Research & Resources

How To Implement AI In Our Business ?
November 18, 2019

AI is already in the mainstream having entered smartphones, homes solutions apart from largescale transformations in the society like self-driving transports and more. Implementing AI in business is already in place as they reduce workload, streamline tasks, help make better decisions and improve productivity. From healthcare to banks, job industry to insurance sectors, all have seen implementation of AI.

Here is top 5 pointers to implement AI successfully to any business or operation:

  1. Use real intelligence for implementing AI in business

Your business needs to leverage AI transformation but, by focusing on real intelligence. Take a close look at your operations, break them down into independent and interrelated segments. Identify areas, functions and processes that require repetitive tasks and will do well with intuitive automation. This is the roadmap to implement AI in your business successfully.

Demonstrate to yourself the daily value that AI can pump into your operations and how it smoothens and optimizes workflow. Remember, that AI is here to make things easier for you and not complicate them. Mark out the areas that suck maximum time and effort and at the same time are crucial to business output.

  1. Set up pilot project for implementation of AI

You will definitely need the help of experts to analyze build a precise AI implementation strategy. AI switchover should have concrete project goal/s. Build your own team of experts or get professionals from companies specializing in AI consultation. They are the right people to give you longterm and short-term objectives of your business AI transformation.

Unless you have an in-house team of experts, it is difficult to close the gap between the technology and its appropriate use. A thorough understanding of the precise type of data and corresponding model creation are crucial to making the best use of AI for any business. Devote time in strategic planning of AI use to get the best return on investment through optimized operations or sales. In fact, the success of the pilot project should be the guideline for further implementation of the technology.

  1. Overcome AI implementation challenges with small steps forward

The secret to leverage AI for businesses the right way is to start small and use it incrementally with time. The best way to overcome all AI implementation challenges is feed selective data into the upgraded system. Pick the area that throws up maximum data and needs most repetition. It will prove the value of AI in your business process where you can collect feedback and move to the next phase of expansion accordingly.

There are two parts to introducing AI to your business operations. One is hardware and the corresponding systems, the other is humans that are responsible for running these systems. You cannot leave out your human resources when it comes to planning and forming strategies as you are likely to face the maximum challenge from this part. Discuss with your team, prepare them for a large operational change and the advantages of it. Put to rest efficiency and job insecurities explaining how it will increase overall efficiency and output.

  1. AI implementation strategy should include storage and hardware

AI is all about data, data warehouse, resulting solutions and their storage. Building the most accurate model for your business will need colossal amount of data use and sorting them for further use. Only the best algorithms can bring about result-oriented AI transformation in your business. Optimizing storage for data ingest, workflow and modeling is the way to ensure maximizing AI integration.

There is investment involved in implementing AI in business effectively to any business. Weigh out all possible options with the experts and see which will have the maximum positive impact. Keep in mind future scalability and addition of extensions to the AI arm. The overall AI system has to be built with balance taking into consideration limitations and challenges that both hardware and software pose. Data security too forms a big part of the system and safeguarding all storage is definitely an important part of any AI makeover. The kind of data will determine the type of security to be used.

  1. Cost of implementing AI includes training staff

Bringing in AI into your operations does not mean replacing existing team but training them or re-skilling them to handle new ways of operating. Large corporates and enterprises have reported enhanced worker skills and capacities with the use of AI. Training on the domain may be an expensive affair for start ups and small businesses. But surely there are smart workshops and online training sessions that can help upgrade the skills of your staff well within your budget. Remember that only an empowered workforce can help leverage the technology and balance the cost of implementing AI.

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10 Data Science Tools to Look For In 2020
November 18, 2019

The demand for competent data scientists is on a rise. They are hired by top organizations to get perceptions about the market, evaluate organizational risks, forecast sales, appraise risks within an organization, boost customer experience and improve the quality of consumer and industrial products. For implementing these tasks data scientists utilize various data science tools.

These tools help them to interpret, extract, leverage, pre-process and generate predictions from vast and complex structured and unstructured data. We have put together the list of top 10 data science tools to look forward to in 2020.

 

  1. Tableau

Tableau is a powerful software that visualizes and analyzes data. It is loaded with advanced graphics that help in visualizing geographical data and simplifying unprocessed data into a format that is easy to understand. The software can interface with databases, spreadsheets, OLAP, and cubes.

 

  1. TensorFlow

TensorFlow is an open-source data science tool with multi-dimensional arrays famous for its efficient analysis of raw data and computational capacities. With its ability to function on CPUs and GPUs, it has emerged as a powerful platform. The tool finds application in advanced ML algorithms such as Deep Learning.

 

  1. Matplotlib

Used for generating graphs by utilizing the analyzed data, this powerful data science tool is a popular choice to look forward to in 2020. With the help of this tool, you can generate complex histograms, bar graphs, and scatterplots. These complex graphs in the analytics tool can be created by simple coding.

 

  1. MATLAB

MATLAB is an advanced closed-source software that processes mathematical data. The analytics platform can be automatically generated by using code, output, and formatted text. The versatile and multi-paradigm numerical computing environment tool for data scientists can be used for image and signal processing, execution of algorithms, and demonstration of data through statistics. The tool also features a graphic library that is used for creating powerful visualization, processing of image and signal.

 

  1. SAS

SAS is a leader in the domain of analytics and cloud computing. It is closed source software that is used for analyzing and presenting data powerfully and interactively. It is mainly used by large organizations for statistical operations.

The trusted technology empowers businesses by converting their data into better decisions. So whether you are a data scientist or statistician, business analyst or forecaster, you can surely benefit from it.

 

  1. BigML

Next in the data science tools list of our countdown is BigML. It is a powerful data analyzing and visualization tool that is going to become more popular in 2020. It provides a fully communicative, cloud-based GUI (graphic user interface) setting for handling machine learning Algorithms.

BigML is the ideal tool that specializes in predictive modeling. Therefore, it is mainly used for estimating sales, analyzing risks, and creating new products. The best feature of the tool is that it is equipped with different automation methods and facilitates interactive data visualization.

 

  1. ggplot2

ggplot2 is an advanced software that creates powerful illustrations and visualizations. You can create maps of different styles such as chloropleths, hexbins, and cartograms. It can also deliver customized visualizations for enhanced storytelling with it.

The tool facilitates data scientists and statisticians to add text labels to data points and increases interactivity and aesthetic value of the graphical representations.

  1. Weka 

Weka (an abbreviation of Waikato Environment for Knowledge Analysis) is open-source GUI software with an interactable platform. The advanced software is an assortment of different machine learning algorithms that can be used for data mining. The most popular data science tool includes tools such as classification, regression, clustering, visualization, and data preparation.

 

  1. NLTK

NLTK is one of the powerful data science tools and techniques that’s going to dominate in 2020. It assists computers to learn and decode human language by developing statistical models that form a vital part of machine learning.

NLTK is an abbreviation of natural language processing and finds extensive application in different language processing methods like tagging, stemming, parsing, tokenization, and machine learning.

 

  1. Apache Spark

Apache Spark is a powerful analytics tool that is particularly created to deal with batch processing and stream processing. It features many application programming interfaces for helping data scientists to create repeated data access for the purpose of Machine Learning.

To conclude, the above-mentioned data analyzing and visualization tools are going to have a major impact in 2020. They are going to transform businesses and revolutionize the ways organizations operate in a competitive scenario.

 

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How AI is Revolutionizing Cyber Security & Preventing Breach of Data?
November 18, 2019

There is little doubt that Artificial Intelligence (AI) & Machine Learning (ML) are transforming the way industries are operating, analyzing risks, solving problems and increasing overall productivity. AI is also reducing street traffic, improving customer experience while shopping online, and making life comfortable with voice-activated digital assistants.  When it comes to the role of AI in cyber security, it’s going to be vital for protecting confidential information and intellectual property of users.

The adaptive machine learning algorithms and security software detect derelictions, phishing attacks, and suspicious activities pertaining to data theft and cyber-attacks.

 

How AI can be implemented in Cyber Security?

The incidents of cybercrimes and data thefts are on a rise. Large and small organizations transmit a huge amount of data through vast networks and online systems locally and globally, making them vulnerable all the time.

However, the conventional technology the organizations use cannot spot unauthorized access by hackers and potential threats of real-time data thefts. They cannot improvise and keep up with the tricks and contrivances adopted by the hackers.

Such loopholes and breach of cyber safety can only be prevented by using AI and adaptive machine learning algorithms. AI cybersecurity thwarts the stealing of confidential information and data, and perceives any cyber threat or suspicious activity.

The machine learning algorithms become effective and identify ongoing cyber-attacks on time. They are designed in such a way that informs the user about the cyber-attack as soon as it happens. They have the ability to learn and react in time to spot those malpractices.

So how do they work? Machine Learning facilitates computers to utilize and adapt algorithms according to the data received. The algorithms can learn and understand the data and take the appropriate steps automatically to prevent breaching of online security.

When the AI algorithms observe any anomaly or notice unusual activities that are beyond the standard patterns, it can block a user who is trying to get an unauthorized access. Thus, AI is competent to find solutions to complex cybercrimes by taking intelligent decisions at the right time.

The scope of AI is vast. Large and small organizations have used many methodologies and tactics of integrating AI into the domain of cybersecurity. Some of them are as follows:

  • Login Access through Biometric
  • Network Intrusion Detection and Prevention
  • Use of Ratings in Cybersecurity
  • Natural language Processing through Machine Learning

 

Advantages of Using AI on Cybersecurity

 

How AI is changing cybersecurity?

Well, the answer is, AI is going to have an extremely positive impact on cybersecurity. Here are some of the advantages of integrating AI and machine learning algorithms on cybersecurity.

Automated Detection of Cyber Crimes

Websites depend on AI and ML, to prevent unauthorized access, phishing emails (the process of obtaining username or password or credit card details by sending emails) and other malicious activities of hackers. AI-powered security systems can prevent cyber malpractices without manual help or human intervention.

They will detect the attack before pauses the entire system. No matter how tactful or adept the hackers maybe, AI will successfully thwart all such efforts.

The AI-powered cybersecurity in organizations utilizes predictive analysis that provides a faster and reliable approach to protect data of users and prevent suspicious activities of hackers. For individual users, AI uses a multi-factor authentication process that gathers appropriate credentials to give access to the system.

 

Secures Platforms

Biometric logins enabled by AI-powered systems have added to cybersecurity. AI-based cybersecurity systems investigate fingerprints, palm prints, and images of the retina which assures security to biometric login access. Face recognition technology is another potent biometric authentication. Here facial features of a user are identified by using in-built infra-red sensors and neural engines.

 

Ensures Accuracy

AI and ML are facilitating systems to predict cyber malpractices ahead of time and detect anomalies with a great deal of accuracy. The speed with which AI-powered cybersecurity systems carry out the task and beef-up the level of accuracy, can never be emulated by humans. Thus AI empowers organizations and individual users when it comes to protecting confidential information and phishing.

With the world embracing digital transformation at an unprecedentedly rapid speed, AI has already made its presence felt in cybersecurity. In years to come, there are going to be more significant developments and innovative approaches to incorporating AI in cyber security.

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Importance Of Data Science in AI Transformation?
November 15, 2019

Artificial Intelligence (AI), Data Science, Data Analysis and related terms remain as jargons unless one has a clear understanding of how it all works. The importance of data science is growing with each passing day and not without reasons. Let’s explore it in clear terms.

 

What is data science & data science importance today?

Data Science is the study of the heap of exabyte data emerging every day from various sources to extract readable and actionable knowledge and create data products. The methods, techniques and theories used, span a wide range of fields from statistics and probability models to signal processing and high-end computing.

 

Importance of data science importance

Data science is dedicated to finding patterns within the massive data available all around and in all fields. Depending on industry verticals and product use, Data scientists analyze the type of data available and form constructive models.  It is done using statistical methods that analyze and scrutinize to come to comprehensive results. A data scientist takes the responsibility of making predictions based on the data analysis. Data science importance today lies in the fact that these models are then used to analyze data from warehouses or other sources to augment business strategies or better the use of products.

What is Artificial intelligence

Artificial Intelligence is the simulation of human intelligence processes by computers and machines. Algorithms and computing systems reason and classify decisions that are usually done by the human brain. This is where the importance of data science comes in. The mentioned processes include acquisition of information and how they can be used, and using the rules to come to definitive conclusions. Artificial Intelligence can range from the simple narrow AI of same and repeated task to the large and strong ability to emulate human cognitive skills.

Machine learning

Artificial Intelligence can be viewed as a universal set with Machine Learning (ML) as a subset that concerns enterprise and consumer applications. Deep Learning (DL) in turn is a subset of machine learning and a power tool for areas like image recognition, natural language processing, sentiment analysis and other areas containing high-quality data to achieve models of high accuracy.

Deep learning

Deep Learning is the specialized subset of AI embedded within Machine Learning where machines emulate the working of human brains, process data likewise and create patterns used in decision making. It comprises of networks much like neural lines that are capable of learning from unsupervised, unstructured and unlabeled data. This has also given it the name deep neural learning.

An example of deep learning can be that of feeding random images of cats to a system. It learns about the basic features of a cat like its size, shape, whiskers, eyes, ears and tail helping it identify a cat among other animals.

Basic steps in data processing leading to AI transformation

The entire process emulates the structure of a pyramid moving from the base to the apex. This is how it data leads to AI transformation:

Data collection: The rock-solid bottom of the pyramid is formed of the right data sets triggering the processes of AI and ML.  It is crucial that all factors for allowing the flowing in of the data are enabled.

Data analysis & treatment: Understanding the types of data, their value range, any data missing from the scene and the statistical analysis of all that is available helps making them comprehensible.

Data exploration & transformation:  Relationship between all the data available is now mapped, those irrelevant dropped and data that will have a bearing are derived. This step makes the pyramid stronger.

Data training: This is the stage when analytics or business/product intelligence is built. In fact, it is the base on which the ultimate and goal-specific Artificial Intelligence is built. Importance of data science here is all about knowing predictions and organizing the training data with precise labels.

Data experimentation: Experimentation is the final stage where all potential problems are mitigated. Changes are incorporated and their results assessed before they are passed on for final use.

Why is data science the future of AI?

The answer is that AI has developed over the years based on data science. Systems follow human-generated pre-programmed tasks and carry them out independently. From automated responses to customer-service desk assistance, identifying and reaching out potential customers to self-driving cars, virtual doctors to natural language processing and emulating emotions, the use of AI encompasses almost everything that humans is about.

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Predictions on the Impact & Future of Artificial Intelligence
November 15, 2019

Artificial Intelligence is touching every sphere of our lives. Technologically it is making its presence felt in online retail marketing, smartphones, games, robotic vacuum cleaners, and assistive car systems, and the list only grows! Even businesses, IT, transportation, healthcare, are transformed and revolutionized by AI-driven cutting-edge tools.

It is expected that AI and robotics will be entirely integrated into business operations and change the way organizations operate in the future. AI will help organizations become more methodical, increase ROI, reduce operational costs, assess risks and add value to them.

 

A brief study on the future of artificial intelligence

Artificial Intelligence is expected to transform our lives with driverless cars, AI algorithms identifying our health issues precisely, and educational programs becoming personalized. It promises to take us to an unthinkable virtual world.

AI made its presence felt some time back when IBM developed Watson in 2011, that defeated human champion players at the US TV game show Jeopardy.

Watson is a computer system that comes in question-answering format. It is capable of answering questions posed in natural language. In recent years, Google developed DeepMind AI called AlphaGo that defeated the world champion Go player.

However different experts, researchers, and entrepreneurs have different opinions about the future of artificial intelligence when it comes to having an impact on our lives in the future.

Michael Milford, a renowned professor of Electrical Engineering at the Queensland Institute of Technology and a researcher of Robotics and Automation, feels basic machine learning algorithms strengthen many technologies that we interrelate within our daily lives. He believes AI can play game-changing roles in voice and face recognition, online marketing, and assistive car systems.

Peter Stratton, a reputed researcher of Robotics and Automation believes automobile manufacturers and education institutions are going to benefit immensely from advanced AI tools if they utilize them at an optimum level.

AI is fast taking over the domain of transportation. With self-driving cars and driverless trains, it is all set to revolutionize the transportation sector. No more dependence on humans to drive us on road.

Similarly, global education will get a new dimension with AI-powered tools. It will empower and equip students with fundamental IT skills. Students can utilize it for personalized learning options, getting smart content by eliminating the barriers of boundaries and undertaking any course from anywhere.

When it comes to personalized learning options, Artificial Intelligence is set to take over classroom assignments and final exams. This, in turn, will benefit students and ensure that they get the best academic assistance.

 

Artificial Intelligence future ideas: How it will affect jobs & society?

As debates continue how AI will impact the job scenario in the future, there is a growing fear among people that several jobs will be digitized or managed by robots.

While it is true that many jobs will be automated, on the other hand it will also create unlimited job opportunities too. With automation, the demand for highly-skilled jobs that require complexity and non-standardization will be on rise.

The service sector is likely to get benefitted with substantial job gains. It is because the increasing economic growth is expected to empower consumers to spend more on services.

There will be setbacks too. Machines can never have the same emotional competencies as that of humans, but there are certain jobs that will be under threat. It includes jobs that have the most elementary levels of emotional understanding and communication.

In the healthcare sector, a virtual doctor will be able to provide customized treatments as per the lifestyle, health conditions, environment and genes of a patient. They will also be able to monitor the exact condition of the patient and provide suitable exercise and lifestyle suggestions.

 

Is AI really the future?

Experts believe that within the next 10 years, Artificial Intelligence and Machine Learning will take over and dominate by empowering businesses, and increasing security and generating new products. So, is AI really the future? Yes, surely.

Those organizations that will integrate AI tools in their business operations will become more methodical. Automated businesses will enjoy more productivity, increased ROI, reduced operational costs, and assessment of risks.

When it comes to security, drones will transform the way we live. They will accelerate the speed of urgent package delivery, especially of medicines. It is expected AI-driven drones will become an integral part of our lives and change it drastically just like the internet and smartphones have done.

Another way AI will revolutionize our future is by generating new services including better customer services in start-ups. By going automated, companies can integrate chatbots that will result in superior customer experiences. AI and robotics are expected to improve our problem-solving abilities and bring out innovative ideas.

To sum up, artificial intelligence, robotics and machine learning are surely going to change our future for the better. We have started feeling the effects already.

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Awesome Tips to Select Data Science Consulting Company
October 20, 2019

Data has become a quintessential part of every business. Every large and small organization has to deal with complex data to run their business efficiently.  With data becoming so valuable for businesses, data science consulting company and AI (artificial intelligence) specialists have risen into prominence. Business-transforming big data, machine learning, and analytics are trending and generating a lot of interest.

analyzing data

Data analytics companies have experts who amalgamate mathematics, algorithms, science, statistics, and business knowledge to analyze the data presented to them. However, if you are an entrepreneur, it may not be an easy task to select a professional data science consulting company. But we have got 7 valuable tips that will surely help you in this direction.

 

Understand your business requirements

Clarify your goals and prioritize your objectives before hunting for a professional data science firm. At first, you need to find out why do you need to hire the consultation service of a big data consulting company? Does your business need third-party solution integration, or R&D or custom-made solution development? Are you looking for Big Data analytics or business intelligence?

If your own company does not have a team of dashboard experts and specialists, then, you need to start from scratch while choosing for a data science consulting company.

 

Evaluate your organizational goals

You need to understand the business objective or purpose for which you need it. Do you need the service for acquiring insights about your business or is it for boosting your operational efficiency?

Are you looking to increase your ROI, automation of processes, developing chatbots, becoming cost-efficient, evaluating business risks, enriching customer experiences, or it is just for leveraging exponential growth of your overall business? So, it is important to assess the objective of your organization.

 

Look for expertise

Before hiring a data science consulting firm, you must ensure that it has the requisite expertise and prior experience that will help your business reach the next level. Besides sound technical knowledge, the company must possess good presentation and communication skills.

Top data science companies have extensive knowledge and flawless understanding of a gamut of related areas such as:

  • Machine learning
  • Online analytical processing
  • Data mining
  • Data warehousing
  • Technical and data architecture
  • Big data and analytics
  • Data engineering
  • Risk analysis
  • Price sales or demand forecasting
  • Dashboard development
  • Market and customer segmentation and others

 

Find out the technologies used by the company

analyzing the data using data science

For analyzing and extracting the data, applying the algorithms, and visualizing the findings, data science professionals use advanced tools and technologies. You need to find out what latest technologies do they use or integrate while providing the solutions. Nowadays data companies use SAP, Python, Java, Hadoop, Matlab, Qlik, Tableau, Cassandra, Sisense, .NET, PHP, Spotfire, Pentaho, and others for efficiency and real-time results.

 

Take a look at the skills and experience level

Make sure the data science consulting firm you are hiring develops and tests analytics and algorithmic models with accuracy. For this, check out their accreditations, skills and experience levels.

 

Read the testimonials

Go through the testimonials and read what previous clients say about the company in terms of services, expertise, knowledge, and customer care services. You can find several feedbacks on the official website of the company itself. You can also take a look at the portfolio of the company that you intend to hire.

 

Discuss the pricing technique

You must know that every company operates differently and have a different approach to pricing. Hence, before hiring a data science consulting company, you should discuss the mode of payments, time of payments, pattern of payments, and extra fees (if any) to keep disputes at bay.

With a plethora of data consulting companies in the market, getting confused is easy. But if you have a clear project objective and fundamental understanding of the expertise you want, then you can surely hire the right company. Follow the above-mentioned 7 tips that will surely help you in this direction.

 

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A Comprehensive List of Top 10 Business Intelligence Tools in 2019
October 18, 2019

Business Intelligence tools are tools that leverage a set of methodologies and techniques to analyze, prepare, and present raw data.

According to industry analysts, business intelligence tools turn the raw data into actionable business information and help business owners to make more effective and data-driven decisions. They further add that the set of methodologies and technologies used by these tools depends on the end goal of the business.

In this blog, we’ve provided a list of 10 business intelligence tools that you must use for an effective BI strategy in 2019.

 

  • BIRT

BIRT is one of the most popular open-source business intelligence tools. The tool is immensely beneficial for creating data visualizations and reports. The best part is that it can easily be embedded in web applications. The main components of the tool are report designer, a runtime component for generating designs, and a charting engine.

  • ELK STACK

This is yet another open-source tool commonly used for centralizing data streams and then visualizing the information in real-time. Easy to use dashboard and simple user interface make it one of the best business intelligence tools for an effective BI strategy.

  • SAP Business Intelligence

SAP BI Tools

SAP Business Intelligence offers a plethora of advanced analytics solutions. This includes real-time BI predictive analytics, machine learning, and planning and analysis. The business intelligence platform, in particular, offers reporting and analysis, data visualization and analytics applications, office integration and mobile analytics. To put it simply, SAP is a robust software that has a variety of functionalities and serves a variety of roles.

  • MicroStrategy

MicroStrategy is a business intelligence with a powerful dashboard and data analytics system. According to experts, MicroStrategy helps in monitoring trends, recognizing new opportunities, and improving productivity. Best of all, users can use this tool whenever and wherever they like. It doesn’t matter whether the data is fed from a spreadsheet or cloud-based software, the tool is capable of analyzing all.

  • Yellowfin BI

Yellowfin BI is a business intelligence tool and end-to-end analytics platform. The USP of the tool lies in combining visualization, machine learning, and collaboration. Users can easily filter through the data with intuitive filtering. The multi-functional dashboard is another significant advantage of this business analytics tool.

 

  • Dundas BI

Dundas BI is a flexible, browser-based intelligence tool that enables users to connect to data sources in real-time. The tool offers great visualizations in the form of charts, tables, and graphs. The best thing about this tool is that the data sets are completely customizable and can be viewed from desktops, as well as, mobile devices. Users can also build their reports and monitor performance metrics.

  • TIBCO Spotfire

TIBCO Spotfire is a premier self-service business intelligence tool that has a multi-functional dashboard, data visualization, analytics, and KPI push to mobile devices. Experts believe this tool is one of the best in the industry for collaborating with the digital team. Users can define their own KPIs and send customized alerts to iPhones, as well as on androids. The most exciting part is that it can be easily integrated with CRMs, ERPs, Excel, and Access.

  • Microsoft Power BI

Power BI Tool

Microsoft Power BI is a web-based business analytics tool suite that is known for its industry-leading data visualization capabilities. The tool allows users to identify trends in real-time and predict future trends and behavior of customers. Being a web-based application, Microsoft Power BI can be accessed from all devices.

 

  • Helical Insight

Helical Insight is an open-source BI framework that is known to provide e-mail scheduling visualization, exporting, multi-tenancy, and user role management. The API-driven framework of the tool allows users to up additional features and customize it according to their needs. Additionally, the instant BI feature enables users to type questions in a Google-like interface and receive relevant reports and charts.

  • Kyubit Business Intelligence

The business tool from Kyubit is unarguably among the best free business intelligence tools available out there in the market. It allows users to create dashboards and reports from their OLAP, SQL, and CSV data. The tool is easy to use and is appropriate for professionals having knowledge of business analytics.

So, here was our list of top 10 business intelligence tools for an effective BI strategy. Let us know in the comment section if we’ve missed out any.

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Impacts of Artificial Intelligence in Everyday Life
October 17, 2019

Before we highlight the impact of artificial intelligence on society, let’s understand what the term ‘AI’ or ‘Artificial Intelligence’ means.

Artificial Intelligence or AI is a branch of computer science that focuses on creating intelligent machines having the ability to react and work like humans. According to experts, knowledge of engineering is essential for developing advanced artificial intelligence algorithms.

artificial intelligence future world

They further add that artificial intelligence in the future is expected to revolutionize every area of life if they’re fed with abundant information relating to the modern world. This includes data related to objects, categories, properties, and relations between all of them.

Machine learning is the heart of AI. It primarily deals with the capability of the machines to use sensory inputs to deduce the different aspects of the world and visual inputs.

Experts further add that the sole purpose of developing smart machines integrated with AI is to make life easier and convenient, bolster economic growth, and detect chronic diseases. Now, let’s understand how it impacts our everyday life.

 

Examples of artificial intelligence in everyday life

  • Email Filters in Gmail – The search engine giant Google leverages the power of AI to ensure that all the email landing in your inbox is authentic and no spam is delivered. Their filters sort emails based on the following categories –
  • Primary
  • Social
  • Promotions
  • Updates
  • Forums
  • Spams

Furthermore, the program helps organize the emails based on the importance of the conversation so that the users don’t feel any difficulty while finding important communications. Google claims that they have succeeded in preventing more than 99% of spam emails from reaching their user’s inbox. As per industry veterans, this is one of the greatest advantages of artificial intelligence.

  • Smart Replies in Gmail – This is yet another classic example of artificial intelligence in our everyday lives. The ‘Smart Reply’ feature on Gmail allows users to respond to emails with phrases like “Thanks” or “Let’s do it” in just a single click. According to Google, smart replies are tailored according to the content of the mail.

 

  • Nudging Reminders in Gmail – The nudging feature of Gmail reminds you to follow up emails that you’ve forgotten to reply. Using AI, Google tries to determine the mails that most probably require a response from the user. Then, it highlights the set of mails after it’s being ignored for a few days.
  • Employee-Employer Matches on LinkedIn – LinkedIn is a social media giant that aims to connect employees with employers and vice-versa. LinkedIn leverages AI to provide better suggestions to job seekers and employers seeking skilled employees.
  • Facebook Proactive Detection – In 2017, Facebook introduced a Proactive Detection Feature that leverages AI for scanning posts and detecting patterns. The goal is to check whether a user is considering self-harm or posting something that is violating the community guidelines.

artificial intelligence advantage

If the algorithm by any chance detects suicidal thinking patters or detects some content that is violating the guidelines, it automatically sends signals to friends or relatives or bars the user from posting the content.

  • Mobile Banking – AI is used by banks for personalizing the experience of the users on their mobile applications. For instance, Wells Fargo (one of the largest commercial banks of the U.S) analyzes account information to provide personalized alerts such as bill-pay reminders, overdraft alerts, and transfer alerts.

 

  • Maps and Direction – There is barely a netizen who doesn’t use Google Maps. All the features of this application have been developed using advanced artificial intelligence. The ability to calculate traffic to find the quickest route to the destination is due to the advanced AI algorithms.

 

Artificial Intelligence Conclusion

AI powers a lot of other programs and devices that make our lives easier. This includes connecting with friends, using email programs, or booking a cab. To get a better insight into artificial intelligence and its role in day-to-day activities, you can get in touch with Visvero.

We have a team of highly-dedicated professionals having in-depth knowledge of artificial intelligence and machine learning.

 

 

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What is The Importance of Business Intelligence Tools and Techniques in Industry?
October 16, 2019

21st Century is the age of information technology. With the world now drifting towards digitization, the amount of data generated every second is massive. Veteran market analysts are of the view that if businesses can capture, analyze, and predict the trends of the consumer from these raw data, they’ll succeed in breaking the ceiling and paving the path to success. Business Intelligence Tools and Techniques, in such a situation, can be of great help!

business intelligence tools and techniques

However, business owners need to understand that analyzing the data and predicting consumer behavior is not an easy affair. Compiling petabytes of data and creating a valuable product(s)/ service(s) requires sheer analytical skills.

What is business intelligence and analytics?

business analytics industry

In simple terms, business intelligence is the art of analyzing raw data and predicting consumer behavior. According to experts, it’s the method of using business intelligence technologies for collection, integration, analysis, and presentation of business data to improve the decision-making capabilities of a business.

Business intelligence tools are the heart of BI and data analytics. They’re primarily software or applications developed to help locate, retrieve, analyze, and document data. According to expert industry analysts, business intelligence makes the flow of data manageable and help businesses extract maximum information from unstructured data sets.

Advantages of leveraging business intelligence tools and solutions

Now that you’ve understood what business intelligence is and how it helps businesses leverage the power of data, you might be thinking about how it can help the global economy leap forward. Well, then let’s understand the power of business intelligence and its importance.

  • Completely eradicates guesswork – Often, business owners or directors are left with no other option, but to make the best guesses or go with their gut feel. Needless to mention that both these strategies can put the business at stake. However, business intelligence techniques for decision making help business owners by providing them with accurate data, real-time updates, and predicting trends.
  • Reduces Response Time – Business queries are common. Data integration approaches in business analytics help business owners get immediate answers to their questions. This way, they’ll be able to make quicker decisions, based on the analysis provided by the business intelligence tools.

 

  • Helps obtain important business metric reports – Business intelligence software provides access to important company data such as business metrics, dashboards, and other critical reports. Best of all, it can be accessed even through mobile devices. As long as there’s internet access, business data can be obtained and utilized by any member of the organization.
  • Provides valuable insight into consumer behavior – It’s almost impossible to sustain in this highly-competitive market without proper knowledge about the types of goods the customers prefer. Business intelligence tools and techniques provide business owners with a clear insight into consumer behavior and also an opportunity to turn such insights into profit by altering the existing product(s) or service(s) or introducing new ones.

 

  • Improves Efficiency – Extracting the requisite information from data is a herculean task. This is where business intelligence technologies step in to fil in the gap! The SAP business tools not only help in gathering the data but also help in converting and merging the same in a single domain. The best part is that it can be easily accessed from one dashboard and converted into a report. This again saves a lot of time and energy and also helps reduce inefficiency.

 

  • Streamlines Operations – Streamlining business operations is now the biggest challenge. Business intelligence has proven its prowess on multiple occasions as an effective procedure for streamlining business operations by providing detailed insights into business performance. The premium business intelligence tools available also pinpoint the areas that are required to be modified for streamlining the operations.

 

Boost Your Sales and Revenue with Strategic Business Intelligence Services

If you’re seeking ways to improve your sales figure, then you must avail strategic business intelligence services from a reputable company. Visvero is one of the leading companies in the business intelligence industry and has been helping businesses expand their sales and revenue for over a decade.

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Data Virtualization: The Modern Data Integration Solution
July 10, 2019

Data virtualization is a modern data integration approach that is already meeting today’s data integration challenges, providing the foundation for data integration in the future. This paper covers the fundamental challenge, explains why traditional solutions fall short, and introduces data virtualization as the core solution.

Click Here to read more (PDF, 11 Pages, 1.84MB)

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