Big data for a long time has been handled wrongly, as there is problem lies in assigning meaning to data. There is acute skill shortage and the human factor also complicates big data. Here are a few ways how we can get better benefits from big data.
1. The business has to say what it wants to achieve from collating and analyzing data.
2. Asking relevant questions, so the data can provide answers.
3. Start small and then get bigger, trim irrelevant data.
4. Managing costs better
5. Understanding what data matters to the business the most.
To know more, follow: http://www.information-age.com/technology/information-management/123459617/big-data-phenomenon-broken-5-tips-doing-analytics-right-way

To provide correct business decisions, we need to address data quality consideration like accuracy, timely, consistency, etc. There is a sheer growth of data which needs to be accounted for and properly identified from the source. Quality movement focuses on many diverse aspects. The origins of defects therefore failed to be identified. The challenge is to understand the data, by use of data models and in context. Analysts build models based on continual consultation with business stakeholders. Metrics are established to quantify the relative importance and evaluate progress. Continual improvement is an ongoing discipline which gives breakthrough results and competitive advantage. To know more:
http://www.thoughtsoncloud.com/2015/07/enabling-a-data-culture-through-continuous-improvement/

The increasing number of startups have surpassed the big players with data driven business models. Data science has become obsolete! The next big thing is predictive analytics.

But there are fears associated with adopting this new technology like the fear of complexity, replacement and failure. 

How effective predictive analytics will be depends on how the organization perceives it. When everyone in the business starts thinking about how predictive analytics can improve their organization, it results in big wins for the company. 

To move forward with predictive, the business needs to leave data science behind. Predictive analytics is a completely different approach. Applied to business, predictive models are used to analyze current data and historical facts in order to better understand customers, products and partners and to identify potential risks and opportunities for a company. It uses a number of techniques, including data mining, statistical modeling and machine learning to help analysts make future business forecasts.

To know more- https://icrunchdatanews.com/3-keys-smooth-migration-data-science-predictive-analytics/

Big data is growing now days because companies are demanding more output from big data. Data and data management solutions have helped companies to modify the customer interaction and tackle the risk of uncertainty in timely communications. After having good views of customers, agents can help transforming marketing qualified leads into sales qualified leads with smarter interactions. There has been a change in the role of Chief Data Officer (CDO) who was earlier working as data scientist to help transforming big data into new repositories. Today there is compensation for CDO for the value they extract from all sources of data. The large volume and variety of data from other sources and social media has made the marketers very busy. CDO’s are held responsible to judge the relevance of consumer data and assign a particular value.  Read more at: http://www.smartdatacollective.com/gayle-nixon/332221/data-within-and-data-without

In healthcare, the main challenge when big data is concerned is to ensure that both healthcare providers and patients are benefiting from the huge flow of information, and are not getting confused by it. Big data is not to be forced and is to be adopted by the healthcare system, in a stable and efficient way. There is a need for targeted and personalized data to improve service quality, even at doctors’ levels. Restricted accessibility to information often creates hurdles for healthcare organizations. The healthcare providers are now deriving both medical and financial information from the insurance sector. EHR data can act as a major playground for big data analytics, as the healthcare sector are adopting more and more e-measures. Read more at: http://healthitanalytics.com/news/how-healthcare-big-data-analytics-drives-systematic-improvement

As soon as Big Data started transpiring into the future, it is believed that it would transform the way companies sell, market, communicate, educate etc. There has been an enormous progress of Big Data which helps in data integration and analytics. Nowadays much of the work is concerned on future and therefore it focuses on identifying the consequences of it. The prospect of Big Data has enlarged in recent days and there are key "first-order", second and third order, sample "second-order", sample "third-order implications of Big Data. With the conversion of Big Data into Implications Wheel, it furnishes everyone with new perception and control.

Read more at: https://channels.theinnovationenterprise.com/articles/7899-the-implications-of-big-data

A recent survey on how organization's active data is growing on a year on year basis, looked at two distinct groups:
• Companies experiencing data growth exceeding 50% annually.
• Companies experiencing data growth of 10% or less annually.
So what did the survey reveal?
For starters, companies experiencing rapid data growth were more likely to uncover business opportunities and drive growth. These companies were better able to leverage a wide variety of data types that are both structured and unstructured. And, in comparison to companies with lower rates of data growth, rapid data growth companies:
• 64% more likely to have an executive champion for their Big Data initiatives.
• 68% more likely to have the ability to discover and classify all relevant business data as it arrives.
• 4.3 times more likely to have defined a chief data manager role.
All evidences points to the conclusion that there cannot be a case for excess data. When it comes to data – the more the better.

For more information visit:
http://www.attunity.com/blog/data-growing-out-control-that%E2%80%99s-good-news-your-business

There is an upward trend in the use of big data in the present scenario of the business world. The structured and the unstructured data comprise of the big data. The big data gets analyzed from the market view point and looks for the right time to fetch the right customers. The big data not only helps in sustainable productivity but a development for the employees on an individual levels as well .The use of big data have benefited many sectors in the business and will help more with the combined effect of the modern technology.

Read more at: 

http://www.business2community.com/big-data/big-data-a-big-impact-on-productivity-01274278

Health care providers, lose track of patients as soon as they relocate from the healthcare facilities. The process of relocating within hospitals is equally complicated for patients. A well-defined IT infrastructure can be used to address both the issues. It is important to match patients with their appropriate health care facilities, by collecting and analyzing the data on the patients’ needs. It will also be possible for healthcare providers to draw valuable insights, by collecting and analyzing data on patient transfer procedures. Hence, we have both big data and data analytics, coming into play, by increasing the operational efficiency, but dampening the revenue earnings of the healthcare industry. Read more at: http://healthitanalytics.com/news/big-data-on-patient-transfers-raises-quality-snags-revenue

Big Data is perceived to be a high tech thing that allows us to gain insights and solve problems like never before. Big Data is processed using brand new computers possessing huge processing power. To utilize data to its full potential, constantly updating systems is necessary. Contrary to our belief that data is a new age concept, data has been in use since a long time as is evident from Willard Brinton’s book Graphic Methods for Presenting Facts published in 1914. Most of the techniques discussed in the book are relevant even today. The only difference between then and now is that the size and availability of data has increased manifold thanks to our growing digital footprint. Big Data is becoming bigger with time but the relevance and use of data remains unchanged. Read more at:https://channels.theinnovationenterprise.com/articles/big-data-its-not-new

In our daily life we came across new technologies and making our life simpler. Today people are enjoying benefits of internet in form of online shopping, easily sending emails and transactions with friends online. Data is collected on the bases of these activities and improvements are made according to people’s preferences and tastes. Data mining is the key to collect big data and most of the businesses are dealing with these data to provide good services to consumers. Both of the large and small organizations are using big data. Earlier big data was providing benefits to retail and sales industry but now it also giving advantages to learning and development industries. Big data is also beneficial for employee training and can improve performance of current employees. Transformation of training process from traditional to modern techniques there has been a significant improvement in employees’ performance. Employers can easily motivate and inspire their employees. After getting good output from employees, employer gives more importance and satisfaction to employees. So, big data help employers to better understand their employees. Big data help companies to understand people’s requirements and providing facilities according to their preference. It also came in a form of learning and development tool for businesses to improve the performance of employees. Read more at: http://www.smartdatacollective.com/briggpatten/331869/how-big-data-shaping-future-learning-and-development-industry

Metrics are performance indicators for the markets. The rules in choosing the right metrics are:
1. Easy to use and understand
2. Easily replicated
3. Metrics should provide useful, actionable information that impacts the business.
With the availability of a wide variety of advanced analytics, it is easy to get sidetracked. Pressure to measure to many things makes it difficult o determine where to focus. Background data on customer is a useful metric. Effectiveness of targeting is related to marketers identifying customer personas. The right metrics such as calculating the potential lifetime values of various customers can help differentiate who is most likely to be profitable over the long term.
To know more: https://hbr.org/2015/07/identify-the-marketing-metrics-that-actually-matter

 After big data and internet of things, the new buzzword is the Analytics of things. Though on a similar note, we do not have an exact definition, we know what it means for the economy and the world, as a top strategic trend in technology. As better algorithms for IOT digital infrastructure are being built to index our world to every smaller level, connection based analytics can be used to better predict future conditions and prescribing future actions. AOT fuels the process as new devices are created, there is a potential for new analytics further leading to modification.  Read more at: http://www.forbes.com/sites/teradata/2015/07/15/analytics-of-things-what-does-it-mean-and-where-is-it-taking-u

Sports analytics have become very popular now days. Predicting winners, player performance and team selection has taken a new form with the help of sports analytics. It has now become a new way of making money and building reputation in sports world. Analysts use the previous data to make predictive models and make future prediction using those models. There has been a shift from qualitative data that was traditionally used to quantitative data. Sports analytics have not been so easy in all sports. American Football, which has large number of variables that can change overtime, faces some difficulty seeking advantage of analytics. NFL teams hardly play 16 games a season implying very small sample size; it is very hard to get some pattern of data. Knowledge of the game and watching the games is equally as important as collecting data. In fact it is part of the data. Read more at:https://channels.theinnovationenterprise.com/articles/how-people-are-beating-the-bookmaker-with-sports-analytics

Data is the most crucial component of every organization but lately some businesses have started to deal in data. Therefore we need to focus on some basic steps before we begin to trade data and they are as follows:

1. Selling and Accumulating data - Companies that are engaged in selling data have suffered from serious adverse criticisms. If an organization has more data, then it has to bear higher risks and management costs.

2. Making Sense - Big data improves business efficiency and has assisted in the Internet of Things but management costs have to compensate against the money made.

3. Making Money - Organizations search for suitable techniques to create money from data.

4. Risks and Returns - Data helps to improve the quality despite of the risk aspects associated with it.

Read more at: http://www.business2community.com/big-data/can-businesses-profit-data-quality-01275752

External factors have a great influence on businesses thus understanding these factors is very crucial when building a statistical forecast. To know whether an external factor has influence on our analysis or not we should consider the following aspects-

1.Consistency- This means how volatile is the data.

2.Accessibility-This is the ease with which we get data.

3.Frequency of getting data- For yearly decision making a quarterly data would do good but for frequent decision making daily data fits best.

4.Data Granularity-The granularity of data refers to the size in which data fields are sub- divided.

To read more- http://revenueanalytics.com/blog/uncovering-external-influences-in-your-analytics/

Companies are constantly generating large amounts of data both from internal and external sources. They are even storing these data. But some of it are useful for future projects while others are never used again. In today’s world of big data, storing data does not create a challenge as does managing and utilizing information. Storage costs are beginning to hurt companies but connectivity allow for some relief. Timing is crucial as presenting data at the right time matters a lot. Retrieval of the data with relevancy, accuracy and integrity can sometimes pose a challenge. With storage and backup infrastructure in place, storing data becomes relatively cheap. Read more at: http://www.dataversity.net/how-to-overcome-the-big-data-hoarding-monster/

Machine learning has showed tremendous potential to transform companies from inside out. Everyday new algorithms are coming up that are being used to encounter data and tackle new problems. On the other hand, a closer look at machine learning reveals it to be nothing more than a branch of statistics for a world of big data. Business executives with a thorough understanding of machine learning have the ability to reach efficient business outcomes. In this age of data, firms have to work with large scale data. Both advanced software and hardware is needed to manage, analyze and store it. Herein lies the applicability of machine learning. To know more, please follow: http://www.dataversity.net/what-business-execs-need-to-know-about-machine-learning/

Understanding and collecting data is an important part of viable businesses nowadays. Big data helps us in that with its many applications in various spheres. Big data is not only limited to marketing applications, it can analyze structured and unstructured data searching for purchase patterns, build logs and store day to day information. Big data has helped optimize business performances, has led to an increase in productivity and thus it has made its impact felt on the profit margins. Big data empowers organizations with knowledge of their employees thus enabling interactions on an individual level. This is bound to make an impact on employee productivity eventually leading to growth of the organization in the long run. Read more at: http://www.business2community.com/big-data/big-data-a-big-impact-on-productivity-01274278

Big data and analytics are the buzz words in any industry now, but one should not forget that data inaccuracy can lead to huge losses for any industry. Big data becomes useless unless it possess a reasonable degree of accuracy. In case of industries like healthcare and banking big data mistakes can even take someone’s life. Data should be cleaned before data scientists can leverage it to derive useful insights. Practicing good data management is the need of the hour. Executives, instead of being impressed by the size of data, should question its quality. Systems should be designed in such a manner that it is able to simplify the process of data collection and minimize risks from inefficient data. Read more at:https://channels.theinnovationenterprise.com/articles/7782-big-data-vs-bad-data