With every passing day, data and not instincts, are used for the expanding of business. Data is the new gold, as it helps in determining trend, offering better customer experience, responding better to market demands. However, given the data size is so big, Data Visualization is opted for, making the interpretations easier. The major reasons that data visualization is crucial are: • Data visualizations amplify a story with pictures and visuals. • Data visualizations makes difficult data comprehensible. • Data visualizations help in decision analysis. Read more at: https://www.experfy.com/blog/the-value-of-visual-data-in-decision-making

Big Data gained popularity during mid-1990s. But what exactly ‘’Big Data’’ means? It generally refers to the voluminous data, which the conventional data processing system is unable to process. It is widely used because of its advanced fraud detection and prevention techniques. The severity of frauds in credit card industry, insurance fraud and email based frauds are not new. Using big data to understand the card usage pattern of every customer, building specific fraud detection models, analyzing the geographical location of a person, can help companies prevent fraud. Pattern analysis with the help of big data helps banking sector detect fraud beforehand. Big Data helps prevent Medicare fraud  within minutes! Thus Big Data helps companies perform tasks on a much broader scale than a human being can actually do. Read more at: https://channels.theinnovationenterprise.com/articles/how-big-data-is-being-used-to-improve-fraud-detection

Data is the utmost important thing for any company or individual. Data Breach is an incident that happens when sensitive, confidential or otherwise protected data is been accessed or disclosed in an unauthorized manner. Many companies intentionally or unintentionally expose and leak consumers and commercial data. According to the Breach Level Index (BLI), many organizations fails to safeguard their databases. 

For any person or company, any such incident can be confusing. In such situation, there is a need of a standard policy which must be followed. Following are few steps one should consider in order to save guard the data in case you are at risk:

1. Isolate – Isolate the machine from rest of the network if a particular hardware is on risk.
2. Document – It is important to keep detailed records of everything you do from the moment data breach is discovered.
3. Photograph – Photographs can help in solving digital data breach.
4. Interview – Any person directly or indirectly involved with the systems that were breached should be interviewed.
5. Use Your Knowledge – Learn and take steps.

To know more about it visit:  https://www.techworm.net/2018/04/first-5-steps-when-faced-with-a-data-breach.html

May be your organisation has just started to step into AI, but don’t worry its not too late. It takes plenty of time to embed a good helpful AI into the organisation and there’s no scope of mistake. Giants like Google, Amazon, Microsoft have implemented AI at early stages because they have plenty of data and plenty of resources. For any organisation it is important to find the best suitable aspect for AI implementation which will provide benefit at most. 

Read More at https://www.informationweek.com/enterprises-wade-into-the-ai-pool/d/d-id/1332434?

Data is the most valuable asset for any company and any person dealing with this data needs to be cautious. Modern businesses rely on data. They store, process and access data for information gathering and use it for decision making. According to reports of 2017, a single mistake in handling this data can result into loss of nearly $3.6 million. 

However, data can be loss due to various. Few of them are:

1. Human Error

2. Hardware Failure

3. Theft

4. Online Crime

5. Natural Disaster

The best way to deal with this is to take prevention and keep an up-to-date recovery plan and a 3-2-1 backup strategy, i.e. there should be three copies of data, kept in two different mediums, and at least one of the backups should be off site.

Read about it at: https://www.bigdatanews.datasciencecentral.com/profiles/blogs/five-ways-your-business-is-at-risk-of-data-loss

Analytics is one of the major jobs performed in companies these days. Daily operations are carried out involving data that presents us with results which helps an organization to carry out further processes and helps in decision making. Effective business intelligence is the product of data processed. This data is raw and can be either structured or unstructured. 

Firstly, one needs to manage data before processing it. Rules are to be set for the analytics process which can offer better insight and an easy processing. Below are the five rules that can help in managing your data more effectively:

1. Establish Clear Analytics Goals Before Getting Started
2. Simplify and Centralize Your Data Streams
3. Scrub Your Data Before Warehousing
4. Establish Clear Data Governance Protocols
5. Create Dynamic Data Structures

The field of data analytics is always evolving and thus it is important to create a proper structure that can help in future. By establishing them we can enhance the quality of data processing.

Read more about it at: https://www.sisense.com/blog/data-management-rules-analytics/

Data is very important for various technologies. Whether it be Artificial Intelligence or Machine Learning, Data analysis or Research work, Data is mandatory to implement them. However, the task of finding right data is very tedious and time consuming. One needs to find data that is most appropriate in terms of information available, size and other factors. 

Every day a huge amount is data is generated on internet. To our help there are few open data sources that are free to use. This data can be in raw form which might need further processing. But to start with the process and to get a data set, one could visit below mentioned sites that provides data for free: 

1. Kaggle
2. UCI machine learning repository
3. data.gov

Know more about them at : https://www.technotification.com/2018/04/building-data-science-models.html

Data Sciences is a very vast field and in recent times, there is a high demand of professionals in this field. Dealing with data is not easy. Data sets available with companies are very large and to extract meaningful data is a tough job. Thus, the job of data scientist is becoming very important for decision-making and is based on automation and machine learning. The main role of data scientist is to organize and analyse data. Other than this, data can help in predictions, pattern detection analysis etc. All this can be done the help of some software which is specially designed for the task. The responsibilities of data scientist begin with data collection and ends with decision making on the basis of data.

To know more about the key roles of data scientist, requirements and skills visit: https://www.cio.com/article/3217026/data-science/what-is-a-data-scientist-a-key-data-analytics-role-and-a-lucrative-career.html#tk.cio_rs

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

    

Evidently, there has been massive adaptation of cloud in the industry. As everything begins to operate on cloud, it also generates massive amount of data. Also, just IoT is not just enough, because it’s no more just a “Thing”; this thing, now, can be an automated car, a drone or anything which makes it a necessity to think beyond Cloud computing and brings us a glimpse of Edge computing. Edge computing can significantly provide better throughput, improved performance, and customised processing of data as per the needs of each user.

Data is very valuable for any business. It can help in decision making, planning, and more. Here, Business Intelligence comes into action. BI uses various softwares, applications, tools and services that enables access to and analysis of data. This improves and optimizes decisions and performances. Using Business Intelligence can help you to get more value by improving customer service, employee productivity, and more.

Following are the few ways one could get more value from Business Intelligence:

1. Build real-time BI into your customer-facing services
2. Improve employee performance through BI
3. Improve Customer Service
4. Predict new revenue streams
5. Automate budgeting and forecasting
6. Shift the emphasis to analysis
7. Embed BI into other platforms
8. Cut time wasted on data gruntwork
9. Bring unstructured data on board

Read more about them at: https://www.cio.com/article/3254646/business-intelligence/9-ways-to-get-more-value-from-business-intelligence.html

Every day a new problem statements emerges in the field of technology and machine learning proves to be a solution in most cases. These days, we tend to find smart solutions for our problems and machine learning is the backbone for the same. Thus, we can correctly state that Machine Learning has already invaded in our lives in some way or another.

However, with the emergence of machine learning, misunderstanding and misconceptions associated with it enters the field. There are few common myths about what and what not machine learning can do. Few of them are mentioned below:

1. Machine Learning is AI
2. All data is useful
3. Anyone can build machine learning system
4. Reinforcement learning is ready to use
5. Machine learning will replace people

One could achieve better results if he avoids these common myths.

To read more about this, visit: https://www.cio.com/article/3263776/artificial-intelligence/machine-learning-myths.html?upd=1531678835984

Few challenges being faced in translating the lessons of predicting analytics from other verticals in manufacturing. The objective of this predictive analytics is to get the correct business decisions and it will impact the design and service of the product. The data is being updated continuously through their supply chain. The predictive models are used to connect the real world data to digital twin models of the virtual world. This helps in better understanding and working of their business plus with the on the factory work. Predictive analytics help to find the issues related with the product quality, performance and its features. These helps in better designing the product features and make it to optimum use of it. The predictive model is quite accurate in giving information about the risk failure, improving the machines to put in a better use as well as it gives the best correlation between job characteristics and job failure. Models are being trained through environmental data and IoT data and few factors which affect such data too such as environmental hazards, weather and many more. Its benefit for the business to take predictive analytics into consideration. https://www.ngdata.com/ways-to-improve-customer-experience/

According to authors, modern-day marvels are the result of machine learning, which are programs that combine through millions of pieces of data and start making correlations and predictions about the world , by using machines that uses cold hard data to make decisions that are sometimes  more accurate than a human’s, thereby reducing biases. Computers don’t hold any inherent biases, as machine  knows only one approach i.e. the objective analysis and are capable of analyzing massive amounts of information, thus having  a distinct advantage over humans. Utilizing the data for machine learning, can uncover contradictory and surprising results by making data more accessible, more understandable and  enabling the organization to achieve a new level of business intelligence, thus empowering decision makers at all levels with a powerful tool. Read more at https://aitrends.com/machine-learning/machine-learning-cure-decision-bias/

According to authors, the implication of big data is the quantity is paramount, the returns generated do not match the quantity of data generated. Experts point out, it is not per se the data that should be big, but the primary factor that counts is the diversity of data, the amount of richness they provide and the focus on accelerating human understanding of data , which has the potential to create output subject to increasing returns. More data retards innovation, the speed of experimentation and iteration. However IT teams helps in bringing order to chaos, in data and analytics, by managing data infrastructure, such as data warehouses and production processes . Data scientists, who’re occupying the space between IT and business consumers , have made enormous strides in getting grip on their data, analyzing and acting on it, thereby avoiding imbalance. Read more at https://aitrends.com/big-data/three-big-data-developments-no-one-is-talking-about/

According to authors, the development of Artificial Intelligence (AI) creates several opportunities for retailers to improve their customer services , insights and business processes. AI makes it possible to generate insights on a scale like never before, it does this by combining customer data  across all platforms( from social media to CRM) like past purchases, search habits, click behavior, age, gender, season of the year and various other variables and self learning algorithms. By crawling the web and aggregating various forms of customer data, AI gives retailers the opportunity to engage with customers on a more personal level. Through chat applications such as Facebook and Messenger and by using chat bots on websites, the potential customers can communicate with the retailers using speech or text, that will assess and answer customer queries, thereby assisting in the selection process and helping in the execution of simple tasks. Read more at https://aitrends.com/retail/robots-ai-retail-8-things-must-know/

Deep Learning(DL) is a subfield of Machine learning concerned with algorithms inspired by the structure and function of the brain called Artificial Neural Networks. It  interprets the raw data through multiple processing layers, where each of these layers, uses the output of the previous one as its input, thus  creating more abstract presentation, tackling conceptual problems ,like image classification and natural language processing  and helping to infer logically. Industries that leverage Machine Learning at present , can switch over to DL approaches in future as it’s an approach of AI , which is showing great promise when it comes to developing the autonomous, self-teaching systems which are revolutionising many industries. DL drives sales, increases engagement  and improves user experience, thus it  will be the future of Personalization, which enables a business organization to enjoy amass customer appreciation. Read more at  https://aitrends.com/deep-learning/deep-learning-personalizing-internet/

Business Intelligence software enabled the visualization and deciphering of data but it failed to understand the correlation in the data. Hence, Business Optimization evolved from BI and enabled stakeholders to get wider insights on data interaction and optimize business growth. This free passage of information allows the organization to further utilize raw data only if the company has industry-wide open API standard. Together it is called AoE (Analytics of Everything) now enables organizations to go through an integrated combinations of people, processes, data and technology to have greater insights on the business. Read more at :

https://www.domo.com/blog/transforming-your-data-from-intelligence-to-optimisation/

According to authors, the remarkable impact of Application Programming Interfaces(APIs), is in the analytics front. From accumulating data from new root to evaluation, it has radically changed the face of analytics and the core role of the citizen data scientists ,by gathering right and reliable datasets ,thereby simplifying advanced analytics and enabling them to devote more time behind identifying and interrogating new and valid questions about data. Developers can borrow functionality from other apps by allowing interaction between two pieces of codes , thereby bringing different bits of software together. Efficiency depends on use. Whether APIs will replace citizen data scientists or not, in future, is unpredictable, as, on one hand, business users will have easy access to analytics without their support and on the other hand, their role will also evolve. Read more at http://blogs.sas.com/content/hiddeninsights/2017/06/23/apis-data-scientists/

The basic premise behind the IoT is to connect everyday objects to the internet through tiny sensors, allowing them to communicate with businesses, consumers, and each other.

While hearing about the Internet of Things doesn’t necessarily signify a consumer would not use an item connected to the IoT, the survey results show a lack of awareness and understanding about what can be gained from it.

The Internet of Things sounds good in principle, giving consumers unparalleled convenience and access to the latest technology, but there is one requirement that can’t be ignored: the internet.

One idea for the Internet of Things is to place sensors on roads, traffic lights, utility grids, and buildings, but doing so represents an expensive venture.

Read more at: https://www.smartdatacollective.com/potential-hurdles-limiting-internet-things/