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Role of Big Data and AI in Financial Trading

Considering the recent development of AI / ML, it is worth exploring the role of Big Data and AI in revolutionizing financial trading. Internet accessibility, mobile smartphones, social media platforms increase the information exchange. Financial trading is complicated, requires complex calculations that use formulas and other factors that affect are market influencers. Thus the trading for a common man is challenging.

In 2018, the global trade finance market was valued at $ 59,500 million. It is expected to touch the mark of $ 71,000 million by the end of the year 2024.

In 2016, the International Data Corporation (IDC) had predicted that sales of solutions based on big data analytics would reach $187 billion by the year 2019.

What is Big Data & Artificial Intelligence?

Big data is voluminous data in either raw or structured form collected from various sources by the organizations. This data is important for businesses but the processing is complex. It requires technology-based solutions to clean, format, manage data and make it usable. It helps in improving operations and make decisions faster than before due to the insights available.

Artificial Intelligence is the human intelligence programmed in machines. Machine learning, Deep learning, Natural language processing of AI enables recommendations, forecasts, reporting, and business analytics. AI builds intelligence from initial learning and continuous learning.

Big data has an input of raw data and AI pulls input from Big Data. The Big data is the initialization of data processing and AI is the output that can help you to make better business decisions.

Define Relationship between Big Data and AI:

  1. Data Dependent: Both Big data and Artificial Intelligence need data that can benefit organizations
  2. Accurate Predictions: Insights are precise with AI to support Big Data, which is just a collection of data. Manually it is impossible to find sense out eg. Big Data but AI can speed the process to highlight actionable.
  3. Trading performance: Big Data has a detailed track record of each trade, broker, trading company and stock. AI empowers us to utilize this gathered information to draw promising results.

What is Financial Trading?

Financial trading is buying and selling of stocks, bonds, commodities, currencies, derivatives, and securities. The price of a financial instrument is determined by demand and supply. Factors that affect financial trading are market conditions, economic conditions, and market influencers. The process of trading is shortlisting financial instruments, buying or selling via broking houses or online trading platforms.

Benefits of Big Data and AI in Financial Trading:

We no more rely on human intuition, knowledge and data-based decision-making gained importance with the development of technology.

  1. Quantitative analysis and trading
  2. Trends and patterns in trading
  3. Trading opportunity analysis
  4. Minimize risks
  5. Increases accuracy
  6. Better trading decisions
  7. Market sentiments analysis
  8. Financial market analysis

Revolution in Financial Trading by AI and Big Data:

Each step of financial trading cycle is crucial and the technology can increase the profitability or at least the probability of success. Changes in the financial market are faster than a blink of an eye and at times stagnant. This dynamic or sluggish behavior of the market can tempt traders to take actions out of impatience. This is where advanced technologies play a vital role.

How big data and AI has revolutionized financial trading?

The massive data stored is formatted to benefit data analysis and analytics. AI discloses valuable insights from the data pertaining to the industry.

Intelligent algorithms designed using Big Data and Artificial Intelligence can help us accomplish our financial trading goals.

Distinct information about the trading patterns, market trends, market reviews, and potential trades is possible due to Big Data. AI can predict using this data stored for trading patterns, market trends, etc.

The growth of Big Data leads to better AI solutions. It can encompass more data to learn from and analyze. A combination of AI and Big Data will be in demand as people have started tasting the fruits of this technology. Their interdependencies provide interesting results. AI brings reasoning power, automates learning and allows scheduling tasks relating to financial trading.

Measurable Trading Growth: Financial trading with AI technology-based algorithms will foresee quantitative trading. Growth in the number of traders and trading activities is the result of data-driven intelligent trading systems. Quality data, proper processing and connecting it with applications facilitate users in prompt decision-making. Programs and AI tools have left aside the manual trading strategies that once prevailed. Accurate outcomes are one of the major reasons for using Big data and AI in financial trading.

Offerings: Various applications that AI introduced to the field of financial trading are systems that recommend stocks, an investment able period, and signals buying and selling. Predict price movements, annual returns, link current world affairs and its impact on the markets. It can even help in portfolio management. It can predict new investment models and introduce profitable algorithms.

Reliance: Customers can rely on the mechanisms developed to meet the financial goals of long term and short term. Secured transacting and faster dealings increments the transactions to prevent frauds and meets the requirements of financial market compliances. Surveillance of trading platforms by the stock exchange includes the micro-level check on the technological tools that can disrupt the process.

Bots advisory services: The chatbots assist users in making financial decisions keeping customer preferences in mind. Suggestions and solutions presented by them are free of bias and does not manipulate humans. The time, energy and costs involved are lesser compared to the human agents that provide service.

Risk Mitigation: Human errors and manual processing issues are diminishing with the new technology financial trading implemented. Big data and AI improved the trading process right from reviewing stocks, placing an order, execution of the order, and delivery. We can schedule notifications, information, and confirmations using AI. Fraud detected is analyzed by the exchanges and take corrective measures or levy penalties on the fraudulent parties.

Sentiment Analysis: Evaluating market sentiments requires opinion mining from sources like social media posts, blogs, articles, etc. This huge data processing uses advanced data mining tools to produce a summary of performance on stocks and commodities and influencing market trends.

Transaction Data: Enrichment of transactional data can help customers monitor the stocks, current prices, futuristic price, and trade better. This data shapes up as historic data after a while and the accuracy of this matter in creating efficient algorithms for financial trading.

Market Predictions: There are no complete predictive solutions in financial trading. The tools that AI provides can convincingly improve the trading abilities, reduce the chances of loss-making, and track the market movements. If, in case 100% accuracy is achievable in predicting the markets the trades will never accomplish. The situation of no profit and no loss-cannot be ideal for any business. A market prediction in this industry is its volatility and stability probabilities. Precautionary actions based on predictions or safe trading as a practice can help traders and investors.

The future of financial trading with Artificial Intelligence:

Secured trading is a result of the numerous calculations that AI performs in negligible time. Absolutely eradicating the past methods is possible when current solutions are effective. AI performs operational transactions, enables high- frequency trades, highlights unprofitable transactions, and most important is it keeps learning to improve.

  1. Automated Trading
  2. Fundamental Analysis
  3. Triggers

The drawback is that we just cannot predict future prices based on historic data; hence at least partial automation is possible. AI can assist in creating a trading account and completing the account opening procedure, send a welcome kit, and introduce the user to trading with training videos.

The trading strategy created and modified with the help of technology scans data and market patterns. It helps predict intraday price movements and recommends trading actions. Queries are resolved and responded accurately based on historic data AI inspects. Intelligent search platforms and tools generate valuable insights based on market behavior to improving trading.

The finance sector is full of opportunities for investors and companies. If we implement Big Data and Artificial Intelligence technology in several fields, the difference in results is noticeable. Execute large trading orders in single or multiple groups using AI. Scheduled trading can save time and efforts of human beings. The trade operations are AI automated, they can control activities that are of repetitive nature for each trade that takes place. Manage the calculations, processing of receivables and payables, account balance, stock holdings.

AI can help finance sector and financial trading activities to provide customer service 24×7. It can process settlements, resolve basic level issues, and share the latest updates to the customers. Investing decisions if AI-supported can benefit the user and it can act as the main investment qualifier for the preferences set by them. Observe the stock performance risks and set targets for the risk capacities we hold or price to profit levels.

Conclusion:

Big data and Artificial Intelligence are almost inseparable, especially with their unique abilities that help businesses. Like knowledge is available everywhere the advantages of Big Data and AI are widespread. The established facts that the finance industry uses this technology extensively is enough to draw advantages and having a competitive edge over others. Humans along with machine help can lead a better financial life.

Hottest Trends in Big Data

Before we begin exploring the hot trends of big data, it is important to understand what big data truly represents. The Big data is the tremendous volumes of information produced from various industry spaces. Enormous information, for the most part, contains information accumulation, information examination, and information usage forms. As the years progressed, there’s been an adjustment in the enormous information examination patterns – organizations have swapped the monotonous departmental methodology with an information-based approach.

This has seen more noteworthy utilization of spry innovations alongside uplifted interest for cutting edge investigation. Remaining in front of the challenge currently expects organizations to send propelled information-driven investigation.

When it previously came into the image, enormous information was basically sent by greater organizations that could manage the cost of the innovation when it was costly. At present, the extent of big data has changed to the degree that undertakings both little and enormous depend on huge information for wise examination and business bits of knowledge.

This has brought about the development of enormous information sciences and technology at a truly quick pace. The most appropriate case of this development is the cloud which has let even private ventures exploit the most recent innovation and trends.

Hottest Trends in Big Data

BIG DATA ANALYSIS

Huge information investigation is the regularly mind-boggling procedure of analyzing enormous and differed informational collections, or colossal information, to reveal data, for example, concealed examples, obscure relationships, showcase patterns, and client inclinations – that can enable associations to settle on educated business choices.

On a wide scale, information examination advancements and technology procedures give a way to break down informational indexes and reach inferences about them which help associations settle on educated business choices. Business knowledge (BI) inquiries answer fundamental inquiries concerning business tasks and execution.

Huge information examination is a type of cutting edge investigation, which includes complex applications with components, for example, prescient models, factual calculations and consider the possibility that examination controlled by superior examination frameworks.

Big data investigation advancements and technology 

Unstructured and semi-organized information types regularly don’t fit well in conventional information distribution centers that depend on social databases situated to organized informational collections.

Further, information stockrooms will most likely be unable to deal with the preparing requests presented by sets of huge information that should be refreshed every now and again or even constantly, as on account of continuous information on stock exchanges, the online exercises of site guests or the exhibition of portable applications.

10 HOT TRENDS OF BIG DATA ANALYSIS FOR 2019

Quantum Computing

Industry insiders accept that the fate of technology has a place with the organization that fabricates the main quantum PC. Nothing unexpected that each technology mammoth including Microsoft, Intel, Google, and IBM, are dashing for the top spot in quantum registering. All in all, what’s the enormous draw with quantum registering?

It permits consistent encryption of information, climate expectation, answers for long-standing medicinal issues and afterward some more. Quantum registering permits genuine discussions among clients and associations. There’s likewise the guarantee of patched up money related displaying that enables associations to create quantum processing segments alongside applications and calculations.

Edge Computing

The idea of edge processing among other enormous information patterns didn’t simply develop yesterday. System execution gushing utilizes edge processing pretty consistently even today. To spare information on the nearby server near the information source, we rely upon the system transfer speed. That is made conceivable with edge registering. Edge registering stores information closer to the end clients and more remote from the storehouse arrangement with the handling happening either in the gadget or in the server farm. This strategy has been under development and has been in trend in 2019.

Open Sourcing

Individual small scale specialty engineers will constantly step up their game in 2019. That implies we will see increasingly more programming devices and free information become accessible on the cloud. This will massively profit little associations and new companies in 2019 and in the future. More dialects and stages like the GNU venture, R, will hoard the technology spotlight in the year to come. The open-source wave will enable little associations to eliminate costly custom improvement.

Data Quality Management (DQM)

The examination slants in information quality developed significantly this previous year. The advancement of business insight to break down and concentrate an incentive from the endless wellsprings of information that we accumulate at a high scale brought close by a lot of blunders and low-quality reports: the dissimilarity of information sources and information types added some greater multifaceted nature to the information coordination process.

An overview directed by the Business Application Research Center expressed the Data quality administration as the most significant pattern in 2019. It isn’t just critical to accumulate as much data conceivable, yet the quality and the setting where information is being utilized and deciphered fills in as the fundamental concentration for the eventual fate of business insight.

Artificial Intelligence

We are developing from static, aloof reports of things that have just happened to proactive investigation with live dashboards helping organizations to perceive what’s going on at consistently and give alarms when something isn’t the manner by which it ought to be. Our answer at datapine incorporates an AI calculation dependent on the most progressive neural systems, giving a high precision in abnormality recognition as it gains from chronicled patterns and examples. That way, any startling occasion will be advised and will send you an alarm.

We have likewise built up another component called Insights, additionally AI-based, that completely breaks down your dataset naturally without requiring an exertion on your end. You basically pick the information source you need to investigate and the segment/variable (for example, Revenue) that our choice emotionally supportive network programming should concentrate on.

At that point, estimations will be run and return to you with development/patterns/conjecture, esteem driver, key fragments relationships, oddities, and a consider the possibility that examination. That is an amazing time gain as what is normally taken care of by an information researcher will be performed by a device, furnishing business clients with access to top-notch bits of knowledge and a superior comprehension of their data, even without a solid IT foundation.

Connected Clouds

The pervasiveness of the cloud is the same old thing for anyone who keeps awake-to-date with BI patterns. In 2019 the cloud will proceed with its rule with an ever-increasing number of organizations moving towards it because of the expansion of cloud-put together apparatuses accessible with respect to the market. In addition, business people will figure out how to grasp the intensity of cloud investigation, where the greater part of the components – information sources, information models, preparing applications, registering power, expository models and information stockpiling – are situated in the cloud.

Booming IoT Networks

Like it’s experienced 2018, the Internet of Things (IoT) will keep on drifting through 2019, with yearly incomes arriving at path past $300 billion by 2020. The most recent research reports show that the IoT market will develop at a 28.5% CAGR. Associations will rely upon progressively organized information that focuses to accumulate data and increase more keen business experiences.

Unstructured or Dark Data

Dim information alludes to any information that is basically not a piece of business examination. These bundles of information originate from a large number of computerized organize tasks that are not used to accumulate bits of knowledge or decide. Since information and investigation are progressively increasing pieces of the everyday parts of our associations, there’s something that we as a whole should get it. Losing a chance to think about unexplored information is a big deal of potential security hazard.

Continuous Intelligence

Progressively, organizations need to work in a powerful situation and improve their choices continuously. With the assistance of nonstop information and the previously mentioned enlarged investigation, organizations will have the option to send what Gartner calls ‘Ceaseless insight’ that basically enables organizations to dissect approaching information settings progressively, by utilizing frictionless process duration to get constant business esteem from information and endorsing quick choices to improve results.

Basically, every client connection can help improve the following one. That is the intensity of persistent knowledge. It can perform continuous examination and recommend arrangements utilizing choice robotization and increased investigation.

Ceaseless knowledge will be in trend in the following year or two, with organizations receiving it to send better arrangements progressively. Regardless of what number of information sources the information streams in from, or how immense or complex it is, this cutting edge ML-driven methodology will enable organizations to quicken investigation and basic leadership.

Conversational Analytics

Normal language handling, a subset of AI and ML has quickly turned out to be ordinary and by 2020, half of every single expository inquiry will be created by voice. In 2020, additional clients will collaborate with chatbots and brilliant speakers like Alexa and Google Home.

Social occasion voice information and breaking down it will end up being a vital piece of each business’ information investigation technique. Investigation of conversational information can be dubious and the technology is as yet developing. Nonetheless, voice technology is particularly a typical piece of life today and conversational examination will see more extensive selection without a doubt.

Difference between Data Science and Big Data Analytics

What is Data Science?

Data science is a scientific methodology, to obtain actionable insights from large unprocessed data sets and structured data. It focuses on uncovering things that we do not know. It is a source of innovative solutions for our problems.

It uses a variety of models and means of extracting and processing information. It analyses data on the concept of mathematics and statistics with the help of automated tools. Cleanse data, find data connections, analyse, and predict potential trends. Manipulate, identify disconnected data points, and explore the probabilities and combinations.

It encourages us to try distinct ways to analyze information. Capture data, program it and solve specific problems with data science. It provides a new perspective towards data, enhances usability to provide insights. Data science can support accurate business decisions and tackle big data.

Data scientists use programming languages like SQL, Python, Java, R, and Scala for multiple analytical functions. They write algorithms, build statistical and predictive models to analyze data.

What is Big Data Analytics?

What is Big Data Analytics?

Big Data effectively processes enormous data, extensive information, and complex data that traditional applications cannot attempt. Big data consists of a variety of structured and unstructured data. Introduce cost-effective and latest forms of information to enable enhanced business insights. It can highlight market trends, customer preferences, customer behavior, and buying patterns

Data analytics can help in the organization’s goals by measuring the current and past events and plans form future events. It performs statistical analysis to create a meaningful presentation of data by connecting patterns to strategize business. It eases immediate improvements, problem-solving, and respond to specific concern area. Data analysts require knowledge of Pig, Hive, R, SQL, and Python.

Data Analytics needs well-defined data sets to address particular problematic areas of business. For better results, the data analysts need to have technical expertise and knowledge of mathematics and statistics. Data mining, database management, data analysis, and skills to convey the quantitative results achieved from data.

Data Analysis has important role in Data Science; it performs a variety of tasks such as collecting and organizing data. It assists in presenting the data in charts, graphs, comparative tables, and build relational databases for organizations.

Data analysis and data analytics sound similar, data analysis includes everything a data analyst practices compiling and analyzing data. Whereas data analytics is a subsection of data analysis, it uses technical tools and data analysis techniques to achieve business objectives.

What is the Difference between Data Science and Big Data Analytics?

Data Science is an integral part of Artificial Intelligence, Machine Learning, Search Engine Engineering, and Corporate Analytics. Big Data Analytics is widely used to find actionable items in fields such as healthcare, gaming, and travel industries.

With a greater scope of data, science helps in data mining for varied and unique fields. Big Data analysis mainly focuses on processing large data. Simplification of the differentiation, data science provides thought for questions you should ask and big data analytics helps in discovering answers to questions.

Data science lays a strong foundation by initiating a focus on future trends, improves observations of data movements, and provides potential insights. Big data analytics provides the path for practical application of actionable insights.

Data analytics examines large data sets and data scientists create algorithms, work on creating new models for prediction.

Are there any Similarities between Data Science and Big Data Analytics?

Are there any similarities between Data Science and Big Data analytics?

Similarity, the interconnectivity of Data Science and Big Data Analytics brings wonderful results to benefit organizations. Their dependency can affect the overall quality of action strategy and consequences based on those actions. Companies never apply both Data Science and Big Data Analytics together in every situation yet are useful for different purposes. It can help companies in the technological change they are about to have. Both can help companies to understand the data better.

The relationship between them can have a positive impact on the company.

  • In 2019, the big data market likely to grow by 20% and the big data analytics market headed towards the target of $103 billion by 2023.
  • Worldwide the companies in various sectors using big data technology are telecommunications 94.5%, insurance 83%, advertising 77%, financial services 70%, healthcare 63%, and technology 57.5%.
  • Nearly, 81% of data scientists analyze data of non-IT industries.
  • About 90% of enterprise analytics stated that data and analytics are key elements of initiatives taken by their organization towards digital transformation.
  • Data-driven organizations have 23 times more chances of customer acquisition, and 6 times more likely to retain the customer.
  • Businesses are motivated to get more insights, proves the 30% per year growth in insight-driven organizations.
  • By 2020, we can expect 2.7 million job listings for data science and data analytics.

Applications and Benefits of Data Science and Big Data Analytics:

Tremendous benefits of data science are noticeable with the number of industries involved in technological developments. Data science is a driving force for business improvement and expansion.

  • Agriculture: Surprisingly data science bias-free thus can benefit even sectors that were not data-driven. It is a reliable source for suggestive actions for water frequency or quantity manure required, soil suitable crops, the precise amount of seeds needed, etc. Big data analytics can be of great assistance to farmers in yield prediction, crop failure symptoms due to weather changes, food safety, and spoilage prevention and much more. Companies can rest assured of crop quality, precautions taken by farmers during harvesting and packaging, and on delivery possibilities.
  • Aviation & Travel: Data science can help in reducing operating costs, maximizing bookings, and improving profits. Technology can help flyers in taking decisions of routes, connecting flights, and seats before booking. This is the service industry, for better performance in various areas; companies adopt data science. Big data analytics can enhance customer experience through information shared by the company. Users can find travel discounts, delays, customized packages, open tickets, and personalized air and other travel recommendations, etc. Companies can get statistical and predictive analysis about the selective area such as profits against a particular marketing campaign. Social media activities and its positive impact or rates of conversion are some of the insights that can help in cost reduction.
  • Customer Acquisition: The complete process is of high importance and creates high value for businesses. Data Science can help identify business opportunities, amend marketing strategies, and design marketing campaigns. Redefining strategies, redesigning campaigns and re-targeting audiences is possible with data science. Big data analytics highlights the pain and profit points for business. Identify the best possible method for customer acquisition and improve on the basis of data analysis. Return on Investments, profitability, and other important business ratios presented by big data analytics in the simplest form. Big Data of the telecommunications industry can help in getting new subscribers, retaining existing customers, approaching current subscribers to serve based on their priorities, frequency of recharge, package preferences, and use of internet packs, etc.
  • Education: Implementing data science in this sector can help in the student admission process; take calculated decisions, check enrollment rates, dropouts from institutes, etc. Big data analysis can compare the current and past year’s student data, issues in process or course wise predictions of student performance, etc. Colleges and educational institutes can perform various analyses using the data and plan the changes required. Big data analytics can evaluate students for admissions in other courses based on their eligibility, preferences, or inclination.
  • Healthcare: Data science collects data from various applications, wearable gear, and patient data by monitoring constantly. It helps in preventing potential health problems. The pharma research and new medicine coding are eased with data science. It can predict illness, frequent hospitalizations. Hospitals can use it for new cases, to diagnose patients accurately, and take quick decisions and save lives. Big data analytics can help in cost reduction on treatments, treat maximum patients, improve medical services and the estimations needed to serve better with exciting machines.
  • Internet Search: The search engines use data science to write effective algorithms to deliver the accurate results of search queries in milliseconds. Big data analytics can recommend users on their search, product, or services, or show preference based results. Search engines have frequent visitors, their view history, specific requirements, and many preferences. The speedy suggestions can save time and increases the chances of someone clicking the links. Even digital advertisements have strong data science algorithms and they are effective than traditional methods of advertising. The user experience and profitability of companies improve with the help of big data analysis.
  • Financial Services: Banking, insurance, and financial institutes have to deal with huge data and the complexity, data science efficiently deals with. Big data analytics allows us to focus on relevant data from the loads of massive data that influence customer analytics. It helps in operational issues identification, fraud prevention and improved recommendations for customers.

Now with the scope of data science and big data analytics, we can find why customers are loyal or why they leave you. Find what works in your favor and against you. Know more about customer expectations and if you can meet them. Find more of such indications are available at varied data points that lie on websites, e-commerce sites, mobile apps, and social media interactions.

Data Science and Big Data Analytics consider facts thus it empowers us to plan, face competition and perform better. We can proactively respond to requests and anticipate the needs of our customers. Deliver relevant products with no anticipations but data-supported predictions. Link innovation in product and service with a set of customer expectations and new demands that generate with time and technology.

Services can be personalized and respond in real-time for faster service. Optimize and improve operational efficiency and productivity by using various techniques for analytics for continuous change and growth. Risk mitigation and fraud prevention provides added security.

Data Science increases abilities to understand the customers and their decision-making patterns. Big Data analysis helps in anticipating the potential that lies in the future based on current data and its predictions.

Conclusion:

Modern businesses generate huge data and taking actions based on valuable insights is extremely unavoidable in order to remain in the competition. By 2021, organizations using big data analysis will be in a position to take a share of $1.8 trillion than the ones less informed. We can look into the data relevancy, using before its stale, reduce the customer experience gaps and deliver in real-time if we are committed to using interweave technology with business. Being a data-driven organization is an intelligent choice.

Relationship between Big Data, Data Science and ML

Data is all over the place. Truth be told, the measure of advanced data that exists is developing at a fast rate, multiplying like clockwork, and changing the manner in which we live. Supposedly 2.5 billion GB of data was produced each day in 2012.

An article by Forbes states that Data is becoming quicker than any time in recent memory and constantly 2020, about 1.7MB of new data will be made each second for each person on the planet, which makes it critical to know the nuts and bolts of the field in any event. All things considered, here is the place of our future untruths.

Machine Learning, Data Science and Big Data are developing at a cosmic rate and organizations are presently searching for experts who can filter through the goldmine of data and help them drive quick business choices proficiently. IBM predicts that by 2020, the number of employments for all data experts will increment by 364,000 openings to 2,720,000

Big Data Analytics

Big Data

Enormous data is data yet with a tremendous size. Huge Data is a term used to portray an accumulation of data that is enormous in size but then developing exponentially with time. In short such data is so huge and complex that none of the customary data the board devices can store it or procedure it productively.

Kinds Of Big Data

1. Structured

Any data that can be put away, got to and handled as a fixed organization is named as structured data. Over the timeframe, ability in software engineering has made more noteworthy progress in creating strategies for working with such sort of data (where the configuration is notable ahead of time) and furthermore determining an incentive out of it. Be that as it may, these days, we are predicting issues when the size of such data develops to an immense degree, regular sizes are being in the anger of different zettabytes.

2. Unstructured

Any data with obscure structure or the structure is delegated unstructured data. Notwithstanding the size being colossal, un-organized data represents various difficulties as far as its handling for inferring an incentive out of it. A regular case of unstructured data is a heterogeneous data source containing a blend of basic content records, pictures, recordings and so forth. Presently day associations have an abundance of data accessible with them yet lamentably, they don’t have a clue how to infer an incentive out of it since this data is in its crude structure or unstructured arrangement.

3. Semi-Structured

Semi-structured data can contain both types of data. We can see semi-organized data as organized in structure however it is really not characterized by for example a table definition in social DBMS. The case of semi-organized data is a data spoken to in an XML document.

Data Science

Data science is an idea used to handle huge data and incorporates data purifying readiness, and investigation. A data researcher accumulates data from numerous sources and applies AI, prescient investigation, and opinion examination to separate basic data from the gathered data collections. They comprehend data from a business perspective and can give precise expectations and experiences that can be utilized to control basic business choices.

Utilizations of Data Science:

  • Internet search: Search motors utilize data science calculations to convey the best outcomes for inquiry questions in a small number of seconds.
  • Digital Advertisements: The whole computerized showcasing range utilizes the data science calculations – from presentation pennants to advanced announcements. This is the mean explanation behind computerized promotions getting higher CTR than conventional ads.
  • Recommender frameworks: The recommender frameworks not just make it simple to discover pertinent items from billions of items accessible yet additionally adds a great deal to the client experience. Many organizations utilize this framework to advance their items and recommendations as per the client’s requests and the significance of data. The proposals depend on the client’s past list items

Machine Learning

It is the use of AI that gives frameworks the capacity to consequently take in and improve for a fact without being unequivocally customized. AI centers around the improvement of PC programs that can get to data and use it learn for themselves.

The way toward learning starts with perceptions or data, for example, models, direct involvement, or guidance, so as to search for examples in data and settle on better choices later on dependent on the models that we give. The essential point is to permit the PCs to adapt naturally without human mediation or help and alter activities as needs are.

ML is the logical investigation of calculations and factual models that PC frameworks use to play out a particular assignment without utilizing unequivocal guidelines, depending on examples and derivation. It is viewed as a subset of man-made reasoning. AI calculations fabricate a numerical model dependent on test data, known as “preparing data”, so as to settle on forecasts or choices without being expressly modified to play out the assignment.

The relationship between Big Data, Machine Learning and Data Science

Since data science is a wide term for various orders, AI fits inside data science. AI utilizes different methods, for example, relapse and directed bunching. Then again, the data’ in data science might possibly develop from a machine or a mechanical procedure. The principle distinction between the two is that data science as a more extensive term centers around calculations and measurements as well as deals with the whole data preparing procedure

Data science can be viewed as the consolidation of different parental orders, including data examination, programming building, data designing, AI, prescient investigation, data examination, and the sky is the limit from there. It incorporates recovery, accumulation, ingestion, and change of a lot of data, on the whole, known as large data.

Data science is in charge of carrying structure to huge data, scanning for convincing examples, and encouraging chiefs to get the progressions adequately to suit the business needs. Data examination and AI are two of the numerous devices and procedures that data science employments.

Data science, Big data, and AI are probably the most sought after areas in the business at the present time. A mix of the correct ranges of abilities and genuine experience can enable you to verify a solid profession in these slanting areas.

In this day and age of huge data, data is being refreshed considerably more every now and again, frequently progressively. Moreover, much progressively unstructured data, for example, discourse, messages, tweets, websites, etc. Another factor is that a lot of this data is regularly created autonomously of the association that needs to utilize it.

This is hazardous, in such a case that data is caught or created by an association itself, at that point they can control how that data is arranged and set up checks and controls to guarantee that the data is exact and complete. Nonetheless, in the event that data is being created from outside sources, at that point there are no ensures that the data is right.

Remotely sourced data is regularly “Untidy.” It requires a lot of work to clean it up and to get it into a useable organization. Moreover, there might be worries over the solidness and on-going accessibility of that data, which shows a business chance on the off chance that it turns out to be a piece of an association’s center basic leadership ability.

This means customary PC structures (Hardware and programming) that associations use for things like preparing deals exchanges, keeping up client record records, charging and obligation gathering, are not appropriate to putting away and dissecting the majority of the new and various kinds of data that are presently accessible.

Therefore, in the course of the most recent couple of years, an entire host of new and intriguing equipment and programming arrangements have been created to manage these new kinds of data.

Specifically, colossal data PC frameworks are great at:

  • Putting away gigantic measures of data:  Customary databases are constrained in the measure of data that they can hold at a sensible expense. Better approaches for putting away data as permitted a practically boundless extension in modest capacity limit.
  • Data cleaning and arranging:  Assorted and untidy data should be changed into a standard organization before it tends to be utilized for AI, the board detailing, or other data related errands.
  • Preparing data rapidly: Huge data isn’t just about there being more data. It should be prepared and broke down rapidly to be of most noteworthy use.

The issue with conventional PC frameworks wasn’t that there was any hypothetical obstruction to them undertaking the preparing required to use enormous data, yet by and by they were excessively moderate, excessively awkward and too costly to even consider doing so.

New data stockpiling and preparing ideal models, for example, have empowered assignments which would have taken weeks or months to procedure to be embraced in only a couple of hours, and at a small amount of the expense of progressively customary data handling draws near.

The manner in which these ideal models does this is to permit data and data handling to be spread crosswise over systems of modest work area PCs. In principle, a huge number of PCs can be associated together to convey enormous computational capacities that are similar to the biggest supercomputers in presence.

ML is the critical device that applies calculations to every one of that data and delivering prescient models that can disclose to you something about individuals’ conduct, in view of what has occurred before previously.

A decent method to consider the connection between huge data and AI is that the data is the crude material that feeds the AI procedure. The substantial advantage to a business is gotten from the prescient model(s) that turns out toward the part of the bargain, not the data used to develop it.

Conclusion

AI and enormous data are along these lines regularly discussed at the same moment, yet it is anything but a balanced relationship. You need AI to get the best out of huge data, yet you don’t require huge data to be capable use AI adequately. In the event that you have only a couple of things of data around a couple of hundred individuals at that point that is sufficient to start building prescient models and making valuable forecasts.

Big Data Analytics Tools

Big Data is a large collection of data sets that are complex enough to process using traditional applications. The variety, volume, and complexity adds to the challenges of managing and processing big data. Mostly the data created is unstructured and thus more difficult to understand and use it extensively. We need to structure the data and store it to categorize for better analysis as the data can size up to Terabytes.

Data generated by digital technologies are acquired from user data on mobile apps, social media platforms, interactive and e-commerce sites, or online shopping sites. Big Data can be in various forms such as text, audio, video, and images. The importance of data established from the facts as its creation itself is multiplying rapidly. Data is junk if the information is not usable, its proper channelization along with a purpose attached to it.
Data at your fingertips eases and optimizes the business performance with the capability of dealing with situations that need severe decisions.

Interesting Statistics of Big Data:

What is Big Data Analytics?

Big data analytics is a complex process to examine large and varied data sets that have unique patterns. It introduces the productive use of data.
It accelerates data processing with the help of programs for data analytics. Advanced algorithms and artificial intelligence contribute to transforming the data into valuable insights. You can focus on market trends, find correlations, product performance, do research, find operational gaps, and know about customer preferences.
Big Data analytics accompanied by data analytics technologies make the analysis reliable. It consists of what-if analysis, predictive analysis, and statistical representation. Big data analytics helps organizations in improving products, processes, and decision-making.

The importance of big data analytics and its tools for Organizations:

  1. Improving product and service quality
  2. Enhanced operational efficiency
  3. Attracting new customers
  4. Finding new opportunities
  5. Launch new products/ services
  6. Track transactions and detect fraudulent transactions
  7. Effective marketing
  8. Good customer service
  9. Draw competitive advantages
  10. Reduced customer retention expenses
  11. Decreases overall expenses
  12. Establish a data-driven culture
  13. Corrective measures and actions based on predictions
Insights by Big Data Analytics

For Technical Teams:

  1. Accelerate deployment capabilities
  2. Investigate bottlenecks in the system
  3. Create huge data processing systems
  4. Find better and unpredicted relationships between the variables
  5. Monitor situation with real-time analysis even during development
  6. Spot patterns to recommend and convert to chart
  7. Extract maximum benefit from the big data analytics tools
  8. Architect highly scalable distributed systems
  9. Create significant and self-explanatory data reports
  10. Use complex technological tools to simplify the data for users

Data produced by industries whether, automobile, manufacturing, healthcare, travel is industry-specific. This industry data helps in discovering coverage and sales patterns and customer trends. It can check the quality of interaction, the impact of gaps in delivery and make decisions based on data.

Various analytical processes commonly used are data mining, predictive analysis, artificial intelligence, machine learning, and deep learning. The capability of companies and customer experience improves when we combine Big Data to Machine Learning and Artificial Intelligence.

Big Data Analytics Processes

Predictions of Big Data Analytics:

  1. In 2019, the big data market is positioned to grow by 20%
  2. Revenues of Worldwide Big Data market for software and services are likely to reach $274.3 billion by 2022.
  3. The big data analytics market may reach $103 billion by 2023
  4. By 2020, individuals will generate 1.7 megabytes in a second
  5. 97.2% of organizations are investing in big data and AI
  6. Approximately, 45 % of companies run at least some big data workloads on the cloud.
  7. Forbes thinks we may need an analysis of more than 150 trillion gigabytes of data by 2025.
  8. As reported by Statista and Wikibon Big Data applications and analytic’s projected growth is $19.4 billion in 2026 and Professional Services in Big Data market worldwide is projected to grow to $21.3 billion by 2026.

Big Data Processing:

Identify Big Data with its high volume, velocity, and variety of data that require a new high-performance processing. Addressing big data is a challenging and time-demanding task that requires a large computational infrastructure to ensure successful data processing and analysis.

Big Data Processing

Data processing challenges are high according to the Kaggle’s survey on the State of Data Science and Machine Learning, more than 16000 data professionals from over 171 countries. The concerns shared by these professionals voted for selected factors.

  1. Low-quality Data – 35.9%
  2. Lack of data science talent in organizations – 30.2%
  3. Lack of domain expert input – 14.2%
  4. Lack of clarity in handling data – 22.1%
  5. Company politics & lack of support – 27%
  6. Unavailability of difficulty to access data – 22%
  7. These are some common issues and can easily eat away your efforts of shifting to the latest technology.
  8. Today we have affordable and solution centered tools for big data analytics for SML companies.

Big Data Tools:

Selecting big data tools to meet the business requirement. These tools have analytic capabilities for predictive mining, neural networks, and path and link analysis. They even let you import or export data making it easy to connect and create a big data repository. The big data tool creates a visual presentation of data and encourages teamwork with insightful predictions.

Big Data Tools

Microsoft HDInsight:

Azure HDInsight is a Spark and Hadoop service on the cloud. Apache Hadoop powers this Big Data solution of Microsoft; it is an open-source analytics service in the cloud for enterprises.

Pros:

  • High availability of low cost
  • Live analytics of social media
  • On-demand job execution using Azure Data Factory
  • Reliable analytics along with industry-leading SLA
  • Deployment of Hadoop on a cloud without purchasing new hardware or paying any other charges

Cons:

  • Azure has Microsoft features that need time to understand
  • Errors on loading large volume of data
  • Quite expensive to run MapReduce jobs on the cloud
  • Azure logs are barely useful in addressing issues

Pricing: Get Quote

Verdict: Microsoft HDInsight protects the data assets. It provides enterprise-grade security for on-premises and has authority controls on a cloud. It is a high productivity platform for developers and data scientists.

Cloudera:

Distribution for Hadoop: Cloudera offers the best open-source data platform; it aims at enterprise quality deployments of that technology.

Pros:

  • Easy to use and implement
  • Cloudera Manager brings excellent management capabilities
  • Enables management of clusters and not just individual servers
  • Easy to install on virtual machines
  • Installation from local repositories

Cons:

  • Data Ingestion should be simpler
  • It may crash in executing a long job
  • Complicating UI features need updates
  • Data science workbench can be improved
  • Improvement in cluster management tool needed

Pricing: Free, get quotes for annual subscriptions of data engineering, data science and many other services they offer.

Verdict: This tool is a very stable platform and keeps on continuously updated features. It can monitor and manage numerous Hadoop clusters from a single tool. You can collect huge data, process or distribute it.

Sisense:

This tool helps to make Big Data analysis easy for large organizations, especially with speedy implementation. Sisense works smoothly on the cloud and premises.

Pros:

  • Data Visualization via dashboard
  • Personalized dashboards
  • Interactive visualizations
  • Detect trends and patterns with Natural Language Detection
  • Export Data to various formats

Cons:

  • Frequent updates and release of new features, older versions are ignored
  • Per page data display limit should be increased
  • Data synchronization function is missing in the Salesforce connector
  • Customization of dashboards is a bit problematic
  • Operational metrics missing on dashboard

Pricing: The annual license model and custom pricing are available.

Verdict: It is a reliable business intelligence and big data analytics tool. It handles all your complex data efficiently and live data analysis helps in dealing with multiparty for product/ service enhancement. The pulse feature lets us select KPIs of our choice.

Periscope Data:

This tool is available through Sisense and is a great combination of business intelligence and analytics to a single platform.
Its ability to handle unstructured data for predictive analysis uses Natural Language Processing in delivering better results. A powerful data engine is high speed and can analyze any size of complex data. Live dashboards enable faster sharing via e-mail and links; embedded in your website to keep everyone aligned with the work progress.

Pros:

  • Work-flow optimization
  • Instant data visualization
  • Data Cleansing
  • Customizable Templates
  • Git Integration

Cons:

  • Too many widgets on the dashboard consume time in re-arranging.
  • Filtering works differently, should be like Google Analytics.
  • Customization of charts and coding dashboards requires knowledge of SQL
  • Less clarity in display of results

Pricing: Free, get a customized quote.

Verdict: Periscope data is end-to-end big data analytics solutions. It has custom visualization, mapping capabilities, version control, and two-factor authentication and a lot more that you would not like to miss out on.

Zoho Analytics:

This tool lets you function independently without the IT team’s assistance. Zoho is easy to use; it has a drag and drop interface. Handle the data access and control its permissions for better data security.

Pros:

  • Pre-defined common reports
  • Reports scheduling and sharing
  • IP restriction and access restriction
  • Data Filtering
  • Real-time Analytics

Cons:

  • Zoho updates affect the analytics, as these updates are not well documented.
  • Customization of reports is time-consuming and a learning experience.
  • The cloud-based solution uses a randomizing URL, which can cause issues while creating ACLs through office firewalls.

Pricing: Free plan for two users, $875, $1750, $4000, and $15,250 monthly.

Verdict: Zoho Analytics allows us to create a comment thread in the application; this improves collaboration between managers and teams. We recommended Zoho for businesses that need ongoing communication and access data analytics at various levels.

Tableau Public:

This tool is flexible, powerful, intuitive, and adapts to your environment. It provides strong governance and security. The business intelligence (BI) used in the tool provides analytic solutions that empower businesses to generate meaningful insights. Data collection from various sources such as applications, spreadsheets, Google Analytics reduces data management solutions.

Pros:

  • Performance Metrics
  • Profitability Analysis
  • Visual Analytics
  • Data Visualization
  • Customize Charts

Cons:

  • Understanding the scope of this tool is time-consuming
  • Lack of clarity in using makes it difficult to use
  • Price is a concern for small organizations
  • Lack of understanding in users for the way this tool deals with data.
  • Not much flexible for numeric/ tabular reports

Pricing: Free & $70 per user per month.

Verdict: You can view dashboards in multiple devices like mobiles, laptops, and tablets. Features, functionality integration, and performance make it appealing. The live visual analytics and interactive dashboard is useful to the businesses for better communication for desired actions.

Rapidminer:

It is a cross-platform open-source big data tool, which offers an integrated environment for Data Science, ML, and Predictive Analytics. It is useful for data preparation and model deployment. It has several other products to build data mining processes and set predictive analysis as required by the business.

Pros:

  • Non-technical person can use this tool
  • Build accurate predictive models
  • Integrates well with APIs and cloud
  • Process change tracking
  • Schedule reports and set triggered notifications

Cons:

  • Not that great for image, audio and video data
  • Require Git Integration for version control
  • Modifying machine learning is challenging
  • Memory size it consumes is high
  • Programmed responses make it difficult to get problems solved

Pricing: Subscription $2,500, $5,000 & $10,000 User/Year.

Verdict: Huge organizations like Samsung, Hitachi, BMW, and many others use RapidMiner. The loads of data they handle indicate the reliability of this tool. Store streaming data in numerous databases and the tool allows multiple data management methods.

Conclusion:

The velocity and veracity that big data analytics tools offer make them a business necessity. Big data initiatives have an interesting success rate that shows how companies want to adopt new technology. Of course, some of them do succeed. The organizations using big data analytic tools benefited in lowering operational costs and establishing the data-driven culture.

How is big data generated

Why big data analytics is indispensable for today’s businesses.

Ours is the age of information technology. Progress in IT has been exponential in the 21st century, and one direct consequence is the amount of data generated, consumed, and transferred. There’s no denying that the next step in our technological advancement involves real-life implementations of artificial intelligence technology.

In fact, one could say we are already in the midst of it. And there’s a definitive link between the large amounts of digital information being produced — called Big Data when it exceeds the processing capabilities of traditional database tools — and how new machine learning techniques use that data to assist the development of AI.

However, this isn’t the only application of Big Data even if it has become the most promising. Big data analytics is now a heavily researched field which helps businesses uncover ground-breaking insights from the available data to make better and informed decisions. According to IDC, big data and analytics had market revenue of more than $150 billion worldwide in 2018.

What is the scale of data that we are dealing with today?

  • ·It is estimated that there will be 10 billion mobile devices in use by 2020. This is more than the entire world population, and this is not including laptops and desktops.
  • We make over 1 billion Google searches every day.
  • Around 300 billion emails are sent every day.
  • More than 230 million tweets are written every day.
  • More than 30 petabytes (that’s 1015 bytes) of user-generated data is stored, accessed and analyzed on Facebook.
  • On YouTube alone, 300 hours of video are uploaded every minute.
  • In just 5 years, the number of connected smart devices in the world will be more than 50 billion — all of which will collect, create, and share data.
Social media platforms have shot up human-generated data exponentially.

As an aside, in an attempt to impress the potential here, let me state that we analyze less than 1% of all available data. The numbers are staggering!

Before we get to classifying all this data, let us understand the three main characteristics of what makes big data big.

The 3 Vs of Big Data

3 Vs of Big Data
Image Credit: workology

Volume

Volume refers to the amount of data generated through various sources. On social media sites, for example, we have 2 billion Facebook users, 1 billion on YouTube, and 1 billion together on Instagram and Twitter. The massive quantities of data contributed by all these users in terms of images, videos, messages, posts, tweets, etc. have pushed data analysis away from the now incapable excel sheets, databases, and other traditional tools toward big data analytics.

Velocity

This is the speed at which data is being made available — the rate of transfer over servers and between users has increased to a point where it is impossible to control the information explosion. There is a need to address this with more equipped tools, and this comes under the realm of big data.

Variety

There are structured and unstructured data in all the content being generated. Pictures, videos, emails, tweets, posts, messages, etc. are unstructured. Sensor-collected data from the millions of connected devices is what you can call semi-structured while records maintained by businesses for transactions, storage, and analyzed unstructured information are part of structured data.

Classification of Big Data

With the amount of information that is available to us today, it is important to classify and understand the nature of different kinds of data and the requirements that go into the analysis for each.

Human Generated Data

Most human-generated data is unstructured. But this data has the potential to provide deep insights for heavy user-optimization. Product companies, customer service organizations, even political campaigns these days rely heavily on this type of random data to inform themselves of their audience and to target their marketing approach accordingly.

Classification of Big Data
Image Credit: EMC

Machine Generated Data

Data created by various sensors, cameras, satellites, bio-informatic and health-care devices, audio and video analyzers, etc. combine to become the biggest source of data today. These can be extremely personalized in nature, or completely random. With the advent of internet-enabled smart devices, propagation of this data has become constant and omnipresent, providing user information with highly useful detail.

Data from Companies and Institutions

Records of finances, transactions, operations planning, demographic information, health-care records, etc. stored in relational databases are more structured and easily readable compared to disorganized online data. This data can be used to understand key performance indicators, estimate demands and shortage, prevalent factors, large-scale consumer mentality, and a lot more. This is the smallest portion of the data market but combined with consumer-centric analysis of unstructured data, can become a very powerful tool for businesses.

What we can do for you

Whether one is seeking a profit advantage or a market edge, carving a niche product or capturing crowd sentiment, developing self-driving cars or facial recognition apps, building a futuristic robot or a military drone, big data is available for all sectors to take their technology to the next level. Bridged is a place where such fruitful experiments in data are being utilized and we are endeavoring to provide assistance to companies who are willing to take advantage of this untapped but currently mandatory investment in big data.

The need for quality training data | Blog | Bridged.o

What is training data? Where to find it? And how much do you need?

Artificial Intelligence is created primarily from exposure and experience. In order to teach a computer system a certain thought-action process for executing a task, it is fed a large amount of relevant data which, simply put, is a collection of correct examples of the desired process and result. This data is called Training Data, and the entire exercise is part of Machine Learning.

Artificial Intelligence tasks are more than just computing and storage or doing them faster and more efficiently. We said thought-action process because that is precisely what the computer is trying to learn: given basic parameters and objectives, it can understand rules, establish relationships, detect patterns, evaluate consequences, and identify the best course of action. But the success of the AI model depends on the quality, accuracy, and quantity of the training data that it feeds on.

The training data itself needs to be tailored for the end-result desired. This is where Bridged excels in delivering the best training data. Not only do we provide highly accurate datasets, but we also curate it as per the requirements of the project.

Below are a few examples of training data labeling that we provide to train different types of machine learning models:

2D/3D Bounding Boxes

2D/3D bounding boxed | Blog | Bridged.co

Drawing rectangles or cuboids around objects in an image and labeling them to different classes.

Point Annotation

Point annotation | Blog | Bridged.co

Marking points of interest in an object to define its identifiable features.

Line Annotation

Line annotation | Blog | Bridged.co

Drawing lines over objects and assigning a class to them.

Polygonal Annotation

Polygonal annotation | Blog | Bridged.co

Drawing polygonal boundaries around objects and class-labeling them accordingly.

Semantic Segmentation

Semantic segmentation | Blog | Bridged.co

Labeling images at a pixel level for a greater understanding and classification of objects.

Video Annotation

Video annotation | Blog | Bridged.co

Object tracking through multiple frames to estimate both spatial and temporal quantities.

Chatbot Training

Chatbot training | Blog | Bridged.co

Building conversation sets, labeling different parts of speech, tone and syntax analysis.

Sentiment Analysis

Sentiment analysis | Blog | Bridged.co

Label user content to understand brand sentiment: positive, negative, neutral and the reasons why.

Data Management

Cleaning, structuring, and enriching data for increased efficiency in processing.

Image Tagging

Image tagging | Blog | Bridged.co

Identify scenes and emotions. Understand apparel and colours.

Content Moderation

Content moderation | Blog | Bridged.co

Label text, images, and videos to evaluate permissible and inappropriate material.

E-commerce Recommendations

Optimise product recommendations for up-sell and cross-sell.

Optical Character Recognition

Learn to convert text from images into machine-readable data.


How much training data does an AI model need?

The amount of training data one needs depends on several factors — the task you are trying to perform, the performance you want to achieve, the input features you have, the noise in the training data, the noise in your extracted features, the complexity of your model and so on. Although, as an unspoken rule, machine learning enthusiasts understand that larger the dataset, more fine-tuned the AI model will turn out to be.

Validation and Testing

After the model is fit using training data, it goes through evaluation steps to achieve the required accuracy.

Validation & testing of models | Blog | Bridged.co

Validation Dataset

This is the sample of data that is used to provide an unbiased evaluation of the model fit on the training dataset while tuning model hyper-parameters. The evaluation becomes more biased when the validation dataset is incorporated into the model configuration.

Test Dataset

In order to test the performance of models, they need to be challenged frequently. The test dataset provides an unbiased evaluation of the final model. The data in the test dataset is never used during training.

Importance of choosing the right training datasets

Considering the success or failure of the AI algorithm depends so much on the training data it learns from, building a quality dataset is of paramount importance. While there are public platforms for different sorts of training data, it is not prudent to use them for more than just generic purposes. With curated and carefully constructed training data, the likes of which are provided by Bridged, machine learning models can quickly and accurately scale toward their desired goals.

Reach out to us at www.bridgedai.com to build quality data catering to your unique requirements.