Importance Of Data Science in AI Transformation?

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.

Predictions on the Impact & Future of Artificial Intelligence

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.

Awesome Tips to Select Data Science Consulting Company

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:

 

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.

 

A Comprehensive List of Top 10 Business Intelligence Tools in 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 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.

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 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 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 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 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 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.

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 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.

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.

Impacts of Artificial Intelligence in Everyday Life

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

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.

 

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.

 

 

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.

 

 

What is The Importance of Business Intelligence Tools and Techniques in Industry?

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.

 

 

 

 

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.

Attending EMSDC ROAR (Return On All Relationships) Event

RETURN ON ALL RELATIONSHIPS

On September 17th, we expect a full day of B2B matchmakers, corporate roundtables, dynamic speakers, targeted workshops, AND new this year, “Ideation Sessions” during this wonderful event presented by EMSDC. They’ve also added a pre-conference workshop on September 16th for diverse businesses to work on their pitches followed by the Welcome Reception at Landshark Bar and Grill on the beach.

This is one event you surely don’t want to miss. We look forward to seeing you there. Click here to register.

Digital Transformation : Conagra Case Study

CIO Helps Conagra Turn Food Trends Into Products

A case for how  Conagra’s CIO, Mindy Simon, leveraged the AI platform to use technology to help drive growth, including the launches of new products under the company’s existing brand names. The platform sources data from the likes of Facebook, Google, Instagram, Pinterest as well as from various Market Research companies, with frequent updates. Pulling Data is automated, as is prepping it for analytics applications.

The tool lets the business identify “pockets of growth” that might have otherwise gone unnoticed. This is largely because the platform can tie data together in one place.

 

Conagra AI application ( 2 pages / 850KB)

 



Copyright (c) 2019 Dow Jones and Company, Inc. CIO Journal  07/22/2019

Data Virtualization: The Modern Data Integration Solution

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)

Business Intelligence in the age of Analytics and Artificial Intelligence

ADVANCE TO INTELLIGENT DECISIONMAKING

Jonathan Bach & Arvind Handuu

LET US START WITH GETTING THE BASICS OUT OF THE WAY

– DATA IS NOT THE NEW OIL, IMPORTANT, YES BUT BUSINESS IS ABOUT DOING BASICS RIGHT,

– ALL BUSINESSES ARE NOT INFORMATION BUSINESSES,

– ARTIFICIAL INTELLIGENCE IS NOT INTELLIGENCE, JUST BEST GUESS

– DATA BY ITSELF AND DEVOID OF THE CONTEXT IS WORTHLESS

These days, one cannot pick any trade publication and not notice a convergence on dia del sabor, namely Analytics and its ‘on-steroids version’ Artificial Intelligence (AI). The collective wisdom points to the efficacy of data utilization to transform the business. Many of our clients have gone through this stage of data envy. Some continue to allocate disproportionate scarce resource of time and treasure to “monetize their data”.

Like all gold rush stories, this one is fraught with peril. The primary reason appears to be the significance attached to the technology as opposed to the business objectives. We’ve seen this before with corporations jumping from one tool to another in hopes that the next tool will be the one to save the initiative, company or the project. The problem is in training, or lack thereof, of the training in the organizations to ask the right question. In this, the question becomes the answer.

The goal, it appears, is to replace or at least augment an organizations intuition and experience-based decision making with an element of data centricity to avoid missteps and to shine a spotlight on certain blind spots. The data centricity assumes a level of Data availability and education or without this any further progress is impossible. We propose a following step approach to help clients adequately optimize the operations and leverage data assets where possible and necessary.

Automate and Optimize – most businesses have implemented some level of automation, some more than others, the starting point is that. Before one begins any large-scale transformation, challenge what is already implemented. Even if it is nothing more than a general ledger at this time. This is a foundational step to transforming a company. The key watchword in this stage is flexible and adaptable. Know that what we build here will need to be changed and rebuilt. The automation platform should be flexible and adaptable.

Digital from the outset – This is an appropriate analytical step in the organization’s life cycle, as the model should be to think innovatively about key aspects of the business. The approach here is to develop transformative competencies that have a potential to be disruptive, think Uber to Taxis, Airbnb to hotels and also operations transparency. In this stage the company starts to build data competencies.

Value creation – with the core of data collection in place, the organization may launch the build stage of analytics framework. The organization decides to make key data available to executives for informing tactical decisions and strategic choices. The key success factors in this process are:

Analytics Evangelist – an organization needs to allocate a role of developing data insights and educating line managers on the possibilities of data analysis

Speed to deliver – Time kills energy, this is true in physics as it is in companies. The organization should have the necessary strategy in place to make the results of analytics available to the managers within on a few days of demand

Keep it simple – an organization must simplify the learning and deployment of analytics by reducing rework. Its often helpful to choose technology that offer a consolidated BI and analytics approach.

Adopt and adapt – Reduce human variability – The weakness of the data driven decision-making is often in ignoring the human factors in adoption:

Education- Implement programs to drive overall data competency in an organization

Build Momentum through ease of access, make it easier for executives to get analytical answers

Implement and measure the use of analytical elements in the corporate choice selection

Reward the utilization of analytical tools

New applications – Encourage the use of analysis, by promoting new applications and new analysis. Share and promote.

Critique – Set periodic review points to analyze the decisions made and supported by hard measured data as well as decisions made without the adequate data. Compare outcomes.

Educate and Evolve – Business Intelligence and Analytics is a high ROI and can be implemented very cost effectively. The organization needs to ensure a steady process of training the users about new data and analysis being made available and effective strategies for utilization

In the end, Data can be a very effective tool available to the decisionmakers in an organization, the power of data becomes available to the organization as more investments are made in development of human capital to ask the right questions of the business. Features in modern BI and Analytical Tools can help users overcome delays and difficulties by automating aspects of data exploration and analytics development and delivering information answers and recommendations to users in the context in which they need it.


Jonathan Bach is a Client Solutions Partner at Visvero, Inc. He works closely with various F2000 clients in optimizing the use of professional services for “ADVANCE”; ing along the BI/ Analytics path. Jon is based out of the Visvero, Pittsburgh.

 

Arvind Handuu is a Practice Manager for Business Intelligence & Analytics at Visvero. Arvind is an analytics value purist. He believes that a BI & Analytics platform should be a self-contained and sovereign solution. “Its value drops to zero the instant you are using a different data source to inform your decision.”; Arvind is based out of Visvero, Pittsburgh.