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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The 4 Mistakes Most Managers Make with Analytics

The 4 Mistakes Most Managers Make with AnalyticsThere is a lot of hype surrounding data and analytics. Firms are constantly exhorted to set strategies in place to collect and analyze big data, and warned about the potential negative consequences of not doing so. For example, the Wall Street Journal recently suggested that companies sit on a treasure trove of customer data but for the most part do not know how to use it. In this article we explore why. Based on our work with companies that are trying to find concrete and usable insights from petabytes of data, we have identified four common mistakes managers make when it comes to data.

Mistake 1: Not Understanding the Issues of Integration

The first challenge limiting the value of big data to firms is compatibility and integration. One of the key characteristics of big data is that it comes from a variety of sources. However, if this data is not naturally congruent or easy to integrate, the variety of sources can make it difficult for firms to actually save money or create value for customers. For example, in one of our projects we worked with a firm which had beautiful data both on customer purchases and loyalty and a separate database on online browsing behavior, but little way of cross-referencing these two sources of data to actually understand whether certain browsing behavior was predictive of sales. Firms can respond to the challenge by creating “data lakes”, holding vast amounts of data in their unstructured form. However, the very fact that these vast swathes of data now available to firm are often unstructured, such as in the form of strings of text, means it is very difficult to store them in as structured a way as could occur when data was merely binary. And that often makes it extremely difficult to integrate it across sources.

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Big Data Analytics Can Benefit Manufacturers

Big Data Analytics Can Benefit ManufacturersA recent Honeywell survey of manufacturing executives indicates that a majority of respondents (67 percent) plan to use data analytics to address issues with equipment.

The survey shows that anaytics are becoming core to what Honeywell refers to as the industrial internet of things (IioT) as a way for companies to save money on equipment maintenance and repair by providing insight into when machines need service.

The survey, conducted by Honeywell Process Solutions (HPS) and KRC Research, polled more than 200 North American manufacturing executives. The study, entied “Data’s Big Impact on Manufacturing: A Study of Executive Opinions,” showed that a majority of respondents said they are already investing in data analytics technology.

In addition, the survey indicated that manufacturing executives view unscheduled downtime and equipment breakdowns as the biggest obstacles to maximizing revenue. Yet, more than a quarter of respondents said they don’t plan to invest in data analytics in the next year. These respondents cited inadequate resources and lack of understanding as key reasons for their lack of investment.

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Advanced Analytics 101: Beyond Business Intelligence

Advanced Analytics 101: Beyond Business IntelligenceTrying to make sense of data is nothing new. Companies have been applying analytical methods since before the computer was invented. Once computers and storage became reasonably cheap and powerful, in the late 1980s, companies started using Business Intelligence (BI) software to try to find meaning in their data. Today’s Advanced Analytics go beyond the capabilities of Business Intelligence.

Business Intelligence is driven by an end user who knows the questions they want to ask and has deliberately collected data to support their inquiry. Advanced Analytics let users find answers when they don’t even know what they should be asking by identifying meaningful patterns. Rather than just looking backwards at the data to understand what has happened, Advanced Analytics use the data to look to the future and understand what is likely to happen. While both Business Intelligence and Advanced Analytics are used to improve decision making, the two techniques have different goals and use different methods.

Three Kinds of Analytics

There are three basic kinds of Analytics, increasing in both complexity and power.

  • Descriptive Analytics: Like Business Intelligence, it looks backward to understand what happened. The techniques used in these Analytics are the least complicated, mostly summarizing data through counts, aggregations of metrics, and simple calculations such as averages. These Analytics may also use data mining to find correlations between variables and help identify reasons for previous success or failure.
  • Predictive Analytics: Uses statistical models and other methods to predict what might happen based on previous events. These Analytics use statistics, data mining, Machine Learning methods, and business rules to make probabilistic predictions of the results of certain actions.
  • Prescriptive Analytics: Gives recommendations on what should be done, including for questions that weren’t specifically asked. These Analytics extend upon Predictive Analytics through the use of optimizations and simulations to evaluate possible actions and the potential impact of each option.

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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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How to Boost Customer Loyalty Using Analytics and Actionable Insights

Returns data offers companies priceless insights that improve the customer experience.Returns data offers companies priceless insights that improve the customer experience.

As ecommerce grows, so do return rates: About one in three online purchases is returned. For online apparel purchases, the rate is closer to 40 percent. That has triggered the growth of the returns-management software marketplace, offering retailers solutions aimed at making the processing and dispensation of returned goods faster and more efficient.

That’s important. The more quickly and efficiently a return is processed, the better the outcome: The consumer gets his or her refund faster. The retailer minimizes reverse-logistics costs. And the company offering the product recoups the maximum recovery value.

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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Analytics in Government: Picking Up Speed or Caught in an Eddy? (Industry Perspective)

Analytics in Government: Picking Up Speed or Caught in an Eddy? (Industry Perspective)Whether U.S. voters are advocates for “smaller government,” “better government” or both, analytics has great potential to make federal, state and local government agencies more efficient, faster and able to operate with fewer staff members and make their staffs more effective.

In the private sector, applying analytics and using big data is a major trend that’s gaining momentum — 73 percent of private-sector enterprises said they are now using analytics or plan to start in the next two years, according to a leading research firm’s latest report. Likewise, applying analytics to big data is “one of the hottest trends in the public sector,” according to Tod Newcombe, a senior editor with Government Technology.

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Business Intelligence vs. Business Analytics

Business Intelligence vs. Business AnalyticsThe painful truth about the difference between business analytics and business intelligence is that if you ask two people to tell you the difference, you’re going to get two conflicting sets of differences explained to you. A lot of business analysis terms get misused, reinterpreted and misappropriated until their original meaning is a murky memory from the distant past.

It’s not so much that there is no difference, it’s just that you need to be prepared to acknowledge that people are going to fight you on it, and that you can’t assume that you’re speaking the same language as everyone else in the room until you break it down into plain English. Everyone has their own idea of what every business term means, so until we get down to talking about the actual points of data and the strategies being used, there’s going to be a lot of confusion.

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