7 Ways to Effectively Utilise Big Data in Organisations

7 Ways to Effectively Utilise Big Data in OrganisationsThe buzz around Big Data is undeniable. Regardless of the size of the organisation, managers can use this information, to help drive better, more effective organisational decision making, as a result of accurate analysis.


1. Improve Business Intelligence

Business Intelligence is a process of analysing data which helps managers and corporate executives make more sound business decisions. So if you try to put in some extra effort to ameliorate your organisation’s business intelligence, it will result in a more accelerated decision-making process, optimised internal business processes, increased operational efficiency, generation of new revenues, and identification recent market trends.

2. Practical Business Decisions Based On Customer Behaviour

Big Data contains a wealth of information about the way customers of a particular organisation act and behave, like their interests, habits, and demographics in some cases. By analysing sales, market news and social media data, organisations may collect and analyse real-time insights of their customers.

This article originally appeared in procurious.com .  To read the full article, click here

How Retailers Use Big Data to Gobble Up Sales

How Retailers Use Big Data to Gobble Up SalesBillions of dollars will be spent this weekend as the Thanksgiving holiday gives way to Black Friday, Cyber Monday, and the beginning of the end-of-the-year shopping extravaganza. For retailers eager to get “back in the black,” big data analytics provides a great opportunity juice profits by successfully converting on ample sales opportunities.

The volume of sales this weekend is expected to be massive. According to the National Retail Federation, 137.4 million Americans are expected to shop online or in stores over the four-day holiday, up from 135.8 million last year. Consumer confidence is high, thanks to a 5.2% increase in the median income of American workers (per the Census Bureau) and all-time highs reached on stock market indices.

This article originally appeared in Datanami .  To read the full article, click here.

Why Big Money and Big Data Win Elections

Why Big Money and Big Data Win ElectionsPolitical campaigns are anything but simple. They’ve gotten even more complex over the years as industries lobby for their own interests by sending money to the candidates they believe will serve them best. While campaigns have used whatever data they could get their hands on in the past, the amount of specific information now available (thanks to big data) has been a boon to politicians hoping to target specific voters and gain new support. In 2016, all of the major players are using big data in their efforts to gain office. Here’s how analytics and money play a role in election outcomes.

Sophisticated campaigns can leverage big data to great effect by focusing in on the information that really matters. However, successful predictive analysis requires a talented campaign data analysts to pick out what’s important within the datasets. In order to survive in a cutthroat political climate, politicians need to make sure that their data scientists are up to the challenge of finding the right voters—and that their campaign and project managers are up to the task of spending that money for maximum impact. 

This article originally appeared in datafloq.  To read the full article, click here.

Using Big Data Analytics to Improve IT Operations

Using Big Data Analytics to Improve IT OperationsTo deliver high-performance and stable IT operations, staffs end up taking shortcuts, bypassing automated and manual processes. Eventually, it leads to applicaton defects and infrastructure failures. Nevertheless, IT departments need to be able to control their environments, diagnose and pre-empt problems and incidents. Unfortunately, most of existing IT tools don’t collect adequate data and don’t do a robust job of handling the data they collect. Activity dashboards are cluttered, and have too many alerts that users end up ignoring them.

Making Big Data Analytics Actionable for IT

An organization’s IT environment typically generates Terabytes of data about system metrics, change logs, event logs and other operational data. It is possible to obtain extremely granular data about the history and current state of your IT environment. The key is to make this data actionable. However, the challenge is to do it rapidly, with minimum overhead. As IT is required to drive more output with fewer resources, IT specialists don’t have months to invest it training, rollout and deployment of a typical enterprise solution. The same specialists need to deal with day-to-day IT issues. This leaves them with even lesser time to proactively manage operations.

Data Analytics for IT Operations

The rise of IT Operations Analytics (ITOA) makes IT’s big data usable by blending and correlating this data, automatically coming up with actionable insights to manage & improve operations. This process means collecting as much data as possible, to avoid missing any critical information, and narrowing it down to useful conclusions. The business side of organizations have already adopted this approach, running operations in real-time. Ironically, although IT is supplying the business users with the necessary big data analytics they need, IT itself is lagging in this respect.

In the past, the business side of organization faced a similar challenge due to the growing pile of structured and unstructured data. Currently, business users deal with Big data using technologies for managing and analyzing large, diverse sets of data. These Business Analytics tools are capable of processing large amounts of data in various formats from anywhere, and correlate data to provide business managers & executives with new insights to help run their business. They can be used to design & develop systems for ITOA.

ITOA can provide meaningful insights based on domain understanding of operational data. This will provide IT operations teams visibility into the behavior of business sytems, enable them to automatically identify and isolate critical events, such as system changes, that have the potential to disrupt existing sytems. ITOA enables you to quickly identify potential issues upfront and understand these issues from the mountain of raw data collected by various monitoring systems. This empowers IT operations to efficiently determine the best way to restore systems, meet performance and availability expectations.

Change-driven Data Analytics

IT must adopt a change-driven approach to deal with operational issues swiftly. Changes are a major source of IT issues. Every time there are changes in application, data or infrastructure, business systems are exposed to risks. By focusing on changes and their impact, IT specialists can analyze data about performance, availability, security; identify actual causes and potential issues.

With the help ITOA, IT operations teams can constantly monitor how changes are affecting various systems, what risks they introduce, use data to gain actionable intelligence and respond quickly.



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How HR Departments Can Obtain and Use Big Data

How HR Departments Can Obtain and Use Big DataEnterprises are constantly seeking new ways to use analytics across their organizations, and the human resources department is no exception. According to a Towers Watson survey, companies are spending a good portion of their HR technology budgets on big data and analytics to improve the hiring process, retain employees, and make better business decisions. While big data can give HR a seat at the overall decision-making table when used correctly, many companies struggle with obtaining the data. Additionally, those in possession of hiring and employee analytics struggle with how to best use this data in a way that matters.

The following is an exploration of how HR departments can leverage big data analytics to become strategic business partners in their organizations.

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2016 Big Data, Advanced Analytics & Cloud Developer Update: 5.4M Developers Now Building Cloud Apps

2016 Big Data, Advanced Analytics & Cloud Developer Update: 5.4M Developers Now Building Cloud AppsKey takeaways from the study include the following:

  • 6M developers (29% of all developers globally) are involved in a Big Data and Advanced Analytics project today. An additional 25% of developers, or 5.3M, are going to begin Big Data and Advanced Analytics projects within the next six 13% or 2.6M of all developers globally are going to start Big Data and Advanced Analytics projects within the next 7 to 12 months.  The following graphic provides an overview of the involvement of 21M developers in Big Data and Advanced Analytics projects today.
  • 4M developers (26% of all developers globally) are using the cloud as a development environment today. Developers creating new apps in the cloud had increased 375% since Evans began measuring developer participation in mobile development in 2009 when just slightly more than 1.2M developers were using the cloud as their development platform. 4.5M developers (21% of all global developers) plan on beginning app development on cloud platforms in the next six months, and 3.9M (18% of all global developers) plan on starting development on the cloud in 7 – 12 months.

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Big Data And A Shocking Waste Problem

Big Data And A Shocking Waste ProblemIt’s a shocking fact that in the 21st century more than three quarters of a billion people do not have access to enough food to keep themselves healthy, while 30% of the food produced around the world goes to waste.

It’s partly a problem of logistics – it’s expensive to keep food fresh and get it to the people who need it most. This of course means its ripe for tackling with technology and Big Data.

Recently I had the chance to speak to one of the founders of Food Cloud – a social enterprise which harnesses the power of crowd sourcing in a bid to cut down on food wasted by retailers.

Food Cloud made headlines recently thanks to a partnership with Tesco , the UK’s biggest grocery seller. The simple premise is that it matches retailers that have surplus food stocks with charities that have the facilities to deliver it to those who need it.

Of course even a concept this elegantly straightforward requires some fairly advanced technology if it’s going to be rolled out at the sort of scale necessary to bring about social change. And plans are underway to operate the scheme in 1,000 Tesco stores by the end of the year.

Co-founder Iseult Ward told me “Our data driven approach is what differentiates us from a lot of traditional food surplus redistribution organizations.

“We’re able to collect all sorts of data on all of the food which is donated, and the organizations it is donated to, which allows us to do things like provide full sourcing and measure the performance of the stores and charities.”


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What every CEO/MD needs to know about Big Data & Data Analytics

What every CEO/MD needs to know about Big Data & Data AnalyticsIn order to remain competitive, and viable, businesses now have to deal with a vast and rapidly growing sea of what has been termed ‘Big Data’.  They need to be able to transform this raw data, often in real-time, into more meaningful insights about their markets, customers, competitors, and to measure and manage their performance more accurately using using techniques such as ‘Data Analytics’. In many cases this represents a paradigm shift from their comfort zone of approaches based more on experience, guesswork, or painstakingly constructed models of reality.

It used to be that Big Data and Data Analytics were the preserve of large global corporations but consider this definition for Big Data: When volume, velocity and variety of data exceed an organization’s storage or compute capacity for accurate and timely decision making. Big Data is a relative term. Every organization will rapidly reach a point where the volume, variety and velocity of their data will be something that they have to address.

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Surge in real-time big data and IoT analytics is changing corporate thinking

Surge in real-time big data and IoT analytics is changing corporate thinkingOrganizations want real-time big data and analytics capability because of an emerging need for big data that can be immediately actionable in business decisions. An example is the use of big data in online advertising, which immediately personalizes ads for viewers when they visit websites based on their customer profiles that big data analytics have captured.

“Customers now expect personalization when they visit websites,” said Jeff Kelley, a big data analytics analyst from Wikibon, a big data research and analytics company. “There are also other real-time big data needs in specific industry verticals that want real-time analytics capabilities.”

The financial services industry is a prime example. “Financial institutions want to cut down on fraud, and they also want to provide excellent service to their customers,” said Kelley. “Several years ago, if a customer tried to use his debit card in another country, he was often denied because of fears of fraud in the system processing the transaction. Now these systems better understand each customer’s habits and the places that he is likely to travel to, so they do a better job at preventing fraud, but also at enabling customers to use their debit cards without these cards being locked down for use when they travel abroad.”


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The Big Data Difference: Predictive Analytics

The Big Data Difference: Predictive AnalyticsWhat if you could know in advance which patients would benefit from certain therapies? Or identify patients approaching a medical crisis and intervene before it’s too late? While doctors have traditionally had to rely on instinct to make these calls, predictive analytics could be a game changer for hospitals, healthcare providers and patients.

The Power of Prediction

Medical sensors and data analytics can be used to power medical devices that can predict adverse outcomes before they occur. By analyzing very large data sets, researchers can identify subtle markers, such as small changes in vital signs or patient behaviors that can be correlated to development of serious conditions like heart failure or kidney failure. If we can learn to look for the right signs, we can develop an early warning system for imminent medical crises.

Combining data analytics with body-worn or implantable medical sensors will allow us to better monitor patient health. These sensors can pick up subtle changes in biometrics, biomarkers and other patient data over time. Using predictive analytics, smart sensors could use these readings to detect early warning signs of kidney failure, stroke, heart failure and other medical crises, alerting healthcare providers before adverse events occur. Data analytics could also be used to power smart apps or devices that provide ongoing guidance to patients in response to sensor data in order to help them better manage chronic conditions.

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