MySQL is the pioneering name in enterprise database management systems and is widely used as an open-source RDBMS (Relational Database Management System). Many of the enterprise applications and CMS web development platforms rely on MySQL database as an excellent solution in web-scale business applications. However, when it comes to the changing big data environment, it seems that MySQL architecture; which was developed with the needs of that time in mind, has some limitations too.
MySQL limitations in big data
On having a closer look into the strengths and weaknesses of the MySQL DBMS, we can see that even though it is powerful, you may need some further assistance to get benefited from it. To start with, here we will discuss the major limitations of MySQL in the area of big data.
1. Delivery of hot data
Considering the need for the larger application, we can see that the cache data in the RAM storage may grow huge over time, given that there are hundreds of thousands of requests generated every second. One disadvantage of MySQL here is that it is not a memory-centered search engine. IT is not designed with a higher concurrency in mind so that users may experience bottlenecks in terms of performance. MySQL may saddle in such situations with high overhead and also may not be able to deliver good speed.
Solutions for this problem is to use any applications like Redis or Memcached as external solutions for hot data needs to eliminate any overhead in terms of SQL parsing. Caching is somewhat difficult, and you are at risk of reading the data which is not current. You may also use the scope of internal scalability in order to enhance MySQL-like thread pools as like an enterprise feature of MySQL. Such add-on features will help run multiple queries concurrently.
2. Capacity to deal with volatile data
Simultaneously handling many updates in a single row of database like a flash sale of a new model cell phone etc., it is important to maintain the exact values at each second. MySQL DB is designed around the transactional semantics to support the tasks with disk-based log durability. So, MySQL is suited for the tasks with highly volatile data but has some limitations in it.
An ideal solution for this issue is that, up to an extent, to do proper data design in MySQL. Splitting the counter into multiple rows will help to attain optimal configuration during MySQL installation, and it will ensure better performance than the primary configuration of stock MySQL. Another big problem with MySQL is parallel replication, which is however addressed in the MySQL 5.7 version. If you still face it, try solutions like the Percona XtraDB Cluster. Many users may tend to move their data to Redis or Memcached and then synchronize periodically to RDBMS.
3. Handling huge data volume
MySQL is a single-node database system, which is not compliant with the modern cloud data center concepts. Even the biggest MySQL installations today cannot scale up to that level with MySQL alone and may have to rely on further applications to ensure sharding, data split across multiple nodes, etc. However, most of the sharding solutions are manual, and it makes coding of application very complex. Any advantage in terms of performance gain will be lost when the queries access the data across multiple shards.
The solution for this problem as suggested by RemoteDBA.com is the tools like Vitess, which is a framework released by YouTube for MySQL sharding. ProxySQL is also used to implement better sharding. Redis and Memcached are other front-end solutions for this issue, and MongoDB and Cassandra are the alternatives to consider for MySQL.
4. Analytical capabilities
MySQL is not designed to handle complicated queries in massive data volumes which require to crunch through large scale data loads. MySQL optimizer has limitations in executing one single query at a time with the use of a single threat. The MySQL queries cannot scale across multiple machine cores in a system and also cannot execute the distributed queries at multiple nodes.
As a solution, even though MySQL is offering no comprehensive solution for any robust data processing solution at large-scale DB management environment, you can try many third-party solutions like Hadoop or Apache Spark, etc. Click House and Vertica etc. have also emerged lately as analytical solutions to be used over MySQL. Apache Ignite can also integrate with Spark and Hadoop to use the in-memory technology which completes well to these technologies and ensure better performance at scale.
5. Enabling full-text search
Even though MySQL can handle the basic text searches, with its inability in parallel processing, searches a scale will not be handled properly when the data volume multiplies. One solution to try out for small-scale searches is InnoDB, which was made available with the version MySQL 5.6. When a particular go beyond a few GB, you have to use better search solutions like Apache Solr, Elasticsearch, Sphinx Search, or Crate.io to manage it better.
The convergence of database trends
Even though MySQL is deemed to be an excellent choice for database, the top trends which converge now to change the data processing landscape now are:
The in-memory computing and cache concept has been there around for long, but it has lately come to the forefront, and the need for real-time data processing has now become a standard procedure in all sizes of enterprises. In-memory computing remains as the primary pointer to speed, and the advancement in memory technology will lead to a better world for in-memory storage, making disks just historical backups.
The memory prices are largely dropping over time. You may get a terabyte RSM at the cost of nearly $20,000, which makes storage more affordable to all size of enterprises to meet their real-time data management and analytical needs at a scale.
This further change the large-scale data processing landscape. Above trends come together to force a big change in the database world and database management systems approach.
The reassuring thing is that these as these trends converge; the existing in-memory platforms will deliver more sophisticated distributed systems which are more efficient and architected to provide greater scalability and higher performance.
Working in data analytics is fairly new, but certainly big data is a growing field that is garnering much attention with the ease with which companies collect and store large volumes of information. With rising storage capabilities and the increasing affordability when it comes to collecting, storing and processing data, companies are keen to find potential employees with a background in both analytics and big data.
Computer-related degree courses teach data management, analysis and analytics to varying levels. In this article, we cover the best degree options for students with a serious interest in big data along with an aptitude for numbers.
The 24-week course is undertaken over the internet. It covers data science, business analytics and data visualization. Furthermore, it also includes predictive modeling and machine learning which intersects with the current huge interest in artificial intelligence.
The certificate is only open to existing graduates who, due to the statistical nature of some of the course’s content, must have also passed either a previous statistics module or a statistics course at college.
Master’s in Big Data Management
A Master’s in Big Data Management is an advanced course which often builds upon a Computer Science degree. Working with detailed analytical modeling and learning about a variety of techniques to draw well-reasoned conclusions from data sets is all part of this type of course.
The Big Data courses usually require either a one- or two-year commitment and are commonly campus-based too.
Data Science Degree
A Data Science degree is a common entryway into the world of big data. Most degrees of this type require students who enroll to already have a working knowledge of programming in R and Python, along with the ability to write functions for both of these languages and to know calculus too. This is because of the need to be mathematically and programmatically competent to complete a Data Science course.
Data Science is often studied at the bachelor’s degree level. It is often framed as “Data Sciences and Big Data” for emphasis about the inclusion of big data on the curriculum, however, any Data Science course will cover big data at some point. While there are degrees available in Data Science at the master’s level, usually big data and analytical courses become more business-related instead of a broader course.
Big Data Analytics and Artificial Intelligence
A master’s in Big Data Analytics and Artificial Intelligence aims to merge data science with AI. In the advent of machine learning and AI being used with search engines to predict and better understand what searchers are looking for, this perfectly encapsulates where big data is being crunched to draw meaningful insights in combination with AI and natural language to make those insights immediately practical through better responses to search queries.
This type of degree also has a strong focus on the human side of big data and where it links up with AI and analytics to provide benefits to end users.
There are different approaches to take when possessing a strong interest in big data and data analytics. It does depend on your statistical background and current coding knowledge as to which course is most suitable for you. Most, not all, courses are for post-graduates as a prerequisite.
Big data platforms allow users to draw conclusions that may not otherwise be evident. They can also look for trends in tremendous amounts of data and do so in a substantially shorter time than humans alone could. The pharmaceutical industry is tapping into the wealth of big data to make gains and maximize patient safety.
Making Better Assessments About Drug Safety
It can take years and millions of dollars invested in drug development before new treatments make it to the market. Once the trial phases start, the typical process is to test drugs on animals first, then humans. However, companies are moving away from relying on animals as they did previously and turning to patient data instead.
Some adverse events reported for particular drugs only manifest in animals. There are also biological differences between animals and humans. Moreover, safety concerns and lack of efficacy are two of the top reasons why drugs don’t make it past clinical trials.
A recent study used big data to see which animals ordinarily used for pharmaceutical testing were the best predictors of side effects in humans. It concluded that such accuracy depended on the creature and the event experienced. Those findings could reduce the instances of using certain animals for studies if they aren’t likely to reflect how humans respond to a drug.
Pharmaceutical companies would have fewer unpleasant surprises during human trials, and the subjects have a reduced chance of encountering issues after taking the drugs.
Allowing Better Collaboration to Happen in Global Clinical Trials
Drug companies comprising the Big Pharma landscape often have trials all over the world. Until relatively recently, though, it wasn’t possible to utilize the data from all those investigations at once. In 2016, Novartis developed a system that brought information from its nearly 500 global clinical trials of the past and present under one roof.
It used cloud technology and broke down the siloed structure the company had before. The platform features a component that allows clinical trial representatives from around the world to communicate with each other. There are also risk prediction features that can prevent dangerous patient consequences.
The combination of a wealth of data and the ability of people to give insights throughout clinical trials enhances both the processes and the outcomes.
Ensuring Medications Stay in the Proper Environments
Many of the drugs Big Pharma companies create must stay within consistent temperature ranges. A deviation of only 2 degrees could be enough to ruin an entire batch, resulting in substantial and unexpected expenses. Properly transporting and storing drugs is also necessary for regulatory compliance.
The companies specializing in containers for shipping those drugs usually offer options with data loggers. Big Pharma companies can connect those to their data platforms and rest assured the cargo stayed at the correct temperature before it reached the destination.
From a patient side of things, high temperatures can change the consistency of some drugs and affect their potency, possibly causing problems for the people who consume them.
Confirming Real-World Worthiness
Clinical trials provide essential information about how drugs affect patients, but some findings are only apparent after years of use. That’s why drug manufacturers are anxious to get their hands on real-world evidence from routinely collected patient data and other sources. Fitness trackers, apps and electronic health records (EHR) are some of the things that contain data Big Pharma companies could use.
Getting a handle on real-world evidence allows them to prove to stakeholders that their product works. Moreover, the significant increase in data could lead to advances in highly personalized medicine.
However, some people aren’t on board with turning their data over to Big Pharma. They fear companies will primarily use their information for marketing purposes and give them little or no control over what happens to it.
Fortunately, there’s a growing recognition of the need for data privacy within the Big Pharma sector. Most of the largest companies have tightened up their practices, but some of the smaller entities that deal with medical data still have work to do.
Some people within the industry believe California’s upcoming data privacy law will cause large-scale improvements. After all, it’s not cost-effective to have one set of data privacy standards for California residents and another for everyone else. As it is now, some companies that use medical data aren’t adequately transparent about how they use patient data, and there are no clear global standards to follow.
Big Data Offers Promise if Used Carefully
The examples presented here show how there are ample opportunities to use big data to improve patient safety. However, those efforts could backfire if breaches or other invasions of privacy happen. Big Pharma must continue to keep privacy a priority when dealing with data.
It’s hard enough running your own business, but even more challenging when you think about the ways technology is changing the landscape. Luckily, there are tips available to help you manage your company, even though you may not be very tech-savvy.
What you don’t want to do is become easily frustrated with yourself and in turn make the situation worse. Focus on what you can do in the present moment to help make your life and job easier regarding technology. When you focus on all the benefits it’ll bring to your business, you’ll likely have more of a desire to improve your skills.
One place you can start is to take time to educate yourself about technology. Learn the lingo and what advancements have been rolled out to help advance businesses specifically. Read books, blogs and magazines related to the topic and increase your knowledge slowly over time. Take it one day at a time and try not to get ahead of yourself or you may quickly become confused and agitated.
Use Online Resources
Another tip for how you can run a business when you’re not very tech-savvy is to take advantage of online resources. For example, if you’re computer is giving you trouble or not functioning properly then turn to a website such as Techloris.com, so you can pinpoint and resolve the issue in a timely manner. There are plenty of articles and people out there willing to share their knowledge with you, but you have to be willing to take the initiative to seek it out.
Hire IT Help
There’s nothing wrong with admitting to what you don’t know when it comes to running a business. What you can do if you’re not very tech-savvy but have the desire to implement more technology solutions in your workplace is to hire IT help. There are plenty of people who have the skills and abilities to assist you in this type of situation and get your business on track for succeeding with the latest advancements in technology. This way you’ll always have someone available to you who can answer your questions and point your business in the right direction in this area.
Ask Your Employees
Don’t forget about the fact that the employees you hire at your company could be tech-savvy themselves. Reach out and ask your employees for assistance when you need it so you can start to improve your skills and run a better business. Purposely hire people who will be able to help you improve your knowledge so you can get more comfortable using technology. You’d be surprised at all the information the younger generations know these days and how resourceful they can be for you as a business owner.
Identify Your Weaknesses
Another tip for running a business when you’re not very tech-savvy is to identify your exact weaknesses, so you can work on them. It’s possible you have some knowledge and capabilities in general and are simply falling short in only a couple of areas.
Pinpoint what’s giving you the most trouble or where you feel the most out of touch and focus your efforts on advancing your background regarding these specific topics. For example, it could be that you want to know more about big data and analytics or understand how working in the cloud can help you and your employees become more efficient.
Don’t Give up
What’s most important is that you don’t give up on yourself easily or stop trying before you even grant yourself a chance to succeed. Even though you’re not very tech-savvy, there are ways to get around it and survive while you work on improving your skills. Focus on what you do know and what’s been working for you and use this as a foundation to grow a better understanding of technology in general.
Stay encouraged by setting goals and rewarding yourself when you meet your objectives. Giving up and refusing to get on board with the latest technology updates and improvements will only hurt your business in the long run. The reality is that customers want to make purchases from businesses that are staying up to speed with the latest trends.
Be glad to know you can still run a profitable business even though you’re not very tech-savvy. Recognizing that it’s an area where you struggle is the first step to you improving your technical skills. Use this advice for helping you to manage your company properly and not allow these gaps in knowledge to negatively affect you continuing to grow your business.
Probably, most people got their first taste of Data Analytics is the Hollywood blockbuster, ‘Moneyball’, wherein the coach Brad Pitt selected players for his baseball team by using data analytics to identify undervalued players. Oakland Athletics lost its star players after losing to the New York Yankees; the team needed to rebuild its roster with a limited budget. Brad Pitt was recruited as the General Manager of Oakland Athletics to rebuild the team. Brad Pitt used data analytics to recruit players and selected players on the basis of their on-base percentage. Using data analytics, Brad Pitt formed a completely new team by signing players at extremely low contracts prices, and this team went to win the Western American League. Imagine the return on investment Oakland Athletics made scouting players using Data Analytics.
Now, let’s apply the same principles of Data Analysis in the Stock Market Analysis and imagine the return on investments an investor can earn by making investment decisions through data analysis. Identifying underperforming stocks with the potential of delivering exceptional performance in the near future, and thereby allowing the investors to purchase the stock at rock bottom rates and sell the stock when it reaches its peak. Data analysis can help investors determine their buying, selling and holding the decision to assure maximum capital gains and return on their investment.
What is Data Analysis and Big Data?
Data analysis can be defined as the methodology utilised for analysis and process of random data to enable making sense out of it. There is a treasure of quantitative and qualitative data which businesses accumulate. This data can be highly valuable if analyse and interpreted in the right manner towards deriving useful insights and results.
Big Data can be defined as large sized or highly complex data sets which are generally difficult to analyse and process using traditional methods of data processing and analysis. These difficulties may include analysis of data, capturing of data, metadata, data storage, search, data transfer and sharing, visualisation and data privacy.
Applications of Data Analysis and Big Data
Data analysis can investigate and discover trends and influences which determine consumer behaviour and buying decisions which can help businesses make important decisions pertaining their product design, delivery mechanism, pricing strategy, marketing and promotion strategy etc. in order to obtain a position of competitive advantage.
For instance, e-commerce businesses are employing some of the best minds in data analysis and Big Data towards deciding almost every aspect of its business from changes in pricing trends, logistics, fast moving products etc. Professionals Sports Teams are using data analysis to determine which player to draft or recruit, team formation, in-game strategy etc. The movie industry is using data analytics to understand consumer preference and designing its movies according to the consumer taste, desires and preferences to ensure their production is a box-office success.
Similarly, data analytics can be used in the stock market towards identifying stocks and shares with growth potential, buying them at rock bottom prices and selling them when the share prices are at its peak.
In the age of digitisation, all share market transactions take place online using Demat Accounts. One of the advantages Demat accounts offer is the easy access to historical financial data regarding the transaction history of the user and the historical performance of a stock or a share.
Demat Account is a form of an account which enables investors of stock and shares to hold their shares in electronic or dematerialised form. Post 5th Dec 2018, SEBI has restricted transfer of shares in physical form and transactions in share using a Demat Account enables electronic settlement of all transaction.
A Demat account is operated similar to a bank account wherein the Demat account holds stocks, shares and other forms of financial instruments instead of cash and similar to a bank account wherein the Demat account is credited and debited when shares are bought and sold. Investors can open Demat account with zero balance of shares and also maintain a zero balance of shares in their Demat account.
Applications of Data Analysis and Big Data in the Stock Market
Data Analysis and Big Data are on the cusp of completely revolutionising how the stock markets in India will function and how investors will make their buying, selling and investment decisions. The technology of data analysis and Big Data is growing rapidly across industries, and the financial sector is not far behind in the development of data analysis and Big Data technologies.
Trading on the stock market required accurate and timely inputs. The magnitude of data that is generated within the stock market on t a daily basis is impossible to be managed analysed and made sense of by human beings due to the sheer volume of data generated and speed at which this financial data is being generated from various sources.
Find some applications of data analysis and Big Data in the Indian Stock Market.
Leverage Data Analytics and Big Data Analytics into Financial Modelling
In the current day and age, financial analysis alone is no longer adequate for examining share prices and share price behaviour. These financial analyses need to be integrated with external factors such as social and economic trends within the economy, political environment, consumer behaviour and preferences etc. which have the potential of impacting the share prices of a particular stock or stocks prices within a particular industry.
Data analysis and Big Data Analytic can utilise predictive models for estimation of probable outcomes and returns on investment. With the increase in access of these results and increase in the level of accuracy of data analysis and Big Data predictions, investors can leverage this information and prediction towards mitigating their risk associated in trading on the stock market.
The latest buzzword within the world of stock market trading is ‘Algorithmic Trading’. Machine Learning technologies and algorithms are allowing computers towards making investment decisions, execute trades just like human beings, but at a rapid pace and high frequencies which is not possible to be carried by people. These algorithms incorporate best buying prices to be traded at specific times and reduction of manual errors which can occur due to behavioural influences.
Real-time data analytics also has the potential of improving the investing capabilities of individual retail traders and high-frequency traders and firms with the algorithmic analysis providing insights which provide access to valuable information, with this enabling making accurate and timely investment decisions towards maximising returns on investment.
The strength of algorithmic trading is within its limitless capabilities of analysing data, making real-time investment decisions and executing trades at a fast pace and high frequency using a wide array of structured and unstructured data obtain from various sources such as stock market information, social media, analysis of recent news etc. towards making intuitive judgements. This analysis of situational sentiments can be highly valuable in stock market trading.
Machine Learning Technologies are still in its very nascent stages with the technology yet to realise its full potential. Theoretically, the applications of this technology are wide and far-reaching and have its applications in trading within the stock market as well. Machine Learning technology can enable computers to learn to make financial decisions and learn from its past mistakes, employ logic in investment decisions etc.
Machine Learning Technologies have the potential of delivering accurate perceptions and executing profitable trades. Though this technology is in its nascent stages, the growth potential of this technology and the endless possibilities in the stock market trading application can enable implementation a combination of Data Analytics, Big Data and Machine Learning without human involvement in decision making.
William Osler was right on the money when he quoted that the ‘future is today’. Several enterprises promote their innovation techniques with this tagline, more so when it comes to the Internet of Things.
Most of us already have smart speakers, or are considering getting one for ourselves. Its ability to comprehend commands issued makes it indispensable to homes and offices alike. But how exactly does it do this? From wanting the volume up to setting alarm reminders, it all comes down to the data that transmits across interconnected devices, transforming cognitive abilities of man and machine to a reality.
Projects under the IoT wing are technically-complex and richly diverse, requiring a deep dive into the critical drivers that not only let you manage expectations but sets a realistic progression curve, some of which are as follows –
Resource planning with a hyper-specialty skills inventory
Given that IoT projects involve several hyper-specialized skills, as demands fluctuate, the gap between the right people and projects would have to be bridged in time to avoid losing out on high-visibility, high return work.
In this regard, a resource management tool lets you onboard resources with an experienced eye and umbrella of skills. Its skills inventory profiles the most sought-after and available resources such that all IoT projects are optimally staffed. With an extensive database that plugs in timesheets, billable effort estimates, available hours and future projections, the skills inventory lets you know how your staff’s competencies are placed for the duration in question. It even tracks how skills are shared in multi-project activities.
The tool’s reporting analytics capture how your resource pool is positioned on inflight and upcoming projects. It points you to under and over-utilized staff to correct schedule imbalances instantly. Further, it weighs the existing capacity against the project pipeline to let you not only plug skill shortages ahead of time but also scale back on inexperienced or underqualified staff.
By predicting the work influx, you’ll know the type and quantity of staff needed and maximize potential on all available hours. This, after taking into account leaves, and training programs running in parallel. Even better, you’ll fulfill the gap between project vacancies and benched staff. An added advantage a resource-centric tool offers is the ability to notify you of your staff’s availability. This lets you onboard a gig workforce without stretching labor costs.
With expectations riding high and customers queuing up for the next big innovation, enhancing flexibility in a fast-paced environment can seem like prolonging the agony of waiting.
Introducing negotiable times, however, is more preferable to having project teams work in haste on any IoT project. For one, you release products that meet the quality mark and emulate the behavioral model fed into it. And for another, your project team’s schedules aren’t jam-packed with time-sensitive tasks, leading to optimal resource allocations even if your primary staff are working on multi-project activities at once.
Given that the scope changes product outcomes, it makes sense to buy your teams enough time to gauge how and where to inject the changes and to then analyze if the change upgrades or devalues the systems’ performance. This is due to the fact that an interconnected system works by extracting the right data from the right places. Considering the explosion of data sets being processed in real-time, a deviation in the information stream can cause the product to stop working altogether or produce an outcome with an undesirable function.
When alterations to the proposed project are suggested when the project’s underway, negotiating timelines lets you, your team and client hash out priorities against feasibility, or ‘ what needs doing first’. This lets them work on mission-critical and time-consuming tasks first so as to minimize or prevent errors that spill over to the next sprint. Further, they can document how the project progressed in order to pass on the lessons learned onto junior teams for the future.
The shift to agility in terms of both methodology and work culture prevents you from confining your IoT projects to definitive start and finish dates It lets your teams capture reordered priorities as opposed to spending more time detecting and resolving errors. What’s even better here is that you don’t lose the edge to a more fluid competitor. Agile release cycles over the more rigid traditional stagewise model proves beneficial in letting teams stay updated of everyone’s progress, milestones reached and technologies that are emerging which will be of relevance and use to them in innovation.
A key driver propeling Iot projects in the right direction is your team’s ability to share what they know and don’t know. Information gaps can be bridged only when your teams collaborate.
The first step to doing so is to have your workforce involved wholly in enterprise-wide knowledge-sharing initiatives, right from your full-timers to hires temporarily contracted. This move not only lets them stay aware and informed of different bodies of knowledge but also lets them explore where their skills fit in strategically. Since they’re at the core of the actual work, your teams can document their contributions on previous IoT projects and determine new lines of work they can take ownership of.
A knowledge-sharing platform lets you vet out the credibility of different business verticals in order to prioritize the ones that generate the maximum number of the best opportunities. With more information plied on, every IoT project offers a learning journey such that your organization’s demonstrable value increases and sustains for the years to come.
Did these critical drivers put you back in the driving seat again?
As the subject matter expert of Saviom’s resource management tool, Aakash Gupta writes extensively on efficiency sciences and the role software play in the space. Keep pace with the latest news from his desk, here
Since the recession, the real estate and housing industry has bounced back considerably. Market prices are higher than they’ve been for years, and estimates show continued growth along with renewed millennial demand. Yet even with an ever-improving market, things aren’t ideal.
The ecosystem, as well as a variety of regulations, processes and legacy solutions within it, are plagued with issues. The most obvious is the time it takes to complete a real estate transaction, not including communications or back-and-forth negotiations between parties.
There are other flaws too, of course. Real estate agents, for example, take a huge portion of money exchanged during a property transaction — up to as much as six percent in some cases. As a result, intermediary fees can balloon quickly, decreasing potential gains of a solid investment.
According to the National Association of Realtors, nearly 80 percent of all home buyers still use an intermediary during a transaction — what you know of as a real estate agent. But why is this activity a big deal? After all, the real estate industry has been powered by agents and intermediary companies for decades now.
The issue is that today, there’s really no need for it, especially with modern digital platforms and technologies. Homebuyers and investors could do exactly what agents do, provided they have the right tools and guidance. It’s the transactions, paperwork and outdated processes — especially when working with banks and financial institutions — that cause the most fuss. Get rid of those old-school problems, and you’re looking at a new, innovative industry with a very bright future.
Many 2018 moving trends, for example, honor a crowdsourced market where buyers and sellers are empowered, exactly the kind of thing blockchain can help facilitate. Today’s sellers, for example, will spend more time researching an area they’re considering moving to. Even so, they’re more inclined to make short-term moves with ideal location traits which means they will constantly be exploring market opportunities and doing their own scouting sans real estate agents. Effectively, we could see a market where the middlemen or main players — who thrive in yesterday’s market — no longer play a role.
The question then becomes, how do we make all this happen? With blockchain, because it provides immense potential for advancing the real estate and global housing industry.
What Is Blockchain and How Can It Help Real Estate?
The blockchain is a relatively new technology originally developed to support Bitcoin, one of the most popular cryptocurrencies in existence. If you know nothing about crypto, don’t worry — it’s not significant here. What we’re really interested in is the tech or foundation that supports the use, transfer and valuation of cryptocurrencies, called Blockchain.
In short, it’s nothing more than a digital ledger in which transaction histories and vital data can be recorded and reviewed. The nature of the technology allows for increased transparency and traceability, as well as much-improved accessibility for all. Perhaps even more important are the additional layers of security it offers, particularly in regard to authentication for users accessing the chain and exchanging data. If deployed or implemented within the real estate industry, the aforementioned traits would prove invaluable.
On the surface, blockchain can help eliminate the need for intermediaries altogether, including not just agents but also lawyers and financial experts. Buyers all along the chain would have the proper verification to deal directly with sellers and property owners.
How Could Blockchain Do Such Remarkable Things?
To understand why blockchain technology would provide the aforementioned benefits, you need to grasp how it works. Blockchain is a shared digital ledger that includes data pertaining to transactions. The information is stored in something called a chunk or a block, and each dataset (block) is connected to the ones that came before it. It creates a long chain of authenticated and verified information, hence the name. Anything stored within the blockchain is incredibly secure because once recorded, the information cannot be modified.
Furthermore, because of its public nature, data in the blockchain is more accessible and easier to verify, track and audit. The technology is already being used in the real world in a variety of ways.
Big data is now becoming more widely recognized as being integral to the functions of most types of business and organizations, and the world of investing is one that is embracing the opportunities that big data can provide. Big data is fundamentally about information and its analysis, and professional investment decisions are based on information, so logically the more information investors have, and the better that data is analyzed, the more accurately they can predict the rise and fall of stocks and shares and other forms of investment.
Traditional forms of investment data
Trading decisions have always been based on the best available knowledge of the time, starting with the information about the company such as its history, prospects, and financial statements. Added to this would be information about the current market, including pricing, margins, growth, and debts. These forms of information have been used since trading began and form the basis of a broker’s buying and selling decisions, but now there are new and powerful sources of information adding to the knowledge base.
New forms of investment data
The growth of the Internet and the communication of information it facilitates is key to the use of big data. There are numerous forms of information that can be captured and analyzed according to requirements, including such diverse aspects as web traffic statistics, filings for patents, spoken information such as interviews, information written in other languages, even images taken by orbiting satellites.
How new forms of data are used
When assessing future prospects for potential investment decisions, traders are looking for the businesses with the best combination of attributes to ensure the optimum return on their investment. By gathering all the information they can and using it to calculate the likelihood that a business will do well, traders can make the most accurate decisions and therefore make more gains than losses.
Big data is adding a new dimension to this pool of information, by providing insights that would have been too complex, or too hard to analyze in the past. New forms of technology mean it’s now possible to analyze information that is hard to quantify, unstructured, or disorganized. The key to unlocking the information contained within the quantities of data it’s now possible to collect is the development of high-speed forms of data capture and analysis.
What do investors gain from big data?
All information adds weight to the credibility of the assessment process for a potential investment. For traders, some of the most valuable insight is gained from being able to more readily define specific investment themes that have previously been too hard to quantify, such as momentum. By using big data to provide the information that may not be available to others in the market, you can get ahead of other traders and act to secure profitable investments ahead of your competitors. The more information you have about a prospective investment, the bigger an advantage you have over the people who are basing their decisions on more traditional data sources. The information harvested from big data is used to find new investment factors that assist traders in improving their stock selection and enable them to test their ideas and strategies to inform future decision-making.
People are still the key to sound investing
Data is only a collection of information, and it still needs to be analyzed and assessed by skilled people who can get to the heart of what the information is telling you. The final decisions have to based on human judgment; informed by the data on the one hand and experienced people who have the skills to interpret it on the other.
It’s like having a ton of dirt and sifting through it to find the specks of gold that will make you a fortune. People like Michael Robinson investor extraordinaire have years of experience in mining successfully for the gold amidst the spoil, and these skills are not ones that machines are yet able to replicate.
What does the future hold?
The development of big data analytics is proceeding at an accelerating rate, and it’s expected that as data collection and analysis becomes ever more sophisticated, big data will make an increasingly significant contribution to investing successfully. There are still challenges, particularly in the emerging markets, where there is a lack of data availability. Companies all across the world are entering the emerging markets, but the standards of the information recording and data quality varies enormously from one country to another. That means there are still substantial gaps in the quantity and quality of data that can be used to assess emerging markets.
Like all challenges, the gaps in data availability present an opportunity for traders who possess the experience and analytic technologies to gain an advantage, by monitoring data sources and assimilating as many of them as possible into the knowledge base. The more big data is utilized to gain insights into markets, the bigger becomes the foundation of knowledge on which decision-making is based. In turn, the analysis of the data and decisions based on the knowledge gained can be further analyzed in light of the results of trading decisions, providing evidence that supports or disputes the actions taken.
There’s no telling how influential big data could become for investors in the future, but there’s no doubt it’s playing an increasingly important part in the investment sector. It’s possible that in time technology will develop to the point where computer systems are able to collect, analyze and cross-reference all the information available so efficiently and effectively that the need for human input will dwindle, or even disappear.
If you consider the introduction of investor bots and artificial intelligence to operate the more straightforward aspects of making investments, then combining the two technologies would seem to be a logical progression.
If humans do get replaced by computers, it won’t be any time soon. For now, investments need the guiding hand of human computing power to realize their potential, but change is on the horizon.
The past few years have involved huge changes in terms of tech and everyone is looking for upgrades to adopt in order to make their lives visibly easier. In-memory computing has been around for quite a while, but specialists are now redefining it. It opens new doors by significantly speeding up processing in various industries. One of the industries affected is represented by customer service.
The extremely fast processing in-memory computing can offer leads to a significant increase in terms of performance, by delivering fast reporting, managing events better, helping entrepreneurs make better decisions and eventually improving the customer satisfaction. In-memory computing is a broad topic that deserves people’s attention at the moment, so here are a few details about it:
A quick explanation
In-memory computing represents a method of processing where all data is stored in RAM. The data is processed in parallel, across devices that are connected, which leads to a faster processing speed. Normally, data is stored in databases which involves some requirements that can make the processing times longer than usual. Moreover, disk databases are limited by the network and disk speeds. In-memory computing enables fast response times by eliminating these limitations and requirements for obtaining optimum performance.
In customer service, making use of this type of technology could entirely change the way processes are being handled. For instance, in-memory computing can cache huge amounts of data, making it accessible in an instant. Storing session data is also possible when using in-memory computing. Analyzing large volumes of data at a high speed is exactly what people who work in customer service need.
Why choose in-memory computing?
Businesses need real-time data processing, meaning that the faster data can be manipulated, the better the results. In this age of hyper advertising, instant analysis and reports, Big Data, IoT and all the applications and platforms that are continuously growing in complexity, the emerge of a better option to handle data was essential. In this sense, in-memory computing comes with a series of benefits that entirely change the game in customer service and not only.
First of all, it is cost-effective. RAM pricing drops every 18 months, which reduces the investment for bigger companies that need efficient and safe storage. The IT departments in companies are constantly pressured to upgrade to a better processing method because of the major benefits it has. Besides the speed advantages of in-memory computing, it can change the game in terms of risk assessment and fraud detection. The interaction with the customer is thus, modified entirely. Harmonizing data by choosing in-memory computing is the best option that companies have at the moment for an upgrade.
Secondly, in-memory computing has simulation capabilities, which means a person is able to simulate what would happen in specific situations. Such a process used to be impossible and – if not impossible – it would take days to complete. Now, simulations can be completed at a glance with in-memory computing.
How quickly can it be implemented?
Well, people believe that making this step further and going for in-memory computing instead of other data storage options is not efficient from the point of view of investment. Actually, the investment will be returned due to the greater productivity that is going to be obtained in time, given the performances of in-memory computing. The industry disruption in customer service came with a new way of both storing and analyzing data at a very fast pace. As mentioned before, IT sectors already feel a pressure coming from companies that already noticed an increase in their revenue and a vastly-growing pipeline. This makes the implementation of in-memory computing in most companies much rapid than expected in the first place. For example, one case study from Pharmacy OneSource reveals how this healthcare SaaS provider increased performance by an incredible 600% by leveraging the power of in-memory computing. Pharmacy OneSource integrated GigaSpaces in-memory computing solution to develop a next-generation patient surveillance platform that allows clinicians to accurately detect risk factors, and perform interventions.
Does it truly resonate with the customers?
In-memory computing became a key in customer service, as it changes the way client engagement is perceived. Debating and assuming are no longer part of the picture, as the power of simulation, reporting and analysis offered by in-memory computing transform the company who uses it into a trusted adviser for its clients. The situation is much more convenient for both the company and the customers who make use of the benefits of such technology. In-memory computing is a next generation method of processing data, that shouldn’t be ignored at any cost.
The rapid advancement of technology is changing the world in new, exciting and unimaginable ways. We’ve gone from brick-and-mortar establishments to online shopping, and from people-driven production lines to fully automated factories. Now it seems one of the next changes will revolve around financial technology – fintech – and next-gen banking technology.
Migrating to Online Banking
Banking technology has been tiptoeing the line between traditional forms of service and next-gen technology for years. Ever since the inception of the automated teller machine, or ATM, in the 1960s, most banks have been willing to offer greater convenience to their customers. According to a recent ABA survey, two-thirds of Americans now use some form of digital banking service.
This trend continued with the rise of the Internet and the consumer embrace of smartphones – and it soon resulted in the birth of the entire fintech sector. Unfortunately, that’s where the support ends – at least from the banking industry.
Some of the later breakthroughs in fintech, like cryptocurrency, don’t benefit from the same level of acceptance as ATMs and smartphones. Nonetheless, consumer interest in digital currencies like Bitcoin is growing.
Examining the Emergence of Bitcoin and Cryptocurrency
Modern and next-gen cryptocurrencies, notably Bitcoin and a few similar platforms, have the potential to cause significant disruption among traditional banking channels. According to the latest statistics, the price of Bitcoin has skyrocketed by a total of $6,620.99 in the last seven years. On a more local scale, it’s increased by nearly $2,000 – or more than 35 percent – in the past year.
Bitcoin isn’t the only cryptocurrency in use today. While many have sprung up recently, mainly to capitalize on the success of Bitcoin, some of these platforms have been around for years. Some of the most popular alternatives to Bitcoin include:
There are hundreds of alternative options available on today’s cryptocurrency market – but some of them have more potential for success than others. While a few individuals and organizations have made it big on their cryptocurrency investments, there are still many untold fortunes to be accumulated by tech-savvy investors and fintech experts.
Seeing Competition from Around the Internet and Across the Globe
Traditional banking and financial institutions are wary of another threat, too. PayPal initially disrupted the banking industry when it first hit the scene, but they continue to introduce upgrades and improvements to their service that attract even more customers. After all, it’s hard to ignore their free accounts that boast no monthly fees and no minimum balance requirement.
Amazon.com is also playing with the idea of adding a bank account service to their already extensive platform. If they do it correctly, Amazon could become a major player in fintech within the next few years. They already offer a site-specific credit card and some items, like their popular Kindle reader, are available in a payment-based structure as opposed to a one-time, outright purchase.
Competition is coming from other countries, too. Germany’s Revolut card, marketed as a luxury accessory in and of itself, provides a bevy of incentives and promotions. Users enjoy cash back in the cryptocurrency of their choosing and more.
France’s breakthrough offering, Shine, was designed specifically with freelance workers in mind. Not only does it provide access to legal and taxation services, but the company has integrated it with other fintech innovations to streamline and standardize processes like billing and invoicing.
Bracing for the Future
Although banks and other financial institutions have historically sided with technology, their recent hesitation to embrace cryptocurrency, PayPal and other fintech systems has many consumers looking for alternatives. While many still rely on traditional banking channels to control their day-to-day finances, banks that don’t give the consumers what they want are bound to drive even more business to their competitors.