“Meetings…Meetings, Meetings, I don’t like it, I avoid it but Meetings likes me!”

Is that how you feel? It’s just not you- but almost everyone feels so. Meetings are terrible, says Elon Musk (ref: Elon Musk Meeting Rules: Tesla CEO Shared Productivity Tips (marketrealist.com)). Problem with meetings is that  they tend to take a lot of your time. You accomplish a very small thing but you end up wasting a lot of time. Agree?

Take a moment and think how you can solve this? We have a solution with geniFLASH,

Visit our previous blogs for “Did you know? – geniFLASH features” @

#1 – Brand Watch

For more information,
visit www.geniflash.com
send your queries to customer.success@genisights.com

geniFLASH has lot many features and it is not just another BI/AI product. Lets learn many unique features of geniFLASH under “Did you know?” blog series.

Introducing “Brand Watch”, a plug n play connect to social media, web media

For more information,
visit www.geniflash.com
send your queries to customer.success@genisights.com

In continuation with our previous blog series – http://blog.genisights.com/in-search-of-actionable-insights/

Visionary Manufacturing Company meets geniFLASH team for demo.

Want to schedule your demo with geniFLASH? Reach out to customer.success@genisights.com.
For more information visit www.geniflash.com

Visionary manufacturing company is loosing its dealership sales. Angelina, the Chief Sales officer has convened a sales review meeting to address the problems.

What’s going to happen next Monday? Wait for our new blog on Monday. Can’t wait till next Monday? Reach out to customer.success@genisights.com

Joe’s point of view

Large organizations have lots of tools, big teams, and huge infrastructure. Do they act fast when it comes to analytics? Let’s find out.

Joe transfers his stress to Micheal.

Micheal asks Joseph. Poor Joseph always gets the word in the eleventh minute!

The team hopes to deliver the best in such a short span of time. Looks good to them.

Time – the precious thing. The clock is ticking. Tik tok tik tok …

Finally a solution indeed! Before the board meeting: Joe and Michael finished the demo meeting with the FLASH team. Joe is in complete awe with FLASH‘s AI-driven features to generate desired insights in real-time. Joe especially liked the call for insights feature provided by FLASH in a few seconds. During the board meeting, Joe converses with FLASH to generate the insights and visuals.

FLASH is available to all you. You can also schedule a demo with the Team FLASH to know more about FLASH capabilities. Contact the FLASH team at info@genisights.com or you can visit the website https://flash.genisights.com/ 

Today’s businesses are reinventing themselves with Artificial intelligence capabilities, be it chatbot or robotic process automation.  The increase in AI-enabled services are also creating higher demand for high-performance computing to process large gigabytes of data. This has also led to Big data processing using cloud environments.  Virtual assistants like Alexa, Google Assistant, and more have brought cloud, analytics, and AI a connected experience in our daily lives. 

Market Size:

According to Infotronic, the Cloud Computing Service industry is predicted to reach a revenue of $150 billion by 2020. They also predict that 83% of enterprise workloads are going to be on the cloud by this year without considering the pandemic and how it has now forced many companies to move to cloud solutions. Data analytics in the cloud will help your business be a part of the 90% of companies that use the cloud in some or the other form.

What is cloud computing?

Simply put Cloud Computing is like renting a house instead of buying a house. You pay for the time you stay without having to have the additional responsibilities of maintenance and up-gradation. Without physically downloading the software you can use the internet to remotely access everything.  Everything you need is located across multiple data centers having dedicated servers, data warehouses, and multiple backup data centers to make sure your data is safe.

Essentially there are 3 types of cloud deployment

• Private cloud: A private cloud is a cloud environment dedicated to one organization.

• Public cloud: A public cloud is a service shared by multiple organizations using “multi-tenancy”, where virtual machines are used for renting the same server space among multiple tenants.

• Hybrid cloud: They are a combination of public and private clouds. In Hybrid clouds, companies use a private cloud for some confidential services and public cloud for other services.

Cloud computing can be categories into 4 types:  

• Infrastructure as a Service (IaaS) – where you can rent out the entire virtual infrastructure like servers and data storage spaces;

• Platform as a Service (PaaS) – where you can rent platforms to build your own applications;

• Software as a Service (SaaS) – where you as a business can access developed applications that run on the cloud and

• Data as a Service (DaaS) – where your entire business data is on the cloud available for remote access

Can we analyze data without the cloud?

Advances in big data and cloud computing have developed these technologies to complement each other. While data analytics interprets huge data into meaningful insights, cloud computing makes this process light and convenient. But can they work without each other? Let’s consider how your data analytics platform would work without cloud computing. Your business will need to purchase computing infrastructure to handle the huge amount of data processing. You will need a system with high processing speed and huge storage capacity. This system will need access to servers, bandwidth, power, and cooling. It will be similar to the time you installed heavy gaming software by clearing out your favorite vacation photos.

Once you have found the system that can handle this you will need to purchase a robust analytics application. You will need to install, configure, and run the application in this system and feed it with all the data of your business. It will process this data and give you meaningful insights.

Need a change? The application will need to be modified and deployed to reflect the change. You will probably rethink every change you need. Even with all this, sharing insights becomes complicated. Let’s not forget the dependency on the system in use. These traditional systems require huge investments and always fear the test of time. With technology changing every fortnight it takes a huge commitment to be using outdated systems simply to justify the money and vision invested in them.

Data analytics in the cloud will reduce your dependency on the desktop. The big data cloud architecture will handle all the processing of the data at a remote server. By using aggregators and integrators, the client’s data sources are integrated into data warehouses that use SQL, Hadoop, Hibase, MongoDB, DynamoDB, and other such data management frameworks. This is then passed through a cloud-based analytical engine that interprets this data followed by a reporting engine that uses data visualization to bring you what you need. This will clear up the processing space of your system. 

You will have remote access to the entire data computing library that is integrated easily with your existing system. What happens when you request a change? It is simply built and integrated directly to the cloud, reducing your deployment time while you continue to access your daily reports uninterrupted. Sharing these insights becomes convenient with SaaS Dashboards. You simply sign in through your preferred browser, view the reports, export them into the format you prefer, and share them. Everyone who has access to the data cloud is connected and on the same page. It bridges the dependency of your local desktop environment by transporting everything to the cloud.

Check out our exclusive interview with Google tech sales specialist and a startup mentor, Mr. K C Ayyagari to gain insights on cloud technology and its role in the analytical transformation.

What is big data’s relationship to the cloud?

Big data management forecast- Courtesy Forrester

A Forrester Research survey in 2017 said that big data solutions via cloud subscriptions will increase about 7.5 times faster than hosted desktop options. Let us consider a cloud-based analytics system. According to comparethecloud.net, “The SaaS Dashboard concept is a way in which to present a cloud-hosted application suite to a user without the need to use a hosted desktop interface.”

Big data impacts and benefits using cloud computing

1. Just a click away

Having everything at your fingertips means that you don’t need to depend on anyone. Besides having all your data insights readily at your fingertips, cloud services are on demand. You can choose how long you want the service and pay only what you use.

2. Easily shareable

Do you need
your sales team to know that they need to up their efforts to meet targets? Simply export the dashboard reports or let your managers view the SaaS dashboard.

3. Integrated

You can easily have all your applications work easily with each other through cloud-based analytics.

4. Never lose your data

All your data is backed up all the time on remote servers and data warehouses. You save a lot of storage space by simply putting everything on the cloud.

5. Fast and lightweight

Do you want to change the way your data looks? Need your data insights designed specifically for your system? Cloud-based analytics reduces deployment time required to generate reports and helps you control your data anytime.

Big data processing in cloud computing environments helps you rent access to software without creating a hole in your pocket. At geniSIGHTS, we understand big data implementation costs and how resource sharing still needs a personalized design for your business needs. With FLASH, our AI-powered dashboard that gives you data analytics in cloud environments (or on-premises), you will never have to worry about anything but your business. Using the cloud has made FLASH highly customizable to fit your business needs. You can simply integrate it into your existing systems. It’s AI-based data insights that use machine learning to adapt based on your decisions. Learning from you as you learn from it. Conversational AI-powered voice control reports that are lightweight and easily shareable through the cloud.

Check out our AI-driven intelligent solution, FLASH, on our website: https://flash.genisights.com/

#AI #ArtificialIntelligence #IoT #bigdata #machinelearning #analytics #datascience #cybersecurity #deeplearning  #EmergingTech #Robotics #chatbots #CloudComputing #SelfDrivingCars #finserv #ArtificialIntelligence #iiot #ML #4IR #AI #ML #CustomerExperience #CX #Chatbots #VirtualAssistant #VirtualAssistants #Omnichannel #Tech #Blockchain #businessanalytics  #analytics #techthatmatters #data #datascientist #datascience

When we think of risk management data analytics, the first thing that comes to our mind is financial business analytics. The risk involved in financial transactions is well known. Analyzing data eases the uncertainty in stock market investments. It is also widely used in banking systems to predict loan defaulters and predict fraud.

Read our article on: How AI can help revive industrial operations from COVID losses?

Why should you invest in Risk Management through AI?

1.       Saves bigger future losses

Art or risk management goes beyond financial data science. The world of business is more than relying on investments in insurances to tackle risk. Today, it is important for every business to foresee potential risks and work on solutions to remove or normalize the effect before the crisis hits. Investing in data reporting dashboards that use predictive analysis helps your business save much future damage. Reporting dashboards make repetitive work like monitoring and weekly reporting automated. This not only saves precious man-hours but reduces the risk involved in humans handling repetitive tasks. A global bank used technology to detect unacceptable false-positive rates in anti-money laundering (AML) detection—which were as high as 96 percent. Using machine learning and artificial intelligence they were able to identify crucial data flaws. The data-quality improvements made saved an estimated 35,000 investigative hours. Another example is the use of advanced analytics by Des Moines Public Schools that identifies and helps at-risk students. Using predictive analytics they are able to give students the attention they need so that they don’t have to drop out of school. They help analyze risks in real-time and provide insights to improve your business.

2.       Market analysis and customer response monitoring

Analyzing the response of customers to existing products can help you predict responses for future launches. Breaking down customer data and customer behavior trends by looking at your customers’ transactions to better understand and predict the attitude and behavior of your customers is called Market analysis. Let’s consider the reason for the failure of the famous case of Pepsi Blue. The lack of market research before launching the product in India failed to point out how people felt the color resembles that of Kerosene. Pepsi’s decision to sell the product under the name “Pepsi” confused the customers who were not expecting a berry flavor to their preferred soft drink. The failure of the product clearly points to the importance of analyzing your market. Artificial intelligence can help you understand your customers and their needs. Continuous monitoring of customers’ behavior trends and by adapting to their ever-changing moods, AI can point you to the preferred audience for your products. It can give you insights like the right time to release a product, spending habits of your customers, and their response to the social, economical and political issues of the world by analyzing their social media presence. This will help you make better decisions and reduce the risk of making these decisions.

3.       Helps you manage a disaster

Real-time Artificial intelligence monitoring helps you predict the possibility of a disaster. But in these uncertain times, we all know that disaster can come from any direction in any form. How then can you help your business restore its health?

Today’s complex algorithms help you come up with solutions by looking at the bigger picture. They break down your data into tiny units and analyze the possible ways to revive. Let’s consider how many chess combinations can you come within a second? IBM’s Blue Gene/L can routinely handle 280 trillion operations every second. A single scientist with a calculator would have to work nonstop for 177,000 years to perform the operations that Blue Gene could do in one second. Today’s complex algorithms can help you analyze all your options before deciding the one right for your business.

How can AI help in calamity management?

HCL recently partnered with the Tamil Nadu government to set up a Disaster Management-Data Analytics Centre. Data analytics using complex AI algorithms can help governments capture data trends in real-time and help the government modify decisions on the go by analyzing the response of the people to these decisions. Use of AI-powered drones to monitor and report vast areas in disaster-affected areas was used by the government of Uttarakhand and Kerala. Analyzing this huge amount of data allows response teams to mark affected regions and conduct risk and damage assessment promptly from remote locations. By predicting the displacement of people using AI rescue teams can meet citizens midway and even direct them to nearby shelter locations. With the world slowly trying to revive the economy, it is important for the governments around the world to keep a close watch on the data generated and use meaningful insights to improve the failing economy. Governments can analyze emergency calls and improve turnaround time in solving these emergencies. They can also monitor the availability of essential services and suggest ways to improve the supply chain management of these sectors.

1. Identify vulnerabilities and hotspots

The use of processes like the analytic hierarchy process (AHP), helps to identify the factors that can cause failure and analyze the effect of these factors by determining the probability of these factors. A study conducted on mapping disaster vulnerability in India using Analytical Hierarchy process helps mark the level of disaster vulnerability across the country and identify hotspots

Even in our business, we need to identify the areas where there is a high possibility of failure. AI can help in identifying these hotspots and make us aware of our business

2. Examine interdependencies

Interdependencies turn disasters into a full-blown domino effect. Being aware of interdependencies can help make better decisions. In times of calamity, you need to take into account the shortage of essential services and the grocery supplies as well. AI algorithms recognize these interdependencies and see help connect the dots to form a big picture. Real-time monitoring helps keep a check on all possible pain points and reduces the effect of a disaster by giving quick insights to stop the chain reaction of a disaster. Even in business, it is important to understand the dependencies of each node and ensure a plan in case the tiles start falling.

3. Connect the links

In case of a disaster, it becomes important to achieve clear communication. You have recognized the problem, found a solution, but does everyone know what to do? Human communication depends on perception
and can cloud the message. AI can help come up with a clear roadmap and distribute responsibilities. Having a common dashboard for communication reduces assumptions and helps keep everyone clear about the plan of action. Reporting through drones and sensors, broadcasting plans of action, and forecasting where people will go through predictive analysis help the process of disaster management to be more controlled.

4. Understanding the cause and effect relationship

We have already established that AI can try multiple combinations at a time to come up with an optimal solution. But it is important to monitor the effectiveness of the decision taken by monitoring the response. Social media monitoring can be a powerful tool to understand the effect of a disaster. It can be used for communicating with the public and for sentiment analysis to understand how people are reacting to a situation.

geniSIGHTS has developed an AI-driven platform for unstructured data. Its complex algorithms analyze social media trends and predict sentiments on business & business parameters – Net Sentiment Score, Sentiment trends, etc. Rightly named as “Ordo Ab Chao” which means Order from Chaos. It structures and filters social media to derive meaningful insights into how a disaster is accepted by the general public. The damage done to the image of companies in the face of a disaster can be viewed and monitored. Efforts to revive the trust can be analyzed to plan better disaster management.

Check out Ordo Ab Chao to see how people are reacting to the COVID-19 pandemic at: https://flash.genisights.com/ordoabchao/

Also check out our Tamil Nadu COVID19 Information Tracker bot for quick updates on Tamil Nadu cases: https://genisights.com/CovidBot/

The disaster was once considered unpredictable and uncertain. Today Artificial Intelligence Risk Management is helping the world change this definition. Predictive analytics risk management helps us be prepared and anticipate calamity before it strikes. Prevention is better than cure. Continuous monitoring, highlighting hotspots, and noting the interdependencies can help achieve this. Even with all the preparation when disaster strikes there is much to do to respond to the hit. AI helps come up with effective ways to soften the impact. The next step is to revive from the attack with optimism. Complex algorithms to restore the economy and the people after the calamity strikes are an important part of risk management. The importance of seeing the whole picture and multi-tasking responsibilities can be easily handled by AI. It is important to handle tough times without letting emotions take over. Relying on machines ensures that decisions are unbiased and bold enough to be outcome-oriented.