The Implications of Big Data on Employee Referrals and Recruiting

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Information about The Implications of Big Data on Employee Referrals and Recruiting
Technology

Published on February 19, 2014

Author: RolePoint

Source: slideshare.net

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The Big Data Paper - Big Data and the implications for recruiting and referrals

We have always created and stored data in one format or another. Historians can go back thousands of years to understand how people lived in centuries past thanks to the evidence and indications of what has happened that is stored deep in museums and archives, historical documents and hand curated registers such as the census. The collation, interrogation and interpretation of data and what it means is far from a new concept, but what has now brought the discussion to the fore in all aspects of business is the incredible volume of data that is being created every moment of every day, and the development of data mining and interpretation tools that are available for anyone to use.

B IG DATA: T HE IMP LIC A T ION S FO R R E C R UI T I N G & R E FE R R A LS A R O L E P O I N T W H I T E PA P E R B Y @ B I L L B O O R M A N

RolePoint Inc. © 2014 3 Big Data

B I G DATA INTRODUCTION and data usage information coming from networked devices and packaged goods We have always created and stored data with embedded sensors. The data in one format or another. Historians can generated from these devices offers go back thousands of years to game-changing opportunities in understand how people lived in centuries operational process optimization and past thanks to the evidence and reinvention, as well as in business indications of what has happened that is analytics and intelligence. HR and stored deep in museums and archives, recruiting is just one of the many business historical documents and hand curated functions to benefit from this new way of registers such as the census. The working. collation, interrogation and interpretation of data and what it means is far from a In April 2012, Information Management new concept, but what has now brought reported that we create 2.5 quintillion the discussion to the fore in all aspects of bytes of data every day, with 90% of the business is the incredible volume of data data in the world having been created in that is being created every moment of the last two years alone. Every hour, every day, and the development of data Wal-Mart handles 1 million transactions, mining and interpretation tools that are feeding a database of 2.5 petabytes, available for anyone to use. which is almost 170 times the data in the Library of Congress. The entire collection We are creating data and data trails at an of the junk delivered by the U.S. Postal unequaled rate. Pretty much everything Service in one year is equal to 5 we do is recorded somewhere. Every petabytes, while Google processes that keystroke, click, call or action is recorded amount of data in just one hour. The total and searchable. Our domestic appliances, amount of information in existence is cars, games and everyday appliances estimated at a little over a zettabyte. leave a trail while mobile devices leave data trails for geolocation use. This data, The real challenge starts when attempting often called ‘multi-structured’, includes to deal with the wealth of data that is loosely structured social media data and available. What organizations really want content, as well as the deluge of is to be able to navigate the data machine-generated data like geolocation landscape in order to find and interpret RolePoint Inc. © 2014 4 Big Data

what is useful in order to make informed 23 percent. When data is visual and real business decisions. The hottest job in time, everyone benefits from being business over the past 18 months has informed and current. It’s an old saying, been data analyst, as more and more but it has never been more applicable: “In businesses look to understand just what God we trust, everyone else bring data!” this data means to them and how they can use it to their advantage in every aspect of their business, not least recruiting. Internal data alone has massive implications for all HR functions throughout business, because decisions based on data are based on fact. In this paper we will not attempt to make the case for big data in recruiting and referral - you ignore it at your peril. Rather, we will look at practical applications within recruiting. The easiest way to understand big data concepts is that big data practices take numbers from multiple sources structured and unstructured (such as social media channels), identify relationships and interpret meaning to different data sets and deliver an output that is easy to understand without expert knowledge (data visualization.) A 2013 report by the Aberdeen Group found that of organizations that use visual discovery tools, 48 percent of business information users are able to find the information they need without the help of IT staff. Without visual discovery, the rate drops to a mere RolePoint Inc. © 2014 5 Big Data

B I C I ON S E G TD A T A H E A D E R INTERNAL DATA by actual performance. Companies have always recorded and Learning and Development System: stored data. One of the challenges Contains training data and companies face is that their corporate assessments, skills profiles, reviews etc. data is kept in silos within individual The learning and development system databases. For the most part, the is an accurate data record of historical business database was built for employees’ development, progress and data storage rather than data retrieval. potential, and enables the Within HR, companies may be collating measurement of skills gaps and and retaining data in 5 - 8 individual workforce planning. places with no link from one to the other. Applicant Tracking System: HR systems might consist of: Contains applicant and employee data Payroll System: past and present, recording progress Contains pay data and personal data through the application process. The relating to past and present ATS is an accurate data record of all employees, earnings, benefits, applications for employment, company reporting structure, successful or unsuccessful, and employment history etc. The payroll important recruiting metrics such as system is an accurate data record of time to hire, source of hire and work history, rewards and other applicant to hire ratio. Includes personal information. metrics that are important for understanding recruiting efficiency, Performance Management System: enabling best use of resources. Contains performance data, reviews, However, these are often hard to track. appraisals, report, performance disciplinary records etc. The Candidate Relationship Management performance management system is System: an accurate data record of the work Works like the client relationships completed by employees against management system and commonly management expectations, enabling used in marketing. The CRM enables the ranking or grouping of employees the indexing and tracking of candidate RolePoint Inc. © 2014 6 Big Data

communication. The CRM follows the from sources such as LinkedIn profiles. trend for talent communities and Communication lines are vertical talent networks. between candidate and company in contrast to a talent community, where A talent community, such as those anyone can connect and communicate. operated by the likes of Microsoft, The real benefit and appeal of the focus on a single discipline or interest, talent network is that candidate data enabling members of the community can be analyzed to ensure relevance in to communicate with each other whilst messaging (the same concept that creating a dynamic profile and leaving applies to referral messaging). a data trail. While this approach is Relevance of topic and content is a often confused with a talent network, critical factor in success, with 75% of its popularity has exploded over recent messaging being opened on a mobile years fueled by social media discussion device and the recipient often making and debate. A community can be instant decisions over ditching the defined as a collection of people who message or opening and reading - are connected by a topic, where each research indicates this decision is member can raise or comment on a made in a maximum of 3 seconds. discussion and connect directly with each other. A good example would be Other HR Systems: a LinkedIn group. Individual companies may well have other systems housing additional HR A talent network, by contrast, can be data. The problem for most compared to targeted e-mail lists of organizations is that each HR system past years, although other operates and stores data in isolation. communication channels such as SMS The benefit of utilizing internal data is or via a mobile app also qualify, as well that it is owned by the organization as traditional methods of and is considered structured data. The communication. Registration for a challenge is combining all of the talent network is usually as simple as datasets for interrogation and one-click connections of a potential understanding how one dataset might candidate with the company, with relate to another, enabling the profiles populated with external data discovery of trends and relationships RolePoint Inc. © 2014 7 7 Big Data

B I G DATA LATENT DATA for interpretation and decision-making. What hampers this is often internal resistance to opening up HR data Latent data is dated and becomes old the between departments and opening the day it is recorded. A good example of this APIs between one technology and within talent acquisition is the vast another. An application programming number of resumes that have been interface (API) specifies how some submitted to a company for job openings software components should interact as they arise. When we consider the with each other. For organizations to resume, it becomes an historical, out of unlock the potential offered by internal date document from the day it is big data depends on: submitted as an e-mail address or contact number may change over time. • • Furthermore, candidates will add to their A single data flow in one direction Data conversion in to a single experience and skills through promotions, transfers, learning or changing employer. format effectively speaking the None of these changes are recorded on same language the resume held within the ATS, hiding • Data retrieval from all technologies many potential matches to new made possible by an open API • opportunities. Companies like London based Work Digital identified the benefit Refreshed latent data pulling from of searching unstructured social data to real time unstructured social data, update the latent resume data contained combining internal data with public within the ATS, because social data is real data sources such as LinkedIn, time. Combining a historical resume with Facebook or Twitter the data contained on a LinkedIn profile, a Facebook profile, an about me page and other similar sources refreshes the ATS keeping candidate details up to date in real time, retrievable and relevant in search. RolePoint Inc. © 2014 8 Big Data

THE SEARS STORY career gets added in real time. When you have data, you can interrogate it. How In a presentation at SourceCon in 2013, many organizations recognize that this Donna Quintall, the Senior Manager type of data would be useful to recruiters, Executive Talent Acquisition at Sears and how many people think it should be Holdings, told the story of how they had locked away from the hiring team? When implemented this methodology in to their you have data, you can influence recruiting function. Quintell commented decisions. that HR teams very often have all the data recruiters needed to plan recruiting, Recruiters get to know when people are but it is hidden behind the walls built by high-risk—triggered by actions like HR and recruiting. Sears set about tearing performance plans—and this models the down the walls to create the HR data recruiting plan. The recruiters source warehouse, fed by their HR systems. Data according to projected needs rather than feeds come from each of their HR simply reacting to jobs as they come up systems including performance at the 11th hour. The sourcing plan covers management, CRM and ATS to produce internal employees as well as external reports, dashboards, and analysis. This targets. When you have data, succession means the recruiters can interrogate data planning and internal mobility become a to understand what they should be reality. recruiting for, costs, speed of hire, loss due to not hiring and other things like the Sears have worked hard to allow best companies to hire from, the best recruiters to have conversations about industries and the best schools. The data internal opportunities freely without includes things like performance needing to go through layers of management, reviews, appraisals and permission. This takes some doing. I financials: every aspect of HR data in remember having the same conversation order to be proactive, predictive and to with Arie Ball at Sodexo. Companies talk be able to use data to influence hiring internal mobility but block the access to managers. it through politics and turfism. The best recruits with the least risk, who are Every new starter at Sears completes a already known, usually live within the profile and all the HR data through their company, but many recruiters are driven RolePoint Inc. © 2014 9 Big Data

B I G DATA to source outside. The key in all of this is is where their hires are and what they are transparency of data and trust. Sears has interested in. profiles on over 400,000 employees. That’s a huge data source. I love the direction Sears is going with this thinking. Data drives decisions further Every interview is a source of competitor than opinions, just as soon as the walls information that goes into the system, come down and recruiters get access, hired or not. Recruiters are trained to allowing sourcing from a reactive, just-in- gather data in the interview (they jokingly time function, to playing a more strategic compare this to being interviewed by the part in business planning. Decisions are CIA). When I think about how much driven by data and what is really market information recruiters could happening, and Sears are reaping the collect to help influence sourcing and benefits. hiring decisions, the potential is frightening. This means building a whole UNSTRUCTURED DATA process for data collection and an emphasis on retrieval through data mining rather than storage. According to Wikipedia, unstructured data is defined as “information that either When recruiters have the data to does not have a pre-defined data model influence and advise recruiters—what or is not organized in a predefined they need to be doing—the perception of manner. Unstructured information is the role changes from being reactive typically text-heavy, but may contain data people-finders to strategic partners. such as dates, numbers, and facts as well. Sears runs their talent community This results in irregularities and through Find.ly. They track all social ambiguities that make it difficult to media activity to see what topics are understand using traditional computer trending. Things like Family Guy and programs as compared to data stored in Eminem are massive trends. They switch fielded form in databases or annotated this back into their thinking on content (semantically tagged) in documents.” and content placement. This is starting with a Family Guy campaign because that In 1998, Merrill Lynch cited a rule of thumb that somewhere around 80-90% RolePoint Inc. © 2014 10 Big Data

of all potentially usable business • information may originate in unstructured connections: 150 billion as of 2/1/13 form. In recent years the volume of • unstructured data has exploded as a sites and the adoption of mobile devices. • Lets consider some of the stats relating • Marketing Ramblings (http:// Total number of location-tagged Facebook posts: 17 billion expandedramblings.com): • Facebook User Stats Total number of uploaded Facebook photos: 250 billion as of 9/17/13 Total number of Facebook users: 1.26 billion • as of 10/6/13 Average daily uploaded Facebook photos: 350 million as of 2/1/13 Total number of Facebook monthly active • users (MAU): 1.23 billion as of 01/29/14 • Average daily Facebook likes: 4.5 billion as of 5/27/13 to Facebook, taken from the blog Digital • Total number of Facebook likes since launch: 1.13 trillion result of the embedding of social media • Total number of Facebook friend Average number of photos uploaded per Facebook user: 217 photos as of 9/17/13 Total number of Facebook daily active users • (DAU): 757 million as of 1/29/14 Average number of items shared by Facebook users daily: 4.75 billion as of • Daily active users in the US: 128 million as of 9/17/13 8/13/13 • • Size of user data that Facebook stores: Number of Facebook messages sent daily: 10 billion as of 9/17/13 more than 300 petabytes as of 11/7/13 • once a day: 76% as of 7/2/13 Facebook User Activity Stats • • Number of times daily that the Facebook Percentage of users that check Facebook multiple times a day: 40% as of 12/30/13 Like or Share buttons are viewed: 22 billion (Tweet this stat) as of 11/6/13 • Percentage of Facebook users that login • Average number of page likes per Facebook user: 40 as of 7/12/13 Number of sites that contain Facebook Like or Share buttons: 7.5 million (Tweet this stat) as of 11/6/13 RolePoint Inc. © 2014 11 Big Data

B I G DATA Twitter stats • When we consider the volume of data being posted in public social media Unique monthly visitors to Twitter.com channels, documents, forums and other (desktop only): 36 million as of 5/1/13 places, it is easy to understand that, if we • can sort the data and sift social data by of 10/3/13 • Total Number of Tweets Sent: 300 billion as relevance and value, 80% of the world’s data is considered to be unstructured. Monthly Active Twitter Users: 231.7 million The challenge of unstructured data is not as of 10/17/13 availability but sifting for relevance and • determining meaning. Unstructured data U.S.: 77% as of 10/3/13 • Percentage of Twitter MAUs located outside is facing the same challenges that structured information faced in its early Percentage of MAUs that accessed Twitter days. CIOs must overcome fragmentation via mobile device: 75% as of 10/3/13 of information and processes; the • Daily Active Twitter Users: 100 million infamous three Vs of information – (Tweet this stat) as of 10/3/13 • volume, variety and velocity; security; and governance issues. To get meaning we Average Number of Followers per Twitter need to separate the signal from the User: 208 (Tweet this stat) as of 10/11/12 • noise and that’s not easy to do with unstructured data. Average Number of Tweets Sent Per Day: 500 million as of 10/3/13 • Average Number of Tweets per Twitter User: 307 as of 1/11/13 • Number of tweet impressions outside Twitter properties in Q3 2013: 48 billion as of 10/3/13 Percentage of Twitter Users Accessing Via Mobile: 60% as of 12/18/12 RolePoint Inc. © 2014 12 Big Data

In terms of recruiting, this means the products and services you like and identifying what you want the data to tell use outlines how, where and when you you as an outcome, the questions that buy things. need answering and the areas of decision making that have historically been based In November 2012, Analyst Josh Bersin, of on opinion. Social data enables profiling Bersin Deloitte wrote: of candidates and audience, identifying the best fit and the most receptive “Start with the problem, not the data. We targets. The challenge here is three fold: are all flooded with data: employee data, location data, social data, compensation • Volume of data created • data, and much more. If you start an Velocity at which data is analytics project by collecting all the data you can find, you may never come to an multiplying in real time, for end. Rather you have to start with the example, Twitter recorded 58 problem: What big decisions would you million tweets/day mark - over a like to be able to make? What problems billion tweets a month. There are would you like to solve? over 2-1bn Twitter search engine queries each day. That’s velocity of data. One common talent problem, for Source: http://www.statisticbrain. example, may be sales productivity. What com/twitter-statistics factors contribute to a predictable high- Variety of sources we can access performing sales person? Every company data from. From Twitter, Facebook, would like to understand this better. And LinkedIn or YouTube to Pinterest or once you understand these Instagram, new data sources are characteristics, how can you better being added every day. • source, attract, and hire such people? Another may be turnover. What factors Data is multiplying at an exponential rate contribute to high turnover in your day-by-day, week-by-week, year-by-year. company and in particular groups? Everything that we do leaves a data trail. Our every action is recorded even if we are not connected, Your mobile leaves a trail of your location, your credit or debit card outlines your spending habits and RolePoint Inc. © 2014 13 Big Data

B I G DATA These questions are worth millions of openings in the companies surveyed dollars to answer. Be careful you don’t filled by internal candidates: start by only looking at data. It leads to “77,200 positions out of 185,450 lots of money spent, systems built, and positions were filled in the US by the often little or no return.” responding firms through internal movement and promotion. This is Source: http://www.bersin.com/blog/post. ~42% of all the openings filled and aspx?id=574b5527-ce55-4ec6-8c45-c8f05f85162e reminds us that the largest source of hire by far is our own employees”. The point Bersin is making is an important one. Big data is, well, big. There Source: http://www.careerxroads.com/news/ is such a range of data sources that is SourcesOfHire2013.pdf possible to just keep mining and never 3. Next we need to identify who fits the extract any real value. Big data projects profile externally and how we are should begin with objectives and a connected to them? The best desired outcome. In the case of recruiting, candidate may have already applied we want to achieve certain clear and could be hidden in the ATS, talent objectives: 1. network or connected with the company in some other way, for We want to understand what ‘good’ example, as a fan of the company looks like. Who are our best page on Facebook or a follower on performers? Who are the best LinkedIn. Alternatively, they may well performers in the industry? What did be connected via social networks or their background look like? What data e-mail with our existing employees. trends connect them? 4. When we have targets, we want to 2. When we have identified the best understand the best way to reach candidate profile, we want to know them so that we can personalize the who fits the profile internally, or if message whilst determining the best internal mobility is actually the best method of delivery and the best time option. The CareerXroads Source of to get a response. Hire Survey for 2013 indicates that the principle source of hire in the USA is internal hiring, with 42% of job RolePoint Inc. © 2014 14 Big Data

5. We want to understand our recruitment process so that we can identify any blockage in order to reduce time and cost of hire. There may be many other objectives that can be solved by taking a big data approach to hiring. The key point Bersin is making is to begin with the end in mind. Understand the problem you are trying to solve and then apply data mining, collection and analytics to find a solution, or at least to understand why the problem exists. RolePoint Inc. © 2014 15 Big Data

B I C I ON S E G TD A T A H E A D E R THE GOOGLE FLU TRENDS “ A study in Clinical Infectious Diseases shows that a Google tool can predict It might seem strange that we are surges in hospital flu visits more than a discussing this particular initiative in week before CDC. For the study, Johns relation to recruiting, but there are some Hopkins School of Medicine researchers clear lessons to be learnt from this that compared Baltimore-specific data from apply to talent acquisition and big data. the Google Flu Trends website, which Every time you search Google, you leave estimates influenza outbreaks based on a trail as to the information you want now online searches for flu information, to ED and meaning can be derived from this, as crowding and laboratory statistics from well as an understanding of the sources Johns Hopkins Hospital. Using Google Flu of information you trust. Trends, researchers found that the number of online searches for flu A controlled experiment between 2009 information increased at the same time and 2010 validated this using search data that the hospital’s pediatric ED to forecast hospital visits for influenza experienced a rise in cases of children over a 21 month period. By monitoring with flu-like symptoms. The Google Flu millions of users’ health tracking Trends data had a moderate correlation behaviors online, the large numbers of with patient volume in the adult ED. Google search queries gathered were Moreover, Google Flu Trends signaled an analyzed to reveal whether there was the uptick in flu cases seven to 10 days earlier presence of flu-like illness in a population. than CDC’s U.S. Influenza Sentinel Google Flu Trends compared these Provider Surveillance Network. Based on findings to a historic baseline level of the findings, the researchers suggested influenza activity for its corresponding that platforms like Google Flu Trends region and then reported the activity could help hospital administrators level as minimal, low, moderate, high, or anticipate flu outbreaks and make intense. These estimates have been appropriate staffing and capacity generally consistent with conventional planning decisions.” surveillance data collected by health agencies, both nationally and regionally. Source: http://www.advisory.com/dailybriefing/2012/01/13/google-flu RolePoint Inc. © 2014 16 Big Data

Whilst this study was concluded four AND THOSE THAT ARE JUST years ago, the tracking has continued via EXPRESSING SOME FLU-LIKE the Google Flu Trends report, and much SYMPTOMS. TRYING TO DETERMINE has been learnt about using search data THE ACTUAL FLU INCIDENCE REQUIRES to forecast outcomes in a meaningful SOME CAREFUL DISAMBIGUATION. THIS way. There has been some variance to the IS ONE PLACE WHERE SMARTER forecast from Google Flu Trends over the ALGORITHMS MAY COME INTO DATA past year which presents another VIGILANTISM: PULLING OUT THE challenge when using data to predict INFORMATION THAT YOU ACTUALLY outcomes: that of context and meaning in WANT TO MEASURE FROM YOUR BIG, unstructured data. This article from the MESSY PILE OF DATA.” Harvard Business Review outlines the real challenge of taking data based forecasts Source: http://blogs.hbr.org/2013/07/how-google- as verbatim: flu-trends-is-getting-to-the-bottom/ “AT THE CORE OF THE ISSUE WITH FLU This highlights the need to continuously MEASUREMENT (AND MOST PROJECTS measure predicted results against actual INVOLVING LARGE AMOUNTS OF DATA) outcomes and to constantly measure the IS AMBIGUITY; BOTH IN THE INTENT OF variance, adjusting the algorithm A SEARCH QUERY, AND IN THE SENSE accordingly. The benefit we have in THAT THE REFERENCE RATE FROM THE recruiting is that we have access to plenty CDC MEASURES INFLUENZA-LIKE of structured data in the ATS and other ILLNESSES, WHICH MIGHT INCLUDE HR systems to test outcomes against. By NON-FLU AILMENTS THAT CAUSE reverse engineering the data on hires, we FEVER, COUGH, OR SORE THROAT. can measure if the historical data trail SEARCH TERMS DIRECTLY RELATING follows the same path. It is important to TO A FLU SYMPTOM OR COMPLICATION regularly challenge our datasets and ARE CONFLATED BETWEEN PEOPLE predictions against known favorable WHO ACTUALLY HAVE THE FLU AND outcomes in order to ensure the integrity THOSE THAT ARE EXPRESSING of predictions. CONCERNED AWARENESS ABOUT IT — AND CDC MEASUREMENTS MINGLE PEOPLE WHO ACTUALLY HAVE THE FLU RolePoint Inc. © 2014 17 17 Big Data

B I G DATA VARIETY OF DATA SOURCES sourcing where a percentage of the desired workforce population is Another area to pay attention to is the underrepresented by placing a greater variety of data sources available, weighting to data from sources where the particularly when using unstructured data target population is represented. We can to influence strategy. The data source also ensure data integrity by adjusting needs to be representative of the the weighting given to certain data to population in order to avoid bias or a lack allow for the data demographics. In order of diversity. Pew internet research to derive meaning from data we need to highlights this problem when looking at understand the DNA of the sources we the demographics of internet users. The are mining to allow for any bias. report from December 2013 identifies that whilst Facebook is popular across a mix In recruiting terms, we can apply this of demographic groups, other channels thinking to technology by targeting the have developed their own unique data we want to collect, identifying the demographic user profiles. For example, data source to protect against bias and Pinterest holds particular appeal to applying contextual understanding. This female users (women are 4 times more involves investigating meaning in the data likely to visit the site then men) and being analyzed. In the case of Google Flu LinkedIn is particularly popular among Trends, the variance occurred because college leavers and users from higher the reason people were turning to Google income households. Twitter and for advice changed from those who were Instagram have particular appeal to concerned about symptoms they were younger users, urban dwellers and non- experiencing to people wanting to take whites and there is a strong overlap precautions in advance of an epidemic between Twitter and Instagram users. because of increased publicity and news. When we can understand data trends, we Source: http://pewinternet.org/Reports/2013/ can apply meaning to what triggers this Social-Media-Update/Main-Findings/73-of-online- reaction. One of the real benefits we adults-now-use-social-networking-sites.aspx have in the recruiting space is that we can use this thinking to identify the Data demographics can be especially internet behaviors of people who are useful when applying diversity to beginning to prepare for a job change. A RolePoint Inc. © 2014 18 Big Data

candidate in a talent network preparing “the global mapping of everybody and for a job change needs greater attention how they’re related”. The term was in matching results against job openings, popularized at the Facebook F8 and we know they are likely to be more conference on May 24, 2007, when it was receptive to a referral message. Our used to explain that the Facebook tracking has shown that on LinkedIn, for Platform, which was introduced at the example, a user thinking about a change same time, would benefit from the social will update their profile, seek and add graph by taking advantage of the new recommendations, increase their relationships between individuals, that connections, particularly with recruiters Facebook provides, to offer a richer and follow more companies. Whilst these online experience. The definition has been actions in isolation might mean very little, expanded to refer to a social graph of all when they are combined over a four week Internet users. period, they are likely to be thinking about moving. The same thinking can be When this is applied to referral networks, found in other social channels, with mapping the social graph of the predictive behaviors identified by mining organization and employees highlights the social media behaviors of candidates the best sources for distributing targeted as they apply in order to understand what messages and opportunities. The average candidates look like in terms of their data number of LinkedIn connections per footprint so that people with a similar employee is 225, with 130 Facebook footprint can be identified. connections. This offers targeted reach for relevant messaging, and tracking employee activity and response to THE SOCIAL GRAPH messaging will highlight those employees most likely to participate in employee Facebook CEO Mark Zuckerberg referral networks. Collecting on-going popularized the concept of the social data over user behavior and outcomes graph to describe his approach to enables recruiters to identify their most mapping the world’s social relationships, active referrers with the strongest in the process, unlocking untold value for relationships. The strength of a people by digitizing their social networks. relationship can be calculated by mining The social graph has been referred to as all of the channels where there is a RolePoint Inc. © 2014 19 Big Data

B I G DATA connection in order to understand the the greater the likelihood of eliciting a nature of the relationship. Whilst we positive response to the referral message. might identify a relationship as a connection between two people, e.g. they A big problem that exists when sourcing are friends on Facebook or connections talent is over messaging. This is a on LinkedIn, we can use other factors to fundamental problem with many social identify the depth of the relationship in recruiting platforms where matching and order to apply a weighting to the depth targeting is based on a limited data set of connection. such as job title and location. Typically, an individual will likely have multiple Interactions between users in social connections in the same organization. If channels that indicate the depth of each contact sends the same referral relationship can include the measurement message in the same way, the risk is over of: messaging considered spam. In the same way as the social graph can be used to • Number of connection points identify connections with a target profile, • Shared connections • Frequency of interactions e.g. @ reaching out and through which channel. messages on twitter, comments The relationship graph leads to the most and likes on Facebook, shared likely route for a successful outcome. so too the relationship graph can be applied to prioritize who should be pictures etc. • Social reputation e.g. Stackoverflow PEOPLE PROFILING AND RELEVANCE votes to answers or questions • OF MESSAGING Shared work history In the first section of this paper, we Whilst this list is by no means exhaustive, identified the way in which structured it is easy to see how relationships data from internal sources can be used to between target candidates and existing profile the best performers in the employees can be ranked to identify who company, and how their skill sets, has the closest relationship. It stands to aptitude and other factors can be used to reason that the closer the relationship compile job specs and a sourcing plan. and the more mutual respect that exists, RolePoint Inc. © 2014 20 Big Data

When you know what good looks like, study of the visual representation of data, you can plan how to find it again. Once meaning information that has been you have profiles of your best employees, abstracted in some schematic form, you have a data footprint of what you are including attributes or variables for the looking for, and a template to match units of information. The vast majority of against, This can be used in a variety of us are not data experts or analysts, and ways including identifying potential hires that’s why we use tools to do the work. now and in the future or identifying an We find it hard to understand numbers in audience for your employer brand columns and rows, but we understand content. Mapping the people you want to pictures, graphs, maps and symbols. hire against the social graph of your current employees enables you to identify Whilst we may not be able to spot a your employees with the deepest possible problem hidden in a row of data, relationship with the highest likelihood of we can easily locate a possible problem if being heard. it is indicated by a red flag. Whilst we may find it difficult to identify the extent The unstructured social data enables the of a skills shortage in a given location by profiling of targets of professional and studying academic data and comparing it personal data, online behaviors, interests with job openings, if you overlay the etc in order to deliver a personalized same data on a map to create a heat map tailored message from a trusted source. of talent against openings, you can In a world of noise, tailored marketing to immediately spot where there is a skills an audience of one greatly improves the shortage or over supply. probability of success, and probability is what big data is all about. Airline caterers Gate Gourmet were able to apply this technique in San Francisco to change their whole recruiting strategy. DATA VISUALIZATION Recruiting unskilled labor in Silicon Valley is no mean feat; historically Gate Gourmet In the age of big data, data visualization had targeted all the towns surrounding is becoming critical to deriving meaning the city, took a truck out and announced from the masses of information available job openings. They did this for a number to us. Visualization is the creation and of years, and were always RolePoint Inc. © 2014 21 Big Data

B I G DATA understaffed. The big problem as they occurring, allowing them to make saw it was a combination of their improvements. The process map made location, the availability of unskilled labor every stage visible, and the addition of and the fact that being airside and a low data revealed where significant margin business, they paid $2 below improvements could be made. It is minimum wage. common in organizations that poor practice continues year on year because Seeking a solution to an on-going it is the way things have always been problem, they overlaid where they had done or just the way things are, and experienced success in hiring with a map making data visual has the potential to of the local area. This told an interesting change that. Data needs to be presented story that had previously been hidden in a format that is easy to digest without from them: their hires came from 3 towns. expert knowledge in order to bring about Changing strategy, rather than try to best practice. blanket cover the whole state, they concentrated all of their efforts on the 3 SELF-SERVICE TOOLS ON DEMAND towns identified on the data map with increased activity and investment. The net result just 12 months later is that they More users are expecting self-service now have a waiting list for opportunities access to data, without the need to call at the airport. The decision to change on data or computer experts. This means direction was made simply by making the that the users need to be able to set their obvious visible in a format everyone own parameters for data interrogation understood. and access results in a format they can understand. A good example of this A multi-national company was able to would be giving recruiters control to set reduce their time to hire from 95+ days to their own search criteria, eliminating, for 16 by mapping out their end-to-end example, employees from a certain process from the raising of a requisition company. This gives the recruiter control to a hire starting. They then measured the of the data at their disposal without time taken at each stage and overlaid this being dependent on a machine dictated data on the end-to-end process map. This algorithm. As much control as possible highlighted where blockages were needs to be in the hands of the user, and RolePoint Inc. © 2014 22 Big Data

this instantly results in a format they can learning tasks and successful applications. understand, because making data Optical character recognition, in which accessible and understandable improves printed characters are recognized decision making, operates in real time automatically based on previous from the latest data when it is needed examples, is a classic example of machine most. learning. Source: http://en.wikipedia.org/wiki/ Machine_learning MACHINE LEARNING When it comes to big data, machine learning is a branch of artificial Machine learning concerns the intelligence (AI), the practice of getting construction and study of systems that computers to think like people. Analytics can learn from data. For example, a of user interpretation of data enables machine learning system could be trained technology to make decisions and rank on e-mail messages to learn to distinguish results based on the past reaction of between spam and non-spam messages. users. A good example of this might be a After learning, it can then be used to recruiter who rejects the result of a classify new e-mail messages into spam search or removes similar resumes from and non-spam folders. those under consideration. Machine learning interprets these actions in order The core of machine learning deals with to change the way future results are representation and generalization. arrived at and determined in the future. Representation of data instances and Google uses machine learning to order functions evaluated on these instances search results by past actions, ranking are part of all machine learning systems. search results by items the user has Generalization is the property that the shown trust in previously, like blog posts system will perform well on unseen data from a particular writer or academic text. instances; the conditions under which this This works on the principle that the more can be guaranteed are a key object of the user interacts with the technology, study in the subfield of computational the better the technology gets at learning theory. understanding the user in the same way a person would. Interaction is rewarded by There are a wide variety of machine RolePoint Inc. © 2014 a continually offering a better user 23 Big Data

B I G DATA experience. Machine learning is applied to MORE ACCURATELY TO THE big data for recruiting in the same way. COMPANIES WHO FIGURE OUT HOW TO Modern recruiting technology facilitates COLLECT AND USE HUMAN CAPITAL this methodology, bringing the way the DATA SUCCESSFULLY. THAT’S BECAUSE technology thinks closer to the elusive THE COMPANIES THAT CAN artificial intelligence. CONSISTENTLY HIRE GREAT PEOPLE, THROUGH IDENTIFYING PEOPLE AND BASING HIRING DECISIONS ON DATA AND NOT INTUITION AND SUMMARY CONVENTIONAL WISDOM, ARE MORE In September 2011, super sourcer Glen LIKELY TO DEVELOP THE BEST TEAMS. Cathey, the SVP of Talent Strategy and AND THE BEST TEAMS WIN.” Innovation at recruiters KForce, published a post, entitled “Moneyball Recruiting”, on See more at: http://booleanblackbelt.com/2011/09/ his excellent resource, the Boolean Black big-data-data-science-and-moneyball- Belt blog. In this post Cathey drew on the recruiting/#sthash.rcfD4Xgh.dpuf story of Billy Bean, who used a computer to select baseball team Oakland A’s. The When Cathey wrote this post, we were story of the success this team achieved is still speculating about the potential well documented elsewhere, but Cathey offered up by the information age. Other expressed the opinion that such areas of business like marketing and sales methodology could be applied to change were showing themselves as the early recruiting and human capital adopters of data mining and analytics to management. derive business value, and a lot of what is being developed in the recruiting space can be attributed to the lessons learnt at Cathey concluded with the statement: this time, lessons we are now applying to talent acquisition and hiring. “I AGREE WHOLEHEARTEDLY WITH MIKE LOUKIDES THAT: “THE FUTURE BELONGS TO THE COMPANIES WHO FIGURE OUT HOW TO COLLECT AND USE DATA SUCCESSFULLY.” HOWEVER, I’D ADD THAT THE FUTURE BELONGS RolePoint Inc. © 2014 24 Big Data

Josh Bersin, of Bersin Deloitte, has created a model to outline the spread of big data analytics across business functions on the journey towards HR and Recruiting: ANALYTICS IS DEFINITELY COMING TO HR The Evolution of Business Analytics in other Functions The Waves of Business Analytics Integrated ERP and Financial Analytics Predictive Customer Behavior - CRM Integrated Supply Chain 1980s Financial & Budget Analytics Logistics & Supply Chain Analytics Predictive Talent Models HR Analytics Web Behavior Analytics Business-Driven Talent Analytics Integrated Talent Management Workforce Planning Customer Segmentation Shopping Basket Recruiting, Learning, Performance Measurement Customer Analytics CRM (Data Warehouse) The Industrial Economy The Financial Economy The Customer Economy and Web The Talent Economy Steal, Oil, Railroads Conglomerates, Financial, Engineering Customer Segmentation Personalized Products Globalization, Demographics, Skills and Leadership Shortages Early 1900s 1950s-60s 1970s-80s Today The case now in 2014 is overwhelming. We can utilize the vast volume of structured and unstructured data in all areas of recruiting, talent acquisition, workforce planning and hiring. We can expect big data to play an increasingly important role in identifying, pipelining and hiring the best talent in the most effective way, and we have only just started. RolePoint Inc. © 2014 25 Big Data

B I G DATA NEXT STEPS CONTACT US TO SCHEDULE A FREE EMPLOYEE REFERRAL CONSULTATION WITH BILL BOORMAN CONTACT US TO FIND OUT MORE ABOUT ROLEPOINT AND ARRANGE A DEMONSTRATION W W W. R O L E P O I N T.C O M I N Q U I R I E S @ R O L E P O I N T. C O M BILL BOORMAN Nasdaq clients, building the principles that help companies generate 70%+ referral The author, Bill Boorman, has over 30 rates into a software-as-a-service platform. years’ experience in and around recruiting. He has spent the last 3 years working with Understanding that at the core of any social recruiting technology start-ups on successful referral program is the product and with corporate clients employee, RolePoint focuses on providing including Hard Rock Café, Oracle and the an engaging, transparent and frictionless BBC to integrate social into their recruiting experience, making it easy to identify practices. Bill has also hosted recruiting talented connections to refer. events in over 30 countries worldwide. For recruitment teams, RolePoint offers a ROLEPOINT comprehensive set of tools, enabling tracking, automation and recruitment RolePoint delivers employee referral intelligence for greater control and insight solutions to a range of Fortune 500 and into referrals within your organization. RolePoint Inc. © 2014 26 Big Data

ROLEPOINT THE MOST POWERFUL SOURCING SOLUTION AT DISCOVERING TALENTED CANDIDATES WITHIN YOUR EMPLOYEES’ PROFESSIONAL NETWORKS RolePoint Inc. © 2014 HIGHER QUALITY CANDIDATES REDUCED TIME-TO-HIRE LOWER COST-PER-HIRE IMPROVED EMPLOYER BRAND 27 Big Data

RolePoint Inc. © 2014 29 Big Data

W W W. R O L E P O I N T. C O M I N Q U I R I E S @ R O L E P O I N T. C O M

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