Financial analysis s beneficial for businesses in the following ways: • Cutting costs: Financial data relating to investments and cash flows are analysed. • Making investments: Financial analysis helps in predicting the returns from investments, thereby enabling the companies to go for profitable investments only. • Forecasting the future: The future of the company can also be forecasted. • Following business trends: Financial analysis relies upon the current business trends and success rates of businesses in the sector. Such analysis helps in recovering faster in case the market suddenly drops. • Management: Financial management is also tracked by the financial analysts which helps in increasing efficiency overtime. Read more at: https://bigdataanalyticsnews.com/big-data-improve-ecommerce-for-businesses-customers/

Few factors where data analytics can be seen creating an impact on this sector as stated below: • Reporting and Business Intelligence: With the help of reporting and dashboards, the travel companies can draw inferences. The most important factor for this industry is seasonality, which can be studied by performing analytical operations on the data. • Alerting and monitoring system: Anomalies and rare observations can be detected which is not possible manually due to huge chunks of data available. • Optimization and efficiency building: Analytics help organizations prioritize their investments and redirect them towards high priority factors. • Personalization: Personalizing always attracts attention of customers. • Enhancing business strategy and customer experience: Along with variations in seasonality, forecasting is equally necessary to define the business goals. Read more at https://www.moneycontrol.com/news/technology/travelling-on-data-how-data-analytics-is-transforming-the-travel-industry-4131941.html

Few factors where data analytics can be seen creating an impact on this sector as stated below: • Reporting and Business Intelligence: With the help of reporting and dashboards, the travel companies can draw inferences. The most important factor for this industry is seasonality, which can be studied by performing analytical operations on the data. • Alerting and monitoring system: Anomalies and rare observations can be detected which is not possible manually due to huge chunks of data available. • Optimization and efficiency building: Analytics help organizations prioritize their investments and redirect them towards high priority factors. • Personalization: Personalizing always attracts attention of customers. • Enhancing business strategy and customer experience: Along with variations in seasonality, forecasting is equally necessary to define the business goals. Read more at https://www.moneycontrol.com/news/technology/travelling-on-data-how-data-analytics-is-transforming-the-travel-industry-4131941.html

In 2014,NASA had lost an instrument located on the Solar Dynamics Observatory that measured UV rays coming from the sun,to forecast solar storms and to alleviate their affects. This is when scientists and engineers turned to artificial intelligence,with the thought that well-trained data can fill the data void. Four years of data captured by space instruments that included images of the sun were used to design processes.Using the best software tools to test these models,the scientists concluded that CNN is a good fit for the data giving 97.5% accuracy. Superior images of the sun generated were used to predict UV measurements.Our question stands at- if AI can be used to fill data gaps,can it forecast UV spectra as well and can it address a wider spectrum of problems?

Read more at: https://www.aitrends.com/neural-networks/how-ai-came-to-the-rescue-of-scientists-studying-the-sun/

One of the most trending and look for technology, Predictive Analysis is a powerful tool that can help us to forecast and predict what lies ahead us. However, it is usually accompanied by few issues that user encounters while using it. They might not be visible during early stages of development but they can become great concern when they will not be able to deliver results to customer. Prevention is always better than cure and thus it is recommended to study the technology well before use. 

Following are few tips that one should use to avoid and resolve common project challenges:

1. Create and execute a formal strategy
2. Ensure data quality
3. Manage data volume
4. Respect data privacy and ownership
5. Maximize usability
6. Control costs
7. Choose the right tools

   To read more about them visit: https://www.cio.com/article/3287937/predictive-analytics/7-tips-for-overcoming-predictive-analytics-challenges.html?upd=1532674958240

    

Investment in shares requires analysis of huge historical data. Analysis is the primary phase and it forecasts the future investment process. Broking house plays a clinical role in this context and charges a percentage of hike in shares. But these days such information is frequently available in different websites and are updated on a regular basis. The usage of technology for predictive analysis is hugely correlated with big data. These are limited to institutional buyers. The automation in predictive analysis requires a huge precision which is cost effective, but its implementation could be a game changer.

To read, follow: http://www.thehindubusinessline.com/markets/stock-markets/big-data-robo-analytics-to-drive-next-phase-of-growth/article8249330.ece

FP&A and S&OP are the two most essential processes for every organization. In modern times both have developed successfully and have started to take part in managerial decision making processes. The advantage of FP&A is that it envisages the results of the decisions that the organizations undertake. It achieves this by scrutinizing both the past performances and present activities and hence it makes the data readily accessible wherever required. While S&OP being one of the integral components of supply chain management, reduces the inefficiencies of the supply chain. It helps the firms to alleviate risks. FP&A on the other hand, manages everything from budgets to predictions. Therefore, both are equally important to a business for running efficiently.

Read more at: https://channels.theinnovationenterprise.com/articles/fp-a-or-s-op-which-adds-more-company-value

Over the past few years, the impact of globalization, amongst other things, has also been seen on Supply Chain. It’s not restricted to one place or country, rather, Supply Chain function has also spread its wings across countries. This change has affected the entire working of businesses. The change in supply chain has also affected the finance department of the firms. It has to adopt its practices to comply with these changes.

Now, a new era of supply chain is on its rise i.e. Digital Supply Networks. Be it the use of robots or 3-D printing, ‘digital’ is the key word.  Once again, its impact on the finance team is huge. This impact has been discussed by David Axson and Gary Hanifan in their article on IndustryWeek. They state the following 3 major effects:

• Release of working capital
• Improved forecasting
• New control philosophy

But the big question is- Are the CFOs and their finance teams ready for this?

To know more, please visit the following link:

http://www.industryweek.com/finance/finance-ready-demands-digital-supply-chains

The most important part in determining a company's stock growth is extremely difficult. But, by asking a few key questions, investors can improve the accuracy of their guesswork. Firstly, take some time to examine the market growth rate- For example, Apple less than a decade ago, was known only for computers, but now it has a market share on the phone and tablet market. To get some hints of their future prospects, you need to estimate the percentage of people who already have smartphones and the percentage of customers who will buy new smartphones etc. Secondly, a company's market share impacts in a big way on its future sales growth. Coffee-retailer Starbucks and automaker Honda are examples of companies that have used their brand name to grow their market share. Also, pricing is an important factor and needs to be considered as it has a big impact on sales revenue growth of any company. Read more at: : http://www.investopedia.com/articles/stocks/04/100604.asp

Big data is the basis for business intelligence, which is about taking all that information and turning it into knowledge to drive better business decisions. Whether its data about retail consumers or homebuyers, it's all the same game.  The business intelligence industry has been analyzing large data sets in corporations for years — decades, really. It’s only now coming to the real estate industry. The amount of data used in the real estate industry isn’t that large. A single major retailer will generate more sales data in a year than the entire real estate industry will in a decade. However, it’s all relative, and the real estate industry is still trying to figure out what data it has, let alone how to use it.

The point is that big data in real estate is about presenting a “whole consumer” picture. It’s about using data to find out who buys what, when, where, why and how. It’s about finding out who will sell a house — when, where, why and how. 

All that data can be used to create tangible insights into consumer behavior using forecasting and modelling software. It’s the analysis that makes the magic happen, that is identifying customers or providing them better services. Analytics is where raw data and the algorithms that crunch it come together. Mining census information, the results of consumer surveys, listings of homes for sale and rent, geographic information systems data and more combine what they draw from numerous databanks with their own proprietary user-generated content. The tools can deliver to consumer’s information about their property's potential value and help them understand home-value trends within a particular milieu, such as a neighborhood or a ZIP code. 

Beyond the consumer and industry-facing aspects of big data, institutions such as banks can plug into big data resources to determine whether a foreclosure or short sale is really worth what a buyer or investor might be offering.

For now, the analysis of big data is likely to stay with those who gather it and companies willing to pay for access, such as the lead generation companies. What real estate agents need to know now is that the data is there and it’s available, in some form or another, to those who are willing to use the right tools.  Read more at: http://mashable.com/2014/07/09/big-data-real-estate/

Big models and big data have long been a feature of weather and climate modelling. Computer-generated global weather forecasts are initialized from millions of diverse observations from satellites, weather balloons, surface weather stations, ships and buoys. Data assimilation, the procedure of ideally mixing these perceptions into the estimate model, is the most computationally difficult part of making a worldwide conjecture, and is a basic component of forecast skill. The international climate modelling community has evolved interesting infrastructure and social institutions that enable a diverse community of interested users to obtain standardized results from leading climate models developed around the world, to capture aspects of climate modelling certainty and uncertainty and help inform decision-makers and the interested public.

Past the thriving information administrations industry, weather has huge monetary and well-being ramifications. Weather Analytics, an organization that gives atmosphere information, evaluates that climate affects more than 33% of overall GDP, influencing the farming, tourism, angling, amusement, and air transport commercial enterprises, to name simply a few. Dubious climate conditions likewise affect little entrepreneurs. Moreover, public safety is of vital concern when officials aim to understand the impact of extreme weather events such as hurricanes, tsunamis, or wildfires. To know more about this aspect go through Per Nyberg (Senior Director of Business Development at Cray)’s article link: http://www.informationweek.com/big-data/big-data-analytics/3-ways-big-data-supercomputing-change-weather-forecasting/a/d-id/1269439