Data can be put to use in many ways and it should be explored. Data should be used to its full potential, this is not about technology but management. A team of data scientists employ a series of various analysis to look into the entire series from data insight to profit. There are 7 methods to put data into work. 1) Use data to make better decisions along the chart. 2) Use innovations in products, services and processes. 3) Making existing products more valuable. 4) Improve quality, eliminate costs and build trust. 5) Sell or license richer data. 6) Asymmetries should be exploited. 7) Connect providers and those who need the data. These help to provide greater value to others. Read more at: https://hbr.org/2017/06/does-your-company-know-what-to-do-with-all-its-data

The present business environment is volatile and full of uncertainties. Therefore, a need arises to improve efficiency and profitability. Though many organizations rely on traditional techniques, predictive analytics is the new trend of managing risks and monitoring frauds which eliminates all the guesswork. Predictive analytics help us in reaching the source of fraudulent transactions and in dealing with future plausible attacks. Lack of corporate transparency and missing public trust should be dealt with by using advanced tools for managing huge data and ensuring accountability. Predictive analytics helps in building the customer profile to know his credibility which is useful for banks. Read more at : https://blogs.metricstream.com/ready-predictive-analytics-revolution/

Big data has now become old, organizations are very well familiar with it. But some of them are still struggling with data. Data lakes provides easy access of data and data mining. Due to management defaults data may turn into data swamps making analysis difficult. Data Lake has a lot of benefits, but the data growing in size becomes difficult to handle. To avoid this problem following steps are taken at the time of creation. 1) Too much data must not be collected at the beginning. 2) Data insighting cannot be done manually, so machine-learning capabilities should be enabled. 3) Businesses should keep an eye on changing data statistics and the employed models. To make it successful one needs to integrate it with business strategy and outcome. Read more at: https://www.readitquik.com/articles/elastic-computing/smart-ways-to-manage-your-big-data/

SecondMuse, place of work that collaborates with organizations to assist solve complicated issues, looked to the most recent drone and 360 video technologies to assist showcase cultivation — the farming of aquatic life-forms — in United Republic of Tanzania.

Led by the Australian Department of Foreign Affairs and Trade’s InnovationXchange, in partnership with SecondMuse, the goal was to award comes that each facilitate scale back environmental impact and increase property.

Ben Kreimer, a drone and computer game individual, and Brett Garling, producer and founding father of Cut Canvas inventive, shot 360 video underwater, on land, and within the air to document 3 of those initiatives.

More than seventieth of Tanzanian alga farmers square measure ladies, and rising ocean temperatures square measure reducing the economic viability of alga farming, per the Blue Economy Challenge web site.Read more at : http://blogs.discovermagazine.com/d-brief/2017/06/08/drones-showcase-aquaculture-in-tanzania/#.WUK_3BN968V

The difference between steam video and live casting s that live casting is uninterrupted steaming directly into the web. Livecasting offers a much better job at communicating. It allows the views to feel the experience as if they were alongside the participants. Also it lets the audience draw their own conclusions. Video take livecasting altogether to a different level. Livecasting creates an opportunity which adds that missing human element, traditional advertising is thus pushed away. Live video allows brands to communicate emotions in a way that is less likely to be misconstrued. The relationship between AI and livecasting is evolving and is focused around user-generating content. Livecasting creates a platform for brands to connect, build, trust and convey emotion. Read more at: https://blogs.adobe.com/digitalmarketing/social-media/hey-brands-busy-lifecasting-arent/

Artificial intelligence and improved technology without structured analytics can prove to be costly and can leave the company paralyzed. However, companies with strong analytics can make excellent use of it. Before Adopting artificial intelligence, the following points should be considered. 

1. The basic processes should be automated.

2. Structured analytics and centralized data processes.

3. Integration of structured analytics with artificial intelligence.

Artificial intelligence, if exploited to its full potential, can reinvent business. Read more at https://hbr.org/2017/06/if-your-company-isnt-good-at-analytics-its-not-ready-for-ai

Machine learning can be used in our daily lives such as filtering the spams in our mailbox or for banks judging the credibility of customers before issuing credit cards to them. We can even deposit checks through our phone. Machine learning models train itself, gather inputs and generate output. Machine learning tools are used as a part of business intelligence. Through Natural Language Processing (NLP) machine learning can comprehend speech or written words and generate graphs and figures. It can correct any anomalies in business and find out when the demand for your product is high. The more inputs one feeds in faster it learns. Read more at: https://www.sisense.com/blog/beyond-hype-machine-learning-unlocking-power-bi/

Traditional methods of procurement of inputs like person hours, capacity and other components are ineffective and costly for the clients. According to output-based pricing model output is pre-determined by the vendor and he’s accountable for achieving that level. There has been reduction in checkout time as analytics solutions for optimization are available to the vendors. Vendors face the overall risk of an outcome and at the same time have a share in profitability. In India, due to unawareness of analytics, companies have not yet adopted the model. Companies in India are unwilling to buy analytics services. A more output based approach can cost-friendly. Read more at:http://analyticsindiamag.com/outcome-driven-pricing-model-analytics-really-work/

Google Attribution 360 is the updated version of Google Attribution, an extremely useful app for Marketers. It helps you understand whether your marketing is working, turn audience insights into action, and deliver more relevant customer experiences. Salient features: Fast setup, Flexible data, Measures TV, Easy to take action, works across channels and across devices. Attribution 360 is also designed to be highly customizable and can measure ads from DoubleClick Campaign Manager. Read more at https://analytics.googleblog.com/2017/05/solving-enterprise-attribution-challenge.html?m=1

Machine Learning models are useful in solving a number of business problems, but there is always a trade-off between accuracy and interpretability of models. Businesses tend to use easily interpretable algorithms which come with lower accuracy. In this article, the author attempts to resolve this issue and shows how powerful black-box algorithms can be used for predictions. He has taken the help of one such algorithm called LIME (Locally Interpretable Model-Agnostic Explanations) that can effectively explain the predictions of any regressor by approximating it locally with an interpretable model. Read more at: https://www.analyticsvidhya.com/blog/2017/06/building-trust-in-machine-learning-models/

Adobe Analytics Cloud is a premier customer-intelligence engine which helps brands to not only analyze vast amounts of dispersed data, but also translate that data into customer profiles. It is built on the Adobe Cloud Platform which uses Adobe I/O and Adobe XDM, and an artificial-intelligence framework called Adobe Sensei. Virtual Analyst, the latest algorithm that was introduced in the market, delivers valuable personalized insights into incongruities which impact your business. Also to integrate Adobe Analytics with additional data sources new content packs for Microsoft Power BI have been introduced, including:Traffic Analysis to quickly discover and analyze digital-traffic trends; and Mobile App Analysis to take an in-depth look at mobile app user engagement and performance. Read more at : https://blogs.adobe.com/digitalmarketing/analytics/next-chapter-virtual-analyst-powered-adobe-sensei/

GST-Network (GSTN) is trying to provide hi-tech security and analytics center to give protect the data under the Goods and Services Tax (GST) for the cyber threats. GSTN will be appointing security companies by August to build up Security Management and Analytics Centre (SMAC). SMAC will implement data analytics to protect the GST system from cyber attacks and will provide a better and protected environment for GST ecosystem. Read more at: http://cio.economictimes.indiatimes.com/news/digital-security/paranoid-about-security-setting-up-analytics-centre-gstn-ceo/59092397

Artificial intelligence (AI) has been a revolution in the field of technology. Big companies are leading the AI revolution and that's given them an edge over most consumer goods companies and retailers. AI marketing tools are both easier and less expensive to acquire, with software-as-a-service. AI tool was successfully used by Albert to increase the sale of Harley Davidson.AI evaluated what was working across digital channels to create more opportunities and allocated resources judiciously eliminating guesswork, gathering and analyzing enormous volumes of data, and optimally leveraging the resulting insights.AI can process millions of interactions a minute, manage thousands of keywords, and run tests in silica on thousands of messages and creative variations to predict optimal outcomes.. By acting instantaneously and autonomously it can modify its buying strategy based on performance parameters. The best way to discover AI's potential is to run some small, quick, reversible experiments, within a single geographic territory, or channel. Read more at https://hbr.org/2017/05/how-harley-davidson-used-predictive-analytics-to-increase-new-york-sales-leads-by-2930

In today’s scenario businesses need faster access to everything. Organizations keep looking for effective methods. Analytics can be of very much help in this. Organizations waste a lot of time, they should select a project that has a potential which does not require too much work or heavy investment. Analytics can start at any level in the organization. Executive should be involved to get the project succeeded as the executive level understands the value of the project. Analytics dashboard helps explore data. People new to analytics may overlook the data quality. There is a tradeoff between high quality and time and expense. An expert helps to find the balance. Analytics can demonstrate value quickly and cost=effectively. Read more at: http://informationweek.com/big-data/big-data-analytics/tips-for-getting-your-company-started-with-analytics/a/d-id/1329014

The creation and consumption of Data is growing at a very fast rate, so does the responsibility to manage it. A report said that 90% of the data available today has been created in last two years. So, companies are using Business Intelligence (BI) to manage quintillion bytes of data per day. Data Mining, Data Handling is used to handle the data. The sharing of data intelligence across an organization doesn't only benefit the entire business, but creates a culture of collaboration and uncovers synergies between departments to better reach an end-goal. Creating a data driven culture is not an easy task. Only 37% companies have found success by adopting it. So, how to do it. First, start at the top. Implementing this lies in the hand of top executives and managers. To start something new is very difficult, so it fully depends on the top tow they manage and impose it. Additionally, education is important when we introduce something new to the company. Read more at: http://dataconomy.com/2017/05/data-nirvana-develop-data-driven-culture/

Quantum computing is the area of study focused on developing computer technology based on the principles of quantum theory, which explains the nature and behavior of energy and matter on the quantum level. In Quantum it can’t be clearly predicted which key element of the technology has entered commercialization and resulted in a massive change. Commercial applications like Temporal Defense Systems (TDS), Westpac, Commonwealth, and Telstra, and QuantumX are being adopted in Quantum computers by Lockheed Martin. Quantum computers can be used for simulation, optimization and sampling. Therefore, the most important action for data science is plotting how Quantum will disrupt the way we approach deep learning and artificial intelligence. Read more at: http://www.datasciencecentral.com/profiles/blogs/quantum-computing-and-deep-learning-how-soon-how-fast

Given the power of the consumers, their demands and the most extreme competitions in the market every business is fighting to survive. Recently the businesses have invested mostly in digital transformation. Since data play a very important role, retail businesses are using the following trends to analyze those data. Firstly, retailers are considering data from in-store technologies and analyzing them to measure their effect on consumer's decisions and thus providing the products demanded more by customers.  Secondly, by location analytics, geo-targeted push notifications are sent out to customers and products are arranged more effectively in the stores.  Thirdly, with the help of explanatory analytics and predictive analytics more demanded products are stocked up. And lastly, cross-platform analytics helps retailers understand the customer's demands and provide effective support to them. Therefore, we can say that data analytics are an essential part of growth. Read more at: http://www.datasciencecentral.com/profiles/blogs/top-data-analytics-trends-for-reatilers-of-2017

Customers do online and offline research, take the recommendations of friends and family and go through product reviews before buying a product . This is where social media plays an important part in marketing. Brands find social media analysis necessary to quantify purchase intent. The three ways to get richer insights are

1. Analyze social media to understand the number of buyers interested in the product.

2. Buzz around the launch of a product reflects the purchase intent of the product.

3. Conducting a competitive analysis of the brands with the help of social media can help the brands to be better.

Read more at http://marketingland.com/social-shopping-use-social-media-analytics-quantify-purchase-intent-214173

The study of consumer behavior and modernization are required by the adoption of data analytics in order to fight the water industry's everlasting challenges. Water industries are looking for sustainable water supplies so that they can serve the water industries for the future. Water industries are trying to integrate the system with the user need with whatever capital is available to them. The most important issue for water utilities are maintaining and expanding assets. The water industries are trying to analyze the available data and collecting new data to increase their efficiencies and smartly manage their assets.  Read more at : http://www.waterworld.com/articles/2017/06/water-utility-industry-report-examines-role-of-data-analytics-and-true-cost-of-delivery.html

Automobiles are becoming highly advanced with the high-performance engines and technologies. Telematics provides the information about the performance of both vehicles and drivers. This automotive technology along with Internet of Things (IoT) is creating favorable impact on the car industry and its related industries like insurance, transportation and logistics service-based companies with fleets and infrastructure. The concept of autonomous vehicle has been there for ages, but with the application of AI it has gained new life. AI can act as an assistant to the human driver providing them a safe driving and with the help of biometric it adds a new level to the car security. Information asymmetry is the main problem in the insurance industries, but with predictive analytics using the available data this problem can be solved. The AI creates a win-win situation for both the user and provider. Read more at: https://www.forbes.com/sites/brianrashid/2017/05/16/how-ai-pioneers-will-affect-the-car-industry-and-why-its-a-good-thing/#7c2fcfde57c2