety of courses and programs worldwide. Micro-credentials, brief, concentrated programs that teach specialist skills or information, are quickly obtained and allow people to demonstrate their skills to future employers. Skills-based credentials can show companies a worker's ability.

Job search websites may both teach and display new talents. LinkedIn Learning provides courses and certificates in business and technology. Lensa is primarily a job search engine, but it also provides career advice. Job search, rsum, and interview tips are provided.

Evolving Work Landscape

Predictions about the future of work are open to a wide range of possibilities. The importance of diversity, equity, and inclusion in the workplace is expected to grow. As our society diversifies and becomes more socially conscious, it will become increasingly important for businesses to provide welcoming environments where all employees feel safe and valued.

Employee happiness and mental health are also expected to receive more attention in the future. Companies should make it a priority to create workplaces that promote the mental and physical health and wellness of their employees in light of the outbreak of the pandemic. Such programs might take the form of anything from workplace flexibility to access to mental health care to wellness courses.

New kinds of labor, such as the gig economy and freelance labor, are projected to contribute to a more dispersed and decentralized workforce in the future. This has the potential to alter our views on work and careers as a whole, allowing people to juggle several jobs and employers.

To sum up

The future of work will be influenced by automation and artificial intelligence (AI), remote work and flexible work arrangements, lifelong learning and upskilling, diversity and inclusion, employee well-being and mental health, and decentralized and distributed work. While the future of work is uncertain, it is clear that in order to thrive in a changing world, individuals and organizations must adapt and evolve.

The post The Future of Work: Trends and Predictions for The Next Decade appeared first on Datafloq.

Data Observability has been all the rage in data management circles for a few years now and has been positioned as the panacea to all data quality problems. However, in our experience working with some of the world's largest organizations, data observability has failed to live up to its promise.

The reason for this is simple: data integrity problems are often caused by issues that occur at the “last mile” of the data journey – when data is transformed and aggregated for business or customer consumption.

To improve data quality effectively, data observability needs to detect the following three types of data errors:

Meta Data Error (First Mile Problem): This includes things like detecting freshness, changes in record volume, and schema changes. Incorrect or obsolete data cause metadata errors, changes in the structure of the data, a change in the volume of the data, or a change in the profile of the data.

Data Error ( Middle Mile Problem): This includes detecting record-level completeness, conformity, uniqueness, anomaly, consistency, and violation of business-specific rules.

Data Integrity Error (Last Mile Problem): This includes things like detecting loss of data and loss of fidelity between source and target system.

However, most data observability projects/initiatives only focus on detecting Meta Data Errors. As a result, these initiatives fail to detect Data Integrity Errors – which impact the quality of the financial, operational, and customer reporting. Data integrity errors can significantly impact a business, both in terms of cost and reputation.

Data integrity errors are most often caused by errors in the ETL process and incorrect transformation logic.

Current Approach for detecting data integrity errors and Challenges

Most data integrity initiatives leverage the following types of data integrity checks:

Schema Matching: It checks for schema and record count mismatches between source and target systems. This is a very effective and computationally cheap option when data does not undergo any meaningful transformations. Generally used during migration projects or data refinement processes as depicted in the data flow

Cell to Cell Matching: Data often undergoes transformations throughout its journey. In this scenario, cell-by-cell matching of data elements between the source and target system is done to detect data loss or data corruption issues.

Aggregate Matching: Data is often aggregated or split for business or financial reporting purposes. In this scenario, one-to-many matching of the aggregated data elements between the source and target system is done to detect data loss or data corruption issues due to aggregation errors.

Most data team experiences the following operational challenges while implementing data integrity checks:

Time it takes to analyze data and consult the subject matter experts to determine what rules need to be implemented for a schema check or cell-by-cell matching. This often involves the replication of transformation logic.

Data needs to be moved from the source system and target system to the data integrity platform for matching resulting in latency increased compute cost, and significant security risks.

Solution Framework

Data teams can overcome the operational challenges by leveraging machine learning-based approaches:

Finger Printing Technique: Traditional Brute force data matching algorithms becomes computationally prohibitive to match all source records with all target records when the data volume is large.

Fingerprinting mechanisms can be used to identify if two data sets are identical without the need to compare each record in the data set. A fingerprint is a small summary of a larger piece of information. The key idea behind using fingerprints for data matching is that two pieces of information will have the same fingerprint if and only if they are identical. There are three types of advanced fingerprinting mechanisms – Bloom filters[1], Min-Hash, and Locality Sensitive Hashing (LSH).

Fingerprinting techniques are computationally cost-effective and do not suffer from scalability problems. More importantly, fingerprinting technique eliminates the need to move the source and target system data to another platform.

Immutable Field Focus: Cell-by-cell matching should focus only on immutable data elements – business-critical columns that do not change or lose their meaning because of transformation. For example, the total principal loan amount of a mortgage should remain unchanged between the source and target system irrespective of the transformation. Matching all data fields requires replication of the transformation logic which is time-consuming.

Autonomous Profiling: Autonomous means for identifying and selecting immutable fields help data engineers focus on the most important data elements that must be matched between the source and target system. When these critical fields are matched successfully, the entire record has likely been transformed correctly.

Conclusion

So, is data observability the silver bullet for all data quality problems? In short, no. However, if you are experiencing data integrity issues at the “last mile” of your data journey, it is worth building a data observability framework that not only detects metadata errors but also data errors and data integrity errors. Automated machine learning can be leveraged to eliminate the operational challenges associated with traditional data integrity approaches.

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As the nature of data changes, so does data security. Securing data in the modern context goes beyond preventing unauthorized access. It is also crucial to ensure that data is not corrupted, encrypted for ransom (in the case of ransomware attacks), or intercepted to be used in other forms of cyber attacks.

Data security is a necessity in the digital economy and in modern life in general. Everyone who uses digital devices and participates in digital transactions deals with some form of data and the need to observe data security practices.

Secrets management: Not as simple as encrypting and decrypting

One example of how complex data security has become is the increasing importance of secret management. Ordinarily, the phrase “secret management” would mean the keeping or handling of personal information that is not supposed to be made public. In the context of cybersecurity, secrets management is a jargon that refers to something similar but more technical.

What is secrets management? Simply put, it is the systematic practice of securely storing and regulating access to sensitive information including passwords, tokens, API keys, certificates, and encryption/decryption keys. It sounds simple and straightforward, but it can actually be quite challenging.

Securing secrets is unlike securing sensitive enterprise files or documents. There is encryption involved, but the access controls are not as simple. Also, the encryption technology used is different from what is often used when storing large amounts of confidential files. It is particularly more complex when it comes to cloud and hybrid setups.

Organizations use diverse kinds of secrets, and managing them is not going to be as easy as using a form or password manager application. In the absence of a sensible secret management solution, some resort to the manual sharing of secrets, which usually leads to the use of weak passwords (so they are easier to memorize or write) and the recycling of passwords across different projects. Some embed the secrets in their code or configuration.

Secrets are also used in app-to-app and app-to-database communications and data access. In many cases, the secrets used here are hard-coded or embedded in devices, which is inadvisable as hackers can easily crack these embedded secrets through scanners, dictionary attacks, or even plain guesswork. It is important to put in place a more secure system of storing and accessing secrets, and this is certainly not going to be as simple as securing a company's confidential documents.

Moreover, secrets and other sensitive data are employed in the operation of privileged apps and tools, microservices, containers, virtualization and administration consoles, as well as third-party and remote access accounts. Managing and securing sensitive data in these scenarios require sophisticated tools or systems that may not be familiar to many organizations.

Complexity: data security's enemy

Cloud computing and security expert David Linthicum of InfoWorld shares a sound reminder: complexity is the enemy of cloud security. This actually applies to data security in general. Managing data can be confusing, especially with the kind of IT infrastructure many organizations have and the diversity of IT assets they oversee (or overlook). It's easy to miss some steps or overlook security best practices, resulting in vulnerabilities and data compromises.

“Complexity is not new; it's been creeping up on us for years. More recently, multi-cloud and other complicated, heterogeneous platform deployments have accelerated overly complex deployments….As complexity rises, the risk of breach accelerates at approximately the same rate,” explains Linthicum.

Complexity often aggravates human errors. Security problems emerge because of misconfiguration and other mistakes committed by those who run an organization's security system. Given how complex IT infrastructure and the extensive variety of the components being managed in an IT ecosystem are at present, it is understandable why there are missteps. However, this should not be an excuse. Complexity is a reality everyone has to deal with, and achieving proper data security means addressing this complexity issue head-on.

To address the complexity issue, it is essential to acknowledge it and include it as one of the factors to consider when formulating data security strategies and looking for data security solutions. Reducing the complexity and implementing an intuitive system for data protection are vital first steps toward dependable data security.

Using the right data security solutions

Generally, real data security cannot be achieved by using a single cybersecurity solution. Again, the data organizations are dealing with now are much broader and more complex. There is no one-size-fits-all solution for it. Different solutions are required to effectively handle different kinds of cyberattacks that target data.

Phishing and social engineering – Addressing data theft attempts through phishing and other social engineering tactics requires more than spam filters and link and email scanners. Effective social engineering defense at present only partly relies on software tools. The core of social engineering defense is the effort to educate people, who are often regarded as the weakest link in the cybersecurity chain, for them to learn how to detect instances of social engineering attacks and respond accordingly

Ransomware, spyware, and other malware – In the case of malicious software that may siphon, corrupt, delete, or encrypt data, the solution involves multiple tools including automated data backup, antivirus, web application firewall, email, and link scanners, and malware detection and prevention systems, among others.

Communication interception tactics – Man-in-the-middle (MIM) attacks or those designed to intercept data exchanged between communicating parties are complex problems that require sophisticated solutions. Two of the best-known solutions are authentication and tamper detection. In authentication, public key infrastructure like Transport Layer Security (TLS) may be employed, wherein clients and servers exchange certificates that indicate secure communications. In tamper detection, there are tools used to examine the latency of responses or discrepancies in response times. MIM attacks are suspected if responses take longer than usual or are not in line with response times patterns established for legitimate connections.

This is not a comprehensive list and discussion of data attacks and their corresponding solutions. However, these should highlight the point that data security is bigger than how most organizations perceive it. As such, multiple tools are needed to attain reassuring data protection.

In conclusion

The data security solution an organization uses should evolve with the threat landscape. It would be inevitable to use multiple tools in the process or transition from one to another. The use of multifunction platforms that integrate various data security solutions is also likely to become commonplace. However, the tools are only a part of the concerns organizations should pay attention to. Foremost, it is essential to properly identify the sensitive data used in an organization and understand the increasing complexity of modern IT infrastructure.

The post The Growing Complexity of Data Security: Secrets Management and More appeared first on Datafloq.

This is somewhat of a misconception distracting IT staff from things like critical configuration flaws, which are just as common and cannot be remedied by simply applying updates and patches.

Some of these security loopholes stand out from the crowd as they are encountered by literally every other company and remain unattended for years.

Email from the Boss

Social engineering and phishing are the biggest scourges undermining enterprise security because they slip under the radar of conventional defenses. You can enhance your employees' security awareness by instructing them to treat emails with extreme caution, but things get more complicated in the case of staff receiving dozens of emails a day.

Have a look at your SMTP server. It is quite likely to process emails without sender authentication. We are not talking about a mail relay harnessed to spew out tons of spam – this is a rare scenario these days.

The issue is different here: an external threat actor can connect to your email server and send a message to any local user without having to go through authentication. The sender's email address can be arbitrary, that is, either external or internal.

Want to send a message on behalf of an organization's IT support? It's a piece of cake. Even if the local sender's address is unknown, you can try and pick one.

The mail server is typically configured to verify whether the local user who sent a message exists.

The attack workflow is fairly prosaic. The threat actor takes advantage of the recipient's trust in the email author to manipulate the victim into opening suspicious attachments or following an embedded link.

It is a mere technicality to get around antivirus software these days. Most security suites easily detect files containing code that exploits cataloged vulnerabilities. Very few are capable of identifying customized malicious code, though.

It is possible to obscure harmful code by means of specially crafted tools, such as obfuscators and “cryptors” that prevent antivirus engines from detecting suspicious commands being executed.

Cybercrooks can equip their code with timers to circumvent sandboxing and other advanced behavioral analysis features. This way, the malware will remain dormant inside the target host for a predefined amount of time.

Attackers are often one step ahead of antimalware vendors because they can prep and test their perpetrating code extensively without facing any risks. Consequently, a well-motivated hacker succeeds in getting a backdoor on a victim's machine in 8 cases out of 10.

The Frail Network

Penetration testers often deal with properly segmented networks featuring strict access control lists (ACLs). Complementary layers of administrative account security, such as LAPS (Local Administrator Password Solution), considerably reduce the surface of attacks aimed at obtaining account credentials from the memory of contaminated Windows hosts.

The hosts are sometimes so reliably protected that the adversary has to resort to attacking the application layer or network infrastructure. The latter, by the way, is a disconcertingly common scenario.

Here's an example of a heavily exploited security flaw in very common Cisco-based networks. The problem is that the Cisco Smart Install (SMI) service, which is used by the majority of modern Cisco Catalyst switches, allows for modifying device configuration without authentication.SMI is intended to streamline the initial setup of network devices, which explains why a lot of switch models go equipped with it by default.

Furthermore, in some cases, it cannot be disabled (for instance, on Cisco Catalyst 6500 Series), so the only protection method boils down to using ACL.

Go ahead and check the TCP port 4786 on your Cisco network equipment – you are quite likely to spot a running service on it. It is wrong to treat SMI as an obsolete service. It has acquired new features over the years. From Cisco Internetwork Operating System (IOS) version 15.2 onward, it accommodates a feature that allows launching post-installation scripts in the “Enable” mode. As before, no authentication is required to do it. The configuration upload/download procedure is available for all builds of the Internetwork Operating System.

The ability to retrieve and alter the configuration of a network device provides threat actors with vast network attack opportunities, ranging from disclosure of admin-level credentials and network traffic interception, all the way to editing access control lists and infiltrating any VLAN on a remote switch.

In case a network device adjoins a protected network segment, compromising the former means the adversary can penetrate the latter.

Although Cisco claims that they have introduced new technology to prevent negative network issues, it is worth taking care of yourself by using additional protections such as Virtual Private Networks, Next Generation Firewalls (NGFW), and secure web hosting platforms.

The Inalienable Prerogative of Control

Group Policy is one of the kernel components of the Active Directory (AD) catalog service that allows administrators to implement a number of configuration scenarios requiring account credentials.

The configuration of these policies is stored in XML files under the domain controller's SYSVOL directory and can be accessed by all group members.

The caveat here is that the passwords to accounts listed in the policy are stored in the CPassword attribute in an encrypted format, where the encryption key is known to the group and cannot be modified. Therefore, any group member can obtain and decrypt the values of account passwords stored in the following files: groups.xml, datasources.xml, scheduledtasks.xml and services.xml. Obviously, the accounts stored this way are most likely to have administrative privileges.

Microsoft released security patches that disallowed storing usernames and passwords in Group Policy configuration. However, the company did not disable or change the existing policies forcibly. It is no longer possible to create new Group Policy parameters containing user access credentials, but the previously defined settings continue to be valid, with the system displaying appropriate security alerts to the domain administrator every so often.

Microsoft has offered administrators some workarounds applicable to domain configuration scenarios but has had hardly any success with that.

Bottom Line

There are lots of critical security issues that cannot be addressed via software patches alone. When it comes to the components of information systems, maintaining configuration standards is an effective and affordable security approach that shouldn't be underestimated or ignored. The only viable alternative is to leverage costly detection mechanisms, such as integrity controls and Network Behavior Anomaly Detection (NBAD).

The post Eliminating Weak Links in Your Company’s Security appeared first on Datafloq.

Deriving value from data has become an important part of organizations since it tells how and what drives business. But as this data comes from different sources, it may often have inconsistencies, duplicate records, invalid, and incomplete entries, etc. And, to make this raw unstructured data ready for analysis, companies need an efficient data clean-up process in place.

Especially after the deadly COVID-19 pandemic, when data has become one of the most precious business assets, keeping a check on its accuracy is vital. Clean and standardized data aids in sales and marketing, networking, and various other facets of an organization.

Consider the case in point that your official database has invalid email ids and missing phone numbers. Are they of any use? Quite obviously, no! Hence, this is where data cleansing & enrichment helps-when the data is cleaned regularly, all the inaccuracies and inconsistencies get rectified, leaving behind supreme quality information. The right quality data positively impacts business efficiency and your bottom line is going to thank you for a lifetime.

Making the Most of Data Cleansing Services

Quality of data matters the most and cleaning is an important part of it if you are looking forward to building a data-centric culture. However, clean-up is a time-consuming and resource-intensive process; businesses cannot afford to compromise here. Therefore, engaging in professional services is the best way out to make the most of your valuable business assets. Listed here are 8 ways data cleaning helps businesses:

Improved Decision-Making

Also mentioned earlier, effective decision-making needs quality data. As your organization generates massive amounts of data, errors are quite natural. But failing to prioritize data quality management might lead to poor decision-making, ultimately leading to business loss. On the tiff side, having clean & accurate data acts as a catalyst for better analytics and enhanced business intelligence.

Optimized Marketing Campaigns

Here you get another important reason to prioritize the data clean-up process– be assured that your marketing campaigns are optimized and targeting the right target audience. Customer habits don't stick for too long and change frequently, making the markets highly volatile. As a result, all the previous data stored might not be irrelevant now. Staying relevant to the latest customer habits and cutting through the curve needs regularly updated databases.

Streamlined Workflows

There are numerous business actions, decisions, and processes that have to be fed with data. Functions such as data analytics, data processing, data entry, etc., need this data to help you successfully manage your data, uncover rich insights, and put it to the best use. If the data is not clean, accurate, or up-to-date, the outcomes might deviate and the efforts will simply go in vain.

Time & Cost-Effective

Imagine you have all the cluttered information stored in your company's database. What if you need some important information immediately, but all your data is cluttered, unorganized, or full of errors? Isn't it going to be a daunting task! Spending time and effort is surely not an affordable option in such a case. But with data cleansing services, the work becomes easier as the information can be fetched in no time.

Free-Up Storage Space

The data cleaning experts have many data-deduplication techniques rolled up their sleeves. They work day in and day out on such tasks. As a result, you can remove data that is of no use and reduce space consumption. Having obsolete or repetitive data which is no longer relevant only takes up your storage.

Maximized Productivity

When employees have clean and updated company data, it can be put to the best use that fuels productivity and drives growth. They need not spend time looking for correct phone numbers, valid email addresses, or filling in the missing information. Besides, when the experts take care of your data quality, you get an edge. Therefore, management can easily raise the productivity of their resources and focus on other aspects of the business as well.

Ending Note

Going forward, businesses can unlock tremendous opportunities with cleansed and enriched data at their disposal. Data cleaning practices cannot be ignored and must be given utmost priority, especially when the demand for quality data is constantly rising. So, for businesses to unlock game-changing data insights and stay relevant in the dynamic market matrices, collaborating with top-notch data cleansing service providers is the best solution to enhance your bottom line. Now that you know where to begin, it is time to make the move!

Got questions? Damco is an IT services and solutions providing company with 25+ years of experience in the industry. They have successfully delivered numerous projects over the years and have carved a unique niche for themselves in the industry. Reach out to us today.

The post Importance of Data Cleansing: 6 Reasons Why the Quality of Data Matters appeared first on Datafloq.

Big data touches so many aspects of our lives today it‘s become a pivotal element in the development of various industries. Despite cybersecurity being an age-old concern, it isn't immune to rapidly developing technology.

Security threats have long been increasing in both scope and complexity. Over the past year, more advanced attacks have emerged, utilizing everything from new phishing lures to environment-based ransomware.

Not only is security at greater risk, but user privacy threats have risen exponentially. The privacy risk aspect is arguably even more significant since legitimate companies are mining our data alongside cybercriminals.

Yet all is not lost. The situation highlights changes implemented by cybersecurity companies. Thanks to big data, new strategies are constantly being added, which give many cybersecurity tools today a more dynamic working model.

In the past, cybersecurity companies typically relied on a reactionary model in product development. It began with risks identification and assessment, then the push of updates into security product databases.

While this model is still in force, it has become only part of most cybersecurity packages. To understand the advantages that big data brings to cybersecurity, let's consider some of the more obvious benefits.

1. Big Data Improves Security Incident Response

Aside from anticipatory actions, big data also enhances security incident response processes. Where cybersecurity once focused solely on prevention, some applications today can work past prevention towards identifying, isolating, and removing threats.

While not an entirely new development, big data helps enhance the process, potentially leading to even more advanced response capabilities in the future. This potential is vital, given that various encryption protocols, including Transport Security Layer (TLS) and such, have proven less than optimal in data protection.

2. Big Data Dynamically Predicts the Scope of Threats

Big data also gives the foresight to predict the intensity and extent of potential security threats. By analyzing data patterns and sources, organizations can feel if an incident is a probe that might lead to a full-on attack.

Some tools today already have these capabilities. The data analysis engines they include can provide the basis for judgment on what threats are acceptable or not.

At the same time, predictive modeling based on the same data can also foresee potential attack points. Such modeling works in tandem with two other relatively new areas of interest; Machine Learning (ML) and Artificial Intelligence (AI).

As advances in related fields move ahead, the overall competency of products using these elements can move forward together.

3. Big Data Helps Anticipate Cybersecurity Threats

Given enough data, security applications today can conduct behavioral analysis to match changes between current and past actions. These dynamic means of monitoring provides customized protection that isn't possible with traditional standards.

One good example of this is websites or servers that are capable of assessing changes in user profiles. If Bob is working from home, he typically logs in from a range of IP addresses that's relatable to his known location.

Once Bob switches on his Virtual Private Network and attempts the same, the potentially vast discrepancy in IP or even country source can raise red flags.

4. Big Data Enables Automated Monitoring On A Large Scale

The human factor remains a glaring weak spot where cybersecurity is concerned. Most typical employees don't have cybersecurity skills and are less familiar with the threats faced. If something happens, they will also not know how to respond.

With big-data-driven tools, applications today can monitor activities on almost any scale, proving defense-in-depth across businesses of all sizes. These tools serve as an effective stopgap that plugs potential blind spots arising from human factors.

5. Enhanced Security Comes at A Manageable Cost

Due to the economy of scale, cybersecurity companies employing big data to produce more effective tools can do so relatively cheaply. These solutions can then be packaged and sold as services, allowing access to powerful products to a broader audience.

The most obvious example where this can be seen lies in consumer cybersecurity products. Internet security applications produced by top brands in the business can offer robust products even more cheaply than ever.

Where these used to be solid in single-license copies, prices have not risen significantly despite an expansion towards multiple-device licensing to meet the demands of modern-day consumers.

Big Data Still Has Some Disadvantages In Cybersecurity

Despite the many and clear advantages that big data brings to cybersecurity, it isn't entirely flawless. Some of its benefits introduce new elements of risk, even if outside traditional cybersecurity borders.

The essence of big data lies in the collection and analysis of data. Within organizations, that might mean the data of hundreds of employees. On a larger scale, the implication leans towards even more significant numbers of individuals.

That data, in the right hands, serves as the biggest asset for big data and cybersecurity. Yet if not sufficiently protected, it becomes a boon to cybercriminals. Leaked data or data breaches are increasingly common, leading to high liability and potential new risks.

While many organizations rely on encryption to prevent data from being useful without the appropriate keys, such standards aren't universally respected. Facebook, for example, famously stored unencrypted user data for years – until cybercriminals stole the data.

To understand the scope and potential liability, here are some of the data breaches that came to light in 2021 alone;

• Facebook, Linkedin, and Instagram lost 214 million data records in 2021 alone.

• 3.3. Million data records of Audi and Volkswagen in Canada and the US were stolen.

• 20/20 Eye Care Network had 3.25 million data records removed or deleted.

Prediction Remains Imperfect

Aside from data risk, there is also the problem of incorrect predictions, resulting in unnecessary overhead to business models. While it's always better safe than sorry, the involvement of large-scale IT teams in analyzing false positives can be frustrating and costly.

Regardless of how advanced big data analytics and cybersecurity tools become, anything predictive will be supremely challenging to perfect. Even as technologies mature, cybersecurity threats will continue to evolve, resulting in a potential status quo.

Conclusion

While big data brings significant advantages to cybersecurity, companies stand considerable risk due to the inherent danger of holding large amounts of data. Despite this risk, there is much more unexplored potential in the field.

For customers of said cybersecurity products, however, the benefits far outweigh the potential cost. Smaller companies and individuals stand to gain the largest benefits through increased security at a manageable cost.

The post How Big Data Is Affecting Cybersecurity Tools appeared first on Datafloq.

Data drives everything, yet as you can imagine, problems tend to arise when using AI tools in a genuinely neutral environment. They are natively blind to issues that seem overly influenced by society today, resulting in a potential clash with regulations or ethics.

Today we'll examine some of the advantages and concerns towards more significant AI deployment in this multi-billion-dollar industry.

Benefits of AI in Hiring

Before we begin, it should be clear that this discussion leans towards in-house engagement with full-time employees. For temporary talent engagement, using an existing freelance platform will be more cost-effective and efficient.

Less Potential for Human Bias

Regardless of guidelines, policy, or other factors, ensuring an utterly bias-free hiring process can be incredibly difficult. It's a human trait to be biased towards certain aspects – in fact, a defining characteristic.

AI, on the other hand, is purely data-driven. As long as the intent is not present in the AI tool, it won't introduce unnecessary characteristics into the hiring process. While data-driven assessment may sound a bit cold, it is impartial.

Speeds Up the Hiring Process

Recruitment times vary widely depending on role, industry, and other factors. Yet, it's undeniable that you can speed up at least part of the process using AI tools. For example, you can use them to create or improve job descriptions, match applications with requirements, carry out candidate screening, and more.

Taking too long to connect with potential talent is something that can cost you dearly. Remember, while there are ample candidates, almost all of them will be applying to multiple companies. If your hiring process drags on, talented individuals may quickly get snapped up by the competition.

Reduce HR Expenditure

From start to finish, hiring new talent is expensive. An increasingly large number of HR tasks can quickly lead to ballooning staff costs simply for talent acquisition. By introducing AI tools, not only can you speed up the process (as discussed above), but repetitive tasks can be kept away from expensive HR personnel and reduce cost.

Remember that the cost of AI tools is often much less in the context of large companies. As your organization grows, it makes much more financial sense to replace rote tasks with automation. You may be surprised at the overall impact on ROI.

Lowers Chances of Talent Leaks

When HR is recruiting, the focus is often on specific roles that need filling. While some companies do keep applications on file, cross-matching doesn't always occur for various reasons. This shortcoming can easily lead to talent leaks where a candidate suited for an alternative role is lost.

Cross-matching often gets neglected due to the amount of time consumed trying to match multiple candidates and roles. Rather than onboarding more HR to fill this gap, you can leverage AI for much faster results.

Matching a potentially leaked talent with alternative roles also saves on the future need to hire specifically for that purpose. Keep these identified talents on file, or simply hire them early in anticipation of filling a need.

Improve the Sourcing Process

The traditional hiring process makes extensive use of job agencies or boards. While this helps save time and money, AI tools can give you many of the capabilities these channels offer. For example, an AI scraper can collate data from many sources and assess them for suitability.

With a single tool, you gain access to a massive potential talent pool that may not directly apply for a vacancy in the company. In this aspect, AI tools are even more important given how well individuals today reduce their digital footprint.

Disadvantages of AI in Hiring

Increasing Regulation

Like many other IT-related elements, AI remains but a tool in the hiring process. Unless you take great care selecting these tools, some form of bias may remain. The reality is that regulation isn't seeking to eliminate bias but to direct it towards the desired outcome.

Because of this, many countries often have some form of discrimination built into regulatory systems – for instance, mandates for specific proportions of gender, domestic versus expatriate labor, or other mandated ratios.

One example of this is New York's proposed legislation to regulate AI algorithms allowed for use in the hiring process. Similar proposals also exist in the European Union, with initial legal frameworks already in draft.

There are also varying general guidelines on occasions, such as privacy laws concerning video interviews, data collection activities, and such. Since 2019 the US state of Illinois has regulated the use of AI in video interviews mandating disclosure and specific prohibitions.

Specific Areas of Challenge Exist

AI and data often work well together and can introduce elements of analysis effectively as well. However, it isn't perfect, and when assessing individuals, there may be some areas challenging to factor in and match.

Intangible factors are especially prominent in this area and can include company culture, values, and mission cohesion. If too much weight is placed on tangible areas of analysis, mismatches in this area can still result in poor hires.

The risk of this happening is exceptionally high if the AI algorithms deployed are less intelligent than optimal. For example, some AI algorithms do nothing more than field matching and are extremely poor in a human relationship context.

Lack of Transparency in the AI Industry

Most companies will rely on external sources for AI algorithms used in the hiring process. Unfortunately, like many commercial products, exactly how they work is often considered proprietary. The result is a high risk as they may introduce areas contrary to the company culture or legislation.

May Lower Company Image

People often have different attitudes towards the use of tech tools. These varying attitudes can mean alienating a proportion of potential candidates who prefer more direct human interaction with a prospective employer.

What makes things worse is that AI elements are often used in the first line of the hiring process. Only when data has been sorted are results provided to human recruiters to make the final judgments.

This prospective alienation may lead to a poor impression of the brand among prospective employees, which may spread in the community and be hard to counter should processes change in the future.

AI Recruiting Tools Currently Available

If you'd like to try some of the available AI recruitment tools, the good news is the abundance of choice. There's a lot of noise in the industry, so picking the right one can be a long process for each company.

Some of the available are;

XOR – You can design this AI chatbot to fit perfectly with your brand and serve as the first line of interaction with prospective hires. It can be highly customized to reflect branding, possible queries, and more. Many big brands are already using XOR, including Ikea, McDonald's, and Mars.

Arya – For something more comprehensive, Arya serves as a complete recruitment platform that can work relatively independently. At the same time, it offers recruiters the necessary features to reach out to candidates directly via the platform. Arya takes care of employee screening and can help drastically reduce the cost of hiring.

Seekout – If your company needs to reach out to extend the reach of HR, then Seekout is a solid choice. It's a talent-sourcing platform capable of scouring a massive database to find candidates based on job descriptions. The scope and scale of Seekout make it more suited to enterprise-scale users.

Pymetrics – Professionals today often make use of gamification in multiple professional settings. Pymetrics does that for hiring and adds behavioral science into the mix. The result is a very modern tool that most younger professionals can relate with easily as they take Pymetrics tests.

HireVue – Originally a video software, HireVue entered the AI recruitment space relatively late, in 2020. It offers a HR chatbot suite capable of end-to-end assistance in the recruitment process. The platform helps source, screen, and naturally act as a video interview system.

Final Thoughts: Will AI Replace Human Recruiters

As with most industries new to the adoption of technology, HR is currently in a state of flux. This state is partially due to transient technology coupled with developing regulations. Overall, AI at the moment won't replace human recruiters.

Instead, they should be seen as valuable assets capable of lowering overall recruitment costs and process enhancement.

The post Pros and Cons of Using AI in Your Hiring Process appeared first on Datafloq.

Following suit, the California Consumer Privacy Act or CCPA was quickly introduced and passed by the California state legislature and is slated to go into effect on January 1st, 2020.

While there is some overlap between the two laws, they do not share the same requirements.

So, how does the CCPA compare to the GDPR?

Let's start with a few similarities.

Both CCPA and GDPR compliances allow consumers to request a copy of the data that organizations might be storing.

Furthermore, every organization should include the ability to share what data they've collected and offer easy ways to delete them upon request. Failure to comply can result in heavy fines.

Another similarity between the two data compliance laws is the requirement to report a data breach to consumers, if and when it happens. The affected consumers will then have the right to take stringent action against companies if their data is exposed.

Moving on, the differences.

One of the biggest difference that sets GDPR apart from CCPA is the right to opt-in and opt-out of data collection. While GDPR requires customer consent for opt-in, the CCPA only offers customers the right to opt-out.

In simpler words, GDPR specifically requires the consent of its users before collecting any data. CCPA, on the other hand, allows organizations to automatically collect data when signing up to a new site or for example, making a purchase online.

However, in both cases, organizations are not allowed to treat customers any different when they choose to opt-out of sharing their data. Like, they are not allowed to offer fewer features or throttle loading time.

Similar goals, a different jurisdiction

GDPR and CCPA have a lot of overlapping areas, but neither the legislations cover the same jurisdiction nor do they cover the same issues.

With that being said, going through the GDPR compliance will give organizations a head start on their CCPA compliance efforts in terms of operations and adaptability.

Stay up-to-date on both the laws. Check out the infographic by LoginRadius.

The post CCPA vs GDPR: What Do Companies Need to Know? appeared first on Datafloq.