How big data is changing the game for backup and recovery

| June 27, 2016

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It's a well-known fact in the IT world: Change one part of the software stack, and there's a good chance you'll have to change another. For a shining example, look no further than big data.

Spotlight

Quantifind

Quantifind’s mission is to empower people to make better decisions that combine human intuition with the voice of intelligent data. Our on-demand insights platform uses explanatory analytics to help financial crimes analysts reduce false positive alerts and help marketers explore, understand, and change their impact on revenue. Quantifind’s clients are some of the world’s most recognized brands, spanning industries such as automotive, consumer packaged goods, entertainment, financial services, pharmaceutical, retail, and telecom. Founded by two PhD’s in atomic physics, the company has offices in Silicon Valley, New York, and DC.

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KAIST Develops Technology for AI-based High-resolution Image Creation

Article | April 7, 2020

The Korea Advanced Institute of Science and Technology (KAIST) announced on April 6 that professor Yoo Hoi-jun and his research team have succeeded in developing a generative adversarial networks processing unit (GANPU) as an AI chip processing GAN with low power and high efficiency. The AI chip is capable of quickly processing arithmetic operations required for image synthesis and restoration on a mobile basis. The single chip is capable of realizing image recognition, inference, learning and determination with low power and high efficiency and is expected to contribute to the use of AI in mobile devices for more purposes.

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Big Data The Role of Predictive Analytics in Sales Growth

Article | March 31, 2020

The analysis of a large volume of data is already an indispensable part of the decision-making process for any business, regardless of its volume. Big data is used to resolve routine problems, such as improving the conversion rate or to achieve customer loyalty for an eCommerce business. But did you know that you can also use it to predict situations before they occur? This is the added value of predictive analytics, the use of big data to anticipate user behaviour based on historical data and act accordingly to optimise sales.For online businesses, periodically performing predictive analytics is synonymous with improving your understanding of the customer and identifying changes in the market before they happen. The predictive models extract patterns from historical and transactional data to identify risks and opportunities. Self-learning software will automatically analyse the data at hand and offer solutions for future problems. This will allow you to design new sales strategies to adapt to changes and boost profit growth.

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What is Data Integrity and Why is it Important?

Article | July 19, 2021

In an era of big data, data health has become a pressing issue when more and more data is being stored and processed. Therefore, preserving the integrity of the collected data is becoming increasingly necessary. Understanding the fundamentals of data integrity and how it works is the first step in safeguarding the data. Data integrity is essential for the smooth running of a company. If a company’s data is altered, deleted, or changed, and if there is no way of knowing how it can have significant impact on any data-driven business decisions. Data integrity is the reliability and trustworthiness of data throughout its lifecycle. It is the overall accuracy, completeness, and consistency of data. It can be indicated by lack of alteration between two updates of a data record, which means data is unchanged or intact. Data integrity refers to the safety of data regarding regulatory compliance- like GDPR compliance- and security. A collection of processes, rules, and standards implemented during the design phase maintains the safety and security of data. The information stored in the database will remain secure, complete, and reliable no matter how long it’s been stored; that’s when you know that the integrity of data is safe. A data integrity framework also ensures that no outside forces are harming this data. This term of data integrity may refer to either the state or a process. As a state, the data integrity framework defines a data set that is valid and accurate. Whereas as a process, it describes measures used to ensure validity and accuracy of data set or all data contained in a database or a construct. Data integrity can be enforced at both physical and logical levels. Let us understand the fundamentals of data integrity in detail: Types of Data Integrity There are two types of data integrity: physical and logical. They are collections of processes and methods that enforce data integrity in both hierarchical and relational databases. Physical Integrity Physical integrity protects the wholeness and accuracy of that data as it’s stored and retrieved. It refers to the process of storage and collection of data most accurately while maintaining the accuracy and reliability of data. The physical level of data integrity includes protecting data against different external forces like power cuts, data breaches, unexpected catastrophes, human-caused damages, and more. Logical Integrity Logical integrity keeps the data unchanged as it’s used in different ways in a relational database. Logical integrity checks data accuracy in a particular context. The logical integrity is compromised when errors from a human operator happen while entering data manually into the database. Other causes for compromised integrity of data include bugs, malware, and transferring data from one site within the database to another in the absence of some fields. There are four types of logical integrity: Entity Integrity A database has columns, rows, and tables. These elements need to be as numerous as required for the data to be accurate, but no more than necessary. Entity integrity relies on the primary key, the unique values that identify pieces of data, making sure the data is listed just once and not more to avoid a null field in the table. The feature of relational systems that store data in tables can be linked and utilized in different ways. Referential Integrity Referential integrity means a series of processes that ensure storage and uniform use of data. The database structure has rules embedded into them about the usage of foreign keys and ensures only proper changes, additions, or deletions of data occur. These rules can include limitations eliminating duplicate data entry, accurate data guarantee, and disallowance of data entry that doesn’t apply. Foreign keys relate data that can be shared or null. For example, let’s take a data integrity example, employees that share the same work or work in the same department. Domain Integrity Domain Integrity can be defined as a collection of processes ensuring the accuracy of each piece of data in a domain. A domain is a set of acceptable values a column is allowed to contain. It includes constraints that limit the format, type, and amount of data entered. In domain integrity, all values and categories are set. All categories and values in a database are set, including the nulls. User-Defined Integrity This type of logical integrity involves the user's constraints and rules to fit their specific requirements. The data isn’t always secure with entity, referential, or domain integrity. For example, if an employer creates a column to input corrective actions of the employees, this data would fall under user-defined integrity. Difference between Data Integrity and Data Security Often, the terms data security and data integrity get muddled and are used interchangeably. As a result, the term is incorrectly substituted for data integrity, but each term has a significant meaning. Data integrity and data security play an essential role in the success of each other. Data security means protecting data against unauthorized access or breach and is necessary to ensure data integrity. Data integrity is the result of successful data security. However, the term only refers to the validity and accuracy of data rather than the actual act of protecting data. Data security is one of the many ways to maintain data integrity. Data security focuses on reducing the risk of leaking intellectual property, business documents, healthcare data, emails, trade secrets, and more. Some facets of data security tactics include permissions management, data classification, identity, access management, threat detection, and security analytics. For modern enterprises, data integrity is necessary for accurate and efficient business processes and to make well-intentioned decisions. Data integrity is critical yet manageable for organizations today by backup and replication processes, database integrity constraints, validation processes, and other system protocols through varied data protection methods. Threats to Data Integrity Data integrity can be compromised by human error or any malicious acts. Accidental data alteration during the transfer from one device to another can be compromised. There is an assortment of factors that can affect the integrity of the data stored in databases. Following are a few of the examples: Human Error Data integrity is put in jeopardy when individuals enter information incorrectly, duplicate, or delete data, don’t follow the correct protocols, or make mistakes in implementing procedures to protect data. Transfer Error A transfer error occurs when data is incorrectly transferred from one location in a database to another. This error also happens when a piece of data is present in the destination table but not in the source table in a relational database. Bugs and Viruses Data can be stolen, altered, or deleted by spyware, malware, or any viruses. Compromised Hardware Hardware gets compromised when a computer crashes, a server gets down, or problems with any computer malfunctions. Data can be rendered incorrectly or incompletely, limit, or eliminate data access when hardware gets compromised. Preserving Data Integrity Companies make decisions based on data. If that data is compromised or incorrect, it could harm that company to a great extent. They routinely make data-driven business decisions, and without data integrity, those decisions can have a significant impact on the company’s goals. The threats mentioned above highlight a part of data security that can help preserve data integrity. Minimize the risk to your organization by using the following checklist: Validate Input Require an input validation when your data set is supplied by a known or an unknown source (an end-user, another application, a malicious user, or any number of other sources). The data should be validated and verified to ensure the correct input. Validate Data Verifying data processes haven’t been corrupted is highly critical. Identify key specifications and attributes that are necessary for your organization before you validate the data. Eliminate Duplicate Data Sensitive data from a secure database can easily be found on a document, spreadsheet, email, or shared folders where employees can see it without proper access. Therefore, it is sensible to clean up stray data and remove duplicates. Data Backup Data backups are a critical process in addition to removing duplicates and ensuring data security. Permanent loss of data can be avoided by backing up all necessary information, and it goes a long way. Back up the data as much as possible as it is critical as organizations may get attacked by ransomware. Access Control Another vital data security practice is access control. Individuals in an organization with any wrong intent can harm the data. Implement a model where users who need access can get access is also a successful form of access control. Sensitive servers should be isolated and bolted to the floor, with individuals with an access key are allowed to use them. Keep an Audit Trail In case of a data breach, an audit trail will help you track down your source. In addition, it serves as breadcrumbs to locate and pinpoint the individual and origin of the breach. Conclusion Data collection was difficult not too long ago. It is no longer an issue these days. With the amount of data being collected these days, we must maintain the integrity of the data. Organizations can thus make data-driven decisions confidently and take the company ahead in a proper direction. Frequently Asked Questions What are integrity rules? Precise data integrity rules are short statements about constraints that need to be applied or actions that need to be taken on the data when entering the data resource or while in the data resource. For example, precise data integrity rules do not state or enforce accuracy, precision, scale, or resolution. What is a data integrity example? Data integrity is the overall accuracy, completeness, and consistency of data. A few examples where data integrity is compromised are: • When a user tries to enter a date outside an acceptable range • When a user tries to enter a phone number in the wrong format • When a bug in an application attempts to delete the wrong record What are the principles of data integrity? The principles of data integrity are attributable, legible, contemporaneous, original, and accurate. These simple principles need to be part of a data life cycle, GDP, and data integrity initiatives. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What are integrity rules?", "acceptedAnswer": { "@type": "Answer", "text": "Precise data integrity rules are short statements about constraints that need to be applied or actions that need to be taken on the data when entering the data resource or while in the data resource. For example, precise data integrity rules do not state or enforce accuracy, precision, scale, or resolution." } },{ "@type": "Question", "name": "What is a data integrity example?", "acceptedAnswer": { "@type": "Answer", "text": "Data integrity is the overall accuracy, completeness, and consistency of data. A few examples where data integrity is compromised are: When a user tries to enter a date outside an acceptable range When a user tries to enter a phone number in the wrong format When a bug in an application attempts to delete the wrong record" } },{ "@type": "Question", "name": "What are the principles of data integrity?", "acceptedAnswer": { "@type": "Answer", "text": "The principles of data integrity are attributable, legible, contemporaneous, original, and accurate. These simple principles need to be part of a data life cycle, GDP, and data integrity initiatives." } }] }

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THE NOT-SO-DISTANT FUTURE OF WORK

Article | November 20, 2020

As smart machines, data, and algorithms usher in dramatic technological transformation, its global impact spans from cautious optimism to doomsday scenarios. Widespread transformation, displacement, and disaggregation of world labor markets is speculated in countries like India, with an estimated 600 million workforce by 2022, as well as the global labor market. Even today, we are witnessing the resurgence of 'hybrid' jobs where distinctive human abilities are paired with data and algorithms, and 'super' jobs that involve deep tech. Our historical response to such tectonic shifts and upheavals has been predictable so far - responding with trepidation and uncertainty in the beginning followed by a period of painful transition. Communities and nations that can sense and respond will be able to shape social, economic, and political order decisively. However, with general AI predictably coming of age by 2050-60, governments will need to frame effective policies to respond to their obligations to their citizens. This involves the creation of a new social contract between the individual, enterprise, and state for an inclusive and equitable society. The present age is marked by automation, augmentation, and amplification of human talent by transformative technologies. A typical career may go through 15-20 transitions. And given the gig economy, the shelf-life of skills is rapidly shrinking. Many agree that for the next 30 years, the nature and the volume of jobs will get significantly redefined. So even as it is nearly impossible to gaze into the crystal ball 100 years later, one can take a shot at what jobs may emerge in the next 20-30 years given the present state. So here is a glimpse into the kind of technological changes the next generation might witness that will change the employment scenario: RESTORATION OF BIODIVERSITY Our biodiversity is shrinking frighteningly fast - for both flora and fauna. Extinct species revivalists may be challenged with restoring and reintegrating pertinent elements back into the natural environment. Without biodiversity, humanity will perish. PERSONALIZED HEALTHCARE Medicine is rapidly getting personalized as genome sequencing becomes commonplace. Even today, Elon Musk's Neuralink is working on brain-machine interfaces. So you may soon be able to upload your brain onto a computer where it can be edited, transformed, and re-uploaded back into you. Anti-aging practitioners will be tasked with enhancing human life-spans to ensure we stay productive late into our twilight years. Gene sequencers will help personalize treatments and epigenetic therapists will manipulate gene expression to overcome disease and decay. Brain neurostimulation experts and augmentationists may be commonplace to ensure we are happier, healthier, and disease-free. In fact, happiness itself may get redefined as it shifts from the quality of our relationships to that between man-machine integration. THE QUANTIFIED SELF As more of the populace interact and engage with a digitized world, digital rehabilitators will help you detox and regain your sense of self, which may get inseparably intertwined with smart machines and interfaces. DATA-LED VALUE CREATION Data is exploding at a torrid pace and becoming a source of value-creation. While today's organizations are scrambling to create data lakes, future data-centers will be entrusted with sourcing high-value data, securing rights to it, and even licensing it to others. Data will increasingly create competitive asymmetries amongst organizations and nations. Data brokers will be the new intermediaries and data detectives, analysts, monitors or watchers, auditors, and frackers will emerge as new-age roles. Since data and privacy issues are entwined together, data regulators, ethicists, and trust professionals will thrive. Many new cyber laws will come into existence. HEALING THE PLANET As the world grapples with the specter of climate change, our focus on sustainability and clean energy will intensify. Our landfills are choked with both toxic and non-toxic waste. Plastic alone takes almost 1000 years to degrade, so landfill operators will use earthworm-like robots to help decompose waste and recoup precious recyclable waste. Nuclear fusion will emerge as the new source of clean energy, creating a broad gamut of engineers, designers, integrators, architects, and planners around it. We may even generate power in space. Since our oceans are infested with waste, a lot of initiatives and roles will emerge around cleaning the marine environment to ensure natural habitat and food security. TAMING THE GENOME As technologies like CRISPR and Prime-editing mature, we may see a resurgence of biohackers and programmable healthcare. Our health and nutrition may be algorithmically managed. CRISPR-like advancements will need a swathe of engineers, technicians, auditors, and regulators for genetically engineered health that may overcome a wide variety of diseases for longer life-expectancy. THE RISE OF BOTS Humanoid and non-humanoid robots will need entire workforce ecosystems around them spanning from suppliers, programmers, operators, and maintenance experts to ethicists and UI-designers. Smart robot psychologists will have to counsel them and ensure they are safe and friendly. Regulators may grant varying levels of autonomy to robots. DATA LOADS THE GUN, CREATIVITY FIRES THE TRIGGER Today's deep-learning Generative Adversarial Networks (GANs) can create music like Mozart and paintings like Picasso. Such advancements will give birth to a wide array of AI-enhanced professionals, like musicians, painters, authors, quantum programmers, cybersecurity experts, educators, etc. FROM AUGMENTATION TO AUTONOMY Autonomous driving is about to mature in the next few years and will extend to air and space travel. Safety will exceed human capabilities and we may soon reach a state of diminishing returns where we will employ fewer humans to prevent mishaps and unforeseen occurrences. This industry will need supportive command center managers, traffic analyzers, fleet managers, and people to ensure onboarding experience. BLOCKCHAIN BECOMES PERVASIVE Blockchain will create a lot of jobs for its mainstream and derivative applications. Even though most of its present applications are in Financial Services, Supply Chain, and Asset Management industries, very soon its adoption and integration will be a lot more expansive. Engineers, designers, UI/UX experts, analysts, auditors, and regulators will be required to manage blockchain-related applications. With Crypto being one of its better-known applications, a lot of transaction specialists, miners, insurers, wealth managers, and regulators will be needed. Crypto exchanges will come under the purview of the regulatory framework. 3D PRINTING TURNS GAME-CHANGER Additive manufacturing, also popularly called 3D printing, will mature in its precision, capabilities, and market potential. Lab-grown, 3D-printed food will be part of our regular diet. Transplantable organs will be generated using stem cell research and 3D printing. Amputees and the disabled will adopt 3D-printed limbs and prosthetics. Its applications for high-precision reconstructive surgery are already commonplace. Pills are being 3D printed as we speak. So again, we are looking at 3D printers, operators, material scientists, pharmacists, construction experts, etc. THE COLONIZATION OF OUTER SPACE Amazon's Blue Origin and Elon Musk's SpaceX signal a new horizon. As space tech gets into a new trajectory, a new breed of commercial space pilots, mission planners, launch managers, cargo experts, ground crew, experience designers, etc. will be required. Since we have ravaged the limited resources of our planet already, mankind will need to venture into asteroid mining for rare and precious metals. This will need scouts and surveyors, meteorologists, remote bot operators, remotely managed factories, and whatnot. THE HYPER-CONNECTED WORLD By 2020, we already have anywhere between 50-75 billion connected devices. By 2040, this will likely swell to more than 100 trillion sensors that will spew out a dizzying volume of real-time data ready for analytics and AI. A complete IoT system as we know it is aware, autonomous, and actionable, just like a self-driving car. Imagine the number of data modelers, sensor designers and installers, signal architects and engineers that will be needed. Home automation will be pervasive and smart medicines, implants, and wearables will be the norms of the day. DRONES USHER IN DISRUPTION Unmanned aerial and underwater drones are already becoming ubiquitous for applications in aerial surveillance, delivery, and security. Countries are awakening to their potential as well as possibilities of misuse. Command centers, just like that for space travel, will manage them as countries rush to put in a regulatory framework around them. An army of designers, programmers, security experts, traffic flow optimizers will harness their true potential. SHIELDING YOUR DATA With data come cyber threats, data breaches, cyber warfare, cyber espionage, and a host of other issues. The more data-dependent and connected the world is, the bigger the problem of cybersecurity will be. The severity of the problem will increase manifold from the current issues like phishing, spyware, malware, viruses and worms, ransomware, DoS/ DDoS attacks, hacktivism, and cybersecurity will indeed be big business. The problem is that threats are increasing 10X faster than investments in this space and the interesting thing is that it is a lot more about audits, governance, policies, and compliance than technology alone. FOOD-TECH COMES OF AGE As the world population grows to 9.7 billion people in 2050, cultured food and lab-grown meat will hit our tables to ensure food security. Entire food chains and value delivery networks will see an unprecedented change. Agriculture will be transformed with robotics, IoT, drones, and the food-tech sector will take off in a big way. QUANTUM COMPUTING SOLVES INTRACTABLE PROBLEMS Finally, while the list is very long, let’s touch upon the advent of qubits, or Quantum computing. With its ability to break the best encryption on the planet, the traditional asymmetric encryption, public key infrastructure, digital envelopes, and digital certificates in use today will be rendered useless. Bring in the quantum programmers, analysts, privacy and trust managers, health monitors, etc. As we brace for the world that looms large ahead of us, the biggest enabler that will be transformed itself will be Education 4.0. Education will cease to be a phase in your life. Life-long interventions will be needed to adapt, impart, and shape the skills of individuals that are ready for the future of work. More power to the people!

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Spotlight

Quantifind

Quantifind’s mission is to empower people to make better decisions that combine human intuition with the voice of intelligent data. Our on-demand insights platform uses explanatory analytics to help financial crimes analysts reduce false positive alerts and help marketers explore, understand, and change their impact on revenue. Quantifind’s clients are some of the world’s most recognized brands, spanning industries such as automotive, consumer packaged goods, entertainment, financial services, pharmaceutical, retail, and telecom. Founded by two PhD’s in atomic physics, the company has offices in Silicon Valley, New York, and DC.

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