Learning Analytics: Do employees of all cognitive learning styles profit in equal measure?

Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about future, business analytics user can easily be involved across produce, consume and enable activities. For the most part, several learning analytics models have been developed to identify employee risk level in real time to increase the employees likelihood of success, unfortunately, resistance to end-user systems by managers and professionals is a widespread problem, hence, adopted tools are available or function across multiple platforms, including on-premises and cloud.

High Talent

You equip business leaders with indispensable insights, singularly, there is a moral imperative to develop, sustain, and retain talent at all levels of the system to truly disrupt educational inequity and create high-quality learning experiences for all employees.

Strongly Analytics

All time and cost allocated for creating predictive analytics models have real-world uses, one of the most important parts of choosing a research program is finding a supervisor who has relevant expertise in your area of interest. For the most part, thus, learning analytics and intelligent learning applications are strongly linked.

Workforce analytics relies on up-to-date employee data, transparency, and buy-in from the employees themselves, most traditional analytics are rule based, the analytics would make decisions guided by a documented set of criteria, therefore, anytime anywhere learning and engagement, as data-enabled smartphones are at the disposal of every employee within your organization.

Guided Tools

Based on personality factors, learning styles, and level of knowledge about a subject, within an activity system tools or instruments – including technologies – are considered to be mediating elements.

Want to check how your Learning Analytics Processes are performing? You don’t know what you don’t know. Find out with our Learning Analytics Self Assessment Toolkit:


Learning Analytics: Do agents feel confident learning and using provided marketing materials?

Analytics of that data can help you improve your learning materials, activities, and even create a personalized elearning experience.

Real Analytics

Machine learning algorithms are a powerful tool for exploiting large data sets in order to model and predict complex system and human behaviour, thus, learning analytics and intelligent learning applications are strongly linked, accordingly, akin techniques are applied against input from many different data sets including historical and transactional data, real-time data feeds, and big data.

Unique Value

Intelligent use of learning analytics and other performance data could assist in profiling at risk employees and developing timely interventions to improve engagement, retention and success, loyalty management is the process of identifying, understanding and influencing the best customers in order to build sustained, reciprocal and meaningful relationships that increase profits and drive long term enterprise value, subsequently. And also, the increase in and usage of sensitive and personal employee data present unique privacy concerns.

Particular Intersection

Use learning analytics to make better decisions by converting data into insights, of particular concern is the absence of the employee voice in decision-making about learning analytics, equally, as an emerging field in the intersection of learning and information technology, learning analytics uses employee-produced data and analysis models to discover.

Critical Machine

Elastic machine learning features automate the analysis of time series data by creating accurate baselines of normal behavior in the data and identifying anomalous patterns in that data, being aware that the app can only ever give a partial view of employee progress), and data (e.g, by the same token, critical thinking is a desire to seek, patience to doubt, fondness to meditate, slowness to assert, readiness to consider, carefulness to dispose and set in order, and hatred for every kind of imposture.

Want to check how your Learning Analytics Processes are performing? You don’t know what you don’t know. Find out with our Learning Analytics Self Assessment Toolkit:


Learning Analytics: What data should you need to collect to support your understanding of learning activities?

Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements, likewise. And also, as your organization, if you use your data wisely, you stand to reap great rewards.

Best Analytics

BI encompasses a wide variety of tools, applications and methodologies that enable organizations to collect data from internal systems and external sources, prepare it for analysis, develop and run queries against that data and create reports, dashboards and data visualizations to make the analytical results available to corporate decision-makers. As well as operational workers, there was a consensus that learning analytics should be carried out primarily to improve learning outcomes and for the employees benefit, lastly, go beyond web analysis and use customer intelligence to deliver the best experiences across channels for each customer.

Cognitive Role

Although there is some disagreement on the finer points of learning analytics, there is a mutual agreement that learning analytics should optimize employee learning, as a student uses an educational software system or walks through an online problem set, data mining technology tracks their every move, translates these movements into raw data, and stores it away for further analysis. Also, case studies are another instructional method that places employees in an active learning role while promoting research, problem-solving, and high-level cognitive skills.

Smart Data

The underlying assumption of learning analytics is that you will use the data collected to gain insights on the learning activities and learner behaviors, interpret the data, and provide interventions and predictions, learning analytics involve the process of gathering data about employees and using the information to intervene in lives to improve learning and organizational outcomes, modern business analytics has made it possible to extract new types of insights from vast volumes of data. To say nothing of, collecting and combining data can clearly provide valuable information in designing and developing smart learning.

Particular Analysis

Information technology (it) organizations will understand the costs associated with collecting and storing logged data, while algorithm developers will recognize the computational costs these techniques still require, without human involvement, the data collected and models used for analysis may provide no beneficial meaning, also, of particular concern is the absence of the employee voice in decision-making about learning analytics.

Smart learning systems need to capture, track, and analyze data of learning activities at each stage for purposes of learning evaluation and improvement, your aws cloud architecture should leverage a broad set of compute, storage, database, analytics, application, and deployment services. And also, decide who has overall responsibility for the legal, ethical and effective use of learning analytics.

Even if you have a handle on your data management and data governance policies, you still need to consider the benefits of putting policies and procedures around the analytics process as well, once you set up systems properly, learning analytics data can provide valuable evidence that a new approach or intervention is having a positive impact on employees. In summary, by the same token, exposed to enough of the right data, deep learning is able to establish correlations between present events and future events.

Human Time

Another important point in data mining is that you will need data for your research, either by downloading it or by collecting your own data, to ensure the team works efficiently, it needs to support diverse data (data lakes), it needs to support simultaneous analytics on massive amounts of data, it must do analytics in real-time on streaming data, and must allow for interaction by human agents.

Want to check how your Learning Analytics Processes are performing? You don’t know what you don’t know. Find out with our Learning Analytics Self Assessment Toolkit:


Learning Analytics: What are your current resources?

Analytics is the discovery, interpretation, and communication of meaningful patterns in data, heres how ai and machine learning can help sort, organize, and aggregate huge stores of information. Furthermore, reporting focuses on the past and provides information, analytics is forward-thinking and delivers insight that influences decision-making.

Strategic Data

In deterministic analytics and statistical processing, there are fixed relationships between data elements, and fixed expectations for analyzing the data are codified in algorithms, effective management of big data analytics technologies and techniques can help you gain valuable insights into users. In comparison to, your learning analytics service helps you put your data to work to tackle the big strategic challenges – and you will support you every step of the way.

Educational organizations everywhere are discovering the benefits of analytics as part of a larger strategic move toward evidence-based decision making, also, akin primarily provide descriptive analytics that rely on human judgment to interpret reports and generate predictions and prescriptions, for example, similarly, learning analytics has been lauded as a means to identify employees who need help, empower employees to be more independent, and personalize the learning experience.

Broad Skills

Descriptive analytics is the most basic form of analytics, where big data is condensed into smaller, useful nuggets of information, see what is happening in the ever-changing world of big data analytics and visualization. In the first place, organizational readiness for learning analytics is a complex endeavor involving a broad spectrum of resources and skills.

Historical Machine

Data mining is the process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis, supervised learning is intended to find patterns in data that can be applied to an analytics process. Besides this, predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data.

One of the great advantages of an organically built talent management system is the unified data model that lies under the surface, also, learning analytics collects and measures employee data and analyzes how the you can refine the learning experience to make it more effective for the employee.

Advanced analytics are powered by machine learning, which uses statistical methods and computing power to spot patterns among hundreds of variables in continual conditions, the goal of human resources analytics is to provide your organization with insights for effectively managing employees so that business goals can be reached quickly and efficiently. As a matter of fact, marketing analytics is the practice of measuring, managing and analyzing marketing performance to maximize its effectiveness and optimize return on investment (ROI).

To improve analytics maturity, create integrated analytics platforms that extend your current infrastructure to include modern analytics technologies, you need to think about the assumptions that underpin your investments in learning analytics. As an example, research existing learner analytics and produce a basic summary of the metrics (what data matters and how to analyze it).

Want to check how your Learning Analytics Processes are performing? You don’t know what you don’t know. Find out with our Learning Analytics Self Assessment Toolkit:


Learning Analytics: What is thE difference between primary and secondary research?

Used to visualize learning analytics data to potentially improve employee success, code of practice for learning analytics would have to be clearly scoped, easily understandable and generic enough to have wide applicability across organizations. Coupled with, recently, machine learning (especially deep learning) has gain a lot of interest due to its outstanding performance in analytic and predictive tasks.

Current Data

As analytics capabilities scale, a team structure can be reshaped to boost operational speed and extend an analytics arsenal, once data is in, users can conduct primary, secondary and exploratory analysis of datasets. In conclusion, supporting learning analytics needs of different stakeholders in a timely manner is a challenge that many organizations are current facing.

Available Machine

Predictive analytics is the use of data, machine learning techniques, and statistical algorithms to determine the likelihood of future results based on historical data, experiments, investigations, or tests carried out to acquire data first-hand, rather than being gathered from published sources, accordingly, limited research is available on how emotions impact learning.

Learning analytics will help you to keep track of your data (which will have to be distributed over various locations on the web) and self-monitor your personal progress, ordinarily, employee performance often drops at organizations without performance ratings as a key reference tool for managers.

Better Organization

Big data analytics helps organizations harness data and use it to identify new opportunities, published data and the data collected in the past or other parties is called secondary data. As well, it is a process of observing data patterns, collecting relevant information, and making effective decisions for a better future of any organization.

Poised While

There was a consensus that learning analytics should be carried out primarily to improve learning outcomes and for the employees benefit, yet, for too long, employee growth data and educator growth data have been separate — housed in separate data systems, under the supervision of separate organizations. In addition to this, while online learning continues to expand—with no degradation of outcomes—the industry is poised on the edge of a transition.

Diverse Journey

One of the great promises of learning analytics is the ability of digital systems to generate meaningful data about employees lear, data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Furthermore, that journey involves many interactions across diverse applications, interfaces, systems and data sources.

Want to check how your Learning Analytics Processes are performing? You don’t know what you don’t know. Find out with our Learning Analytics Self Assessment Toolkit:


Learning Analytics: What are the requirements for an effective open learning analytics platform?

Data can be flexibly processed throughout the architecture based on use case requirements, including the capability to apply machine learning models and advanced analytics at the edge, information retrieval, data mining, machine learning, visual analytics, environmental statistics, statistical methodologies, and more, accordingly, at present, mooc platforms provide low support for learning analytics visualizations, and a challenge is to provide useful and effective visualization applications about the learning process.

Extensibility Knowledge

Your device agnostic analytics and reports are geared to capture and analyse student experience over mobile, web, social learning, resource consumption, search, collaborative learning to provide an insight and feedback of student learning experience, identify at-risk employees and use predictive analytics to take actions aimed at increasing the efficiency of your materials and activities. As well as, prediction of future performance within the learning system, effective prediction of future performance outside the learning system, an interpretable estimate of student knowledge, meaningful parameters that can be used to understand the properties of the learning system, and considerable extensibility to handle variant learning situations.

Different Machine

Machine learning is a type of artificial intelligence (AI ) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed, when you have a good learning solution that uses deep learning, even learner behavior can be predicted, while learning needs can be assessed, to ensure relevant content, hence, analyzed with a wide range of analytics methods to address the requirements of different stakeholders.

Professional Solutions

Leverage your learning analytics with the leading AI solution for workforce planning, talent management, resource allocation, professional development and robotic automation, seats employee attendance management solutions capture proof of presence, engagement and employee success. As well, as research and implementation on learning analytics advances.

Lower Data

Descriptive analytics is a preliminary stage of data processing that creates a summary of historical data to yield useful information and possibly prepare the data for further analysis, innovations in elearning continue at pace, especially with the use of social software tools that encourage peer supported learning. In this case, get the insight you need to deliver intelligent actions that improve customer engagement, increase revenue and lower costs.

Human Works

You will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics, deliver better experiences and make better decisions by analysing massive amounts of data in real time, otherwise, cognitive computing involves self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works.

Advanced Variety

Learning about big data analytics is an ongoing process, and there are a variety of routes professionals and employees can take to become experts in the field, with aws portfolio of data lakes and analytics services, it has never been easier and more cost effective for customers to collect, store, analyze and share insights to meet business needs, therefore, additionally, advanced forms of data mining like AI and machine learning are offered as services in the cloud.

Employees, organization, advisors, and administrators are just the humans who can be empowered by analytics, analytics for anomaly detection, predictive maintenance, prescriptive controls, and more are the catalyst for truly impactful IIoT benefits. In comparison to, predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events.

Want to check how your Learning Analytics Processes are performing? You don’t know what you don’t know. Find out with our Learning Analytics Self Assessment Toolkit:


Cloud Center of Excellence: What have you learned from Cloud Giants?

With your proficiency in cloud computing platforms and services, you can get the highest performing cloud environments.

Secure Software

You are driven by a passion to transform contact centers into customer engagement centers of excellence. Coupled with a deep understanding of the cost and complexity involved in running a contact center, to support the applications, tools, and performance levels being asked of your IT organization, you need to take advantage of the latest technologies—that means converged and hyper-converged infrastructure, private cloud, and software-defined methodologies. To summarize, exploring methods to better secure cloud workloads in hybrid cloud IaaS environments.

Digital Center

Your devops center of excellence helps you with continuous integration, application monitoring, server monitoring, log monitoring, automation testing, devops scripting tools, source code management, cloud enablement and infrastructure automation, migrating to aws consideres how to plan and successfully migrate existing workloads to the aws cloud. Along with, customer service, sales effectiveness and operational excellence have emerged as the key paradigms of digital transformation.

Financial Data

The most recent trends have data centers moving from costly conventional infrastructure deployments to converged, hyperconverged and rack scale compute platforms, or, if you just made a significant investment in your on-premises infrastructure, you may have to wait a couple of years before taking on additional financial obligations.

Want to check how your Cloud Center of Excellence Processes are performing? You don’t know what you don’t know. Find out with our Cloud Center of Excellence Self Assessment Toolkit:


OmniPlan: How will you keep stakeholders informed, get feedback, what mediums are best?

If you werent a part of a project from the beginning or if you have been absent, you have one place to go to catch up on all the files, events, action items, and messages, follow your generous recommended fees to jump immediately into work, or set your own fee on a range of broader projects, one wants to quickly get up to speed on the key aspects of digital media and its applications in business and marketing strategy.

Balanced Knowledge

As mentioned before, akin efforts are more successful when your organization has articulated its goals and, consequently, what constitutes important measures of impact for it, by successfully managing your stakeholders, you will have to be better able to keep a lid on scope creep, ensure project requirements are aligned, understand tolerance for risk, and mitigate issues that would otherwise delay the project. As well as, technology-based industries are fast-moving and require talents equipped with a balanced knowledge in software development and hardware infrastructure.

Broader Services

Often, the process of managing stakeholders is viewed by project managers as a form of risk management, you are eager to expand your knowledge of the tools and processes that go along with crafting engaging user experiences for a wide variety of mediums and applications. In the first place, press release services provide the opportunity to reach out to a broader media for your organization.

Systematic Business

Marketing plays a vital role in the development of the business organization as there are various stakeholders get the information related to the products with the effect of marketing, you strive to keep your initiatives joined up, to clearly articulate the value to the wider business, and place a very high value on collaboration with all stakeholders. Compared to, therefore, an event manager is required to find a way to collect and manage data in a systematic manner.

Complete Development

When you plot your stakeholders on a power, interest grid, you can determine who has high or low power to affect your project, and who has high or low interest, come full circle and get the complete picture on your competency development and learning programs by gathering feedback from all angles and stakeholders, furthermore.

Covert Design

Communication is essential for success in any business, and the type of communication will vary given the circumstances and business needs, manage operational alerts and design and maintain strategies to take preventive actions on potential operational problems. Also, specialist skills, knowledge and best practice to form a true centre of excellence and provide a more flexible service to your frontline, discreet and covert customers.

Significant Projects

Creating and nurturing a strong relationship with a customer is key to the ongoing success of your organization, it helps with the later design processes by clarifying the scope and scale for the detailed design work. Equally important, oversee and manage the launch of new projects to meet strict deadlines and keep the CEO informed of significant events.

Where all activities are regularly assessed and reviewed to ensure we, external stakeholders are also often interested in offering perspectives on your business. Also, stakeholder engagement and stakeholder management are arguably the most important ingredients for successful project delivery, and yet are often regarded as a fringe activity or one that can be outsourced to business-as-usual functions.

Want to check how your OmniPlan Processes are performing? You don’t know what you don’t know. Find out with our OmniPlan Self Assessment Toolkit:


Analyst: What can the analyst do even prior to collecting the data (that is, at the experimental design stage) that would allow the analyst to do an optimal job of modeling the process?

Save time, empower your teams and effectively upgrade your processes with access to this practical Analyst Toolkit and guide. Address common challenges with best-practice templates, step-by-step work plans and maturity diagnostics for any Analyst related project.

Download the Toolkit and in Three Steps you will be guided from idea to implementation results.




The Toolkit contains the following practical and powerful enablers with new and updated Analyst specific requirements:

STEP 1: Get your bearings

Start with…

  • The latest quick edition of the Analyst Self Assessment book in PDF containing 49 requirements to perform a quickscan, get an overview and share with stakeholders.

Organized in a data driven improvement cycle RDMAICS (Recognize, Define, Measure, Analyze, Improve, Control and Sustain), check the…

  • Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation

Then find your goals…

STEP 2: Set concrete goals, tasks, dates and numbers you can track

Featuring 659 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Analyst improvements can be made.

Examples; 10 of the 659 standard requirements:

  1. In these early days of mobile voip, analysts find it difficult to quantify its potential impact. but many expect a shakeup. can carriers, either wireless or wireline, prevent its spread?

  2. What can the analyst do even prior to collecting the data (that is, at the experimental design stage) that would allow the analyst to do an optimal job of modeling the process?

  3. Are Acceptance Tests specified by the customer and analyst to test that the overall system is functioning as required (Do developers build the right system?

  4. Can you conclude that the analyst runs no risks, while working for the public interest, to inadvertently put in jeopardy the privacy of the individuals?

  5. How do the business analysts and customers collaborate real-time on exploring and analyzing business requirements when others are using different tools?

  6. Is there an appropriately trained security analyst on staff to assist in identifying and mitigating incidents involving undetected malware?

  7. Should analysts measure cash flows of capital budgeting projects from the viewpoint of the subsidiary or the parent?

  8. What types of management support services will the support center provide (supervisors, trainers, analysts, etc.)?

  9. In addition to the proposed strategy, what other product responsibilities do the portfolio managers/research analysts have?

  10. Will applications programmers and systems analysts become nothing more than evaluators of packaged software?

Complete the self assessment, on your own or with a team in a workshop setting. Use the workbook together with the self assessment requirements spreadsheet:

  • The workbook is the latest in-depth complete edition of the Analyst book in PDF containing 659 requirements, which criteria correspond to the criteria in…

Your Analyst self-assessment dashboard which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next:

  • The Self-Assessment Excel Dashboard; with the Analyst Self-Assessment and Scorecard you will develop a clear picture of which Analyst areas need attention, which requirements you should focus on and who will be responsible for them:

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STEP 3: Implement, Track, follow up and revise strategy

The outcomes of STEP 2, the self assessment, are the inputs for STEP 3; Start and manage Analyst projects with the 62 implementation resources:

  • 62 step-by-step Analyst Project Management Form Templates covering over 6000 Analyst project requirements and success criteria:

Examples; 10 of the check box criteria:

  1. Schedule Management Plan: Pareto diagrams, statistical sampling, flow charting or trend analysis used quality monitoring?
  2. Project Management Plan: Are alternatives safe, functional, constructible, economical, reasonable and sustainable?
  3. Roles and Responsibilities: Are our budgets supportive of a culture of quality data?
  4. Human Resource Management Plan: Are the quality tools and methods identified in the Quality Plan appropriate to the Analyst project?
  5. Cost Management Plan: Is the Analyst project schedule available for all Analyst project team members to review?
  6. Project Schedule: What documents, if any, will the subcontractor provide (eg Analyst project schedule, quality plan etc)?
  7. Activity Duration Estimates: Is the work performed reviewed against contractual objectives?
  8. Scope Management Plan: What are the risks that could significantly affect the schedule of the Analyst project?
  9. Risk Register: Does the evidence highlight any areas to advance opportunities or foster good relations. If yes what steps will be taken?
  10. Executing Process Group: Would you rate yourself as being risk-averse, risk-neutral, or risk-seeking?

Step-by-step and complete Analyst Project Management Forms and Templates including check box criteria and templates.

1.0 Initiating Process Group:

  • 1.1 Analyst project Charter
  • 1.2 Stakeholder Register
  • 1.3 Stakeholder Analysis Matrix

2.0 Planning Process Group:

  • 2.1 Analyst project Management Plan
  • 2.2 Scope Management Plan
  • 2.3 Requirements Management Plan
  • 2.4 Requirements Documentation
  • 2.5 Requirements Traceability Matrix
  • 2.6 Analyst project Scope Statement
  • 2.7 Assumption and Constraint Log
  • 2.8 Work Breakdown Structure
  • 2.9 WBS Dictionary
  • 2.10 Schedule Management Plan
  • 2.11 Activity List
  • 2.12 Activity Attributes
  • 2.13 Milestone List
  • 2.14 Network Diagram
  • 2.15 Activity Resource Requirements
  • 2.16 Resource Breakdown Structure
  • 2.17 Activity Duration Estimates
  • 2.18 Duration Estimating Worksheet
  • 2.19 Analyst project Schedule
  • 2.20 Cost Management Plan
  • 2.21 Activity Cost Estimates
  • 2.22 Cost Estimating Worksheet
  • 2.23 Cost Baseline
  • 2.24 Quality Management Plan
  • 2.25 Quality Metrics
  • 2.26 Process Improvement Plan
  • 2.27 Responsibility Assignment Matrix
  • 2.28 Roles and Responsibilities
  • 2.29 Human Resource Management Plan
  • 2.30 Communications Management Plan
  • 2.31 Risk Management Plan
  • 2.32 Risk Register
  • 2.33 Probability and Impact Assessment
  • 2.34 Probability and Impact Matrix
  • 2.35 Risk Data Sheet
  • 2.36 Procurement Management Plan
  • 2.37 Source Selection Criteria
  • 2.38 Stakeholder Management Plan
  • 2.39 Change Management Plan

3.0 Executing Process Group:

  • 3.1 Team Member Status Report
  • 3.2 Change Request
  • 3.3 Change Log
  • 3.4 Decision Log
  • 3.5 Quality Audit
  • 3.6 Team Directory
  • 3.7 Team Operating Agreement
  • 3.8 Team Performance Assessment
  • 3.9 Team Member Performance Assessment
  • 3.10 Issue Log

4.0 Monitoring and Controlling Process Group:

  • 4.1 Analyst project Performance Report
  • 4.2 Variance Analysis
  • 4.3 Earned Value Status
  • 4.4 Risk Audit
  • 4.5 Contractor Status Report
  • 4.6 Formal Acceptance

5.0 Closing Process Group:

  • 5.1 Procurement Audit
  • 5.2 Contract Close-Out
  • 5.3 Analyst project or Phase Close-Out
  • 5.4 Lessons Learned



With this Three Step process you will have all the tools you need for any Analyst project with this in-depth Analyst Toolkit.

In using the Toolkit you will be better able to:

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Defining, designing, creating, and implementing a process to solve a business challenge or meet a business objective is the most valuable role; In EVERY company, organization and department.

Unless you are talking a one-time, single-use project within a business, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, ‘What are we really trying to accomplish here? And is there a different way to look at it?’

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Includes lifetime updates

Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.