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Quantum Computing Going Commercial: IBM and Google Leading the Trail

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Quantum computing is all set to make a debut in the commercial world – tech bigwigs, like IBM and Google are making an attempt to commercialize quantum computing. Julian Kelly, a top notch research scientist at Google’s Quantum AI Lab announced with a joint collaboration with Bristlecone, a quantum processor that offers a testbed for various research activities on quantum technology and machine learning, quantum supremacy can be achieved and this could be a great stepping stone for building larger scale quantum computers.

QUANTUM COMPUTING GOING COMMERCIAL: IBM AND GOOGLE LEADING THE TRAIL

After Google, IBM is also making significant progress in commercializing quantum computing technology by taking it to the cloud in 2016 with a 5 qubit quantum computer. Also, last year, November they raised the bar by declaring that they are going to launch third generation quantum computer equipped with a 50 quibit prototype, but they were not sure if it will be launched on commercial platforms, as well. However, they created another 20 qubit system available on its cloud computing platform.  

Reasons Behind Making Quantum Computing Commercialized:

Might lead to fourth industrial revolution

Quantum computing has seeped in to an engineering development phase from just a mere theoretical research – with significant technological power and constant R&D efforts it can develop the ability to trigger a fourth industrial revolution.

Beyond classic computing technology

Areas where conventional computers fail to work, quantum computing will instill a profound impact – such as in industrial processes where innovative steps in machine learning or novel cryptography are involved.

Higher revenue

Revenues from quantum computing are expected to increase from US$1.9 billion in 2023 to US$8.0 billion by 2027 – as forecasted by Communications Industry Researchers (CIR).

Market expansion

The scopes of quantum computing have broadened beyond expectations – it has expanded to drug discovery, health care, power and energy, financial services and aerospace industry.

From cloud to on-premise quantum technology

To incorporate quantum computing into the heart of the business operations’ computing strategy, the companies are contemplating to add a new stream of revenue by implementing quantum computing via cloud. In the future, it’s expected to see a rise in on-premise quantum computing – because the technology is already gaining a lot of accolades.

Better growth forecasts

In the current scenario, the quantum enterprise market is still at a nascent stage with a large user base in the R&D space. But by 2024, it has been forecasted that this share would be somewhere around 30% and the powerful revenue drivers will be industries, like defense, banking, aerospace, pharmaceutical and chemical.

IBM or Google? Who is a clear winner?

In the race to win quantum supremacy, IBM is a sure winner and has made stunning progress in this arena, even though it is receiving stiff competition by Google recently. Google’s new quantum processor Bristlecone has the ability to become a “compelling proof-of-principle for building larger scale quantum computers”. For this, Julian Kelly suggested, “operating a device such as Bristlecone at low system error requires harmony between a full stack of technology ranging from software and control electronics to the processor itself. Getting this right requires careful systems engineering over several iterations.”

 

As last notes, quantum computing has come out from being a fundamental scientific research to a structural engineering concept. Follow a full-stack approach, coupled with rapid testing and innovative practices and establish winning control over this future tool of success.

In this endeavor, DexLab Analytics can for sure be of help! Their business analytics certification online courses are mindblowing. They also offer machine learning using python courses and market risk training – all of them are student-friendly and prepared after thorough research and fact-finding.

 

The article has been sourced from – https://analyticsindiamag.com/why-are-big-tech-giants-like-google-ibm-rushing-to-commercialize-quantum-computing

 

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How to Develop a Data-Driven Culture among the Employees and Organization?

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Data creation and consumption is exploding. So is the challenge of analyzing data and transforming it into actionable insights.

How to Develop a Data-Driven Culture among the Employees and Organization?

The expert consultants at IBM say – 90% of the data in the world has been created in the last couple of years at a rate of 2.5 quintillion bytes per day. Just imagine, when such gold mines of information go unused, what toll does it take on companies who harbors them? The loss is insurmountable, isn’t it? 

But the question that makes us brood is how do companies empower employees to use such vast pools of data to reveal hidden business insights?

To keep pace with an accelerating growth of data generation and robust competitive landscape, new age companies need to shift their focus to sound Business Intelligence solutions that helps in collecting and analyzing data to determine patterns and alert users, in case of anomalies in the nature of business. And the good news is that they are doing so.

Once companies start off with data handling and data mining, the issue of utilization needs to be addressed next – the trickiest problem with data utilization is the insights that take place within the departments –while creating data silos. Siloed data results in creating a lot of issues, but the larger one is that it creates only a partial view of whatever is happening within an organization and a bigger picture is not available.

As a result, a data-driven culture is to be adapted – but how?

 

Let’s Take Your Data Dreams to the Next Level

Right from the top-level

The true DNA of any organization lies within its top-level management team, including the founders and managing directors – and that’s where the foundation stone of data-driven culture should be implanted. Implementing something new and offbeat is intimidating, but when the company leaders promote it, the idea gains familiarity and merges with other data-driven decisions.

Empowerment

Employees need to feel empowered – then only they can independently mine data and share crucial findings with colleagues and seniors without asking for help from the IT guys. A common misconception that exists around is that data analytics is a cumbersome task which requires heavy involvement from IT- but in reality, things are changing – BI tools are being revamped to make users more independent and self-sufficient to take better business decisions.

Time and again, it’s important for the executive management to show some token of appreciation to employees for their hard work. It is in these subtle ways the data-driven culture gets promoted across company walls.

 

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Sharing is caring

Now, the data siloes come into the picture. Once employees are comfortable with using data and bearing the fruits of success – insights need to be shared across the business fronts to draw a much larger picture.  Not only it promotes cross-team and departmental collaboration, but also brings in newer data into the limelight that wouldn’t have been possible before. Hence, sharing of insights is crucial for business success.

From the above discussion it is clear, insights gained from data is extensively beneficial for business, as they offer new answers for innovation and development. However, achieving data nirvana is no mean feat – the steps highlighted above should be followed, and then only the companies would be able to achieve their desired goal, i.e. a seamless data-driven culture.

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Sources: http://dataconomy.com/2017/05/data-nirvana-develop-data-driven-culture

How This Bengaluru Startup Is Using AI to Detect Early Stage Breast Cancer in Women

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The World Health Organization says – one out of two women diagnosed with breast cancer dies within five years in India. In the US, the fatality is less than one out of five, and in China, one out of four. Shortages of technology for early detection and radiographers coupled with the expense of regular screening, which normal people find too expensive to afford in India have led to an increasing number of breast cancer cases of late. Today, breast cancer has outstripped cervical cancer as the major cause of cancer death among women in this country.

 
How This Bengaluru Startup Is Using AI to Detect Early Stage Breast Cancer in Women
 

A Bengaluru-based tech startup and the brainchild of Geetha Manjunatha (CEO) and Nidhi Mathur (COO), NIRAMAI offers breast cancer screening solution by combining artificial intelligence, machine learning and cloud. It aims at tackling the issue of accessibility and expenses of breast cancer screening. These two dynamic women had seen cancer very closely in their family and feel an emotional connect with anyone who is diagnosed with this deadly disease. This led to the conceptualization of NIRAMAI, which means BEING WITHOUT DISEASES in Sanskrit. Also, it’s an acronym for “Non-Invasive Risk Assessment through MAchine Intelligence”.

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The Working Principle

The breast cancer screening solution by NIRAMAI is non-invasive, non-contact and non-radiation process of detecting early stage breast cancer amongst women of all ages. The deep technology that it claims to have patented is Thermalytix technology – a fusion of top-grade machine learning algorithms over thermal images.

“Thermography is well known to sense earliest signs of cancer. However, traditional manual interpretation of a thermogram has not been accurate enough to become accepted as a standard of care. Interpreting 400000 colour values in thermograms and to diagnose breast abnormality is a huge cognitive overload to a radiologist – use of machine learning enables automated analysis and helps in better interpretation of thermal images and considerably improves the overall accuracy of diagnosis”, says Geetha, one of the cofounders of NIRAMAI.

The working mechanism of screening in NIRAMAI is quite simple, and effective. The women who want to get screened need to relax for the first 10 minutes before taking up the test. Then a high resolution thermal sensor is kept at a distance of 3 feet from her to measure the temperature distribution on her chest and generate thermal images. Next, the NIRAMAI software scans these thermal images to automatically initiate a screening/diagnostic report and hands over a radiologist-certified report to the women. The test is performed in a highly intimate manner, the women undertaking the screening is neither touched nor seen by anyone.

“This is unlike mammography which is based on X-Ray and is recommended for women above 45 years only once in 2 years. It is also noncontact and doesn’t require any breast compression; hence not painful. Since the equipment is very portable, it is amenable to be used in outreach programs being a rural camp or urban corporate screening,” she shares.

Overcoming challenges

In healthcare space, analytics and AI are dubious topics. It takes a lot to coax a doctor to use an AI tool as an aid in his diagnostic procedure – countless discussions, several experimental trials and after a lot of effort, NIRAMAI could finally step into and create a niche of their own.

Another challenge was to have an edge over their competitors, who once knew that they are out with a revolutionizing technology, would like to sell everything to copy that. For that, they have armed themselves with 10 patents in this area, which is somewhat protecting them from other players.

Since breast cancer is a big health issue in India, the NIRAMAI team feels that it is extremely important for women to go for regular screening. It is safe and in most cases, early detection helps keep cancer at bay.

The power of analytics is huge. Arm yourself with a powerful data analyst certification Delhi NCR. It will help you go a long way!

Some parts in this blog have been sourced from:

https://analyticsindiamag.com/this-women-led-startup-is-using-ai-thermal-imaging-to-detect-breast-cancer

https://www.techinasia.com/startup-patented-ai-tech-breast-cancer-screening

 

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Data Analytics: The Key to Track and Curb Leakages in GST

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Though our country may have got a One Nation, One Tax Policy, in the face of GST, its revenue collection figures are not so encouraging. In the beginning, GST revenue collection for the first three months went over 90000 crore, but the figures started dropping from October to 83346. And in November, it further slipped to 80808 crore. Since then, the figures mostly lingered around 86000 in the recent months.

 
Data Analytics: The Key to Track and Curb Leakages in GST
 

The Union Ministry of Finance had to figure out the reason of this discrepancy, the reason behind such huge revenue leakage in GST collection before it’s too late, and for that data analytics came to the rescue. After carrying out a thorough analysis, on its 26th meeting on Saturday, GST Council discovered several major data gaps between the self-declared liability in FORM GSTR-1 and FORM GSTR-3B.

 

Highlighting the outcome of basic data analysis, the GST Council stated that the GST Network (GSTN) and the Central Board of Excise and Customs have found some inconsistency between the amount of Integrated GST (IGST) and Compensation cess paid by importers at customs ports and input tax credit of the same claimed in GSTR-3B.

 

 

“Data analytics and better administration controls can help solve GST collection challenges” – said Pratik Jain, a national leader and partner, Indirect Tax at PricewaterhouseCoopers (PwC).

 

He added, “Government has a lot of data now. They can use the data analytics to find out what the problem areas are, and then try and resolve that.” He also said that to stop the leakage, the government need to be a lot more vigilant and practice better controls over the administration.

 

Moreover, of late a parliamentary committee has found that the monthly collection from GST is not up to the mark due to constant revisions of the rates, which has undoubtedly affected the stability of the tax structure and had led to an adverse impact for trade and business verticals.  

 

 

“The Committee is constrained to observe the not-so-encouraging monthly revenue collections from GST, which still have not stabilised with frequent changes in rates and issue of notifications every now and then. Further, the Committee is surprised to learn that no GST revenue targets have been fixed by the government,” said M Veerappa Moily, the head of Standing Committee on Finance and a veteran Congress leader in a recent report presented in the Parliament.

 

The original article appeared in – analyticsindiamag.com/government-using-data-analytics-to-track-leakages-in-gst/

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The role of Big Data Analytics in the World of Media and Entertainment

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The role of Big Data Analytics in the World of Media and Entertainment

A reverberating revolution is on the go in the media industry. Reason: thorough digitization and data-driven marketing.

A seamless amalgamation between digital and analytical solutions is transforming versatile media platforms across the globe. Not only does it help in curating more personalized content for its niche audiences, but also bolsters newer capabilities, such as master data management for digital assets and improved customer engagement programs.

Is Big Data Big Enough?

Facebook gathers and processes more than 500 TB of data every day.

Google processes 3.5 billion requests every day.

Amazon records 152 million customer purchase data every day.

With the rise of digitization, media and entertainment companies are leveraging big data technology like no other for better customer engagement.

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Here are a few examples showing how media companies are using the power of big data:

In predicting audience preference

Large chunks of data helps in predicting and understanding the demand of audiences – right from the genre of shows and music they like to content selection for a given age group or for different channels.

Better acquisition and retention

Any day, big data help to fathom the reasons why consumers subscribe or unsubscribe a particular channel. It aids companies in developing robust promotional and product strategies to attract and retain more loyal user base. Social media data also lend a helping hand to enhance consumer interest.

Content revenue generation and new product development

With the power of accurate and productive data, media houses incentivize consumer behavior and while doing so, they understand the true market value of the content generated.

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How Media Uses Data Anaytics?

Netflix

Netflix assimilates large chunks of viewership data, with which it performs an in-depth analysis of viewer’s behavior for millions of viewings of shows. The analysts conduct thorough research on the attributes and qualities of data about consumers to know which show is the most popular. This analysis also helps them know how long viewers are watching a program or season or any individual show. Hence, in this way it outbids its competitors and owns rights to showcase blockbuster hits.

Bollywood

Talking about our very own Bollywood, SRK’s Chennai Express used big data and analytics to boost social media presence and digital marketing endeavors. And, no wonder, it smashed the box office records of 2013. It became such a raging success that the IT services company Persistent Systems released a statement saying, “Chennai Express related tweets generated over 1 billion cumulative impressions and the total number of tweets across all hashtags was over 750 thousand over the 90-day campaign period.”

This is a single instance. Many other bigwig producers have time and again collaborated with cutting edge big data analytics firms to better understand consumer trends and drive customer engagement.

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Conclusion

Big Data is a surefire boon for media and entertainment houses; it helps companies to solve crucial questions about consumers, things they like, content they feed in, and shows they treasure. Moreover, it aids in tracking clicks, shares and views across multiple devices and media platforms.

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Role of Self Service Analytics in Businesses

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Role of Self Service Analytics in Businesses

Self Service Analytics is proving useful for business users, who are working on business data without necessarily having a background in technology and statistics. It is essentially bridging the gap between trained data analysts and normal business users.

Following are the characteristics of Self Service Analytics:

  1. Business Users Independence:

Self Service Analytics reduces dependency on IT and Data warehousing teams, thereby reducing the turnaround time for a request made by a business user.

It does so by continuously collating and loading real time data into a singular stream without disparity, which is easily accessible through browsers. Thus, it helps business users in taking decisions on Real-Time basis.

This feature benefits organizations because vital decisions made within time can be more profitable as compared to the traditional way of analysing data, which may not be a good idea in respect to the urgency constraint.

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  1. Easier and Reduced Cost of Operations:

Often, the company’s data are fragmented and widespread across various divisions. This increases the headache of channelling the data meaningfully and in a wholesome manner.

Further to this, preparing reports using this data becomes a cumbersome job for the IT department or the department, which is serving such request. Hence, it may lead to increased cost of time or decreased quality of efficiency at which the operations have to run. However, many a times, these reports fail to give an overview of the operations in an organisation.

Self-service BI integrates data from different systems and delivers a “Single Version of Truth”. Accessing this data and running computations on it requires only a browser for access and eliminates the need to install, maintain and administer large-footprint software clients on each user’s workstation.

If Self Service Analytics is hosted on SaaS, it will further reduce the cost of machinery and maintenance associated with it. The provision for usage can be increased or decreased in no time according to the usage pattern. This really means that Self Service Analytics helps you adapt with time and Pay-Per-Use model, which is a leading trend in most of the industries.

  1. Resolving the conflict over accuracy:

Typically, a business user using Excel would have a local copy of data and run computations on it. He can merge and transform it by using various formulas and finally derive a conclusion.

This is dangerous because in live operations, data keeps changing and data integrity is at stake by working on local copies. Thus, accuracy in decision-making becomes a game of luck.

In Self Service BI, the data from the source is extracted, transformed and loaded into a unique data model, which goes with all operations. In this case, data integrity is assured. In addition, all business users have the same source of data, removing the risk that working with different local copies have.

Therefore, from the above stated facts, we can conclude that Self Service Analytics is a need for today’s businesses.

However, there are a few risks involved in Self Service Business Analytics:

  1. Loose corporate governance and make data available to business users directly may be taken advantage of in an undue manner.
  2. Business users may not be properly trained or skilled to make decisions.
  3. Relying heavily on any tool without some real life experience and insight into the background of that data can result into an impaired decision-making.

If all the above-mentioned risks are mitigated and proper corporate governance structure is in place, Self Service Analytics can be very beneficial for the success of any organization.

To excel in Self-Service Analytics, why not take up Machine Learning courses in Delhi from DexLab Analytics! They are informative, interesting and elaborate.





 

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Our Perception Must Include Data-Ception

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Our Perception Must Include Data-Ception

Considering the complex competitive global environment, the world business today is witnessing a paradigm shift from mere data storage to data mining and other subsequent activities.

Thus, from a managerial perspective it is of prime importance to develop a psyche, which can interpret the collection of data. This psyche cannot be theoretically learnt from books, as it requires a knack to make data talk. Data is no more evaluated independently. Today, a cross-domain relationship between data exists, which on analysis depicts patterns, responsible enough to do wonders for the organization.

The question is how can we connect the dots? Following the recent trends, developers are grabbing every opportunity to break a huge chunk of data into meaningful relevant information. From the standpoint of technical professionals, along with an analytical mindset, they need to get hands on experience on the technological perspective to understand the real significance of data evaluation.

 Read Also : DexLab Analytics – Training the Future to be Big Data Analytics Fluent

The data not only aligns with the internal activity of the business but also is an integral part for consumer servicing. There is an intense need to study the needs of consumer and every decision he makes, which broadens the outlook of a business on how he/she is using their product. What are the expectations of the customer from an existing product? What more my customer needs? The answers to these questions cannot always be mapped quantitatively but a qualitative approach towards data is one of the key aspects of data analytics.

In this digital era, slightest technological ripples are going to reshuffle the whole industry scenario. And, that is why the omnipresence of data will aid businesses in setting new benchmarks in consumer and market findings. Growing pace of social media would open a Pandora’s Box for companies, who have their right audience in this particular domain.

The emergence of IOT, which primarily thrives on data, will cause disruption in the current business orientation. The data producing sensor architecture directly connected to the company can help the business to be fast and robust, which is the need of an hour. In addition, this analytics might influence mid-size distribution largely.

Simple example of this model: Sensors attached to tyres could sense data, and alert a tyre manufacturer about the usage of a consumer, which will help in servicing their customer at the right moment.

Thus, on an individualistic note there is need to develop a data analytical mindset and include data-ception in perception.

This blog has been contributed by Team Frontrunners, comprising members Ria Shah, Dishank Palan, Sanjay Sonwani from Welingkar College.

 

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5 Analytics Tools To Improve Your Business Decisions

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5 Analytics Tools To Improve Your Business Decisions

Big Data has proved to be inevitable for business organisations in the quest for stepping ahead of their competitors. Nevertheless, only having Big Data at hand does not solve problems. You also need the availability of efficient analytics software that can put your data to the best use.

A business analytics tool is responsible for analysing massive amounts of data in order to extract valuable information. Such information in turn, can be used for improving operational efficiency and for taking better decisions.

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So, let us here go through the top 10 data analytics tools available in the market.

  • Yellowfin BI

Yellowfin Business Intelligence (BI) is a reporting, dashboard and data analysis software. The software is able to conduct analysis of huge amounts of database, in order to figure out appropriate information. With Yellowfin, your dashboard can be easily accessible from everywhere including company intranet, mobile device or web page.

  • Business Intelligence & Reporting Tools (BIRT)

BIRT is open source software programmed for JAVA and JAVA EE platforms. It consists of a runtime component and a visual report designer, which can be used for creating reports, visual data, and charts and so on. Information gathered from this software can be used for tracking historical data and analysing it and as well as for monitoring ongoing developments in various fields. BIRT can also be used for real-time decision-making purposes.

  • Clear Analytics

Clear Analytics is quite easy to manage as the software is based on Excel spreadsheets. While the software allows you to continue managing data using Excel, it also adds some extra features like reports scheduling, administrative capabilities, version control, governance etc. for better decision making. In short, Clear Analytics can be your choice in case you want high-end performance in exchange of minimal effort.

  • Tableau

Tableau is BI software that provides insight into the data that a business organisation requires for connecting the dots, in order to make clear and effective decisions. Data visualisation in Tableau is much dynamic and elaborative as compared to the other programmes available. Besides, it also provides easier access to data given its extended mobile device support. Additionally, the costs of implementing this program as well as its upgrade are relatively low.

  • GoodData

GoodData is a service BI platform. It takes into account both internal and external datasets (cloud) of an organisation to analyse and provide better governance. The platform is programmed for managing data security and governance thereby, consequently providing the user with the desired results. The most important feature of this platform is that it can analyse datasets of any size, thus making it effective for its users. Recently, the company rebranded their software as an Open Analytics platform.

These are some of the major analytics tools used by organisations irrespective of their scale in order to enhance their business intelligence. Whether you are looking to enhance your career or take better business decisions, a Data analyst certification course can help you to achieve such objectives. Data Analysis helps you to track the competitive landscape and figure out the essentials that needs to be done, in order to get ahead of your competitors. If you are a manager, you can take precise decisions based on quantitative data. Since big data is potential of driving your success, it is your job to master the science and use it for your advantage.

 

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The Best Analytics Tools for Business And How to Make The Most of Them

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The Best Analytics Tools for Business And How to Make The Most of Them

All companies are awash with useable data about their customers, prospects and internal business operations as well as suppliers and partners. But most of them are also ill-equipped with the requisite understanding to leverage this streaming flood of data and cannot convert it to actionable insights to increase their revenue by growing their revenue thus, increasing their efficiency. Business intelligence tools are technology that allows businesses to transform their data into actions for generating better business.

The Business Intelligence and analytics industry has been around for decades now and is considered by most analytics personnel as a mature industry. But this BI market is never static with constant evolution and innovation to prepare for meeting the ever expanding needs of businesses of all sizes and from a diverse range of industries. So, it is imperative that people gather an understanding of the different Business Analytics tools for better operation of their companies.

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Business Intelligence tools can be categorised in three different groups:

  • Guided analysis and reporting
  • Self-service Business Intelligence and Analysis
  • Advanced Analytics

The first category of guided analysis and reporting includes Business Intelligence tools of traditional styles that have long been used for years to perform recurrent data analyses of specified data groups. This system of data analysis was only used for predefined static reporting several years ago, but today it is possible for data analysts to select, compare, visualize and analyse data using various tools and features.

Tool styles in this category include the following:

  • Reports
  • Scorecards and dashboards
  • Spreadsheet integration
  • BI Search
  • Corporate Performance Management

The second category of BI tools which falls under the category of self-service BI and analysis includes the tools BI users utilize to make ad hoc analysis of data. Such analytical practices may be a one-time analysis or building of a recurring analytical system that may with shared by others.

Usually the users of such Bi tools have a dual role to play – consumer of information and producer of analytical systems. They usually share or publish their BI application which they build with the self-service BI tool. The users of such tools will always have the term analyst in their job title. Staff members of the management department may also make use of such tools when they need to perform similar tasks as that of a business analyst, for their peers even if their job title does not imply that.

The Business Intelligence tools include in this category includes the following:

  • Ad hoc analyses and reporting
  • OLAP cubes i.e. online analytical processing
  • Data visualization
  • Data discovery

The third category of advanced analytics includes the tools that a data scientist uses to build predictive and prescriptive models of analysis. These are tools for predictive modelling, statistical modelling and data mining along with rigorous use of big data analytics software. In these cases data analyst spend a huge chunk of their time performing tasks like data ingestion, cleansing and integration.

To understand the full spectrum of different Business Intelligence tool classes here is a visual explanation:

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Who should invest in BI tools?

For a long time now investment and use of BI tools has been growing gradually regardless of the economic conditions. And it has especially accelerated in the recent times as companies crave for data for better growth and more organized operations. While data analytics tools were mainly associated with large enterprises due to their cost, complexity and demand of high skilled personnel, but those factors have now been grossly transformed as more and more SMBs (small and medium sized businesses) now being significant customers of BI tools and software.

Now that you have a good understanding of the different tool categories and how they should be deployed, the next step for you is to understand your  company specific needs and make the best use of these tools that are optimized for so.

 

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