BIG DATA MANAGEMENT
Article | January 21, 2022
Businesses that are data-driven are well-known for their success, as data is widely considered to be a company's most valuable asset. Understanding data, its relationships, and the law requires the use of data modelling techniques. Sadly, people who are not familiar with data modelling best practises see them as a pointless documentation exercise. In the eyes of others, it is a hindrance to agile development and a waste of money.
A data model is more than just documentation because it can be implemented in a physical database. Therefore, data modelling is not a bottleneck in the development of an application. Due to these benefits, it has been proven to improve application quality and reduce overall execution risks.
Data modeling reduces the budget of programming by up to 75%.
Data modeling typically consumes less than 10% of a project budget.
Data Modelling- Today’s Scenario
Data models methodologies for data modelling have existed since the dawn of time. At the very least, it's been around since the dawn of the digital age. In order for computers to deal with the bits and bytes of data, they need structure. Structured and semi-structured data are now part of the mix, but that doesn't mean we've reached a higher level of sophistication than those who came before us in the field of computing. As a result, the data model lives on and continues to serve as the foundation for the development of advanced business applications.
Today's business applications, data integration, master data management, data warehousing, big data analytics, data Lakes, and machine learning require a data modeling methodology. Therefore, data modeling is the foundation of virtually all of our high-value, mission-critical business solutions, from e-Commerce and Point-of-Sale to financial, product, and customer management, to business intelligence and IoT.
"In many ways, up-front data design with NoSQL databases can actually be more important than it is with traditional relational databases [...] Beyond the performance topic, NoSQL databases with flexible schema capabilities require more discipline in aligning to a common information model."
Ryan Smith, Information Architect at Nike
How is Data Modelling Beneficial for Businesses
A data model is similar to an architect's blueprint before construction begins. The visual manifestation of a development team's understanding of the business and its rules is data modeling. The data modeling methodology is the most efficient way to collect accurate and complete business data requirements and rules, ensuring that the system works as intended. In addition, the method raises more questions than any other modeling method, resulting in increased integrity and the discovery of relevant business rules. Finally, its visual aspect makes it easier for business users and subject matter experts to communicate and collaborate.
Let us look into some of the core benefits of data modeling for businesses.
Following Data modeling techniques and best practices prevents the schema from endless searching and give results faster, resulting in a more efficient database. The data model's concepts must be concise to ensure the best performance. It's also crucial to accurately convert the model into the database.
Higher Quality Data
Data modeling techniques can make your data precise, trustworthy, and easy to analyze. Inaccurate data and corruption are even worse than application errors. Data can be adequately understood, queried, and reported on as a good data model defines the metadata. Developers can foresee what can lead to large-scale data corruption before it happens because of the visual depiction of requirements and business rules.
Effective data modeling techniques detect flaws and inconsistencies early in the process, making them significantly more accessible and less expensive to fix. As a result, data models allow you to design apps at a reduced cost. Data modeling often takes less than 5%-10% of a project's budget, and it can help lower the 65-75 percent of a project's budget that is usually allocated to programming.
By documenting fundamental concepts and language, data model methodologies lay the groundwork for long-term maintenance. The documentation will also aid in the management of staff turnover. As an added bonus, many application providers now provide a data model upon request. For those in the information technology field, it's common knowledge that models are a powerful tool for explaining complex ideas in a simple and straightforward manner.
An application database that contains numerous related tables is more complex and thus more prone to failure during development. On the other hand, data model techniques quantify software complexity and provide insight into the development effort and risk associated with a project. Therefore, the model's size and the degree of inter-table connectivity should be considered.
Any business can benefit greatly from data modelling methods and techniques. To the untrained eye, data modelling may appear to be distinct from the type of data analytics that actually add value to a company. In order to make data storage in a database easier and have a positive impact on data analytics, data modelling is an essential first step.
Frequently Asked Questions
What is data modeling?
In software engineering, data modelling refers to the use of specific formal techniques to develop a data model for an information system. This is used to communicate between data structures and points.
Which are the five crucial data modeling types?
The five crucial data modeling types are
Conceptual data model
Physical data model
Hierarchical data model
Relational data model
Entity-relationship (ER) data model
Article | January 19, 2022
Rapid digitization is generating an enormous amount of data every second. The field of data science employs highly unstructured data to draw meaningful insights that can help businesses make smart decisions. It is one of the ever-evolving technologies that is reshaping the landscape of the digital world. To be a data scientist, one must have the skills, methods, and knowledge of top-notch algorithms that process and execute data efficiently.
This article highlights seven apps that can help you grow in your career as a data scientist.
SmartWindows is a productivity app for Windows 10 and Windows 11 users. It is a handy desktop application for data scientists when it comes to getting a 360-degree view of multiple desktop files. A data scientist works on multiple data files at a time. Owing to this, excessive switching between apps becomes very inefficient and time-consuming. SmartWindows eliminates that overhead by allowing users to arrange multiple desktop apps and data files on-screen and save the screen arrangement in the SmartWindows profile. Once saved, users can restore the profile at any time, whereby all files will be restored at the exact same position and same windowsize on the desktop screen will be a single click.
SmartWindows allows users to create unlimited profiles and up to six displays. It auto-restores the screen arrangements on one or many screens. You can work on multiple work zones and save and restore them at any time. SmartWindows supports multiple browsers such as Google Chrome, Firefox, Microsoft Edge, etc., and restores dozens of tabs all with one click. It also restores Smart Layouts on Windows 11 without the hassle of creating new layouts before getting started on work.
QPython is a script engine that runs Python programs on Android. This mobile app contains all the essential components that will help you learn python. Data scientists need to play a lot of data that is hard to interpret and evaluate using manual methods. For complex unstructured data, it is difficult to make decisions, and therefore, for all the preprocessing and advanced techniques, Python is a must-learn programming language to study. QPython gives users leverage to learn Python using their mobile phones. It contains an editor, a console, an interpreter, a terminal, an explorer, and the SL4A library for Android.
The app comes with two basic modules, of which one is for beginners called QPython OL and the other is for experienced Python users called QPython 3x. If you are a beginner, you must choose QPython OL as it provides all the basic understanding and programming techniques to get started with. The app does not require an active internet connection, which means you can open and learn it anytime anywhere you want.
DataCamp is a learning platform to enhance data science skills online. The platform has a variety of programs and skills you can learn from. The platform follows a comprehensive hands-on experience in which you learn, practice, and apply the techniques of data science to real-world problems. It follows a complete curriculum of advanced and non-coding essentials that can help users learn data science and machine learning. It offers hundreds of video courses on different subjects. Practice can be done through various interactive exercises that can help you improve your data science skills.
Elevate is a cognitive training mobile app for Android and iOS. It helps users enhance their mind’s processing speed, speaking abilities, and problem-solving skills. They offer personalized training sessions that include exercises. It is a suitable tool for data scientists to strengthen their analytical and communication skills. Elevate consists of more than 35 games that enhance self-confidence, productivity, math, writing, and learning skills.
5. Math Workout
It is an educational application to test the analytical skills of users, keeping their minds healthy. It involves multiple exercises and tests to increase the processing power of the mind through mathematics. Mental arithmetic helps enhance data science skills. The Math Workout has some cognitive games and mental exercises in more than ten languages that interpret the cognitive psychology of users and train their brains accordingly. The app would make you perform calculations at your fingertips, allowing you to process tough calculations in a matter of seconds.
6. Programming Hub
The Programming Hub is a platform developed for developers. It consists of more than 5000 computer programs and 20+ courses for programmers to practice and learn programming languages. Whether you are a beginner or an expert, the Programming Hub offers a wide range of programming courses to help you with problem-solving skills with multiple programs and software frameworks. The app specializes in providing programs for artificial intelligence, R programming, C, C++, C#, Java, and more.
As the name suggests, NeuroNation is a brain training app that is designed to improve the brain activity of users through various exercises. It enhances the processing power of the brain and the logical thinking capability of users. It has a set of 60 different exercises, games, and activities. It is a website and an app for both Android and iOS. It has extraordinary features to increase scientific basics, personalization, fun, and motivation among the learners.
Article | January 12, 2022
The industry of Data Science has been popular since a decade or so. The aim and workflow of the field has undergone a lot of changes since then. From basic reporting and analytics to predictive and cognitive analytics, data science revolutionized the concept of “Computers that can think”. As of today, Data Science and its subfields are one of the greatest in demand and has a great competition in the industry. Apart from improving businesses, Analytics has proved its capability in various sectors and applications. This has changed the overall structure of the field of Data Science and the opportunities available. The large amount of data and its wide scope comes with plenty of various opportunities and developments.
AI, AI everywhere
AI started as a boom few years back wherein it saw great potential, but now it’s everywhere. From Research Labs to Education, Healthcare and even in personal devices, AI has taken up various forms, solving many problems and improving various products and services. Even now, experts states that the full potential of AI is not completely utilised and is expected to be used in a few years’ time. The wide use of Artificial Intelligence has motivated many start-ups to focus on the use of AI to build solutions and products. Industries at all scales have taken a move to include AI in their services / products to increase efficiency through intelligent behaviour. The “Mimicabilty” of human brain functionality comes with great potential – Namely Automation and Optimization of various tasks.
Data Centric is the trend
With a large amount of data generated daily on servers, there comes a need of shifting from model-centric to data-centric. Let us have a glimpse into what each approach means. In model-centric approach, data is kept constant while the model is tweaked to adjust to the data and get a good result. What’s the drawback of this? Not performing good on real world data. But, then why the model gave good results? That is because, model completely got adjusted to the data you gave instead of generalizing to the real-world problem. This issue came in the recent past, thus the trend of Data-centric came in. Experts like Andrew Ng often hosts talks and campaigns to shift the focus on ML Practitioners and Industries to Data rather than model. According to him, “data is like food for the model”.
Data Engineers on Rise
As a trailing topic to the previous one, Data Engineers will have a good rise in the coming future. As seen before, there is a copious amount of existing data and is getting generated at an un-imaginable rate. This might sound good since, “more is good” for analytics, but comes with the disadvantage of difficulty in ensuring data quality. As more data comes in, maintaining quality can be challenging. One reality here is that not all times a cloud pipeline can be used to ensure data quality and automated cleaning. There are times where raw data is logged as it is. This calls for more data engineers to perform cleaning and tuning of data to ensure it meets quality standards. With the oncoming of Data-Centric AI approach, this will have a great hype in the coming recent time. And speaking from the Industry Point of View, Data is one of the core part of Data Science and has no replacement. No good data, no good results and the crash of Data Science.
“Artificial” becoming more on the point for AI
AI started as a research topic long back. It all started like a big-bang – Basic linear regression to Neural Networks. The latest AI algorithms can now see like a human (Computer Vision), speak like a human (Natural Language Processing) and assist in decision making (Inferencing using Models). The newer models are much more advanced that AI seems to be – Natural Language Processing algorithms can now carry out tasks on speaking, predict a sentence and even tell what sentiment it has. Computer Vision Algorithms are now assisting doctors to detect defects and diseases through X-rays and MRI images. This all points to the immense capability in the field of AI. In future it is predicted that setting up an AI system will be equivalent to setting up the mind of a human being – A situation called singularity.
More opportunities coming up
As seen previously, there are a lot of opportunities coming up in Data Science field. With its vast number of applications in every sector, there are a lot of openings coming up. Start-ups and developed industries are now shifting to AI solutions because of its dynamic nature and intelligent decision-making capability. The number of jobs in Data Science is expected to rise in the coming years. Especially Post COVID, organizations experienced a surge in various technologies, out of which Data Science is one of the major fields. Data Science and Artificial Intelligence is one of the most demanded fields for research to happen. Aspiring researchers have a good demand in the AI and ML field. Tech giants have marked their presence in improving AI algorithms, making the systems more efficient and “Intelligent”. Artificial General Intelligence is one of the applications of AI which is focused on varied problem solving rather than a restricted problem domain and is expected to bring a significant change in the AI focused problem solving.
BIG DATA MANAGEMENT
Article | November 26, 2021
Data breach prevention is going to be the need of the hour as cybercrime continues to grow. Cybercrime is a growing threat to businesses of all sizes. Due to this unprecedented time many companies shifted to work-from-home model. Statics show data breaches are on a rise and can have devastating long-term financial set-back or reputational repercussions to your organization.
As a result, businesses must ensure that their data is secure to avoid substantial loss or theft.
As data breaches happens in different ways, there is no such thing as a one-size-fits-all remedy. Security needs a multifaceted approach to be effective.
In this article we’ll find out different ways prevent data breaches.
Impact of a data breach on businesses
A data breach can destroy a business, especially for small and medium-sized businesses (SMB). Data is a valuable asset for any business especially, the data related to customers and payments. Cybercriminals find this data valuable. Lack of planning and security creates vulnerabilities for criminals to exploit.
It is estimated that 60% of small and medium-sized enterprises will close within six months after the attack. Larger businesses or agencies, on the other hand, will survive. Nevertheless, they too will suffer the consequences.
A data breach can impact businesses in the following ways;
Businesses must compensate for both immediate and hidden fines (fines, public relations, legal fees, and punitive regulatory measures) for a data breach. In addition, business needs to compensate customers, refund any stolen funds, and bear a share value loss. A smart organization will use this opportunity to develop data security and disaster recovery strategies, which entails financial investment.
Fines and fees – The PCI Security Standards Council may impose fines or penalties for a data breach. Both regulatory organisations and card network brands will have different fines.
Forensic investigations – Major consequences of a data breach include, the business that was attacked will be accountable to perform a forensic investigation to determine the causes of the data breach. These investigations are costly and often yield valuable evidence and insights to prevent future data breaches.
Future security costs – Victims of a data breach may have to bear costs of credit monitoring for customers whose data was compromised. This may also include the costs of identity theft repair, card replacement, and additional compliance requirements from the PCI.
Having a good reputation is the most prized asset for any organization. As a business, one must constantly put effort into building and maintaining brand integrity. A single compromising episode like a data breach can trash the best of reputations. According to a PwC report, 85% of customers won't shop at a business if they have concerns about their security policies.
Customers value their privacy, and a data breach will be perceived as a lack of regard for their data and privacy. Furthermore, 46% of businesses reported that security breaches harmed their reputation and brand value.
The product blueprints, business strategies, and engineered solutions are some of your most valuable assets for any organization. Your trade secret gives you an added advantage over your competitors. Hence it needs to be protected as some may not hesitate to use breached intellectual property.
Other significant consequences of a data breach include;
A data breach can pit the CEO against the CISO
Poisoned search results on your corporate brand
Loss of sales after a data breach
Less attractive to new employees, especially in tech positions
Legal penalties after a data breach
Understanding the aftermath of a data breach is an important step to safeguarding your business. The next step is to create an action plan is to protect what you've worked so hard on.
How does a Data breach happen?
Data breaches sometimes can be traced back to planned attacks. But, on the other hand, it can result from a simple oversight by individuals or flaws in the infrastructure.
For instance, an employee uses a co-worker's computer and reads files without proper approval or permission. However, the access is unintentional/accidental, and no personal information is revealed. The data was breached, however, because it was read by an unauthorised person.
This person deliberately accesses/shares data with the intent of causing harm to an individual or company. The malicious insider may have genuine authorization to use the data, but the intent is to use the info in nefarious ways.
Lost or Stolen Devices
Any laptop or external hard drive with important information on it that is not encrypted or unlocked goes missing.
Malicious Outside Criminals
These are hackers who attack several vectors to collect information from a network or an individual.
Global cost of data breach
According to the Ponemon Institute's Cost of a Data Breach Report, global data breaches cost $3.86 million on average in 2020. The amount in 2020 was somewhat lesser compared to 2019 when it hit $3.92M. The same report found that the average cost of a data breach in 2020 totaled $8.64M.
Ways to prevent a data breach
Conduct employee security awareness training
Control access to data sensibly
Update software regularly.
Require secure passwords and authentication
Simulate phishing attacks
Limit access to your most valuable data.
Review your user account lifecycle processes
Insist on complex and unique passwords
Protect against authentication bypass
Store sensitive personal information securely and protect it during transmission
Consider implementing a secure SSO solution
Secure all endpoints
Segment your network and monitor who's trying to get in and out
Manage Vendors - Third-party vendors must comply.
Protecting against data breaches may appear to be a time-consuming procedure. You will be in a better position if you take an encrusted step to secure your data using various methods, policies, and procedures to ease security threats.
How does a data breach impact an organization?
Depending upon the company and data type, the consequences may include destruction or corruption of databases, leaking of confidential information, the theft of intellectual property, and regulatory requirements to inform and possibly compensate those affected.
What is the most common data breach?
Hacking attacks are the most common cause of a data breach. However, it is often a weak or lost password that is the vulnerability that the opportunist hacker is exploiting.
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