For any organization, it is necessary to keep a track of the data created, data pulled, the source from where it is pulled, the format of the data they have been using till now, who can access this data and many more. There are many strategies for managing the data but there’s nothing as a perfect strategy. It varies from organization to organization and types of data. Another important factor is size of organization and its budget. The following are the three factors that can help in managing the data in a better way i.e. as per the GDPR rules.

1. Centralization of data

2. Automating data management 

3. Measuring Success.

Read More at https://www.analyticsinsight.net/how-to-overcome-the-challenges-in-managing-big-data

Tons of data is generated everyday in the industry. And making sense of this pile of data has become an important task for many businesses. To achieve this, they are turning into Big Data analytics and Data Science. Data Scientists have knowledge about various algorithms suitable for various types of data and these statistical algorithms are implemented in several programming language. Selection of the programming language depends on many factors. So here is the list of top 6 programming languages that are used by most of the data scientists and analysts.

1.  Python

2. R Programming

3. Matlab

4. Java

5. Julia

6. Scala

Read detailed review at https://www.technotification.com/2018/07/best-programming-languages-for-data-science.html

Just 5 years back, people were debating over whether to accept cloud over in-premise systems, its cost, the billing systems of cloud etc. The topic “Should we use cloud” from that time has turned into “What else should be do on cloud”. Cloud has really grown and have got its roots really deep in the industry. Most of the industry is planning to find new ways to do the business which has cloud as a major part in their systems. 

Read More at https://www.informationweek.com/cloud/software-as-a-service/the-cloud-steps-into-maturity/d/d-id/1332394

Data is like an untapped stream of gold. If used properly, it can help any business to start climbing of the stairs of success real fast. History has plenty of examples like Hershey’s, Caterpillar, Vermont Electric Power Company and many more. These companies began to store, segregate and analyse their data. The data includes their operational activities, sales, purchases, monitoring employees, data about competitors and many more. Proper usage of this data can bring a digital transformation in the organisation. Data analysis often results in cut in operational cost. But its benefits doesn’t just limit till the cost cut. Data analysis can also show a path for revenue generation.

Read more at https://www.informationweek.com/strategic-cio/digital-transformation-turning-data-into-dollars/a/d-id/1332319

Careers in machine learning, Data science, artificial intelligence, deep learning and many more are considered as one of the best choices to pursue. Now these technologies and the related jobs are considered one of the hottest and best jobs today. So, here are the list of top 5 languages prevalent in market for data science, machine learning etc.

1. Python

2. R

3. Java

4. Scala

5. C

Read More at https://www.informationweek.com/big-data/ai-machine-learning/5-top-languages-for-machine-learning-data-science/d/d-id/1332311?

Over past few decades, there has been a rapid advancement in the industry. It has become important that the strategists verify that their end users are getting the latest and quality material. Falling too behind of the advancements may result loses for the organisation. There are several symptoms that may help you to understand it’s the time to make necessary changes in your IT infrastructure to meet the requirements of the end users.

5 such symptoms are as follows :-  

1. Increasing number of IT help desk calls.

2. Hardware/software failures and outages on the rise.

3. Rise in the use of shadow IT.

4. Nobody wants to work for you.

5. New tech initiative is scrapped due to shortcomings in the infrastructure.

Read in Detail at https://www.informationweek.com/strategic-cio/it-strategy/5-signs-your-it-infrastructure-is-falling-behind/a/d-id/1332304?

Github is a very common and well known platform for learning, sharing and developing programs. It’s very helpful especially for team projects. With time Github has turned to be a platform for interactive programming learning. The most common and most chosen programming languages among the Github users are :-

• javascript

• Python

• Java

• Ruby

• PHP

• C++

• CSS

• C#

• GO

• C

Read Detailed review at https://www.technotification.com/2017/11/10-programming-languages-on-github.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?

Cyber security is of utmost important to any organization and of late there has been a tremendous rise in cyber security jobs. This high demand comes from expansion in the interconnectedness of gadgets and computing systems, thus increasing the potential points of intrusion. So, here is a list of some trends in cybersecurity that will affect jobs in the IT sector. They are: Artificial Intelligence, Internet of Things Openings Outbound and Opening Inbounds, Cloud Services and Mobile Devices and Third-Party Apps. to know more, go to: 

https://it.toolbox.com/blogs/davidgillman/5-job-trends-in-cybersecurity-080718

The pace at which technology is moving is unmatchable. Every day some advancement in technology comes into lime light. Among the various fields in technology, one of the major and trending technology is AI. It is growing each day and proving to be a solution in many applications. Below are the 10 areas where Artificial Intelligence can be noticed:

1. Robots predicting the future
2. Robot Soldier
3. Survival Robots
4. Police using AI algorithms to predict crimes
5. AI-based medical treatment
6. Autonomous drones and weapons
7. Supercomputers with imaginations
8. AI communicating with AI
9. AI hackers
10. AI in court

Every thing in this world have a positive side and a negative side. Similarly, a few cases have been there where Artificial Intelligence have gone wrong. However, chances of improvements are always there. 

To know more about these 10 areas and other uses visit: https://www.techrepublic.com/pictures/10-terrifying-uses-of-artificial-intelligence/

Now a days, companies are investing a huge amount of money in Artificial Intelligence, Automation, Robotics etc in order to be up to date with technology. However, still there are few limitations that each of them faces. Even after adopting various technologies, the reports stated that the company still struggles in defining goals. Below are the five steps that companies must follow in order to improve and to meet their expectation for automation technology:

1. Recognize that the use of intelligent automation is transformative, and built on the use of new machines and data sources
2. Formulate a comprehensive approach to automating the service delivery model
3. Measure value vs. risk
4. Consider the “operating model” in all forms
5. Disrupt from within

Bringing innovation in always good but it is also suggested to look for other alternatives for investment as not all technologies proves to be powerful for a company.

Read more about it at https://www.techrepublic.com/article/5-steps-to-help-your-company-benefit-from-ai/

Artificial Intelligence is a combination of various sub topics. Whether it be Machine Learning, Deep Learning, Neural Network etc, each one of them finds shelter under artificial intelligence. Below are the basics of two important topics – Neural Network and Deep Learning.

Neural Networks: Neural network is modelled in the same way as human brain. It compromises various algorithms and aims to find relationship in data provided by us through processes that mimics like human brains. It is one of the trending technologies and is finding its applications in various fields like trading, medicine, pattern recognition etc.

Deep Learning: Deep Learning is basically a network composed of several layers. It is also known as ‘stacked neural networks’. It is a subfield of machine learning and is inspired by the structure and function of brain. It can be widely used in classification, clustering, predictions etc.

We are walking towards the era where technology will dominate and the main technology will be artificial intelligence. Hence it is important to get an insight of what we will be with in future. For a beginner, these topics might be confusing but they form the crust of artificial intelligence.

Read more about them at: https://www.technotification.com/2018/08/neural-networks-deep-learning.html

Digitalizing is becoming the need of hour in every business. Every organization is trying to in cooperate technology as per there needs. Companies believe that cloud storage, analytics, mobile and social advancement are all the tools they require for digital transformation. However, this is not enough. Digital Transformation is still lagging behind even after great efforts by organizations. One reason behind it is the fact that one could not match the speed at which technology is growing. 

Following are few challenges that organizations faces while trying to keep digital transformation up to date:

1. Lack of vision and leadership
2. IT and business don’t see eye to eye
3. Little to no engagement
4. Transforming ops is hard
5. Governance is lagging
6. Critical functions are being shortchanged
7. Shying away from the cutting edge
8. Metrics misalignment
9. Failure to change culture
10. Not failing enough

Read more about them at: https://www.cio.com/article/3274447/digital-transformation/why-digital-transformations-are-lagging.html

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

As the name suggest, Digital Transformation involves thinking and rethinking about how an organization can use technology in business. It helps in improving ongoing processes and bring changes to business. Digital Transformation vary widely based on specific demands of an organization. However, there are few digital transformation elements that should remain in focus. They are – Workforce Enablement, Operational Agility, Customer Experience, Culture and Leadership, and Digital Technology Integration. Even though many companies are trying to in cooperate digital transformation, 84% of them are failing!

Following are the three keys to success:

1. Establish Clarity
2. Prepare from the core
3. Learn to iterate quickly

Read more about them at https://www.cio.com/article/3292262/digital-transformation/3-fundamental-keys-to-digital-transformation.html

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

    

Artificial Intelligence is spreading its wings and is coming into rescue in various fields. One such field which comes into rescue for humans is the health care sector. Combination of these two fields can bring great advancement in health care sector. Artificial Intelligence and Machine learning have already come into action in medicine. Following are the top 4 applications:

1. Diagnosing Diseases: Not all diseases can easily be rectified. This could be time consuming and expensive. Here, various Deep Learning algorithms prove to be a solution. This focus on automatic diagnosis, making diagnosis much cheaper and accessible. 
2. Developing Drugs Faster: Drug development is a time taking and a tedious task. It involves analytics and various rounds of testing. AI has already aced in speeding up the process.
3. Personalizing Treatment: Same medical procedure can not be carried out on every patient. Choosing the course of treatment can be a difficult and a great responsibility. Machine Learning can automate this task. It can help in designing the right treatment plan.
4. Improving Gene Editing: This is a technique that relies on targeting and editing specific location on the DNA. A careful selection needs to be made. Machine Learning models have successfully been able to predict target and effects successfully.

To read more at https://towardsdatascience.com/artificial-intelligence-in-medicine-1fd2748a9f87

Today, Edge, IoT, mobile devices etc are everywhere and are creating plenty of data. By 2025, almost 75% of the world, both consumers and businesses will be connected. If seen in a broader view, there is a huge pile of data and it’s still increasing with a rapid rate. There should be more focus on proper and more usage of this data. Get people who can help you out to resolve the issue of better and more efficient utilization of data.

Cryptocurrency is often seen as an asset. Few months back, there was a hype for purchase and sale of cryptocurrencies, especially, bitcoins. So along with the money, you obviously need a wallet to hold it. And actually there are various kinds of wallets to store this currency. Since Crypto-Currency is just a software, not a hard cash, so it requires a software wallet. There are various types of wallets like Mobile wallet, Desktop Wallet, paper wallet/cold storage, hardware wallet. These wallets have their own traits and the selection of wallet appropriate for you depends on you. 

Have a detailed review about cryptocurrency wallets and their attributes at https://www.analyticsinsight.net/investing-in-cryptocurrencies-you-need-a-cryptocurrency-wallet/