Data blending means combining data from multiple database sources into a single worksheet or table of a given solution in a relatively quick and straightforward way for discovering pattern between data. Big Data blending is gaining importance in businesses as data blending allows uncommon types of data to coexist in a model. New and better strategic decisions can be made by bringing together unusual feeds into one repository. Data blending and data joining are not similar and have significant differences. Joining involves combining data from the same source whereas blending involves combining data from different sources.
Read more at: http://www.cmswire.com/analytics/how-data-blending-can-sweeten-your-insights/

Increase of a site's traffic is normally taken as a brand's visibility and relevance. But often things are not what they appear to be. Hence for making out whether the visitors are real or not data, leveraging is required. Establish What “Normal” Means for Your Business – The data and tracking should be made by such a source that in case of a drastic change the problem can be isolated and identified. In order to recognize what’s normal and what’s abnormal a baseline to work from has to be established. The performance data has to be reviewed consistently and methodically.
Not All Spikes Are Bad Spikes – Identifying whether abnormal spikes in the site traffic or analytics is good or bad is difficult. Logistics issues can be alerted by sudden spikes and dips.
Common Sense and Logic – Looking for twists and reviewing behavioural flows to identify pattern of users can be employed. This helps in understanding the key attributes of the traffic or the keywords. The emerging patterns can be used for proper optimization of the flow of the site.
Read more at: http://www.cmswire.com/analytics/ask-your-data-is-it-a-bot-or-real-traffic/

In recent days, since some data is sensitive and cyber crime has also progressed rapidly so it is essential for companies to ensure right data for the right people. As a result, new laws are coming forward to ensure the safety of data. Accidental leaks are regarded as one of the major cause of data breaches. Firms should make sure that they are closed to data hacking and vindictive fraud cases. Organizations use graph databases to recognize problems in the most efficient manner and to enquire about connected data. With the consequence of graphs, businesses have essential information to recognize the right people for the data and help the companies to secure their data.

Read more at: https://channels.theinnovationenterprise.com/articles/graphs-granting-access

Data breaches have always been a broad level concern, but what is more important is the impact data breaches have. Usually after a company suffers a data breach, strict regulations are enforced curtailing employee use of data. Thus the entire workforce loses data flexibility which in turn affects productivity. At times, hasty decisions can demoralize employees. Having a data-led approach to business throughout the company is definitely a welcome change. There are a number of ways to dispel fears surrounding data. Risks surrounding data can be avoided through three elements namely policy, training and education, and technology. Policy should guide through using data and devices. Training and educating employees is relevant in avoiding breaches. Technology will protect the business in case of a data breach. Having an intelligent, flexible and secure approach to information management will ensure effective use of data while guarding against risks. Read more at:https://channels.theinnovationenterprise.com/articles/don-t-be-scared-of-your-data

In the years, succeeding the financial crisis, the banking sector was restructured ranging from changes in regulations to customer service. The major problems that banks are facing include customer dissatisfaction, fraud, increased competition and regulations and all these issues can be solved using Big Data analytics. By leveraging transactional, behavioral and social data, banks can provide a hyper-personalized customer service. Risk management is another area where analytics can be of help. Big Data analytics can detect cybercrimes and predict the location of attack. It can also identify deviation in customer behavior which is indicative of fraud. Big Data technologies can be used to integrate external watch list screening system and unstructured emerging data sources. There is a huge scope for newcomers in Fintech sector to exploit Big Data as they are built keeping analytics in mind. The big banks should adopt new technologies to leverage the wealth of data they possess and maintain a competitive edge. Read more at:https://channels.theinnovationenterprise.com/articles/analytics-in-banking

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

People analytics is a data-driven approach to managing people at work. Those working in people analytics strive to bring data and sophisticated analysis to bear on people-related issues, such as recruiting, performance evaluation, leadership, hiring and promotion, job and team design, and compensation.

The 5-step path to people analytics:

1.Bridge the gap-To ensure you have everything you need- people, processes and technology

2. Knowing the stakeholders- involves understanding their challenges, goads and opportunities

3. Setting goals and objectives- This involves agreeing upon mutual goals and objectives

4. Assessment- Doing a reality check pf where you are and where you want to be

5.Prove success with data- Highlighting successes and areas that require improvement.

To know more- https://icrunchdatanews.com/5-step-path-people-analytics/

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/

Project management now a days requires a more flexible approach than traditional methods. This involves taking step by step actions which are ranked based on priorities.

4 Good Reasons To Do Agile Project Development-

1. Quality -A key principle of agile development is that testing is integrated throughout the lifecycle, enabling regular inspection of the working product as it develops. This allows the product owner to make adjustments if necessary and gives the product team early sight of any quality issues. 

2. Flexibility- In agile development, change is accepted. In fact, it’s expected. Instead the timescale is fixed and requirements emerge and evolve as the product is developed. 

3. Revenue -The iterative nature of agile development means features are delivered incrementally, enabling some benefits to be realized early as the product continues to develop.

4.Predictability – using an Agile  approach with fixed schedule iterations makes the cost of each iteration predictable.

To know more-http://revenueanalytics.com/blog/agile-project-management-benefits-to-a-revenue-management-project/

A U.S. regional bank while reducing its staff and technology cost wanted to see how it could maintain its collection rate. A consultancy based in Chicago and Bangalore analyzed the actions of a U.S. regional bank such as calls, mailers and IVRs and concluded that the bank was overspending.  The bank was thus able to reduce costs by a million $. Analytical companies are now investing in platforms and products to fill the void in analytic stack. To know more: 

http://www.forbes.com/sites/tomgroenfeldt/2015/07/08/bank-reduces-debt-collection-costs-through-big-data-analytics/

Big Data is the game-changing opportunity for marketing and sales. Airlines are spending on advertising. With the help of big data and analytics airlines can see how to allocate ad dollars. Marketing mix is one example of analytics which helps us understand which media vehicle works best. Following are the three key aspects of marketing mix-

1. Examining the noise- Airlines should take into account the impact of noise.

2. Normalizing the data- This means to adjust the data for external factors (those outside marketing campaign).

3. Performing a regression analysis- To calculate the return on each investment made for marketing and to know the mean impact of each campaign analysis of data is necessary.

To know more- http://revenueanalytics.com/blog/airlines-can-maximize-return-on-marketing-spend-with-predictive-analytics/

Big data is an emerging phenomenon. Nowadays, many organizations have adopted information technology (IT) and information systems (IS) in business to handle huge amounts of data and gain better insights into their business. Many scholars believe that Business Intelligence (BI), solutions with analytics capabilities, offer benefits to companies to achieve competitive advantage towards their competitors. But this journey of increasing adoption of analytics requires much more thinking with an organization.

1. Promotion of decision making based on business outcomes: 

Investment should focus on implementation of analytics rather than infrastructure. Great companies link analytics to results to ensure gains.

2. Data –led decision making: 

Adoption of analytics should be encouraged and intuitive decision making should be discouraged.

3. Technology and business move together: 

Capabilities are built by amalgamating data, technology and business talent.

Therefore, decision makers should realize the importance of big data analytics in improving their business. Changes should be created in organizations for successful adoption of business analytics.

To know more- http://analyticsindiamag.com/successful-adoption-of-analytics-the-journey-from-good-to-great/

Hospitality industry has not been concerned about technology and big data which resulted in widening of the gap between customers and industry. Big Data can result in improved customer satisfaction and personalized marketing campaigns. In addition, Big Data results in a boost in employee productivity and more efficient operations. The advantages of Big Data for the hotel industry are enormous.

1. Improving Customer experience through Personalization  

To improve customer experience, businesses must work towards gauging preferences .There is likely to be an enormous amount of information about every person on the Internet, so the key is to mine this information, and get the most relevant details out of it

2. Marketing Strategies

Big data can help companies devise the best marketing strategies for each target segment, based on their activities and preferences. When businesses understand their customers well, they can devise the right strategies to reach out to them. For example, if a company knows that a particular target group is spending an average of thirty hours a week on social media, they can devise their marketing strategies through social media channels. Such a strategy will give the greatest visibility for the company within their target group. 

3. Competitive Advantage

Companies that make use of big data have edge over others. They are able to foresee trends and make pricing strategies accordingly.

To know more- http://analyticsindiamag.com/how-big-data-is-leveraging-the-hospitality-industry/

Actionable data to support or automate decision making is used by predictive and prescriptive analytics. Prediction of a quantity i.e. regression is required by many use cases. The actions based on the predictions is more important than the actual quantity predicted. Predictions of future events in the context of predictive and prescriptive analytics needs a full probability density distribution. The predictions might give some estimated value of expected fluctuations, but details will still be hidden in the full probability density distribution. In this case, advanced predictive models can be used to derive best point estimator for a given scenario.
Read more at: http://www.blue-yonder.com/blog-e/2015/07/20/predictions-as-a-probability-density-distribution/

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

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

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

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