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

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 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

    

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

Business and IT are two completely different fields. Yet there is always a need to mix them for the betterment of both fields. However, there is a huge gap between these two and to bridge them a Business Analysts is comes into role. A business analyst is responsible to take IT and Business together by using data analytics to analyse the ongoing processes and methods, determine plans and requirements and recommend future plans on the basis of current studies. Now days, data is very important for a business. It can help in planning, decision making etc and thus business analysts are a need for an organization. It is important for a business analyst to be good in both hard skills and soft skills. He must be good at sharing the information he was able to figure out with the team. Similarly, he must have a strong IT background.

To know more about a Business Analysts visit: https://www.cio.com/article/2436638/careers-staffing/project-management-what-do-business-analysts-actually-do-for-software-implementation-projects.html

What could be better than knowing what future lies ahead us? Predictive Analysis is one such branch of data analytics which can be used to make predictions of future unknown events and is growing with a rapid pace. On the other hand, innovation is an ongoing process which finds its application in almost every field. Without innovation, we would not have reached the platform at which we are now. A number of technological achievements have improved our lives.

These days, Innovation has found a guide in Predictive Analytics that helps to walk towards success.  Many innovations are made but majority of them never succeeds. Predictive Analytics is going to play an important role aiming towards new products ensuring greater economic stability and progress in coming years. 

To know more about how predictive analysis can help in innovation read https://www.smartdatacollective.com/predictive-analytics-methods-make-innovation-successful/

Artificial Intelligence is the trend and need of this hour. It has already found its applications in many fields. This technology is changing and improving the world at a tremendous speed and for our betterment. There is no doubt that AI is future. However not many of us knows its basic application in Business. Business needs time to time changes to meet the requirements. AI can help and change business in many ways.

Top five way in how Artificial Intelligence can help and upgrade your business are:

1. Cheaper Analytics
2. Hiring
3. Customization
4. Anticipation 
5. Security

Know more about it at:  https://www.techrepublic.com/article/top-5-ways-ai-will-change-business/

With many companies still stuck to take advantage of data, analytics has to be number one question because this is a key stage to implement AI successfully. There is a sequence of evolution in analytics, starting from descriptive to prescriptive. 

Nowadays, organizations tend to skip traditional analytics and shift into AI. Many enterprises use the descriptive analytics, applying BI techniques: combine all your data to get a quick review on what’s going on in the company. 

Without the insight that analytics brings, it will be hard to assess the outcome of any artificial intelligence system. Analytics keeps AI transparent, responsible and may help increase the productivity of AI systems. Unproductive information control leads to the unnecessary operational costs. AI analytics helps to find out cost savings and prepares a report with the help of main ROI metrics to keep up productive decision making.

Data is an important aspect of decision making and for valuable insights over the data, BI has emerged crucial in organizations. Some useful ways to manage your data and team are : 1.Invest in a robust BI service like Google Analytics or CoolaData so as to get better insights on behavioral analytics and better collaboration. 2.Break down silos - when organizations go through silo effect and information stops flowing, BI can prevent this through transparency. 3.Teams should acquire data analysis skills and grow as they work. 4.One should relate to the context, not depend solely on the numbers but on the why, how. 5.Decide on how to decide for better data implementation. 6.Drive action through insights, study how trends correlate and act accordingly. Read more at :

http://www.networkworld.com/article/3197753/big-data-business-intelligence/6-ways-to-manage-your-data-and-team.html

Both terms web analytics and digital analytics are interchangeable. But there is a difference between two of them. When Web analytics association changed its name to the digital analytics association then the word digital analytics came up. During the early days of the internet, Web analytics were analyzing the website data, such as users, visitors, links and many more alike. When other forms of online came like emails, search, social, etc., then a new term called digital analytics came into being where all these channels were analyzed. Now all the online channels have been transformed from web analytics tools to digital analytics tools. Web analytics is the analysis of website data, whereas digital analytics is an analysis of all data from digital channels that includes websites also. But till now web analytics are still searched more than digital analytics according to Google Trends chart. Read more at: http://webanalysis.blogspot.in/#axzz4hutH4lMG

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, 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 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 last decade saw the massive growth of big data. During that time, all the technologies did not change but there have been a lot of transformations. Cloud analytics, uses a range of analytical tools to help companies extract information from a massive amount of data and present it in a form that is readily available via web browser, has become popular among the companies with the emerging new data sources. With the need to store and process big data, a whole constellation of open source software such as Hadoop emerged, which is used to store and do a basic processing on big data and is also cheaper than a data warehouse for similar volumes of data. Scripting languages like Hive, Pig, and Python along with many open source tools like Spark are gaining much popularity. Read more at: https://hbr.org/2017/06/how-analytics-has-changed-in-the-last-10-years-and-how-its-stayed-the-same

Customer Value Maximization is a good method used to attract customers to increase transactions and keep active customers of a business for a long duration. The primary factors which contribute to the total revenues that a customer will generate are time, purchase frequency and monetary value of purchases. Maximizing the value of the customer to the business implies maximizing time × purchase frequency × monetary value equation. The pillars of every value maximization strategy are:

1. Customer segmentation 

2. Tracking customers over time.

3. Accurate prediction of future customer behaviour.

4. It should be based on the use of advanced calculations to determine the Customer lifetime value (LTV).

5. Marketing action optimization.

Read more at http://www.optimove.com/blog/maximize-customer-value-smart-way

Many big companies are trying to adopt sophisticated artificial intelligence and other advanced technologies without practicing basic data analysis. But this will affect the companies adversely. Without basic automation and strategic vision of solving basic problems using basic data analytics, companies cannot use the AI and advanced technologies to reach the correct results. The different ways of AI are not yet fully discovered by the companies .AI has the full. potential to reinvent business but before that proper automated and structured analytics are needed to be prepared to nourish the advantages of AI. Read more at: https://hbr.org/2017/06/if-your-company-isnt-good-at-analytics-its-not-ready-for-ai:

Is big data serving these businesses or is it just obscuring the decision-making procedure? Collective with analytics, Big data has numerous applications and is cast-off to find responses to glitches in a variation of businesses, it can benefit them comprehend customer behavior and get the most out of business procedures. But like so numerous things that complete good in concept, it’s not precisely working out for numerous organizations. 36% say that it has caused data overload and has made procedure of decision making poorer. Combination of the info with classy analytics tools can benefit organizations to turn rare and formless data into planned insight to get ahead of the struggle.  Since more business operators need to be able to take this data and produce intellect around it, demand is increasing for analytics tools. Read more at: http://www.hadoop360.datasciencecentral.com/blog/is-big-data-harm-or-good

Nowadays, many organizations use data and analytics to understand customers, develop new products and optimize business processes. 

“Organizations able to take advantage of the new generation of business analytics solutions can leverage digital transformation to adapt to disruptive changes and create competitive differentiation in their markets,” said IDC analyst Dan Vesset in a statement issued in conjunction with the release of IDC’s Worldwide Semiannual Big Data and Analytics Spending Guide earlier this year.  A recent Forrester Research study also found that, 50% of businesses now use data and analytics tools to analyze their existing customers, while 48% use them to find new customers and 47% use them to develop new products and services. Read more at: http://www.cio.com/article/3147293/analytics/analytics-50-how-big-data-innovators-reap-results.html#tk.ciofsb

Many data analytics firms are working for the betterment of the farmers. These companies integrate satellite, weather, and IoT analytics with the agricultural sector. They use its proprietary machine learning and parallel computing techniques, to resolve complex relationships like crop growth and soil health. Using analytics farmers can opt for a smart sampling procedure using satellite – based crop clustering techniques, which reduces the time for identification of these plots and optimize their locations. While the former requires timely crop intelligence, crop insurance companies need highly accurate assessment of risk. The satellite imaging analytics serves two purposes: First, it ensures that the farmers receive a fair and immediate compensation for crop loss due to adverse climatic conditions. Second, it enables insurers to settle claims speedily due to the availability of data in near-real time without any manual intervention. Read more at: https://yourstory.com/2017/05/satsure/