A data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed. According to Gartner, the advantage of Data lakes is: helps in addressing the old and new problem by providing the relevant set of data for analyzing the situation. Disadvantages are:

• Lack of data quality.
• Security and access control.
• Data Lake requires proper infrastructure.

But using purpose built cloud systems security, access control and scalability problem can be solved, but data quality is not good.
 To know more about Data Lake and its advantage and disadvantages, read an article Data Lakes: Emerging Pros and Cons by Joe Panettieri. Link: http://www.information-management.com/news/Big-Data-Lakes-Cloud-Computing-Analytics-10026889-1.html

For any business, demand forecasting is an important function. Demand Forecasting is the activity of estimating the quantity of a product or service that consumers will purchase in near future. It helps you to order inventory and arrange staff for fulfilling customers need.

According to Peter Daisyme (Co-founder of Hostt), some ways to make demand forecasting more accurate are:

• Use the right set of data for making decisions.
• Consider the variables like the seasonal trend, random trend, economic conditions, etc.
• Know your customers and business.
• Each year, you should refine your demand forecasting technique.
Read more at: http://www.entrepreneur.com/article/244823

According to Sujan Patel (Contributor), companies before analyzing big data, must understand the company's goals and mission. In a survey by Price Waterhouse Cooper, only 44% of companies feel that they have the right talent to capitalize big data. When any company chooses tool for data analytics, focus should be on team needs and solution and the team must know how to use that tool. Read more at: http://www.forbes.com/sites/sujanpatel/2015/04/22/how-fortune-500-companies-are-building-big-data-teams-and-how-startups-can-too/

According to Rick Frascona (senior content manager with MadValorem), digital marketing is one of the parts of real estate marketing strategy. Event-driven marketing and predictive analytics can lower costs of direct mail marketing. A Real estate agent can find customers who have a maximum probability of buying or selling houses with the help of predictive analytics.  According to the National Association of Realtors, 92% of homebuyers in 2014 used the internet to search for homes. Read more at: http://www.inman.com/2015/04/21/can-predictive-analytics-breathe-new-life-into-direct-mail-marketing/

In today’s world, data is limitless. But, this Big Data, will help you achieve your targets, if we use it efficiently. Otherwise, you’ll be trapped in a web of data. For this, you need to do a gap analysis i.e. what data you have today and what data would you like to have.

To answer these questions, this article by Mary C. Long, Chief Ghost at Digital Media Ghost, suggests three tracks on which your business can move forward on:

1. Get a handle on your current data
2. Vet potential data sources and set a realistic budget
3. Add ONE new data source to your client insight capabilities

To know more, visit the following link on cmswire.com:

http://www.cmswire.com/cms/customer-experience/dont-let-big-data-keep-you-up-at-night-028895.php

Big data represents a major change in the way businesses and other organizations operate and will require a new mind-set and capabilities. Extracting business value from big data seems to be a complicated task. But, it can be easily accomplished, if done in a planned manner. Following article reports nine steps, given by HCL Technologies CEO, Anant Gupta, in the World Economic Forum’s Information Technology Report, to help organizations overcome the barriers in transition to Big Data and minimize the difficulties that may occur along the way:

http://www.information-age.com/it-management/strategy-and-innovation/123458030/9-steps-realising-benefits-big-datas-promise

According to a survey, the Indian fashion industry is likely to touch $77 billion in a next five year. To understand the changing customer behavior and have a competitive advantage, e-commerce firms and retailers can use analytics. In a research from Technopak, the share of apparels and lifestyle in e-commerce is likely to increase by 30% and e-commerce will contribute about 6% of apparel sales. Fashion industry faces a challenge of understanding customers want and require quick access to market dynamics.  E-Commerce in retail is increasing, and all channels, firms are looking for advanced analytics to understand consumer behavior. Read more at: http://www.business-standard.com/article/pti-stories/data-analytics-can-help-retailers-know-consumer-behaviour-115042000358_1.html

According to Collen Jones (CEO of Content Science and co-founder of ContentWRX), big data help to identify new market opportunities and customers. But, most companies are facing problem with analyzing big data. Therefore, before analyzing big data, an organization also needs to analyze small data. • Understand your situation by collecting and analyzing the data.
• Interpreting the data you collected in a clear and compelling way
• Searching what to do next?
Collected Data should be focused on content and customers and should be accurate and reliable. And while searching what to do next try to find opportunities and threats.
To know more read at: http://www.cmo.com.au/article/573077/thinking-big-data-marketing-get-small-data-right-first/

“If you live long enough, you'll make mistakes. But if you learn from them, you'll be a better person.”

- William J. Clinton

Trial and error are part and parcel of our everyday work. But, learning from mistakes and performing better, is what that makes the difference. In this article, Rachel Sprung, Product Marketing Manager at HubSpot, has shared some of the mistakes in the field of analytics that she had experienced. They are:

• Thinking low numbers are always a bad thing
• Confusing correlation with causation
• Confusing visits with views (and vice versa)
• Equating more time on a page with higher engagement
• Counting internal website visits toward your goal

 To know more and avoid such mistakes, visit the following link:

http://blog.hubspot.com/marketing/marketing-analytics-mistakes

According to Mary Shacklett (president of Transworld Data), organizations need big data and analytics capabilities for immediately transferring big data into actionable decisions. According to a Gartner September 2014 report, there is an increase in investment in big data by 64% from 2013. Jeff Kelley (Big data analytics analyst from Wikibon), says that “customers expect personalization when they visit websites, so companies need to develop analytical capabilities and in the long term apply real-time will grow as Internet of Things.” Preventive maintenance analytics can be developed, if data on the Internet to thing can be analyzed. To know more about real time analytics and Internet of Things read on:  http://www.techrepublic.com/article/surge-in-real-time-big-data-and-iot-analytics-is-changing-corporate-thinking/

Punk in Punk Analytics means working fearless and having an approach to ‘do-it-yourself way’. In the last few years with the rise of new technologies, we entered into the era of punk-style analytics. Some characteristics of punk analytics are:

• No barriers for information search, as you can download easily and free of cost.
• Mistakes are part of the process, but we can overcome by using analytics software and by having familiarity with the data.
• Fast and to the point is good.
• Idea should be transparent and it should have less processed time.
• Punk Analytics are basically concentrating on the issues of the moment, as it is the starting point for active exploration.
•   Working collaboratively is important to achieve a common goal.
To know more about punk analytics follow the article link of James Richardson (Business Analytics Strategist at Qlik): http://www.itproportal.com/2015/04/19/rise-punk-analytics/

Breach of security has always been an important issue for any organization. Analytics has also provided a solution to this problem in the form of Security Analytics.

According to Eric Ahlm, Research Director at Gartner, "Security analytics platforms endeavor to bring situational awareness to security events by gathering and analyzing a broader set of data, such that the events that pose the greatest harm to an organization are found and prioritized with greater accuracy."

Two important applications of security analytics are:

• Security Information and Event Management (SIEM)        
• User Behavior Analytics (UBA)

To sum up, although security industry is still immature in the application of analytics, it still holds huge growth opportunities.

To know more, visit the following link:

http://www.firstpost.com/business/security-analytics-play-major-role-finding-breaches-says-gartner-2199152.html

An important application of analytics is Tax Analytics, which helps organizations, think differently about tax related issues and, thus, facilitates new approaches to improve business outcomes. A new function of tax analytics is to improve the understanding and management of the tax implications associated with short-term business travel. It helps businesses track travel of employees, identify areas of concern in states or countries, address locations of concern, and enhance payroll delivery by analyzing global travel-related data and potential tax implications. This creates a win-win situation for both the parties- the business and the employees traveling on behalf of the organization as it leads to cost reduction for business and satisfaction for employees.

To know more, read this interesting article by Beth Mueller, U.S. Tax Analytics Leader, Deloitte Tax LLP, in The Wall Street Journal:

http://deloitte.wsj.com/cio/2015/04/16/tax-analytics-spotlight-on-business-travelers/

The number of merger and acquisition (M&A) transactions involving insurance brokers and agents have shown a growing trend since last year. It’s important to speed up the post merger integration process for making these M&As successful. This is where the use of analytics technology comes in.

Some important areas where analytics technology is helping to make a difference in post-merger scenario are:

• Driving Efficiency
• Boosting Productivity
• Facilitating Teamwork
• Strengthening External Relationships
• Navigating Logistics
• Reconciling Different Corporate Cultures

To know more, read the following article at insurancejournal.com by Kabir Sayed, CEO of Riskmatch.

http://www.insurancejournal.com/news/national/2015/04/07/363528.htm

Analytics is seen as a ladder to succeed in today’s world. It reminds everyone of the benefits that one can achieve with its application in business. But is analytics always useful? What people chose to ignore is its downside. In retail sector, although analytics helps to predict demand, but it also helps to predict the employees required to meet the demand. This in turn, leads to less stable work hours for part time workforce. To reduce the harm to real people, retail analytics should be used in a responsible manner.

To know more, read the following Forbes article:

http://www.forbes.com/sites/sap/2015/04/15/abusing-the-power-of-retail-analytics/

According to Vincent Weafer (Senior Vice President of Intel Security), analytical capabilities will help you to be ahead of your competitors. Predictive Analytics help you to analyze the future trends more accurately and help you to realize your threats and opportunities which affects budget, purchase and staffing decisions. For prediction, a large amount of data is required from a range of activities which organizations perform, historical events and third party intelligence and to make predictive analytics more effective, you need to build foundational abilities like real-time hunting, prioritization and scoping of security incidents in their environment. You need to analyze your stakeholders for blocking decisions. Read more at: http://www.darkreading.com/partner-perspectives/intel/predictive-analytics-the-future-is-now/a/d-id/1319956

According to a 2014 survey of more than 2,000 business professionals by MIT Sloan and SAS Institute, 87% respondents share a common restlessness to elevate their organizations to the next level of analytics.

Being data driven, has now become a necessity for all businesses, especially financial institutions. Using predictive data analytics to interpret a wide range of internal and external data on customers helps financial institutions to identify best targets for a particular product and earn more by making timely pitches.

Russ Bunham, in his Forbes article, gives four tips on how financial institutions can use big data analytics to their advantage:

1. Creating a customized, consistent customer experience
2. Dissolving internal silos to have one view of the customer
3. Ensuring data insight flows to the right person to make the pitch
4. Using big data knowledge to enhance customer relationships

To understand them in detail, visit the following link:

http://www.forbes.com/sites/centurylink/2015/04/14/4-ways-financial-institutions-can-bank-on-big-data-in-2015/

The focus of any news organization is to understand what it is that readers like, what they actually read and what they really should know about. To achieve these goals, The Guardian, is using its in house analytics engine, Ophan, to track page views, median attention time and identify the type of marketing required for each article. It also provides exploratory data for further research. According to Chris Moran, The Guardian’s digital audience editor, “Being informed by the data can actually lead to higher quality journalism.” 

By measuring site data more closely, The Guardian has now become a news powerhouse. To know more, visit the following link:

http://thenextweb.com/media/2015/04/13/how-the-guardians-ophan-analytics-engine-helps-editors-make-better-decisions/

Over the last two years, Big Data and technological advancement have transformed the way industries operate and compete. Financial Services industry, in particular, has adopted big data analytics to inform better investment decisions with consistent returns. It is widely used by investment banks, asset management firms, insurance firms and stock exchanges, to name a few.

The Investopedia article by Trevir Nath, talks about 3 V’s of Big Data i.e. volume, variety and velocity, how they are applied by financial services industry, algorithmic trading and the challenges faced by this industry in the increasing embrace of big data. In the nutshell, this article explains that despite the challenges, the financial services industry is trending towards Big Data and Automation.

Read more at:

http://www.investopedia.com/articles/active-trading/040915/how-big-data-has-changed-finance.asp

Big Data Analytics is generally used for building models to periodically optimize processes i.e. After-it-Happens business. But this limits the use of Big Data as there is always a delay in the data-to-action cycle and also, the analytical capabilities are limited at the business’s disposal. A better application of big data analytics is using it for As-it-Happens business. It enables us to leverage the data and models moment by moment to make an impact on business operations in real time.

Steve Wooledge, Vice President of Product Marketing at MapR, gives examples of real life applications that leverage a big data environment that integrates OLTP and analytical databases in his following article:

http://www.forbes.com/sites/teradata/2015/04/09/creating-the-as-it-happens-business-analytics-drives-action/