Embedded Analytics helps in improving the revenue figures, user engagement and impart value to software applications by using the reporting and analytical capabilities in businesses. It helps businesses with precise decisions. Business operations coupled with embedded analytics allows for efficient process analysis and improving operations based on real-time situations. Predictive Analysis is the basis for operational decisions and embedded analytics used to make the results of predictive analytics is of the greatest value. People across an organization could use sophisticated analytics output which enables them to question the data. Putting the business intelligence platform inside the application architecture, embedded analytics guide users to choose where in the process the analytics is to be embedded. Integrated with CRM, ERP and financial systems, embedded analytics access core enterprise data and provide accurate information and further helps in reducing the cost. Lying at the roots of a radical revolution, analytics, or embedded analytics to be precise, would help companies accommodate the values to be provided to the end users.

Read More at: https://www.analyticsinsight.net/how-is-embedded-analytics-transforming-organizations-into-smart-enterprises/

Artificial Intelligence has spread to almost all major and minor sectors and has been growing exponentially over the past few years. With the medical breakthrough in 2017 of AI identifying arrhythmia, researchers have developed microscopes for real-time diagnosis of cancer in 2018. It involved remodelling a standard microscope with deep neural networks integrating augmented reality giving a tool what is termed as the Augmented Reality Microscope. In such a microscope, a neural network is integrated for detection of tumor once the biopsy is conducted. Here, the eyepiece plays the role of the eye, AR is the 3-D image captured (which is the Field of View) and the role of the brain is played by the network. The output of the network is in the form of a heatmap which depicts the probability of cancer at each pixel location. The ARM has to its name a number of benefits like low equipment cost, low latency and also keeps pace with semi-continuous movements of the slide thus improving the accuracy of quantitative tissue assessments. With this we are ARM-ed well to fight against the unfortunate and sometimes catastrophic disease called cancer.

Read More at: https://www.analyticsinsight.net/augmented-reality-microscope-to-detect-cancer-cells/

AI has made it completely impeccable, from archaic development to cutting-edge technologies, reaching out to every niche possible, bearing the embryo of a heated competition between China and the US to revolutionize; the two major hubs having AI development in boon. Among the others following in this race include Singapore, Vietnam, Indonesia and Thailand. Vietnam has successfully implemented a crop intelligence system named Sero, that enables farmers to identify sick crops. Once enhanced further, Sero would assist in diagnosis and possible future treatments. An Indonesian telecom operator, Telkomsel, employed only chatbots to deal with clients and answer most of their queries. Sooner or later AI is digging deep roots in the industry although it takes some time for AI developers to present the desired results to the companies.

Read More at: https://www.analyticsinsight.net/asia-accelerates-growth-global-artificial-intelligence-industry/

The ways in which financial enterprises operate today have changed fundamentally due to technological evolutions. Titanic amount of data gathered from individuals’ electronic devices can be analyzed and used by the Fintech sector to deliver reliable services to its clients. Big data have been offering numerous benefits to this industry in areas of fraud detection, customer segmentation, risk management etc. Big data helps financial enterprises understand the transaction pattern of clients and can inform them about suspicious transactions. Big data also partially, if not fully, rules out the potential threats from bad investments and bad payers. Fintech companies aim at creating personalized financial services and big data assists in doing so by meeting the specific demands of the final customers. Big data also leads to better compliance capabilities ultimately translating into better services for both B2C and B2B consumers.

Read More at: https://www.smartdatacollective.com/fintech-big-data-play-role-financial-evolution/

Call centres have started to explore new avenues using predictive analytics which improves their overall effectiveness and streamlines their operations. Traits that best prepare a person as a call centre representative are not clearly evident to officials but predictive analytics has made it possible to identify such traits. Call centres are merging CRM data into predictive analytics models as CRM has played a vital role in most organizations. The approaches used include analyzing variables contributing to customer discontent, building an automated system to transfer calls to representatives with varied expertise etc. Predictive analytics have been transforming industries and benefitting the aging ones by bolstering customer satisfaction and reducing churn rates.

Read More at: https://www.smartdatacollective.com/call-center-analytics-move-industry-21st-century/

With machine learning making a huge leap of progress, it is being debated whether Artificial Intelligence and religion are compatible or not. Humans have been worshipping non-human entities from time immemorial and man-made robots would be superior to their creators. Religious modules have been entwined with technology to bear daily prayer apps and robot priest as the fruits. For example, the human robot named Peppa, an invention of Japan, could perform funeral entailing a cheaper cost than a human priest. Research at Faraday Institute of Science and Technology reported a strong correlation to exist between technology and religion which basically involved three stages-rejection, adoption and adaption. Down the lane, technology would become a ubiquitous part of all religion.  With a more objective analysis, AI provides a consolidated nutshell of all religious dialects.

Read More at: https://www.analyticsinsight.net/are-artificial-intelligence-and-religion-compatible/

A potential threat that all businesses must be aware of is that of cyber attacks. Several data breaches have occurred in the past few years, the Equifax Data Breach in 2017 being the most noteworthy one. As a result, the legal repercussions have grown more stringent tightening the regulations on cyber security. Businesses could face severe consequences which includes consumer lawsuits, criminal charges for evading regulations and potential business shutdown. Consumers have sued companies on the ground of leakage of their confidential information furnished with respective businesses. The Health Insurance Portability and Accountability Act along with few other acts have strongly enforced cyber security regulations. Failure to abide by these brings in huge losses for the business concerned. To avoid such powerful legal ramifications, small and large businesses both must adhere to cyber security regulations.

Read More at: https://www.smartdatacollective.com/3-legal-repercussions-of-cyber-attacks/

Businesses having their analytics processes in place now resort to data monetization to reap benefits to the next level. Data monetization refers to deriving financial benefits from data sources. The largest tech companies nowadays are geared towards delivering the best user experience and collects proprietary data on which researchers perform analysis to derive insights and plan subsequent plan of action. Companies deploy block-chain ledgers to reduce processing loads and specialize in combination of big data and block-chain to bring increased profits. Even the smallest businesses benefit from e-mail monetization by leveraging data collected through the cheapest avenues possible. From managing a blog to small e-mail lists, linking helps in generating leads that turns data into money.

Read More at; https://www.smartdatacollective.com/how-data-monetization-can-add-value-analytics/

Almost 97% of the large companies leverage analytics and such data-driven businesses have emerged to be nearly 6% more profitable as indicated in a study by IDC. Location can be an address, a delivery route, a service boundary or a sales territory and along with disruptive technologies such as machine learning, artificial intelligence, location analytics helps in brisk innovation of the players in the financial realm. It’s required to move towards the analytical maturity curve implementing a 360 degree approach. With the help of location analytics, the real change can be observed between growth of new applications and innovations, taking the trend from static to dynamic content followed by connecting content. There are certain prominent cases where location analytics has helped in increasing profits that include Tamecco, the Tokyo-based company that focuses on customer satisfaction to achieve the moto that location-based analytics assists a lot. Among others are the Grey Jean Technologies and Teemo with the tagline of “Drive-to-Store” engaged in revolutionizing retail advertising. It is expected of industries to benefit from technologies like geospatial intelligence, forecasting and visual analytics.

Read More at: https://www.analyticsinsight.net/how-innovation-leads-to-location-analytics/

The major challenge facing any business organization is to use the massive amount of people data to nurture relevant insights on improving business performances. Analytical techniques varying from reposting to predictive analytics to experimental research help in directing company HRs. Following the comprehensive implementation of the cloud HR systems, companies are using data in all facets of workforce management and operational improvement, moving beyond employee engagement and retention. HR has been emerging as the “intelligent platform” with analytics becoming a critical part of the high-performance hiring in the talent acquisition trend. In order to reduce unconscious bias in hiring, businesses resort to the use of interview data and careful parsing of job posting language. With commensurate technological advancement, analytics will deliver personalized recommendations.

Read More at:  https://www.analyticsinsight.net/using-people-analytics-to-drive-business-performance/

Artificial Intelligence coupled with machine learning algorithms has taken the defenSe industry to great heights by bringing in this paradigm shift from descriptive to prescriptive analytics where soldiers have the knowledge of potential ceasefire beforehand. Data mining and machine learning algorithms help in analyzing unusual movements round the border thus making combat systems more effective at the decision juncture. The US Defense department has approximately spent US$1.6 billion in cyber defense. Although India is not far in the race, it is catching up for the time being. Many India based analytics firms are providing technologies to measure border infiltrations, detect smuggling and possible future agitations. However, India is progressing in the race of cognition of AI with machine learning to give a new shape to the country’s military services.

Read More at: https://www.analyticsinsight.net/data-and-guns-how-ai-is-changing-the-defense-sector/

Big data, AI and Machine Learning has given us an era where technology drives humanity but such digitization comes with certain cons as well. Handling a titanic amount of data seems to be harmless as long as businesses know how to use it and what purpose the data is serving. There should be a thin line between the actual required data and the data that has been irrelevantly collected. Big data has held us high but has failed to keep up with the privacy laws. Major data leaks and cybercrimes are the two prominent threats that growing technology has brought with it. Identity theft, classified as nuisance data breach by the index, has resulted in the loss of nearly 200 million records last year. Data manipulation and data integrity attacks are becoming popular as the world becomes more compact and technology dependent. To ace with this, the company must implement measures such as encryption, user access key etc. Fighting with the big data flood is not a company’s cup of tea as it involves various risks to triumph over. The big data storm must be dealt with carefully with proper planning and execution. 

Read More at: https://www.analyticsinsight.net/big-data-disruption-organizations-need-careful/

To keep track with the increased competition in today’s business environments, business leaders look outward to find potential innovation. Facing competition from start-ups, established businesses are inclined to pursue mergers and acquisitions. Such outward looking attitude might cause leaders to overlook ideas of innovation available internally. With the help of digital collaboration tools, allowing for staff participation in innovation encourages teamwork and camaraderie. Inward looking innovation involves the following few steps:

1) Formalizing the program: Setting up innovation programs on whims leads to major failures and hence, such programs should be formalized with guidelines. Larger companies set up their own incubation centres where their teams operate as start-ups whereas smaller companies enable teams to embark on pet projects.

2) Breaking down data silos: Data silos decelerate growth and innovation programs should break down such silos. Participants should be allowed to tap on a variety of skills and perspectives by using collaboration tools like Trello, Slack and Conceptboard.

3) Track Progress:  There should be a time element in the program lest projects won’t progress. Making events out of program milestones is a good way to inject excitement.

4) Provide executive support: Leadership plays a critical role in any innovation program with the duty of keeping their efforts aligned with the business goals. The momentum should not break down. Proper planning is indispensable for efficient time and workload management. 

Such efforts bring concepts closer to reality and not only seek to generate bright ideas. 

Read more at: https://www.smartdatacollective.com/ways-nurture-employee-innovation-from-within-organization/

In the digital age, social media is the most effective tool that helps improving and promoting any type of business content, get customer feedback and improve the overall reach of the company. This has led to the emergence of social media analytics which involves optimizing analytics and social median data into usable information to interpret how exactly the business content is going with the community of followers. 

A clear definition of the company goals lies at the root of such social media analytics with business objectives varying from prioritizing customer service to establishing a name in a niche or industry. Once a goal is defined, social media analytics could be used to frame strategies for accomplishment of such desired goals. Without a goal in the background, the system might go haywire bringing no effective results.

Being saturated with millions of click bait type contents, generic content does not work for the social media and the marketing world. Social media has restricted amount of characters hence, drawing of these characters should be maximized which helps to see the type of content that resounds best with the followers. Analyzing the increasing leads attained through content makes way for an inflated sales basket for the business. Average content does not acquire any place and being concise and thought-provoking in a post yields better results.

The success of a business lies in the timing of a post. Posting about a particular content at a specific time can increase sales. Also publishing a post on high traffic days can bring in commendable results for the business concerned. 

Read more at: https://www.smartdatacollective.com/use-social-media-analytics-increase-business-success/

Destroying traditional jobs but creating new ones, technical innovations have changed the course of work over the years. The Industrial Revolution of the 18^th century marked the transition to new manufacturing processes, effectively increasing the output levels and discovering the modern industrial marvels. With AI improving the standard of living, the current and future generations are likely to witness taxing employment pattern changes.

AI would change the future of work by bringing about the following changes:

1)      Create new jobs: Tasks requiring the least of the human cognitive mind would be dealt with the application of modern AI powered robotics allowing individuals to devote their time to community services, volunteering etc.

2)      Bring Automation: A research carried out by Carl Benedikt Frey and Michael Osborne of Oxford University in 2013 reported that approximately 47% of jobs would be automated in the next few decades with non-routine jobs and tasks requiring  high cognitive and good social skills having the lowest probability of being automated compared to a greater probability involved in automation of manual jobs and routine jobs like data entry, production logistics etc.

3)      Increase the gap between the owner and the worker: AI is likely to widen the gap between high skilled and low skilled workers and also increase the persistent inequality between the owner and the workers by laying off workers that would inflate the profit margin of the owners as robots and chat-bots would not demand overtime allowances.

Gartner, the global research and advisory firm, reported that AI is creating more jobs than it is destroying by bringing about a net increase of nearly 2 million jobs by 2025. The core objective of AI should be to make human workers more efficient without laying them off. AI coupled with human intelligence is all set to revolutionize the economy we inhabit.

Read More at: https://www.analyticsinsight.net/is-artificial-intelligence-a-threat-to-your-job/

Artificial Intelligence, big data and machine learning have been ruling the industry in recent times. Starting from Amazon to Google, indulgence in predictive modelling is indispensible. When it comes to the human body, well, artificial intelligence plays a pivotal role in saving lives. Rampant use of AI is involved in CT scans in cases of stroke or brain injuries. Radiologists have a backlog of cases which might delay the detection of the criticality involved in a particular case. To the rescue comes AI, which by streamlining the CT scan interpretation workflow by triage process and automation of the initial screening process, radically reduces the time lapse in detection and diagnosis of time sensitive cases. To detect abnormalities demanding urgent attention such as intracranial haemorrhage, cranial fractures, midline shifts etc, Qure.ai has provided automated deep learning algorithms to assist physicians. The algorithms’ accuracy is equal to that of a physician and classification algorithms are used in radiology itself. TITAN X of NVIDIA, cuDNN and GeForce GTX 1080 GPUs were used that achieved almost 95% accuracy rate as compared to that of 97% by radiologists. Such AI algorithms tend to become a life saver in a world where there is an acute shortage of specialized radiologists.

Read More at: https://www.analyticsinsight.net/how-artificial-intelligence-predicts-life-threatening-brain-disorders/

Business Intelligence and Business Analytics are being used interchangeably nowadays in almost every field of businesses worldwide involving, in particular, leveraging the data of a company in order to evolve and grow. In addition to other sectors, predictive analytics greatly benefit the construction business categorizing information with relevance and accuracy. Predictive Analytics assists in the following ways:

1)      Leveraging work packages: Predictive Analytics helps in task breakdown matching the right people for the right job, scans past project documents, including the Work Breakdown Structure (WBS), and assess the fallacies in project execution. With such technical know-how, businesses can scientifically cut on resourcing costs without compromising on potential.

2)      Prescribe, Predict and Describe: Descriptive Analytics creates a database containing the failures and their severity which is followed by predictive analytics analyzing their recurrence. Finally, prescriptive analytics explores options that can prevent such fallacies in future work.

3)      Scanning risks: The construction space advocates vociferously for the health and safety of the crew and this purpose could be served by predictive analysis in conjunction with prescriptive analytics. Pinpointing disaster zones to nth degree accuracy and using pedometer analytics to measure the distance the crew covers, predictive analysis places heavy-duty equipments at various access points improving visibility in low lying areas and also alerts about the resources that demand servicing. It ensures both the project’s progress and the business’s adherence to the crew’s safety standards.

4)      Lowered production costs: Manual monitoring methods are laborious and entail a cost on the business’s profits. GE’s Kimberlite Survey reported that businesses using predictive approach using retrofit sensors and cloud computing experienced approximately 40% less unplanned downtime.

5)      Immersive Insight: Predictive analysis converts dormant data into actionable analysis and prevents any information from lying unanalyzed.

Read More at: https://www.analyticsinsight.net/5-benefits-of-business-analytics-for-the-construction-business/

Data has become a central part of the economy and applications in analytics have been proliferating fast from private to the public sector. Data collection and analysis are at the root of critical economic decision-making which makes the socio-economic issues easy to interpret and comprehend, thus playing a pivotal role in economic battles. Big data analytics help the government in infusing transparency into the system, combat fraudulence and deliver public services effectively and efficiently.

The year 2017 will always be known to have triggered off this big data journey with Demonetization and GST being the two notable data-driven policies injected into the system coupled with the shift in focus to the macroeconomic issues like Aadhar data collection which gained an edge to bring economic reforms. Using big data analytics, the following few untapped areas can positively impact the government:

1)      Tax and Welfare: The ‘Project Insight’ rolled out by the Indian govt. used data mining techniques to counter tax evasion in 2017. It also helped in tracking down deregistered firms and gathered information about black money potholes in existence.

2)      National Security:  The uncertainties faced by national security officers with regards to the unpredictable security situation can be overcome by the use of analytics thus enabling them to combat crime attacks easily.

3)      Healthcare: Healthcare system in India has the opportunity to leverage big data analytics on the data emanating from biometric, patient records and thus provide actionable insights with greater prediction power contributing to effective public health.

4)      Education:  Ranking second in terms of student enrollment,  the titanic amount of student data can be analyzed to predict statistical figures and would help in efficient budget allocation.

In addition to these, analytics has also entered the farming sector where the concept of geo-tagging the entire agriculture infrastructure was implemented. Although the entire process is still in its infancy, the outcomes that big data analytics present to the Indian economy are much more effective. Though big data analytics have not been used in policymaking yet, the budget allocation hints at a widespread adoption of artificial intelligence and big data analytics in the Indian economy.

Read more at: https://www.analyticsinsight.net/how-indian-government-is-using-big-data-analytics-to-improve-economy-and-public-policy/

Google has been dominating the search engine industry over the years, though it has been frequently criticized of not providing search results in non-English languages. To cater to the problem, it has resorted to semantic indexing thereby becoming proficient at providing multilingual search results. The spectrum of search contents have been widening with time thus hinting at an expanding and trending macro environment. The search engines use algorithms which are solely based on Artificial Intelligence which would be rather simpler with limited pre-defined inputs. In its quest to understand the true meaning of different search queries, the algorithms are required to understand the contextual meaning behind various pairs of words which is attributable to deep learning. Despite capturing 70% of the search engine market globally, certain discrepancies arise due to regulatory policies. However, according to Shout Agency, the core problem is not the structure of algorithms as Google can make educated assumptions indexing any language but discrepancies in search results persist. The crux of the matter entirely stems from the fact that Google has had limited opportunities to conduct deep learning in some language than others. A potential risk is involved due to smaller user base and fewer Google employees that can understand the language enough to determine the worth of the content which lowers the chance of Google to conduct manual penalties for content. This could lead to greater pervasiveness of spun content throwing away algorithms dependent on deep learning.

Read more at:  https://www.smartdatacollective.com/google-search-algorithms-use-big-data-multilingual-latent-semantic-indexing/

Of late, Agile methodologies have been taking root in data science boosting complex collaborations between data scientists and other developers. Agile can be easily ported over to Machine Learning and Artificial Intelligence domains due to its feedback-heavy, iterative nature and given that incessant improvement is an innate part of AI. Such methodologies are characterized by fast feedback loops and short development sprints. Agile projects, in distinction to old-school waterfall approaches, involve error correction and cyclical stakeholder input and primarily focuses on short term goals rather than the long-term view. AI researchers should think of research as an iterative, evolving process to remain receptive and adaptive as per Agile’s basic tenets. To ensure that projects do not grind to a halt, maintaining a buffer of solutions for implementation is a priority as data scientists work on multiple projects, each taking months to complete. The iterative nature of Agile well captures experimentation as a core part of AI and ML projects. Agile maximizes value throughout the development process.

Read More at: http://www.dataversity.net/case-agile-machine-learning/