Management Approaches Focused on Advanced Analytics

Facts and Figures

Methodologies: PM (Project Management)

  • Project Management Institute report: the organizations with a well-planned Enterprise Project Management Office (EPMO) report are successful in meeting around 48% higher than their initial objectives & goals.
  • Recent study on several project management cases: with the help of highly efficient PMO (Project Management Office), 42% of the projects attained their targets.
  • Massachusetts Institute of Technology (MIT): data literacy is the ability to read, work with, analyze and argue with data, the ability to consume data.
  • The Data Literacy Index found out that more data literate companies have a higher business value of between 3-5%.
  • This represents US$320-534 million of the total market value of each business.
  • Many companies can integrate data literacy into their existing skills initiatives, while others may choose to buy learning software and even data books to boost workforce growth.
  • When Gartner invited vendors to apply their Augmented Analytics tools against a sample data set at a BI (Business Intelligence) Bake Off in 2016, only one –Salesforce Einstein– allowed business users to accurately identify the root driver.
    • In this case, the tools were presented with a data set relating to college students to see what factors lead to higher long-term earnings.
    • Most of the tools simply reinforced the inaccurate bias that earnings were correlated with Ivy League colleges, when the main driver was parents’ income.
  • Augmented Analytics greatly reduces the need for data literacy to extract insights, but does not obviate the need for learning how Analytics can be misleading.

Sources: The above text is a creative synthesis elaborated from the following sources: Amarendra Babu L (PM Times); Jordan Morrow (IT Brief); George Lawton (Search Business Analytics)

These sources have been selected from a total of 11 articles on the subject matter. Which in turn are the result of sifting through 56 articles.


Methodologies: PM

  • Project Management has been growing at a fast pace and has found its way into every aspect of the business domain.
  • AI is a buzzword in almost all industries. It also has more potential to bring advanced product innovation to Project Management.
  • Companies are widely adopting hybrid methodologies derived from different aspects of Agile & Waterfall techniques.
  • Emotional intelligence skills include:
    • Emotional awareness
    • Motivation
    • Regulation
    • Ability to exploit emotions
    • Ability to apply emotions to tasks.
  • The PMO (Project Management Office) * is a prime factor for the projects to ensure their pre-defined objectives & goals.
  • IoT devices can play a vital role in Project Management such as data collection and team collaboration.
  • Organizations are looking for standard cybersecurity solutions, such as cloud signature technologies and GDPR, to ensure that their project data practices are safe and secure.


* The PMO (Project Management Office) is the leading management group, which as a respected standard setter, establishes the right approaches, guidelines, methodologies and practices to manage programs and projects in whatever sector or line of activity.


How Analysts Can Benefit from Project Management Techniques

  • As an analyst, you really need to be, in a sense, bilingual:
    • You need to have a competency in Analytics
    • And a firm understanding of your organization’s domain,

to be able to translate Analytics into action.

  • While data analysts are incredibly smart and talented individuals, they sometimes have difficulty communicating their takeaways and insights in a way that affects change within their organization.
  • They have to link the various phases of the Analytics project to the general phases of Project Management (PM), in order to bridge the gap in communication.

 The Importance of Project Management in Analytics Engagements

  • Analytics projects have often run significantly over budget, both in terms of cost and duration.
  • Good analysis is done but it’s then not used by the business in its day-to-day operations. These do not provide a good Return on Investment. The key seems to be a good Project Management…
  • One of the most important indicators for any Data Analytics project is Return on Investment.
  • The typical owners of Data Analytics projects are the good data consultants-analysts and the business stakeholders.
  • Good Data Analysts need space to use their natural curiosity to explore data and produce best results using detailed/analytical techniques.
  • For a business manager, what are required are usable results –on time and on budget–. These latter differences can lead to big challenges.
  • The major elements of PM are:
    • Initiation
    • Planning
    • Supporting Execution
    • Monitoring & Controlling
    • And Closing
  • All of which are supported by constant communication.
  • These elements are critical at all phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM) in Analytics Projects.
  • A good Return on Investment can be provided through good Project Management (PM).

An Overview of Common Data Analysis Techniques in Project Management

  • The importance of Data Analysis for a project manager can never be underestimated, as collecting and analyzing business data serves many purposes.
  • The actual data analysis is either exploratory or confirmatory:
    • Most quality management methods such as Six Sigma are statistic intensive:
      • They apply a variety of statistical applications to analyze the production or operations data and confirm the extent of deviation from the standard mean.
    • Time analysis applies in a project to set up a call center:
      • To identify the seasonal and peak hours of demand for telephone operators…
      • And, thereby, make decisions on the number of terminals
    • The preliminary approach to analyze data in Project Management is:
      • Data mining or collecting data from various sources…
      • And converting it into a presentable format for modeling and making predictions.
    • The basic method of Data Analysis in projects is various types of statistical analysis.
    • A time series is a set of ordered observations on the quantitative characteristics of a phenomenon:
      • Undertaken at equally spaced time points
      • To forecast future values of the series…
      • And identify trends, seasonal variations and periodic oscillations.
  • Data can be used to drive more profitable operations and decisions.
  • Data-informed decision-making empowers individuals across the organization
  • In today’s industry, where the data revolution and automation have become pervasive:
    • Business leaders have to consider what skills their workforce will need in the coming years:
      • Data literacy is the most important of the

            Data Literacy and Augmented Analytics

  • Democratizing Data Analytics gives everyone access to tools & information
    • But Data Literacy is still required to analyze data and deliver successful outcomes
  • AA greatly reduces the need for Data Literacy in order to extract insights…
    • But does not obviate the need for learning how Analytics can be misleading.
  • AA could use visualizations and embedded AI models…
    • To remove the barrier between complicated data and business insights.
  • AA tools help reduce the technical know-how for generating basic charts.
  • AA reduces the effort by data scientists in setting up and exploring different data models that might be used by business domain experts.
  • Organizations could explore a hybrid approach in which business analysts and data analysts tackle different tasks:
    • With analysts and BI engineers building data models
    • And data scientists vetting them.

sources: The above text is a creative synthesis elaborated from the following sources: Tim Stobierski (Northeastern University); Mary Kearney (PRESIDION); N. Nayab (Bright hub pm)

These sources have been selected from a total of 12 articles on the subject matter. Which in turn are the result of sifting through 49 articles.


Methodologies: PM

  • The adoption of AI into your future Project Management will change the overall Project Management workflow and increase the productivity of the project outputs.
  • The current competitive world is demanding a significantly higher number of hybrid project managers capable of handling several multiple methodologies.
  • As project managers and team members are looking for flexibility within the workplace, firms are adopting this trending technology for effective communication over a wide range of platforms.
  • Comprehensive Data Analytics –like task reports and predictive analytics– will help PMs identify risks.
  • How Analysts Can Benefit from Project Management Techniques
  • By thinking about a business question in the terms of a business case, you will be able to describe to colleagues and key stakeholders why a particular analytics project matters.
  • The realization analysis explains what benefits the organization will achieve if a project were to be initiated and completed.
  • By thinking about realization in terms of scope, it will be easier to tie your interpretation to next steps and offer guidance to the organization moving forward.
  • The Importance of Project Management in Analytics Engagements
  • If you integrate PM into the solution, you can achieve an effective communication between business stakeholders and data analysts.
  • Overview of Common Data Analysis Techniques in Project Management
  • Probabilistic data analysis techniques will provide a range of possible outcomes for each set of data, and facilitate decision making when encountering data that throws up uncertainties.
  • Augmented Analytics promises to bring BI to a much larger audience of business users.
  • AA will reduce the burden on subject-matter experts to understand the data model used to display trend lines on a graph.
  • With AA, business and data science will get closer and business-oriented data scientists will become increasingly important.


Methodologies: PM

  • To ensure the success of a business, it is essential that the Project Management team should be aware of and embrace the latest trends and strategies:
    • AI and Machine Learning.
    • Hybrid PM methods.
    • Kanban Boards (workflow and visual based methodology, invented and implemented by Toyota to assign workloads using a card system):
      • Kanban methodology enables PMs to communicate with other team or department members…
        • To prioritize workflow efficiently and avoid multitasking.
      • Emotional Intelligence.
      • Project Management Office.
      • Data Analytics.
      • Growth of Remote Teams.
      • Internet of Things (IoT).
  • How Analysts Can Benefit from Project Management Techniques
  • The six phases of the Analytics lifecycle correspond with PM as follows:
    • Business Question = Business Case.
    • Analysis = Preliminary Profit Realization Analysis.
    • Interpretation = Recommendations for a Project:
      • As you interpret the data, you should do so in a way that clearly reflects back upon the initial goals and objectives that your stakeholders care about…
      • Then data are translated into actionable steps for the organization.
    • Realization = Recommendations for a Project
    • Implementation = Monitoring and Controlling:
      • Analysts use Key Performance Indicators (KPIs) and various dashboards to monitor the success and progress of the project.
      • This relates with the monitoring and controlling phase of PM, based on the same measurement methods to ensure projects are on track.
    • Completion = Project Close Out:
      • In PM, the completion of a project is typically tied back to the business question.
  • The Importance of Project Management in Analytics Engagements
  • PM can help to:
    • Ensure that:
      • The business stakeholders do not change the principal aim of the Analytics outcome…
      • And that analysts remain attentive to the tasks at hand to retain the focus of the project.
    • Allow management of risks using the Action & Risk Log to articulate issues.
    • Ensure engagement by all stakeholders so that…
      • The project is successful from the business perspective…
      • And that it transitions into the Business as Usual life of the company.
    • Ensure that the project ends in the solution resolution
      • Thereby ensuring good Return on Investment for the business.
      • Data Literacy

        • To become a data literate enterprise, what is needed is top-down vision, support and
        • Management has to:
          • Discover the importance of data usage in decision-making.
          • Advise on technical and human resources to encourage this.
          • And coordinate access to and use of data across the organization.
          • Emphasize that each individual plays an important role in the exchange of knowledge and techniques with their peers.

                    Data Literacy and Augmented Analytic

        • To make solid logical inferences and ensure their implementation in the real world is in line with the values of the people whose abilities they were designed to augment.
        • Basic literacy involves simply learning how to use the technical features of tools to draw pretty graphs.
        • It is critical to produce transparent explanations that business users can easily understand, verify and act on.

sources: The above text is a creative synthesis elaborated from the following sources: Amarendra Babu L, (; Jordan Morrow, (; George Lawton, (; Tim Stobierski, (; Mary Kearney (; N. Nayab (

These sources have been selected from a total of 17 articles on the subject matter. Which in turn are the result of sifting through 85 articles.