Those using Predictive Analytics easily outpace those who don't. There is a clear incremental sales lift in the marketing campaigns which consider predictive analytics. Every organization strives to achieve a higher return on investment (ROI) from that spend on marketing. Predictive analytics help creation of unique customer profiles by analyzing the data.  Read more about how Predictive Analytics can be useful in B2B marketing in the article written by Laura at: http://blogs.forrester.com/laura_ramos/15-07-02-the_power_to_predict_can_give_b2b_marketers_an_unfair_advantage

Security is an important aspect in any organization. In case of a security breach, company suffers a loss of trust in addition to money. For security, organizations are storing terabytes of data and with big data analytics security has become a nimble and deterrent strategy. It rapidly spots a suspicious pattern. In enterprise security, a small anomaly can be of great importance. Security breach occurs via an unimportant channel in a long period of time. If the organization has right tools, then only it can detect a hacker’s actions in time. With big data analytics data at rest and real time activity can be monitored with ease and organizations can be safeguarded in better way. Read more at: http://www.forbes.com/sites/centurylink/2015/11/23/improving-data-security-through-big-data-analytics-2/

Clickstream is recording of the parts of the screen of a computer user clicks on while web browsing or using another software application. Clickstream Analysis refers to collecting, analyzing, and reporting the data about visitors visit. This is generated by recording the succession of clicks each visitor makes. This helps you to get insights of the behaviour of the visitors. Hence, you can manouver your strategies as per this analysis. It gives you the information of how long a visitor stayed on your website and how often he returns.  This gives a wealth of information to be analyzed.  To know more about clickstream data analysis go through the article written by Jaoa  Correia at: http://www.blastam.com/blog/index.php/2015/04/move-into-limitless-world-of-clickstream-data-analysis

Nowadays, Marketers are re-examining the role of TV advertisement in digital age for making budget modification, as very few are tuning in TV to watch your ads. The number of Youtube viewers are increasing marketers are blending it and new viewing trends to learn new ways of drawing customers. The first driver of Youtube visitors is it  variety of content such are mash-up of memorable events, movies trailers etc. This has lead many personalities to entertain online which are rather becoming more famous among viewers according to a survey. Secondly, Youtube is gaining respect in media industry as it garners credibility to views as reach metrics Third drivers has to be the cord- cutting movement as people are purchasing digital programs instead of cable or satellite which offers them the flexibility of when and what to watch along with engaging in their other important business simultaneously. Moreover, these viewers preference offers analytics opportunities to understand the changing trends in peoples preferences. Read more at :- http://www.cmswire.com/digital-experience/are-you-really-reaching-customers-with-tv-ads/

Surveys show that security concerns are primarily stopping user from using cloud solutions. In reality, the security provided by reputed cloud vendors is much more secure than home grown solutions as seen in Sony's email hack and target credit card breach cases. Cloud solutions are cheaper, adds flexibility, providing tremendous computation capacities to users in just second without even installing software. It updates continuously, where we can store personal data with greater security because the cloud service provider know better protection than a customer. People are reluctant towards transferring all data from premise to cloud just for analysis because they don't understand how much of the data analyzed comes from internal system. Audience at the international conferences uses cloud analytics over Hybrid Transaction/Analytical Processing (HTAP) due to risk of disruption of jobs. The visible risks of cloud solutions are acceptable because of the rule that relying on outside services run by experts completely overwhelms the potential cost. Read more at:- http://timoelliott.com/blog/2015/10/whos-afraid-of-cloud-analytics.html

The relationship between structured and unstructured data in real time can be analyzed by turning the structured and unstructured data (wide data) into fast data. Origin of a problem can be found by wide data. Quicker decision and analysis can be made by fast data. With the boom of Internet of Things (IoT), new challenges relating to the structured and unstructured data may arise. By turning wide data into fast data we will be able to retrieve massive amounts of data from various sources and then use them to identify and potentially assess their behavior in real-time.
Unstructured data can also be used to meet real-time data demands. When wide data is turned to fast data it opens doors to various opportunities.
Read more at: http://www.tibco.com/blog/2015/07/17/big-datas-companion-wide-data/

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/

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

All that glitters is not gold – and the same is true for data as well. To be frank, there is a lot of ordinary in our data management world.
And the most worrying part is that most companies do not actually realize this threat, which is not particularly surprising to be honest, with most companies sticking to well defined data rules which have been in places for quite some time now. This is because data is living under outdated data policies and rules. Data processes and systems are persisting single-purpose data. These outdated processes have given rise to zombie data, which when used can prove to be detrimental rather than beneficial to your corporation.
The only solution is to reassess the founding blocks of data analytics to crush zombie data and ensure progress in the field of data analytics.

For more information visit:
http://blogs.forrester.com/michele_goetz/15-07-14-is_zombie_data_taking_over

Predictive analytics can be seen as business investment rather than an IT investment. But researchers found that there has been shift of funding from general business budget to IT business budget. According to research 15 percent of the organizations prefer to purchase predictive analytics. But truly speaking you will find that there is increasing demand for predictive analytics. This can let businesses to respond faster to market activities and threats. Business investment in predictive analytics has occupied more space in front office but according to research IT and operations are closely related to these operations. To use such analytics technique, the requirement is just a permission from high level management of organization.  Read more at: http://www.smartdatacollective.com/tony-cosentino/332022/predictive-analytics-investing-and-selecting-software-properly

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

The key to achieving better efficiency in CRM is through analytics. CRM will help sales teams to identify leads and retain customers. Marketing teams can use CRM to plan strategies for future. One should keep the following things in mind to successfully leverage CRM data:

(i) Data should be clean and devoid of bad data. Clean, filtered and structured data is essential for gaining meaningful insights.

(ii) Fixing systematic failures and finding ways to improve processes is important. Methods to capture data should not be broken. (iii) Adopting use of data quality tools so that one can use self-service tools and prepare data thus leading to improved productivity with progress of implementation.  

Read more at:http://www.business2community.com/big-data/taking-control-crm-data-01253472

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

More often than not estimates, analytics, data-driven predictions seem confusing and overwhelming. But now the situation demands that benefits of data interpretation is vital. Statements such as data too difficult to access, understand or use are common and so are ignored while making business decisions. Fear of failure affects productivity and trying out new ideas. To dispel fear of data usage, managers need to promote better work ethic; data interpretation must start at the basic level with simple tools and incorporate the habit. To incorporate the total picture in a business decision, every perspective regarding data must be addressed.
To know more: https://hbr.org/2015/07/dispel-your-teams-fear-of-data

While you sit reading this article, if you stretch your arms, you would have electronic help all around you. A smartphone which could pay your bills, plan your schedule and tell you it’s time for a meeting or showing you the nearest food joints when you are hungry. These interruptions imply that we are surviving on advanced, analytics driven machine intelligence. All this said, for machine intelligence to be more powerful, we should be ready to accept a higher level of intrusion. For example, a patient detected with a heart disease, his device could suggest him to take a nap or hit the gym, or could wake him up when he is feeling anxious or stressed. Read more at: http://www.forbes.com/sites/teradata/2015/07/16/why-machine-learning-is-the-next-penicillin/

Businesses should always keep invoking new technology in their systems to keep themselves updated. A recent study has revealed that innovation occurs when business models can be matched with one or more of the six patterns where technological advances and market needs overlap, thus leading to growth and transformation. These six points of overlap are named as Six Degrees of Innovation which are as follows:

(i) Meeting customers’ individual needs with tailor-made products and services.

(ii) Achieving sustainability by minimizing waste and managing resource cost.

(iii) Possessing jointly owned assets to boost efficiency and minimize cost.

(iv) Saving money whenever possible by paying only for the services that are used.

(v) Monitoring supply chains effectively by using tracking systems.

(vi) Using data and analytics to gauge customer needs and easily adapting it. Read more at:      http://www.businessnewsdaily.com/8199-innovation-technology-adoption.html

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 

While running a business, one should consider not only internal data but external ones as well. Due to the inability to access information and integrate it, businesses are lagging behind. Though a huge amount of external data is available, it’s not always easy to find the desired information. Certain software aid the process of finding external data. Bringing both external and internal data together provides a unified view as well as helps in the process of decision-making and discovering insights. Data from all sources should be brought together and technologies that are able to perform this task and are able to put equal importance to all sources of data should only be accepted. Read more at:https://channels.theinnovationenterprise.com/articles/does-your-car-have-more-awareness-than-your-business

The present events ongoing in the world economy give us the impression that we are amidst turbulence and chaos. To become more active and adjust to rapidly changing events, Finance Planning and Analysis (FP&A) departments should consider a vast amount more of data for forecasting. A lot of this can be achieved by making use of advanced technologies like Cloud, Big data and analytics. With Cloud, necessary information can be disseminated to required destinations. FP&A organizations are now moving from spreadsheets to enabling technologies and integrated platforms thus ensuring transparency of assumptions and drivers. Big data and analytics enables faster than ever processing of data. Using software, sifting through a chunk of unstructured data and identifying patterns has become easier. Read more at: https://channels.theinnovationenterprise.com/articles/the-rise-of-the-rolling-forecast-for-fp-a