Data is omnipresent. It exists to be consolidated and yearns to be understood. Data capture the history of a business, and they hold the capability to answer the what, how, why, and therefore of operations.
While discussing data, it is important to define the two commonly interchanged terms in this field: Business Intelligence (BI) and Data Science. Businesses from e-commerce to financial services employ BI and Data Science to gather data that can explain past performance and predict the path forward.
BI and Data Science are the full stack of data analysis. Let’s explain how:
Business Intelligence refers to the conversion of raw data into actionable information. Data Science concerns obtaining information from raw data to forecast future performance and strategize business-critical operations accordingly.
So, how do BI and Data Science contribute to a business’ success?
Let’s break this down with the help of an example. Consider an e-commerce business that has been selling men’s clothing for 10 years. Their offerings range from formal shirts to casual jeans, anything that comes under the broad category of men’s apparel.
The business is at its 10-year mark, and it is looking to rapidly increase sales. How would they go about it?
Understand the present
Firstly, they need to understand the business’s performance to date. What’s the best way to do that? Take the help and expertise of BI specialists to capture all sales and website data for the past 10 years. This process includes collecting, integrating, analyzing, and presenting the available data. The BI team is responsible for the business’ data management, dashboards, data arrangement, and information display.
Performance metrics such as onsite activity (clicks, time spent, bounce rate), e-commerce activity (categories and products visited, searches), etc. are captured and stored in the form of charts and summaries.
Data converted into information sources such as charts measure performance and quantify the business’s progress. The BI team performs quantitative analysis with the assistance of predictive analytics and modeling.
Once the data can be visualized, it is stored in data warehouses. The knowledge that such data offers can be used to develop effective strategies to gain business insights. The data can also warn employees about operational red-flags and suggest improvements.
Strategizing the future
Now that the data is available to be analyzed and understood, here’s where data scientists come in. While the work of data scientists can overlap with that of BI teams, the former functions along the lines of the future. The job of a data scientist is to understand the data at hand, locate opportunities for improvement, and back them with a combination of a logical understanding of trends, and the data at hand.
Examples of business strategies this men’s clothing e-commerce retailer could use include changing product pricing, improving site design to reduce bounce rates and last stage of sale drop-offs, the introduction of new products and cancellation of underperforming ones, etc.
Data scientists recommend such solutions, backed by the data resource accumulated and organized by Business Intelligence specialists.
Important differences between BI and Data Science
BI involves answering questions that may not seem straightforward at first glance. It answers the “what” of a business’ activity. Data science relies on predictive analytics and a creative dissection of the data that’s available. It answers the “how” and “why” of the data’s findings.
To work in Business Intelligence, you could survive with a basic qualification in a science-related degree. Companies tend to be flexible with BI applicants as their main objective would be to understand the data collected and support business decisions.
On the other hand, working in data science is a little more complicated. Companies opt for aspirants that have a background in Data Science. They might also require a thorough understanding of topics such as statistics, machine learning, and programming, to decipher the collected data to create future predictions.
BI teams use tools such as Microsoft Excel, SAS BI, Power BI, Sisense, and Microstrategy to organize and consolidate data. Data scientists use tools such as Python, R, Hadoop/Spark, SAS, and TensorFlow to study past data, discover trends, spot patterns, and predict future business behavior.
A combination of the two equips businesses with reports that provide powerful insights into the present and help draw the plans for the future.
Growing and established businesses collect a lot of data, and this data can provide insights for improving growth and staying on top of their game. There is no debate that business intelligence and data science are crucial to this process. Business intelligence explains what has happened, and Data Science answers why those events took place. Business intelligence can handle static and highly-structured data, while data science can deal with high-speed, high-volume, complex data.
Today’s businesses aren’t new to data. For decades we’ve seen them keep track of their expenses, sales, customer base, etc. But, only until recently has data moved from being a source of bare information to a haven of actionable insights.
Credit for popularizing the usage of data and the coinage of the term “Big Data” arguable goes to McKinsey Global Institute’s May 2011 report. The report cites Big Data as “the next frontier for innovation, competition, and productivity. “
Businesses today understand data, and they’re quickly exploring creative ways to make the most of what’s at hand. Data has transformed businesses to the extent where ignoring its importance is a regretful strategy.
Let’s take a look at how various businesses are benefitting from the power of data:
The first evident benefactors of data are the retail industry, online retail in particular. E-commerce sites harness their data’s capabilities to understand customers better and employ a strategy that improves their retail experience, thus increasing their odds of spending more and increasing profits.
For example, retailers can keep track of their product shelf, and differentiate the successful products from their loss-making counterparts. With this knowledge, retailers can plan to replace unsuccessful products with new additions, and zero-in on the types of products that are making the business the most money.
Financial institutions can use data for use cases beyond stock market analysis and large ticket trading. Banks are using big data to create credit scores that reflect the card holder’s behavior in the most accurate fashion possible. Fraudulent transactions can be identified by understanding the data-backed trends of similar earlier transactions. Employing data in their operations allows financial services firms to make the business of money efficient and safer than ever.
Educational institutions are using data to identify areas of learning difficulty, research better learning strategies, and adjusting syllabi based on what’s trending in the industry.
Students can be understood in a way that objectively provides a road-map to their success in academia. Courses can be planned by online education aggregators using data on each course’s adoption, and they can zero-in on the successful courses and eliminate or replace sub-par ones.
Hospitals and drug manufacturers are using big data to track patients’ symptoms, find new medicines, and avoid preventable deaths. Most recently, data enthusiasts have been using data to track the spread of pandemics such as the coronavirus. Drug manufacturers are also using data to discover new medicines by guiding scientists to potential organic raw materials and sources.
Data helps traditional industries such as agriculture too! Farmers can use data to monitor crop growth and predict the influence of factors such as weather, pesticides, and the market for selling their crops. Also, online forums exist wherein farmers across the globe can show source data on agricultural activities to improve information reach and insight-based decision making.
For decisions wherein referees could go wrong, data can stay right. Sports are using data to ensure gameplay stays fair, by understanding the trends and the movements behind fouls and violations.
Governments, more law-enforcement, have started using data to analyze trends in crime. It also has the potential to be used for identifying missing individuals and victims of criminal activities such as human trafficking, drug dealing, etc.
With growing use cases for data in business, there is no excuse for businesses not taking advantage of this information revolution. Gone are the days where business leaders had to rely on their gut feelings. By harnessing data’s capabilities, businesses can understand the past, evaluate the present, and hack the future a lot closer to their favor.
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?
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.
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?
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.
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:
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
Profits of businesses have seen an increase of 8–10 percent and experienced a 10 percent reduction in overall cost due to 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:
Improving product and service quality
Enhanced operational efficiency
Attracting new customers
Finding new opportunities
Launch new products/ services
Track transactions and detect fraudulent transactions
Good customer service
Draw competitive advantages
Reduced customer retention expenses
Decreases overall expenses
Establish a data-driven culture
Corrective measures and actions based on predictions
For Technical Teams:
Accelerate deployment capabilities
Investigate bottlenecks in the system
Create huge data processing systems
Find better and unpredicted relationships between the variables
Monitor situation with real-time analysis even during development
Spot patterns to recommend and convert to chart
Extract maximum benefit from the big data analytics tools
Architect highly scalable distributed systems
Create significant and self-explanatory data reports
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.
Predictions of Big Data Analytics:
In 2019, the big data market is positioned to grow by 20%
Revenues of Worldwide Big Data market for software and services are likely to reach $274.3 billion by 2022.
The big data analytics market may reach $103 billion by 2023
By 2020, individuals will generate 1.7 megabytes in a second
97.2% of organizations are investing in big data and AI
Approximately, 45 % of companies run at least some big data workloads on the cloud.
Forbes thinks we may need an analysis of more than 150 trillion gigabytes of data by 2025.
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.
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.
Low-quality Data – 35.9%
Lack of data science talent in organizations – 30.2%
Lack of domain expert input – 14.2%
Lack of clarity in handling data – 22.1%
Company politics & lack of support – 27%
Unavailability of difficulty to access data – 22%
These are some common issues and can easily eat away your efforts of shifting to the latest technology.
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.
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.
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
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.
Distribution for Hadoop: Cloudera offers the best open-source data platform; it aims at enterprise quality deployments of that technology.
Enables management of clusters and not just individual servers
Easy to install on virtual machines
Installation from local repositories
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.
This tool helps to make Big Data analysis easy for large organizations, especially with speedy implementation. Sisense works smoothly on the cloud and premises.
Data Visualization via dashboard
Detect trends and patterns with Natural Language Detection
Export Data to various formats
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.
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.
Instant data visualization
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.
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.
Pre-defined common reports
Reports scheduling and sharing
IP restriction and access restriction
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.
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.
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.
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.
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
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
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.
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.
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.
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 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.
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.
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.
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!
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:
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.
There are no desired outputs. The model is programmed to identify its own structure in the given input data.
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.
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?
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
Drawing rectangles or cuboids around objects in an image and labeling them to different classes.
Marking points of interest in an object to define its identifiable features.
Drawing lines over objects and assigning a class to them.
Drawing polygonal boundaries around objects and class-labeling them accordingly.
Labeling images at a pixel level for a greater understanding and classification of objects.
Object tracking through multiple frames to estimate both spatial and temporal quantities.
Building conversation sets, labeling different parts of speech, tone and syntax analysis.
Label user content to understand brand sentiment: positive, negative, neutral and the reasons why.
Cleaning, structuring, and enriching data for increased efficiency in processing.
Identify scenes and emotions. Understand apparel and colours.
Label text, images, and videos to evaluate permissible and inappropriate material.
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.
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.
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.