The development of mobile, IoT, and the cloud has increased the need of analytics to solve challenges in the customer, product, operations, and marketing domains. The established companies need to restructure their business and technology to increase their sales. Organizations need to involve cross-functional teams to establish data governance. Analytics 1.0 was data warehousing and business intelligence; Analytics 2.0 was big data, Hadoop, and NoSQL. Now in the era of Analytics 3.0, when tools make decisions and measure the impact. For more read the article written by chandramohan Kannusamy (Technical Architect) : http://data-informed.com/analytics-3-0-and-data-driven-transformation/

Some of the pros for executing data driven marketing efficiently are:-

1. Joint collaboration with IT department and marketing team is the key to achieve efficiency.

2. Hiring an industry analyst, professor or data scientist to review the data before publishing is necessary to check accuracy.

3. Planning the data collect and analysis before starting the project will help immensely.

4. It is important to focus on what the data means and what are its implications.

5. In order to increase exposures create strong relations with media and analyst (to find out what kind of data suits best for them) before publishing the data.

6. Company should decide whether they want to invest in product growth or data marketing.

7. If the company doesn't have data they don’t need to invest in costly data gathering procedures. Inexpensive tools are easily available.

To know more, read article by James A. Martin on the link : http://www.cio.com/article/3052442/marketing/how-to-do-data-driven-marketing-right.html?page=2

401(k) is a retirement savings plan sponsored by an employer. It is found that retirement planning and wealth management firms are upgrading themselves with client segmentation. Nowadays, segmentation and predictive analytics projects are important to organizations. Segmentation is an important as it help firms continue to add resources. Firms are also trying to progress with the help of big data. For more read the article written by John Sullivan : http://401kspecialistmag.com/segmentation-critical-fully-understand-clients-across-channels-march-2016-boston-new-research-cerulli-associates-global-analytics-firm-finds-firms-approaching-client-segmentat/

Prescriptive analytics is considered the third wave of the Big Data revolution after descriptive analytics and predictive analytics. Prescriptive analytics add a third layer of technology by evaluating possible actions in response to the data and produce a desired outcome. Combination of predictive and prescriptive analytics can be helpful for organizations which wants to do more in less time. Combining prescriptive and predictive analytics add more features into the equation, such as the responsiveness of a prospect to your outreach. Effective prescriptive analytics deliver more transparency and control. For more read: http://insidebigdata.com/2016/03/03/how-prescriptive-analytics-puts-complex-decision-making-on-rails/

Nowadays, computers solve crimes, drive cars, cure sickness and accurately predict political races. But the problem is, it's not enough to just store, access and process data. Machine learning and artificial intelligence algorithms are divided into two factors: huge quantities of timely and accurate data. There are four typical methods to be used to acquire data. They are -1. Required self-declared data,2. Self-declared or observed data 3. Volunteered self-declared data, 4. Crowdsourced observed data. For more read the article link written by David Elkington: http://techcrunch.com/2016/03/01/getting-predictive-about-politics-and-everything-else/

EMR (electronic Medical Record) adoption, big data and other trends are helping a lot in the generation of data in the healthcare industry. But data is not what drives the healthcare industry- managing this data does. In healthcare, data analytics is done in use cases. Multiple use cases are created according to the need like one while patient got admitted in some department, a use case gets created. To provide best services to patients timely, immediate access to patient’s data without the barrier of time or location. Read the full article here: http://www.computerworld.com/article/3038315/data-analytics/accelerate-time-to-value-with-healthcare-discovery-analytics.html

The National Biotechnology Development Strategy 2015-20 plans to make India a world-class bio-manufacturing hub, with intent to launch big missions, creation of new biotech product and creating a strong infrastructure for R&D. The catalyst in this mission is harnessing the power of big data. IT and healthcare have always worked together. Big data and data analytics have helped a lot in cancer research, drug safety, genomics and clinical research. Big data helps in solving the complexities of healthcare that looked impossible to solve before. It helps in making sound decision within a short period of time and with cost effective measures. The error rates have also decreased enormously leading to a significant increase in efficiency. Read the full article here: http://tech.firstpost.com/news-analysis/national-dept-of-biotechnology-sees-big-data-propelling-it-to-100-bn-industry-by-2025-293213.html?utm_source=also_read

According to a report, analytics and big data are going to see most vigorous hiring. Established firms and startups are offering handsome income to talented data scientists. Analytics and big data sector have been on consistent growth over last five years and are expected to increase at a compound annual growth rate of (CAGR) of 33.2% and 26.4 % respectively. The demand for data professionals has increased. It has been predicted that in coming times data science is going to have most exciting jobs. Read the full article here: http://economictimes.indiatimes.com/jobs/analytics-big-data-to-see-robust-hiring-high-pay-packets-report/articleshow/51105814.cms

Predictive analytics and statistical analysis are based on the concept of relationships between observed and future actions. When analyzing people, we observe a small sample of data on people and build a predictive model to identify a number of shared traits they have. Micro-targeting is the idea of finding relationships among variables to recognize the target audience's shared traits. It helps to identify the right people. The steps included to predict purchase behavior and design a campaign to expand customer base are: 1. Create dataset. 

2.  Once we have a dataset loaded, we will analyze the people who have purchased the cloud solution and find what they have in common with one another. 

3.  To do this, our first step is to create a dependent variable. 

4. Then, we build a predictive model which takes that variable containing cloud purchase information, and compares it to other variables in our data set. 

5.  The regression model we build then compare our cloud purchase variable to whichever other variables used for analysis, and then gives us statistical correlations for each variable. 

6. Initiating the cloud solution licensing, then identify more people who fit this demographic.

For more read the full article at:

http://www.econtentmag.com/Articles/Column/Marketing-Master-Class/Why-Predictive-Analytics-and-Microtargeting-is-a-Game-Changer-for-Marketers-109377.htm

Big Data is massive. This article will explore the issues of big data: what it is, how it will improve decision - making, and how to use it correctly.

1) What's the big deal about big data?

Big data is all about three "V's" Velocity, Volume and Variety.

2) How it will improve decision?

# Deliver customer insights.

# Deliver targeted communication.

# optimize performance.

3) How to use it correctly?

# Collect data.

# Clean Data.

# Merge Datasets.

# Analyze Data.

To learn more about big data follow the article by Angela Hausman (PhD) at: http://www.business2community.com/big-data/big-data-like-teenage--01458960

Silent customers create major risks to companies. They don't express their dissatisfaction. Companies can avoid these issues through the proper use of technology with predictive analytics.  Predictive analytics can stop the silent customer attrition by identifying four ways to retain customers:

1. Recognize customers who make a detailed analysis before they determine.

2. Determine the most effective actions to reduce the attrition

3. Distinguish between the best time, message, and channel to reach the customer.

4. Identify the full path to retention rather than one single action.

Companies that adopt predictive analytics to identify who is likely to leave and determine the best plan of action to stop attrition.

For more read :

http://www.information-management.com/news/big-data-analytics/using-predictive-analytics-to-stop-silent-customer-churn-10028295-1.html

Earlier, organizations need to analyze the impact of external factors on the basis of performance. They used old statistical modelling tools and took months of data collection and analysis and guessed the external factor which had more impact on the business. These models became outdated as soon as they were developed as external data were constantly changing. During the economic crisis, companies did not understand the economy. But, nowadays, as consumer behavior and other useful data sets have become more available, businesses can address challenges and opportunities by improving bottom line profits and helps to generate higher revenue by following correlation of real time data model.  For more read the article written by Rich Wagner (President and CEO of Prevedere) at : http://www.informationweek.com/big-data/transforming-an-antiquated-business-intelligence-process-/a/d-id/1324197

Design thinking is at the top of mind for business teams of big giants as well as startups. The traditional "If you build it, they will come," mentality has been taken from techniques like customer journey mapping and empathy-driven prototyping. Many companies are unsure how to implement it to improve their business - especially in areas like data analytics and decision sciences. The first step is to ask:  for whom are we designing and what is the problem they are experiencing? The second: to what end are we modeling the design - to boost consumption and engagement, improve performance, or to achieve scale? These same needs to be asked at the outset of any analytics effort. Here are five simple steps that are key to infusing analytics with a designer mindset.

1) Create a design framework that allows you to fail fast.

2) Empathize with your customer to impart emotion into your product.

3) Focus on problem-solving that allows for rapid experimentation.

4) Employ methods to inspire creative brainstorming across teams.

5) To design the killer solution, let nature be your guide.  

To read more visit at:  http://www.datanami.com/2016/02/16/what-design-thinking-means-for-data-analytics/

Some volunteers of a nonprofit volunteer outfit are going door to door in Odisha. They are collecting data about schools, health facilities, Panchayat, roads, etc. These data points are being analyzed by a not-for-profit development organization which will use this data and will draft a development plan for Member of Parliament. The idea is to filter the data into meaningful information which will help in reducing the gap between real needs and actions. Some big consultancies are also working together with politicians for the same purpose. Data analytics tools are used to determine demographic profiles and socioeconomic aims. Read the full article here: http://articles.economictimes.indiatimes.com/2016-02-02/news/70283182_1_data-analytics-tools-prime-minister-narendra-modi

Enormous demand of Wi-Fi has led to transmission of billions of bytes of data every day. Researchers have predicted that by 2019 mobile devices will exchange 35 quintillion bytes of information every month. This is just for mobile devices, just consider computers, IOT devices and you can get an idea how much data will be transferred. But our traditional Wi-Fi can't handle this entire load. So scientists have discovered a new technology known as LiFi. It uses light as a means of data transmission. Using LiFi we can send 224 gigabits per second. They can be also used in healthcare facilities. The idea that light can pass through walls can increase security because one has to be physically present in the room to access the data. Read the full article here: http://www.forbes.com/sites/bernardmarr/2016/01/12/will-lifi-take-big-data-and-the-internet-of-things-to-a-new-level/#2bceb09c346f

Big data is in its adolescence. Companies have started to treat it as a corporate asset. The focus has been shifted to find those data initiatives that will have biggest and immediate effect. Companies are not looking for more data, but the right data. This helps in making better informed decisions. A correlation between good data policies and financial success is also observed. Big data is becoming less about volume and velocity, but more about value. As technology continues to improve, the "bigness" of big data is becoming a less important factor. Read the full article here: http://www.forbes.com/sites/bernardmarr/2015/11/30/big-data-now-a-top-management-issue/#29598044794d

Most businesses need a combination of multiple marketing techniques to exhaust the full potential and make it more productive. Though internet usage is increasing each day, but offline marketing channels still involve lots of customers. Best marketing strategies merge both offline and online marketing. This might seem a difficult task, but machine learning models make it easier. These lessons from the data field which channel, in what way, and at what time will be most effective for the particular set of customers. This is called intelligent messaging. Thus leading to maximize success of marketing. A significant increase in the success rate has been observed in the marketing strategies using these techniques. Learn more about it in the article written by Brendan O'Kane (managing director and chief executive officer of OtherLevels) at: http://www.itproportal.com/2016/01/04/intelligent-messaging-big-data-machine-learning-a-powerful-combination-for-multi-channel-marks/

Despite the many differences in application, most people agree on that the disruptive innovation are: 1. Cheaper 2. More accessible and 3. use a business model with structural cost advantages. Due to the presence of these characteristics in disruption, it's difficult for an existing business to respond to competition. To read more above disruptive innovation follow: https://hbr.org/2016/01/how-big-data-is-changing-disruptive-innovation

In the Healthcare industry, what we really need? Better healthcare results to improve our lives. Healthcare industry generally takes decision using data- like case histories. Well the good news is, now we have lots and lots of data. This is the era of big data, where millions of bytes of data are generated every second. And the bad news is we can't keep up with this fast rate of generation of data. Big data is unstructured which makes it difficult to hunt down relevant helpful information. Using data science with health care we can predict epidemics, advance cures and can provide better, safer and more pleasant experience for patients. Read full article here - http://www.cio.com/article/3001216/analytics/4-big-reasons-why-healthcare-needs-data-science.html

Hadoop and Apache Spark are seen as the competitors in the world of big data, but now the growing consensus is that they are better convention in together. Here is a brief look at what they do and how they are compared.  1. They do different things: Both are the big-data frameworks, but they do not serve the same purposes. Hadoop is a distributed data infrastructure. It also Indexes and keep track of that data, enabling big-data processing and analytics. On the other hand, Spark is a data processing tool. Secondly, both can be used individually, without the other. 3. Spark is faster 4. You may not need Spark's speed: Spark is fit for real-time marketing campaigns, online product recommendations, cybersecurity analytics and machine log monitoring. 5. Failure recovery: differently, but still good. Read more at: http://www.computerworld.com/article/3014516/big-data/5-things-to-know-about-hadoop-v-apache-spark.html