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How Business Intelligence is different from Data Science

Data Science contains both structured and unstructured data, it’s essential in dealing with the data volumes of modern times. Mainly it is used in technology, finance, and internet-based businesses. It is not useful in business decisions because of its inability to apply the outcomes of applied algorithms. Data Science has a complex and stringent approach to uncover the hidden patterns and trends prevailing in data. It is upgraded business intelligence with refined statistical tools to analyze data and predict better for wiser use of predictions.

Business Intelligence is mandatory with rising data size and complexities. BI is widely implemented in data management solutions. It collects and analyzes data, the purpose to provide insights and to streamline business operations. BI refers to data collection methods, opting technologies, using it in applications using data points to populate business analysis and for data presentation. Business analytics is part of business intelligence that analyzes historical data to predict business trends and generates actionable data. It can minimize operational costs and increase revenues.

BI and Data Science

Differences & Features:

Sr.No.Data Science:Business Intelligence:
1It deals with a variety of structured, semi-structured, and unstructured data.It requires an adequately structured data for accurate predictions.
2Gathers data and has multiple supersets.Gathered data is used for analysis.
3Data input sources are multiple.Limited data input sources as it is of past performance.
4Largely it is data-dependent. Involves data but is not data-dependent.
5Data Science blends data and algorithms to build technology that can respond to a set of questions. It encourages you to discover new questions that can change your outlook. Business Intelligence can draw interpretations based on business requirements. It answers the questions you put forth.  
6Data Science analyzes past data trends or patterns for predictive analysis. BI helps to interpret past data for illustrative analysis.
7Data Science answers queries like geographical influence on business, seasonal factors that affect business and customer preferences. Business Intelligence can respond to the financial aspect of factors affecting business.
8It involves the use of statistics and coding for algorithms and the development of software. Uses statistics but no coding involved.
9Programming languages used are  C, C++, C#, Java, Julia, Matlab, Python, R, SAS, Scala, SQL, Stata, Haskell, Programming languages used in Business Analytics are  C, C++, C#, Objective C, Java, Javascript, PHP, Python, R, SQL, Ruby.
10Data Science findings are not used by decision-makers in business due to the lack of clarity in data sets. BI accesses your organizational data to understand current business performance and improving it.
11Data Scientists use various methods, algorithms, and processes for insights from structured and unstructured data.Business Intelligence is knowledge acquired over a period, its statistical interpretation, and continuous upgrades in the sector.
12Investment costs for data science is higher.Lesser investments for Business Intelligence as data is historic.
13Used in Machine Learning and Artificial IntelligenceUsed in Business Analytics
14Data Science is not useful in day-to-day business decisions.Business Intelligence is useful in day-to-day business decisions.
15Data Science can tell you why things are happening the way they are happening in the business.Business Intelligence can tell you what and why things are happening in the business. 
16It is a predictive and proactive analysis of data. It is more of a retrospective and reactive analysis of data.
17It is a modern and flexible approach to handling business data. It is the traditional and inflexible approach to handling business data.
18Data scientists acquire skills to interpret data sets.Business experts interpret data based on their intelligence and experience.
19Machine analytical can maintain the quality of the analysis.Manual intervention can impact the analytical quality.
20Data Science is also known as AI-enables Data Science.Business Intelligence is not the same as Business Analytics.
21It requires the technical team to extract insights; ordinary businesses are forced to rely on expertise.Non-technical people can easily draw powerful insights if they are trained.
22Data scientists continuously refine algorithms for efficient predictions.These are set processes and based on statistical calculations, the change in formulas will change the outcome.
23Data Science focuses on experimentation.Traditional Business Intelligence systems have no room for experimentation.
24Widely used in healthcare, banking, e-commerce, etc.Widely used in retail, food, oil, fashion, pharma, etc.


Similarities of Data Science and Business Intelligence:

The focus is on data collection, formatting and interpretation in Data Science and Business Intelligence. Business insights can give a competitive edge to decide on the actions. Both provide a high level of support based on a detailed study of data points and help in taking accurate decisions.

Data Science reinforces Business Intelligence with the analysis that gives power to the assessors and decision-makers. Business Experts can work with technology and enhance their work patterns instead of just relying on their knowledge.

The perspective of DS and BI real data and its predictions, to improve processes, transform data interpretation and adds business value with best business decisions.

Benefits of Data Science:

  • Automate redundant tasks and business processes
  • Increased productivity
  • Identifying the target audience
  • Personalized insights, purchases, and customer experience
  • Employee Training
  • Trend based actions
  • Adopting best practices
  • Analyze purchasing patterns
  • Predictive Analysis
  • Assessing Business decisions
  • Better decision making

Benefits of Business Intelligence:

  • Quicker reporting, analysis or planning
  • Precise reporting, analysis or planning
  • Better data quality
  • Improved employee and customer satisfaction
  • Enhanced operational efficiency
  • Increased competitive advantage
  • Reduced costs and expenses
  • Increased business revenues
  • Standardization of business processes
  • Lesser Workforce needed
  • Better business decisions

A survey of 2600 business intelligence users by BI-Survey received a detailed opinion on various comparatives.

About 64% found BI as faster reporting, analysis or planning and 56% voted for its accuracy and 49% could make better business decisions.

  • Target is a company with business intelligence and analytics software is the world’s largest Business Intelligence provider and serves Microsoft too.
  • Kognito offers solutions to companies that need to analyze large and complex data for data migration, a fast and scalable analytical database for telecom, finance, and retail sectors.
  • Host Analytics a leader in cloud-based financial applications helps in planning, consolidation, reporting, and analytics. Businesses benefit from the improved business agility and lowered costs of improved security.

Top 9 Business Intelligence Companies:

  1. Microsoft
  2. Tableau Software
  3. Sisense
  4. IBM
  5. SAS
  6. Tibco Software
  7. SAP
  8. Oracle
  9. Pentaho

Shell a giant oil company used data science to forestall machine failure in facilities globally.

Qubole uses ML and AI to analyze data, integrates with lots of coding languages and open-source tools to automate data processing for data science.

Sumo Logic believes that businesses are incessantly generating data online and the analysis of this data should be done simultaneously. It can give better insights in real-time and for efficient processing, it uses the cloud.

Top 9 Data Science Companies:

  1. Numerator
  2. Cloudera
  3. Splunk
  4. SPINS
  5. Alteryx
  6. Civis Analytics
  7. Sisense
  8. Teradata
  9. Oracle
Data Science

Future of Data Science and Business Intelligence:

Business Intelligence is progressing towards Data Science for real-time insights and profitable business outcomes. Wipro has over 1000 data scientists working across various domains this points towards the current status and rising demand because of changing business needs and the increase in data-driven organizations.

By the year 2020 data science will automate over 40% of tasks, 90% of large enterprises will generate revenues from data as service.

Ventana Research Assertions predicts that by 2021, 66% of analytics processes to go beyond what happened and why and share what should be done and 33% of organizations would want NLP as a capability of Business Intelligence systems.

Business Intelligence incorporated in real-time to improve business decisions by nearly 50% of the companies will change the scenario by 2022. Around 60% of companies that have 20 data scientists will need a professional code of conduct for the ethical use of data by the year 2023.

The year 2025 will make mark forever increasing data-related activities. We will see 150 billion device users, the digital data will rise from 40 zettabytes to 175 zettabytes and IoT devices will generate more than 90 zettabytes and almost 6 billion consumers will interact with the data.

Trends in Business Intelligence include integrated content and capabilities; automation, generate actionable insights, data collaborative to leverage usage, data governance, adoption of AI, machine learning as service, and overall efficiency.                             

Trends in Data Science include data quality management, predictive and prescriptive analytics, data as a service, work towards tight privacy and better personalization.

Business Intelligence Process:

  • Define levels of questions to be answered.
  • Select BI tools you will use.
  • Achieve anything specific and plan in comparison with past performance.
  • Define the reports you need.

Data Science Process:

  • Identify sources to collect data
  • Collect data from multiple sources
  • Integrate different data sources
  • Visualize the data

Whether to Choose Business Intelligence or Data Science:

Define your needs and the approach that suits your business. Business Intelligence earlier meant for large enterprises but not this is available for small and mid-sized organizations. Newer techniques like self-service business intelligence enable users to work on data with no technical knowledge and are user-friendly. BI lets you target the weaker areas by providing actionable insights for the problems. Business Intelligence tools improve productivity, processes and are excellent solutions for less complicated businesses.

Data Science lets you achieve a generous understanding of customer behavior, real-time insights, and predictive analytics for the business to take a competitive advantage. More the business expands and you have to deal with complex and huge datasets data science is the ultimate reliable technology for accurate business decisions.

Summary:

New technologies like Business Intelligence and Data Science have immense capabilities that depend on implementation to bring transformation in business. Data and its value are known to all business enterprises but the pain points should be identified to extract maximum benefits by applying technology.

Questions that arise while applying new technology should make you unstoppable as the same technology or in combination with other technology; opens the realm of possibilities for your business.

The recent developments of Data Science and Business Intelligence are bringing major change to the way data is analyzed and the results are utilized. There is a variety of data that gets generated with increased accessibility and internet usage. This data is useful for business growth and represented in simple formats for the management and other decision-makers. They can rapidly change the current business processes and visible future from the forecasts.

Data in Business

In the course of the most recent two years, huge data has been changing how incalculable organizations work and looking to the future that doesn’t anticipate halting. Enormous information vows to realize further disturbance as its revolution works its way through huge and little associations

What Is Data??

In figuring, information will be data that has been converted into a structure that is productive for development or handling. Comparative with the present PCs and transmission media, information will be data changed over into parallel computerized structure. It is adequate for information to be utilized as a solitary subject or a plural subject. Crude information is a term used to depict information in its most essential computerized position.

Big information is the colossal volumes of data created from different industry spaces. Huge data generally contains data development, data assessment, and data utilization structures. As the years advanced, there’s been a change in the colossal data assessment designs – associations have swapped the dreary departmental strategy with data approach.

This has seen progressively vital use of spry development close by inspired enthusiasm for bleeding-edge examination. Staying before the test at present anticipates that associations should send impelled data-driven examination.

At the point when it recently came into the picture, huge data was fundamentally sent by more noteworthy associations that could deal with the expense of the development when it was exorbitant. At present, the degree of huge information has changed to the extent that endeavors both little and tremendous rely upon enormous data for shrewd assessment and business bits of learning. This has achieved the development of tremendous data sciences, innovations and technology at a speedy pace. The most proper instance of this improvement is the cloud which has let even private endeavors abuse the latest revolution and patterns.

Here are seven zones where they have utilized investigation to change their exhibition which helped income, expanded benefits, and improved consumer loyalty and maintenance.

What is Data

1. Better Business Intelligence

Business insight is a lot of information devices that are utilized to more readily break down a business. It goes connected at the hip with huge information. Before the ascent of huge information, business knowledge was somewhat restricted. Enormous information has offered ascend to business insight as an authentic profession. Numerous organizations are equipping by procuring business knowledge specialists since they help take an organization to the following level.

Business insight can be used in any business that creates information. These days, it’s uncommon to discover a business that isn’t producing any information whatsoever. This implies any business can profit by better business knowledge. New uses for business insight are being conceived normally.

2. Providing Better Customer Insights

Investigation of huge information uncovers what customers lean toward right now. If most of the individuals on a social stage are examining a specific item, that is the ideal minute and spot to spring up the item’s Ad. This builds the exactness of your rundown of objective clients

Huge information’s first enormous imprint on organizations has been its production of more focused on promoting. Huge information has enabled organizations to make laser-focused on advertising efforts. While enormous information investigation isn’t constantly 100% exact, it tends to be profoundly precise. This high exactness enables organizations to target showcasing to saw client needs.

Enormous information examination can enable a business to anticipate what items clients may require later on. Quite a while back, there was a story that Target precisely anticipated a pregnancy dependent on buy history. While current information investigation methods are not exactly at the level to make these sorts of forecasts routinely, they are joining to that.

Envision how your business would profit by having the option to showcase the items that you realized your clients required and knew enough data about them to tailor your message to their unmistakable needs.

Enormous information examination results demonstrate your shopper’s buy designs. As an active advertiser, you can exploit and send them proposals of items they like and consistently select. Building customized correspondence with purchasers is a viable strategy for client maintenance. Preeminent, you can wager that this will doubtlessly be a certain deal!

3. Proactive Customer Service

Organizations can know precisely what their clients need before the client even needs to voice their worry. This sort of proactive client care will alter business that wants to separate themselves dependent on prevalent client care.

Envision calling into a business. Continuous enormous information investigation of the client’s record and even organization site visits can anticipate a couple of issues that the client could need assistance managing. A voice brief could even be utilized to inquire as to whether this was their issue and give computerized help if the client picks.

In any case, client assistance would have a smart thought of what the call was about and convey proficient client assistance. Further enormous information investigation could permit client assistance to proactively contact clients on records where prescient examination confirms that the client may have a future issue.

4. Customer Responsive Products

Enormous information vows to not just improve client support by making it increasingly proactive yet also, it will enable organizations to make client-responsive items. Item configuration can be centered around satisfying the needs of clients in manners that have never been conceivable. Rather than depending on clients to tell your business what they are searching for in an item, you can utilize information examination to foresee what they are searching for in an item. Clients who offer their inclinations using reviews and purchasing propensities. Indeed, even use case situations can make a superior picture of what a future item ought to resemble.

Flower vendor execution can fluctuate dependent on numerous components, for example, time of day, the day of the week, or the item being sold, and so forth.

Breaking down provider execution recognizes which of their numerous providers will give the most noteworthy likelihood of accomplishment for any give requests dependent on area, which expands their request satisfaction.

5. Productivity Improvements

Modern designers are specialists in ineffectiveness. They realize that you can’t make a procedure progressively proficient without having information. Enormous information is providing rich information about each item and procedure. This rich information is recounting a story that keen organizations are tuning in to.

Designers are breaking down huge information and searching for approaches to make procedures run all the more effectively. Enormous information examination functions admirably with the Theory of Constraints. Limitations are simpler to perceive and once perceived, it’s simpler to recognize how if the requirement is the most restricting imperative. At the point when this limitation is found and the expelled, the business can see immense increments in execution and throughput. Huge information helps supplies these answers.

6. Reduce Costs

Enormous information can give the data expected to decrease business costs. In particular, organizations are currently utilizing this innovation to precisely discover slants and foresee future occasions inside their businesses. Realizing when something may happen improves estimates and arranging. Organizers can decide when to deliver and the amount to create. They can decide how a lot of stock to keep available.

A genuine model is stock costs. It’s costly to convey stock. Not exclusively is there a stock conveying cost, however, there is likewise tying up capital in unneeded stock. Enormous information examination can help foresee when deals will happen and in like manner help anticipate when creation needs to happen. Further examination can demonstrate when the ideal time is to buy stock and even how much stock to keep close by.

7. Understanding the market

Through stream handling, it is conceivable to follow the market pattern from all points of view; the past, the present, and what’s to come. By determining the ongoing data from enormous information, it is conceivable to get the present market benchmarks and change your systems to meet them. Settling on value choices is overpowering thinking about the consistent difference in costs in the market continuously.

Assessing huge information will uncover the examples for valuing from a client item point of view – in light of their eagerness to pay. This is the best valuing system in examination with putting together your costs concerning the expense of creation, the cost of a comparable item, or standard edges.

A superior comprehension of business forms through huge information examination helps in augmenting each selling chance. With your item’s transformation rate expanding, you can choose to append minor items to it for the greatest benefits. This should be possible by considering the conduct of your objective purchasers by gathering related data of huge information.

Huge information is something to grasp on the off chance that you need to enable your business to accomplish more. Soon those organizations that haven’t grasped enormous information will end up left behind.

The estimation of enormous information is dictated by its volume, assortment, speed, and veracity. The precision of the outcomes relies upon how it is deciphered, used and above all, how it is applied. Huge information contains all the data you have to prevail in your showcasing procedures and increment your change rates. You should simply utilize the correct assets for gathering, examining, deciphering, and placing it without hesitation

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.

Machine Learning and AI to cut down financial risks

Under 70 years from the day when the very term Artificial Intelligence appeared, it’s turned into a necessary piece of the most requesting and quick-paced enterprises. Groundbreaking official directors and entrepreneurs effectively investigate new AI use in money and different regions to get an aggressive edge available. As a general rule, we don’t understand the amount of Machine Learning and AI is associated with our everyday life.

Artificial Intelligence

Software engineering, computerized reasoning (AI), once in a while called machine knowledge. Conversationally, the expression “man-made consciousness” is regularly used to depict machines that emulate “subjective” capacities that people partner with the human personality.

These procedures incorporate learning (the obtaining of data and principles for utilizing the data), thinking (utilizing standards to arrive at surmised or positive resolutions) and self-redress.

Machine Learning

Machine learning is the coherent examination of counts and verifiable models that PC systems use to play out a specific task without using unequivocal rules, contingent upon models and induction. It is seen as a subset of man-made thinking. Man-made intelligence estimations manufacture a numerical model reliant on test information, known as “getting ready information”, in order to choose figures or decisions without being explicitly adjusted to playing out the task.

Financial Risks

Money related hazard is a term that can apply to organizations, government elements, the monetary market overall, and the person. This hazard is the risk or probability that investors, speculators, or other monetary partners will lose cash.

There are a few explicit hazard factors that can be sorted as a money related hazard. Any hazard is a risk that produces harming or undesirable outcomes. Some increasingly normal and particular money related dangers incorporate credit hazard, liquidity hazard, and operational hazard.

Financial Risks, Machine Learning, and AI

There are numerous approaches to sort an organization’s monetary dangers. One methodology for this is given by isolating budgetary hazards into four general classes: advertise chance, credit chance, liquidity hazard, and operational hazard.

AI and computerized reasoning are set to change the financial business, utilizing tremendous measures of information to assemble models that improve basic leadership, tailor administrations, and improve hazard the board.

1. Market Risk

Market hazard includes the danger of changing conditions in the particular commercial center where an organization goes after business. One case of market hazard is the expanding inclination of shoppers to shop on the web. This part of the market hazard has exhibited noteworthy difficulties in conventional retail organizations.

Utilizations of AI to Market Risk

Exchanging budgetary markets naturally includes the hazard that the model being utilized for exchanging is false, fragmented, or is never again legitimate. This region is commonly known as model hazard the executives. AI is especially fit to pressure testing business sector models to decide coincidental or rising danger in exchanging conduct. An assortment of current use instances of AI for model approval.

It is likewise noticed how AI can be utilized to screen exchanging inside the firm to check that unsatisfactory resources are not being utilized in exchanging models. An intriguing current utilization of model hazard the board is the firm yields. which gives ongoing model checking, model testing for deviations, and model approval, all determined by AI and AI systems.

One future bearing is to move more towards support realizing, where market exchanging calculations are inserted with a capacity to gain from market responses to exchanges and in this way adjust future exchanging to assess how their exchanging will affect market costs.

2. Credit Risk

Credit hazard is the hazard organizations bring about by stretching out credit to clients. It can likewise allude to the organization’s own acknowledge hazard for providers. A business goes out on a limb when it gives financing of buys to its clients, because of the likelihood that a client may default on installment.

Use of AI to Credit Risk

There is currently an expanded enthusiasm by establishments in utilizing AI and AI procedures to improve credit hazard the board rehearses, somewhat because of proof of inadequacy in conventional systems. The proof is that credit hazard the executives’ capacities can be essentially improved through utilizing Machine Learning and AI procedures because of its capacity of semantic comprehension of unstructured information.

The utilization of AI and AI systems to demonstrate credit hazard is certainly not another wonder however it is a growing one. In 1994, Altman and partners played out a first similar investigation between conventional measurable techniques for trouble and chapter 11 forecast and an option neural system calculation and presumed that a consolidated methodology of the two improved precision altogether

It is especially the expanded unpredictability of evaluating credit chance that has opened the entryway to AI. This is apparent in the developing credit default swap (CDS) showcase where there are many questionable components including deciding both the probability of an occasion of default (credit occasion) and assessing the expense of default on the off chance that default happens.

3. Liquidity Risk

Liquidity hazard incorporates resource liquidity and operational subsidizing liquidity chance. Resource liquidity alludes to the relative straightforwardness with which an organization can change over its benefits into money ought to there be an unexpected, generous requirement for extra income. Operational subsidizing liquidity is a reference to everyday income.

Application to liquidity chance

Consistency with hazard the executives’ guidelines is an indispensable capacity for money related firms, particularly post the budgetary emergency. While hazard the board experts regularly try to draw a line between what they do and the frequently bureaucratic need of administrative consistence, the two are inseparably connected as the two of them identify with the general firm frameworks for overseeing hazard. To that degree, consistency is maybe best connected to big business chance administration, in spite of the fact that it contacts explicitly on every one of the hazard elements of credit, market, and operational hazard.

Different favorable circumstances noted are the capacity to free up administrative capital because of the better checking, just as computerization diminishing a portion of the evaluated $70 billion that major money related organizations go through on consistency every year.

4. Operational Risk

Operational dangers allude to the different dangers that can emerge from an organization’s normal business exercises. The operational hazard class incorporates claims, misrepresentation chance, workforce issues, and plan of action chance, which is the hazard that an organization’s models of promoting and development plans may demonstrate to be off base or insufficient.

Application to Operational Risk

Simulated intelligence can help establishments at different stages in the hazard the boarding procedure going from distinguishing hazard introduction, estimating, evaluating, and surveying its belongings. It can likewise help in deciding on a fitting danger relief system and discovering instruments that can encourage moving or exchanging hazards.

Along these lines, utilization of Machine Learning and AI methods for operational hazard the board, which began with attempting to avoid outside misfortunes, for example, charge card cheats, is currently extending to new regions including the examination of broad archive accumulations and the presentation of tedious procedures, just as the discovery of illegal tax avoidance that requires investigation of huge datasets.

Financial Risks

Conclusion

We along these lines finish up on a positive note, about how AI and ML are changing the manner in which we do chance administration. The issue for the set up hazard the board capacities in associations to now consider is on the off chance that they wish to profit of these changes, or if rather it will tumble to present and new FinTech firms to hold onto this space.

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.

8 resources to get free training data for ml systems

The current technological landscape has exhibited the need for feeding Machine Learning systems with useful training data sets. Training data helps a program understand how to apply technology such as neural networks. This is to help it to learn and produce sophisticated results.

The accuracy and relevance of these sets pertaining to the ML system they are being fed into are of paramount importance, for that dictates the success of the final model. For example, if a customer service chatbot is to be created which responds courteously to user complaints and queries, its competency will be highly determined by the relevancy of the training data sets given to it.

To facilitate the quest for reliable training data sets, here is a list of resources which are available free of cost.

Kaggle

Owned by Google LLC, Kaggle is a community of data science enthusiasts who can access and contribute to its repository of code and data sets. Its members are allowed to vote and run kernel/scripts on the available datasets. The interface allows users to raise doubts and answer queries from fellow community members. Also, collaborators can be invited for direct feedback.

The training data sets uploaded on Kaggle can be sorted using filters such as usability, new and most voted among others. Users can access more than 20,000 unique data sets on the platform.

Kaggle is also popularly known among the AI and ML communities for its machine learning competitions, Kaggle kernels, public datasets platform, Kaggle learn and jobs board.

Examples of training datasets found here include Satellite Photograph Order and Manufacturing Process Failures.

Registry of Open Data on AWS

As its website displays, Amazon Web Services allows its users to share any volume of data with as many people they’d like to. A subsidiary of Amazon, it allows users to analyze and build services on top of data which has been shared on it.  The training data can be accessed by visiting the Registry for Open Data on AWS.

Each training dataset search result is accompanied by a list of examples wherein the data could be used, thus deepening the user’s understanding of the set’s capabilities.

The platform emphasizes the fact that sharing data in the cloud platform allows the community to spend more time analyzing data rather than searching for it.

Examples of training datasets found here include Landsat Images and Common Crawl Corpus.

UCI Machine Learning Repository

Run by the School of Information & Computer Science, UC Irvine, this repository contains a vast collection of ML system needs such as databases, domain theories, and data generators. Based on the type of machine learning problem, the datasets have been classified. The repository has also been observed to have some ready to use data sets which have already been cleaned.

While searching for suitable training data sets, the user can browse through titles such as default task, attribute type, and area among others. These titles allow the user to explore a variety of options regarding the type of training data sets which would suit their ML models best.

The UCI Machine Learning Repository allows users to go through the catalog in the repository along with datasets outside it.

Examples of training data sets found here include Email Spam and Wine Classification.

Microsoft Research Open Data

The purpose of this platform is to promote the collaboration of data scientists all over the world. A collaboration between multiple teams at Microsoft, it provides an opportunity for exchanging training data sets and a culture of collaboration and research.

The interface allows users to select datasets under categories such as Computer Science, Biology, Social Science, Information Science, etc. The available file types are also mentioned along with details of their licensing.

Datasets spanning from Microsoft Research to advance state of the art research under domain-specific sciences can be accessed in this platform.

GitHub.com/awesomedata/awesomepublicdatasets

GitHub is a community of software developers who apart from many things can access free datasets. Companies like Buzzfeed are also known to have uploaded data sets on federal surveillance planes, zika virus, etc. Being an open-source platform, it allows users to contribute and learn about training data sets and the ones most suitable for their AI/ML models.

Socrata Open Data

This portal contains a vast variety of data sets which can be viewed on its platform and downloaded. Users will have to sort through data which is currently valid and clean to find the most useful ones. The platform allows the data to be viewed in a tabular form. This added with its built-in visualization tools makes the training data in the platform easy to retrieve and study.

Examples of sets found in this platform include White House Staff Salaries and Workplace Fatalities by US State.

R/datasets

This subreddit is dedicated to sharing training datasets which could be of interest to multiple community members. Since these are uploaded by everyday users, the quality and consistency of the training sets could vary, but the useful ones can be easily filtered out.

Examples of training datasets found in this subreddit include New York City Property Tax Data and Jeopardy Questions.

Academic Torrents

This is basically a data aggregator in which training data from scientific papers can be accessed. The training data sets found here are in many cases massive and they can be accessed directly on the site. If the user has a BitTorrent client, they can download any available training data set immediately.

Examples of available training data sets include Enron Emails and Student Learning Factors.

Conclusion

In an age where data is arguably the world’s most valuable resource, the number of platforms which provide this is also vast. Each platform caters to its own niche within the field while also displaying commonly sought after datasets.  While the quality of training data sets could vary across the board, with the appropriate filters, users can access and download the data sets which suit their machine learning models best. If you need a custom dataset, do check us out here, share your requirements with us, and we’ll more than happy to help you out!

The need for training data in ai and ml models

Not very long ago, sometime towards the end of the first decade of the 21st century, internet users everywhere around the world began seeing fidelity tests while logging onto websites. You were shown an image of a text, with one word or usually two, and you had to type the words correctly to be able to proceed further. This was their way of identifying that you were, in fact, human, and not a line of code trying to worm its way through to extract sensitive information from said website. While it was true, this wasn’t the whole story.

Turns out, only one of the two Captcha words shown to you were part of the test, and the other was an image of a word taken from an as yet non-transcribed book. And you, along with millions of unsuspecting users worldwide, contributed to the digitization of the entire Google Books archive by 2011. Another use case of such an endeavor was to train AI in Optical Character Recognition (OCR), the result of which is today’s Google Lens, besides other products.

Do you really need millions of users to build an AI? How exactly was all this transcribed data used to make a machine understand paragraphs, lines, and individual words? And what about companies that are not as big as Google – can they dream of building their own smart bot? This article will answer all these questions by explaining the role of datasets in artificial intelligence and machine learning.

ML and AI – smart tools to build smarter computers

In our efforts to make computers intelligent – teach them to find answers to problems without being explicitly programmed for every single need – we had to learn new computational techniques. They were already well endowed with multiple superhuman abilities: computers were superior calculators, so we taught them how to do math; we taught them language, and they were able to spell and even say “dog”; they were huge reservoirs of memory, hence we used them to store gigabytes of documents, pictures, and video; we created GPUs and they let us manipulate visual graphics in games and movies. What we wanted now was for the computer to help us spot a dog in a picture full of animals, go through its memory to identify and label the particular breed among thousands of possibilities, and finally morph the dog to give it the head of a lion that I captured on my last safari. This isn’t an exaggerated reality – FaceApp today shows you an older version of yourself by going through more or less the same steps.

For this, we needed to develop better programs that would let them learn how to find answers and not just be glorified calculators – the beginning of artificial intelligence. This need gave rise to several models in Machine Learning, which can be understood as tools that enhanced computers into thinking systems (loosely).

Machine Learning Models

Machine Learning is a field which explores the development of algorithms that can learn from data and then use that learning to predict outcomes. There are primarily three categories that ML models are divided into:

Supervised Learning

These algorithms are provided data as example inputs and desired outputs. The goal is to generate a function that maps the inputs to outputs with the most optimal settings that result in the highest accuracy.

Unsupervised Learning

There are no desired outputs. The model is programmed to identify its own structure in the given input data.

Reinforcement Learning

The algorithm is given a goal or target condition to meet and it is left to its devices to learn by trial and error. It uses past results to inform itself about both optimal and detrimental paths and charts the best path to the desired endgame result.

In each of these philosophies, the algorithm is designed for a generic learning process and exposed to data or a problem. In essence, the written program only teaches a wholesome approach to the problem and the algorithm learns the best way to solve it.

Based on the kind of problem-solving approach, we have the following major machine learning models being used today:

  • Regression
    These are statistical models applicable to numeric data to find out a relationship between the given input and desired output. They fall under supervised machine learning. The model tries to find coefficients that best fit the relationship between the two varying conditions. Success is defined by having as little noise and redundancy in the output as possible.

    Examples: Linear regression, polynomial regression, etc.
  • Classification
    These models predict or explain one outcome among a few possible class values. They are another type of supervised ML model. Essentially, they classify the given data as belonging to one type or ending up as one output.

    Examples: Logistic regression, decision trees, random forests, etc.
  • Decision Trees and Random Forests
    A decision tree is based on numerous binary nodes with a Yes/No decision marker at each. Random forests are made of decision trees, where accurate outputs are obtained by processing multiple decision trees and results combined.
  • Naïve Bayes Classifiers
    These are a family of probabilistic classifiers that use Bayes’ theorem in the decision rule. The input features are assumed to be independent, hence the name naïve. The model is highly scalable and competitive when compared to advanced models.
  • Clustering
    Clustering models are a part of unsupervised machine learning. They are not given any desired output but identify clusters or groups based on shared characteristics. Usually, the output is verified using visualizations.

    Examples: K-means, DBSCAN, mean shift clustering, etc.
  • Dimensionality Reduction
    In these models, the algorithm identifies the least important data from the given set. Based on the required output criteria, some information is labeled redundant or unimportant for the desired analysis. For huge datasets, this is an invaluable ability to have a manageable analysis size.

    Examples: Principal component analysis, t-stochastic neighbor embedding, etc.
  • Neural Networks and Deep Learning
    One of the most widely used models in AI and ML today, neural networks are designed to capture numerous patterns in the input dataset. This is achieved by imitating the neural structure of the human brain, with each node representing a neuron. Every node is given activation functions with weights that determine its interaction with its neighbors and adjusted with each calculation. The model has an input layer, hidden layers with neurons, and an output layer. It is called deep learning when many hidden layers are encapsulating a wide variety of architectures that can be implemented. ML using deep neural networks requires a lot of data and high computational power. The results are without a doubt the most accurate, and they have been very successful in processing images, language, audio, and videos.

There is no single ML model that offers solutions to all AI requirements. Each problem has its own distinct challenges, and knowledge of the workings behind each model is mandatory to be able to use them efficiently. For example, regression models are best suited for forecasting data and for risk assessment. Clustering modes in handwriting recognition and image recognition, decision trees to understand patterns and identify disease trends, naïve Bayes classifier for sentiment analysis, ranking websites and documents, deep neural networks models in computer vision, natural language processing, and financial markets, etc. are more such use cases.

The need for training data in ML models

Any machine learning model that we choose needs data to train its algorithm on. Without training data, all the algorithm understands is how to approach the given problem, and without proper calibration, so to speak, the results won’t be accurate enough. Before training, the model is just a theorist, without the fine-tuning to its settings necessary to start working as a usable tool.

While using datasets to teach the model, training data needs to be of a large size and high quality. All of AI’s learning happens only through this data. So it makes sense to have as big a dataset as is required to include variety, subtlety, and nuance that makes the model viable for practical use. Simple models designed to solve straight-forward problems might not require a humongous dataset, but most deep learning algorithms have their architecture coded to facilitate a deep simulation of real-world features.

The other major factor to consider while building or using training data is the quality of labeling or annotation. If you’re trying to teach a bot to speak the human language or write in it, it’s not just enough to have millions of lines of dialogue or script. What really makes the difference is readability, accurate meaning, effective use of language, recall, etc. Similarly, if you are building a system to identify emotion from facial images, the training data needs to have high accuracy in labeling corners of eyes and eyebrows, edges of the mouth, the tip of the nose and textures for facial muscles. High-quality training data also makes it faster to train your model accurately. Required volumes can be significantly reduced, saving time, effort (more on this shortly) and money.

Datasets are also used to test the results of training. Model predictions are compared to testing data values to determine the accuracy achieved until then. Datasets are quite central to building AI – your model is only as good as the quality of your training data.

How to build datasets?

With heavy requirements in quantity and quality, it is clear that getting your hands on reliable datasets is not an easy task. You need bespoke datasets that match your exact requirements. The best training data is tailored for the complexity of the ask as opposed to being the best-fit choice from a list of options. Being able to build a completely adaptive and curated dataset is invaluable for businesses developing artificial intelligence.

On the contrary, having a repository of several generic datasets is more beneficial for a business selling training data. There are also plenty of open-source datasets available online for different categories of training data. MNIST, ImageNet, CIFAR provide images. For text datasets, one can use WordNet, WikiText, Yelp Open Dataset, etc. Datasets for facial images, videos, sentiment analysis, graphs and networks, speech, music, and even government stats are all easily found on the web.

Another option to build datasets is to scrape websites. For example, one can take customer reviews off e-commerce websites to train classification models for sentiment analysis use cases. Images can be downloaded en masse as well. Such data needs further processing before it can be used to train ML models. You will have to clean this data to remove duplicates, or to identify unrelated or poor-quality data.

Irrespective of the method of procurement, a vigilant developer is always likely to place their bets on something personalized for their product that can address specific needs. The most ideal solutions are those that are painstakingly built from scratch with high levels of precision and accuracy with the ability to scale. The last bit cannot be underestimated – AI and ML have an equally important volume side to their success conditions.

Coming back to Google, what are they doing lately with their ingenious crowd-sourcing model? We don’t see a lot of captcha text anymore. As fidelity tests, web users are now annotating images to identify patterns and symbols. All the traffic lights, trucks, buses and road crossings that you mark today are innocuously building training data to develop their latest tech for self-driving cars. The question is, what’s next for AI and how can we leverage human effort that is central to realizing machine intelligence through training datasets?

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.