Hr Analytics - 18mbahr302 1o4837

HR ANALYTICS – 18MBAHR302 DR PRADEEP N E

UNIT 1 HR Analytics in perspective

WHAT IS ANALYTICS? Analytics is the application of analytic logic in the organizational . Analytics is about analyzing the data to make meaningful information which should be able to organizations in their decision making process.

HR Analytics refers to the application of analytic logic to the HRM Function.

Analytics is an encoming and multidimensional field that uses mathematics, statistics, predictive modeling and machine learning techniques to find meaningful patterns and knowledge in recorded data. Analytics is considered as statistics which not completely true. Analytics implies in itself the Art and Science of data universe. Here Art teaches as how to look at the data in different patterns and Science s in application of the data in various domains. Analytics is the hybrid concept of analyzing data with appropriate metrics utilizing efficient statistical tools and applications. Today, with the gigantic nature of the data which is in huge volumes often called as big data, various third wave generations have come in the way to work more effectively through utilizing advanced technological tools. In turn, this will lead to the application of analytics to the value chain of organizations. https://www.sas.com/en_in/insights/analytics/what-is-analytics.html

TYPES OF ANALYTICS There are three predominant types of analytics in use today.  Descriptive statistics. Descriptive statistics have been around the longest. the Swedes in 1749? Tabulating

population counts was an early foray into descriptive analysis – the summary of collected data points. These are the models that will help you understand what happened and why. There are still plenty of descriptive analytics in use today – everything from how many clicks a page receives to how many units are produced vs. how many are sold.

 Predictive analytics. Predictive analytics has surged in popularity. The desire to predict customer behavior has been a

main driver. Increased computing power with the ability to run hundreds or thousands of models quickly – and widespread adoption of predictive techniques like vector machines, neural networks and random forests – are bringing predictive analysis to the forefront of many organizations. These models use past data and predictive algorithms to help you determine the probability of what will happen next.

 Prescriptive analytics. Prescriptive analytics is the newest kid on the block. Knowing what will happen and knowing

what to do are two different things. Prescriptive analytics answers the question of what to do by providing information on optimal decisions based on the predicted future scenarios. The key to prescriptive analytics is being able to use big data, contextual data and lots of computing power to produce answers in real time.

UNIT 1: HR ANALYTICS IN PERSPECTIVE ROLE OF ANALYTICS The Role of Analytics is itself a vast discipline to learn and engage in the existing business scenarios. Its application extends to various aspects of any organization which includes: 

Enhance organizational efficiency

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Achieve competitive advantage

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Maximization of resource utility

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Establish the best alternatives

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decision making process of organization

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Leverage cost effective methodologies through analytics

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Building effective strategies for human resource management

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Business Intelligence & Market Research (Where should we locate a new manufacturing plant?)

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Customer Intelligence (Who gets the latest catalog or brochure?)

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Pricing strategies & optimization (How much should we charge for a particular item?)

WHY IS ANALYTICS IMPORTANT? From the first known population data collection project by the Swedish government in 1749, to Florence Nightingale recording and analyzing mortality data in the 1850s, to British scholar Richard Doll’s tobacco and lung cancer study in the 1950s, the analysis of data has fueled knowledge discovery for hundreds of years. Each of the above scenarios required an answer to a heretofore unanswerable question. In the 1700s, the Swedes wanted to know the geographical distribution of their population to learn the best way to sustain an appropriate military force. Nightingale wanted to know the role that hygiene and nursing care played in mortality rates. Doll wanted to know if people who smoked were more likely to suffer from lung cancer.

WHY IS ANALYTICS IMPORTANT? Analysis of data can uncover correlations and patterns. There’s less need to rely on guesses or intuition.And it can help answer the following types of questions:  What happened?  How or why did it happen?  What’s happening now?  What is likely to happen next?

With faster and more powerful computers, opportunity abounds for the use of analytics and big data. Whether it’s determining credit risk, developing new medicines, finding more efficient ways to deliver products and services, preventing fraud, uncovering cyberthreats or retaining the most valuable customers, analytics can help you understand your organization – and the world around it.

WHAT WILL BE IN THE THIRD WAVE HR THAT IS NOT IN THE TRADITIONAL HR? HR Analytics , first of all, that we live now in a world that sleeps, breaths with data. So HR will be the host for the departments that will answer questions on how to create the utmost efficiency. Big and Small data will both be handled.

Analysis based on data will play a great role as a decision- mechanism in all processes from hiring to firing. HR Analytics will become one of the most important units of HR. The employees of these units will include graduates of natural sciences and engineering just as much as social sciences. The most important difference of the HR analytics will be their shedding light into the future. Otherwise, HR today also uses employee data and analyses it to understand the past and the future. HR analytics will make predictions for employee loyalty or quitting rates using foresight models and developed artificial intelligence systems to look at today’s picture.

UNDERSTANDING THE HR VALUE CHAIN

UNDERSTANDING THE HR VALUE CHAIN HRM activities and processes: Efficiency metrics On the left of the chain, we find the HRM activities. These are measured using the so-called efficiency metrics. Examples include:  Cost of hire  Time to hire/time to fill  Learning and development budget  Training time in days  Time since last promotion

UNDERSTANDING THE HR VALUE CHAIN All these metrics measure HR processes and give information about how efficient the HR function is. It doesn’t say anything about how well HR is hitting its marks, a.k.a. HR effectiveness. I like to refer to organizations who solely focus on HRM processes level 1 HR organizations. Their main focus is cost savings, reached by optimizing these efficiency metrics. For example, if they can lower the cost of hire while keeping the time to hire metric stable, they are more efficient. This immediately shows the big weakness of these level 1 HR organizations: they focus on reducing HR cost – and thus approach HR as a cost-center instead of focusing on the value that HR adds. In other words, HR efficiency says nothing about how HR contributes to the business.

UNDERSTANDING THE HR VALUE CHAIN HRM outcomes: Effectiveness metrics In the second category, we observe the HRM outcomes. These are the outcomes that are traditionally seen as important HR KPIs. Examples include:  Engagement  Retention/employee turnover  Absenteeism rate  Individual performance

 Team performance  Quality of hire

UNDERSTANDING THE HR VALUE CHAIN All these metrics provide information about how well the workforce is doing. This involves both HR and line management.

For example, when engagement is high, HR is more effective than when engagement is low. The same holds true for retention and (inversely) for employee absence. Part of HR effectiveness is how well the intended HR practices are executed by managers. HR can do a stellar job but with bad managers, employees will be more absent and much more likely to leave!

UNDERSTANDING THE HR VALUE CHAIN It is important to realize that most of our HR activities are aimed at achieving positive HR outcomes. For example:  We don’t want to spend too much time on bringing in new people, otherwise we will lose the best candidates,

bringing our quality of hire metric down  We are training our people to make them perform better and retain them  We engage in wellness promotion in order to lower absence  And so on

Level 2 HR organizations focus on HRM outcomes. They don’t focus on cost savings but on how they can reach their HR outcomes in a cost-efficient way.

UNDERSTANDING THE HR VALUE CHAIN

Organizational objectives The last category is organizational objectives. These are the strategic goals that the organization is trying to reach. Examples of metrics include:  Market share

 Profit margins  Market capitalization  Customer satisfaction  Customer loyalty

These are the kind of outcomes that add value to the business and make the business more viable in the long term. Level 3 HR organizations focus on the business contribution they make with all of their people policies. These are truly strategic HR functions.

THE HR VALUE CHAIN IN PRACTICE An example about how these different levels of HR organizations think. Say we want to increase learning in the organization.  A level 1 HR organization will allocate more L&D budget to employees, believing that

better-trained employees will benefit the organization.

 A level 2 organization will allocate more L&D budget to employees and follow up by

checking if these investments pay off. They test knowledge retention and check if the investments lead to better individual performance. If not, they will test and change training programs and/or training providers in order to optimize return.

 A level 3 organization does it the other way around. They know that the L&D spending

was increased because the organization wanted to become more innovative and profitable. This organization will do all of the above and test how it impacts these two key performance indicators. They will only be satisfied when there’s a positive relationship between the L&D spending and the key performance indicators.

THE HR VALUE CHAIN IN PRACTICE

THE HR VALUE CHAIN AND ANALYTICS This is also where analytics comes in. HR serves the business and should follow the organizational objectives. All HR outcomes and activities that HR focuses on should lead to these business outcomes.

Analytics is a great tool to measure the effectiveness of the HR interventions aimed at reaching these business outcomes. This relates to the two which show the value that is added through HR practices. In this case by hiring the right people and training them on the job. This kind of tangible analytics evidence connects what we do in HR to tangible financial business outcomes, proving once again the added value of HR. https://www.aihr.com/preview-lessons/#leader-m3l3

RIDING THE THIRD WAVE OF ANALYTICS Two decades ago, trained data professionals spent an exorbitant amount in using statistical and analytical software such as SPSS and SAS.  That was the very first wave of analytics transformation – monetization.  This was soon followed by commoditization, as expert programmers started developing analytical algorithms on

open source.  Today, we are witnessing the third wave - democratization of analytics - that is finally getting the algorithms

into everyone’s hands. UI-based tools like Exploratory and Dataiku leverage open source technology, and are widely adopted by business s across marketing, finance, manufacturing, etc.

RIDING THE THIRD WAVE OF ANALYTICS As organizations further democratize data science, three key skills will distinguish the talent of tomorrow –  business thinking,  software and statistical abilities, and  soft skills. Analysis based on data will play a great role as a decision- mechanism in all processes from hiring to firing. HR Analytics will become one of the most important units of HR.

RIDING THE THIRD WAVE OF ANALYTICS

Acquiring a business mindset One of the biggest challenges for a data scientist is the ambiguous nature of requests they get from internal as well as external clients. In such a scenario, using a business mindset can prove to be highly valuable. Instead of fishing for signals in the data alone, thinking like a business stakeholder and working closely with business units can help data scientists decipher answers that are aligned with business objectives.

RIDING THE THIRD WAVE OF ANALYTICS

Augmenting technical data skills

As per a recent ET report, there has been a 400% rise in demand for data science professionals in India across industries, but the supply has grown only by 19% in the past year. The problem becomes more acute as the unstructured data available in most organizations continues to grow. To address this, many enterprises have started data science practices and training programs within their organizations or partnered with educational institutions/edtech firms to grow and mentor data science professionals.

RIDING THE THIRD WAVE OF ANALYTICS Developing data storytelling capabilities The father of modern physics, Albert Einstein famously said, “If you can’t explain it to a six-year-old, you don’t understand it yourself.” This is very relevant to the realm of data science as well.

One of the more challenging tasks for data scientists is to present the insights buried under humongous amounts of data and complex calculations in the form of a simple story that even non-technical business folks can understand. This is where soft skills such as people and communication skills come in handy. They are critical to not only convincing business stakeholders and getting them on board with the findings, but also liaising with other data-driven teams within the organization to enable vital business decisions.

LEAN ORGANISATION SYSTEM

A Lean system describes a business or business unit that holistically applies Lean principles to the way it plans, prioritizes, manages, and measures work.

LEAN ORGANISATION SYSTEM

These are two of the fundamental concepts of Lean: Eliminate anything that does not add value to the

customer, and work systematically and continuously to create more

value for the customer

KEY PRINCIPLES OF LEAN SYSTEM  Optimize the whole - Visualize, optimize, and manage

the entire organizational value stream as one value-generating system.  Create Knowledge - A Lean system is a learning system -- it grows and develops through analyzing the results of small, incremental experiments  Eliminate Waste - Eliminating any process, activity, or practice that does not result in more value for the customer.

KEY PRINCIPLES OF LEAN SYSTEM  Build Quality in - Lean organizations set themselves up for sustainable growth by building quality into processes and documentation. They automate and standardize any tedious, repeatable process or any process prone to human error, which allows them to error-proof significant portions of their value streams.

 Deliver Fast - In Lean, flow refers to the manner by which work moves through your organizational system. Good flow describes a Lean system with a steady, consistent flow of value delivery, while bad flow describes a system with unpredictable delivery and unsustainable habits.

KEY PRINCIPLES OF LEAN SYSTEM  Defer Commitment - This Lean principle says that Lean systems

should function as just-in-time systems, waiting until the last responsible moment to make decisions and deliver work.  Respect People - Lean systems are designed to maximize

customer value while minimizing waste, out of respect for the customer. Out of respect for employees, Lean systems encourage environments that allow everyone to do their best work.

UNIT 2

HRA Frameworks

HRA FRAMEWORKS

Current Approaches to measuring HR & Reporting Value from HR Contributions Traditional Versus Contemporary HR Measures  Measure – Individual Behaviors, Traits or Reactions

 Statistical summaries

HRA FRAMEWORKS

Measure – Individual Behaviors, Traits or Reactions  measures of the reactions of various groups (top

management, customers, applicants, or trainees),  what individuals have learned, or  how their behavior has changed on the job.

HRA FRAMEWORKS

Statistical summaries  Individual measures  Include various ratios (for example, accident frequency or severity),  Percentages (for example, labor turnover),  Measures of central tendency and variability (for example, mean and

standard deviation of performance measures, such as bank-teller shortages and surpluses), and

 Measures of correlation (for example, validity coefficients for staffing

programs, or measures of association between employee satisfaction and turnover)

HRA FRAMEWORKS Four Levels of Sophistication in HR Analytics HR analytics is fact-based decision making. In the sections that follow, we describe four levels of sophistication used by Google’s People Analytics Group:  Counting - All relevant data about the workforce are tracked, organized, and accessible  clever counting - Extrapolating from descriptive data yields new insights. For example, consider workforce planning  Insight - What drivers of the trends do you find through clever counting?  Influence - The results of counting, clever counting, and insight can help make a difference. At this level, the relevant question is, how can we shape outcomes rather than just measure them? Each higher level requires mastery of the lower levels

HRA FRAMEWORKS

Fundamental Analytical Concepts from Statistics and Research: Fundamental Analytical Concepts from Economics and Finance

TODAY'S HR MEASUREMENT APPROACHES Measurement Approach Example Measures Efficiency of HRM operations

Primary Appeal

Cost per hire, time to fill, training costs. Ratio of HR staff to total employees.

Tough Questions

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Fundamental Analytical Concepts from Statistics and Research: Fundamental Analytical Concepts

TODAY'S HR MEASUREMENT APPROACHES Measurement Approach Example Measures Efficiency HRM HR activityofand "bestoperationsindexes practice"

Primary Appeal

Cost percapital hire, time to fill, Human benchmarks. training capital costs. index. Human Ratio of HR staff to total employees.

Tough Questions

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Fundamental Analytical Concepts from Statistics and Research: Fundamental Analytical Concepts

TODAY'S HR MEASUREMENT APPROACHES Measurement Approach Example Measures HR Efficiency dashboard HRM or HR activityofand "bestscorecard operationsindexes practice"

Primary Appeal

How Cost per thecapital organization hire, time to or fill, Human benchmarks. HR training function costs.meets goals of Human capital index. "customers, Ratio of HR financial staff to total markets, employees. operational excellence, and learning."

Tough Questions

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Fundamental Analytical Concepts from Statistics and Research: Fundamental Analytical Concepts

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Primary Appeal

Tough Questions

Vast Explicit Useful array logic cost-value oflinking HR Wouldn't Is thisthis the scorecard outsourcing best pathprove from cut a HR practices are measures associated Can What is the logic can calculations. employee be categorized. variables to connection costs talent even to profits? more? between with familiar financial connecting these activities The Logic financial "balanced of cost outcomes. savings scorecard" is easy people Do Howthese doand our cost strategic HR savings practices come outcomes. with such huge financial concept to Valuable relate isfor toknown ing. organizing to and outcomes? at work the together? price of workforce Data from many effects? business Standardization analyzingleaders. diverse makes data Which value? Whatthe logic numbers can we and use drillto organizations lends Will practices that Software benchmarking elements.allowscomparisons s to downs Why find more should are most our critical costs be like to credibility. worked in connections other easier. analysis. our the this?same success? as the industry's? Suggests there may be organizations necessarily Fundamental Analytical Concepts fromcustomize Economics and Finance practices or combinations work in ours? Source: John W. Boudreau and Peter M. Ramstad, "Strategic HRMthat Measurement in theprofits, 21st Century: From Justifying to generally raise Does having theseHR practices Strategic Talent Leadership." In HRM in the 21st Century, Marshall Goldsmith, & Marc Efron (eds.), 79– sales, etc. ... Robert P. Gandossy,mean theyS.are 90. New York: John Wiley, 2003. implemented well?

Fundamental Analytical Concepts from Statistics and Research:

HR MATURITY FRAMEWORK – FROM LEVEL 1 TO LEVEL 5 Objective of the framework  Improve the ability of the organizations to attract, develop, motivate, organize and retain talent  Focus on Employee development  Ensure alignment between the individuals’ personal aspirations and organizational objectives  Clarity on career progression and growth

 Employee participation & empowerment  Instill the best HR practices and procedures  Transparency in practices read more at: https://www.citehr.com/54238-what-pcmm.html

HR MATURITY FRAMEWORK – FROM LEVEL 1 TO LEVEL 5 Maturity Level 5 Focus Areas:  Improve the ability of the organizations to attract, develop, motivate, organize and retain talent  Focus on Employee development  Ensure alignment between the individuals’ personal aspirations and organizational objectives  Clarity on career progression and growth

 Employee participation & empowerment  Instill the best HR practices and procedures  Transparency in practices read more at: https://www.citehr.com/54238-what-pcmm.html

HR MATURITY FRAMEWORK – FROM LEVEL 1 TO LEVEL 5

MATURITY LEVEL – PROCESS CATEGORIES

HR MATURITY FRAMEWORK – FROM LEVEL 1 TO LEVEL 5 Five Levels of Maturity Framework:

 Initial Level (Typical characteristics: Inconsistency in performing practices,

Displacement of responsibility, Ritualistic practices, and Emotionally detached workforce).

 Managed Level (Typical characteristics: Work overload, Environmental distractions,

Unclear performance objectives or , Lack of relevant knowledge, or skill, Poor communication, Low morale)

read more at: https://www.linkedin.com/pulse/introduction-people-capability-maturity-model-pcmm-zafar/

HR MATURITY FRAMEWORK – FROM LEVEL 1 TO LEVEL 5 Five Levels of Maturity Framework:

 Defined Level (Although there are performing basic workforce practices, there is

inconsistency in how these practices are performed across units and little synergy across the organization. The organization misses opportunities to standardize workforce practices because the common knowledge and skills needed for conducting its business activities have not been identified)  Predictable Level (The organization manages and exploits the capability created by its framework of workforce competencies. The organization is now able to manage its capability and performance quantitatively. The organization is able to predict its capability for performing work because it can quantify the capability of its workforce and of the competency-based processes they use in performing their assignments) read more at: https://www.linkedin.com/pulse/introduction-people-capability-maturity-model-pcmm-zafar/

HR MATURITY FRAMEWORK – FROM LEVEL 1 TO LEVEL 5 Five Levels of Maturity Framework:

 Optimizing Level (The entire organization is focused on continual improvement.

These improvements are made to the capability of individuals and workgroups, to the performance of competency-based processes, and to workforce practices and activities. The organization uses the results of the quantitative management activities established at Maturity Level 4 to guide improvements at Maturity Level 5. Maturity Level 5 organizations treat change management as an ordinary business process to be performed in an orderly way on a regular basis) read more at: https://www.linkedin.com/pulse/introduction-people-capability-maturity-model-pcmm-zafar/

HRA FRAMEWORKS : LAMP FRAMEWORK According to the LAMP model, there are four major components, which are critical to strategic change. These components include: Logic, Analytics, Measures and Process (Cascio and Boudreau 2008).  LOGIC  Analytics

 Measures  Process

HRA FRAMEWORKS : LAMP FRAMEWORK

HRA FRAMEWORKS : LAMP FRAMEWORK  LOGIC : Articulate the connections between talent and strategic success, as well as

the principles and conditions that predict individual and organizational behaviors. The logic element of any measurement system provides the "story" behind the connections between the numbers and the effects and outcomes

For example, beyond providing numbers that describe trends in the demographic makeup of a job, improved logic might describe how demographic diversity affects innovation, or it might depict the pipeline of talent movement to show what bottlenecks most affect career progress.

HRA FRAMEWORKS : LAMP FRAMEWORK

 Analytics

: Use appropriate tools and techniques to transform

data into rigorous and relevant insights — statistical analysis, research design, etc.

For example, understanding whether employee engagement causes higher work performance requires analysis beyond correlations that show the association, to be certain that the reason is not simply that better performers become more engaged.

HRA FRAMEWORKS : LAMP FRAMEWORK

 Measures : Create accurate and verified numbers and indices calculated from data systems to serve as input to the analytics, to avoid having “garbage in” compromise even with appropriate and sophisticated analysis.

HRA FRAMEWORKS : LAMP FRAMEWORK

 Process

: Use the right communication channels, timing, and techniques to motivate decision makers to act on data insights.

For example, reports about employee engagement are often delivered as soon as the analysis is completed, but they become more impactful if they’re delivered during business planning sessions and if they show the relationship between engagement and specific focus outcomes like innovation, cost, or speed.

HCM : 21 FRAMEWORK There is an opportunity to make a quantum leap in human capital management, a leap from obsolescence to innovation, through the application of analytics HCM:21 (human capital management for the twenty-first century), breakthrough program was developed over a period of eighteen months as part of Predictive Initiative, a consortium of major organizations and thought leaders who were committed to transforming people management into a strategic function.

HCM : 21 FRAMEWORK

HCM, or human capital management, is the framework of logic that is used to gather, organize, and interpret data, and subsequently also knowledge, for the purpose of assessing the probability of events.

HCM : 21 FRAMEWORK HCM takes the gambling out of decision making. It helps to overcome a reliance on past data and obsolete experience, and replace it with insights regarding the future and the tools for influencing it. This is called ‘‘Managing tomorrow, today.’’

HCM : 21 FRAMEWORK – SYSTEM CONSISTS OF FOUR PHASES

HCM : 21 FRAMEWORK Scanning: All the external market forces and internal organizational factors are listed in of how they might affect the organization’s human, structural, and relational capital. Additionally, the interdependencies and interactions across these three forms of capital are recognized and ed for. This is the critical, often ignored, point.

HCM : 21 FRAMEWORK Planning: Workforce planning is reconstituted as capability development. The industrial-era, gap-analysis, structure-focused model is replaced with an agile system focused on building sustainable human capability rather than filling positions; in fact, many of those older positions will be restructured or eliminated.

HCM : 21 FRAMEWORK

Producing: Human resources services are studied as processes with inputs, throughputs, and outputs. Statistical analysis is applied to uncover the most cost-effective combination of inputs and throughputs to drive desired outputs.

HCM : 21 FRAMEWORK

Predicting: A three-point measurement system is designed to include strategic, operational, and leading indicators. The causal and correlational aspects of the three points are used to tell a comprehensive story.

SUMMARY HCM:21 is a model and a methodology for managing human capital, talent, or simply people. It is distinct and disruptive in that ‘‘people management’’ has always been a loosely connected, out-of synch batch of processes for hiring, paying, training, and sustaining talent. This is why people claim to ‘‘hate’’ HR. As a result, the function is being dismantled through outsourcing and parceling out to finance and operations. The time clearly is now for HR professionals to face these realities and adopt a future-focused, integrated management model.

TALENTSHIP FRAMEWORK Boudreau's HC Bridge provides a value map connecting investments, activities and business success: ‘The purpose of the model is to provide a framework to articulate the logical connections between investments, changes in the nature or deployment of workforce talents, and sustainable strategic success’.

TALENTSHIP FRAMEWORK Framework involves three stages:  Impact (equivalent to the Impact step in the HCM Value Chain)  Effectiveness (equivalent to Output in the HCM Value Chain)

 Efficiency (including Input and Process measures from the HCM

Value Chain)

TALENTSHIP FRAMEWORK

5 OVERARCHING COMPONENTS OF AN EFFECTIVE ANALYTICS FRAMEWORK In order to conduct good analysis and go through all the steps in the analysis spectrum, analysts are encouraged to use an analytical framework. Analytical frameworks are designed to structure an analyst’s thinking, and to help logical thinking in a systematic manner. In short, analytical frameworks are models that aim to guide and facilitate sense making and understanding. An analytical framework is often presented visually. Analytical framework = theoretical + conceptual framework (secondary data review, analysis plan, methodology, tools)

5 OVERARCHING COMPONENTS OF AN EFFECTIVE ANALYTICS FRAMEWORK

According to Benjamin Lieberman, analytical frameworks incorporate such patterns and also provide a checklist of skills, tools, and techniques that are necessary for researching a particular area, such as business analysis or system architecture.

5 OVERARCHING COMPONENTS OF AN EFFECTIVE ANALYTICS FRAMEWORK Benjamin Lieberman states that an analytical framework consists of five key components that include: 1) An assortment of tools 2) A set of useful solution patterns 3) One or more model forms

4) Multiple research techniques and skills, and 5) Methods for grouping complex information.

5 OVERARCHING COMPONENTS OF AN EFFECTIVE ANALYTICS FRAMEWORK

Source and more details available here:

https://www.ibm.com/developerworks/libra ry/ar-anframe/index.html