Highlights- The First 90 Days: Chapter 1, Prepare Yourself

Michael Watkins

It’s a mistake to believe that you will be successful in your new job by continuing to do what you did in your previous job, only more so.

Preparing yourself means letting go of the past and embracing the imperatives of the new situation to give yourself a running start.

Getting Promoted

You must figure out what it takes to be excellent in the new role, how to exceed the expectations of those who promoted you, and how to position yourself for still greater things.

Balance Between Breadth and Depth

You also need to learn to strike the right between keeping the wide view and drilling down into the details.

Rethink What You Delegate

… the keys to effective delegation remain much the same; you build a team of competent people whom you trust, you establish goals and metrics and monitor their progress, you translate higher-level goals into specific responsibilities for your direct reports, and you reinforce them through the process.

When you get promoted, however, what you delegate usually needs to change… it may make sense to delegate specific tasks… your focus may shift from tasks to projects and processes… entire businesses.

Influence Differently

… the decision-making game becomes much more bruising and politically charged the higher up you go. It’s critical, then, for you to become more effective at building and sustaining alliances.

Communicate More Formally

Establish new communication channels to stay connected with what is happening where the action is… all without undermining the integrity of the chain of command.

Your direct reports play a greater role in communicating your vision and ensuring the spread of critical information.

Exhibit the Right Presence

What does a leader look like at your new level in the hierarchy? How does he act? What kind of personal leadership brand do you want to have in the new role? How will you make it your own?

Four Pillars of Effective Onboarding

Business Orientation

Getting oriented to the business means learning about the company as a whole and not only your specific parts of the business. It’s beneficial to learn about the brands and products you will be supporting, whether or not you’re directly involved in sales and marketing.

Stakeholder Connection

It’s also essential to develop the right relationship wiring as soon as possible. This means identifying key stakeholders and building productive working relationships. Remember: you don’t want to be meeting your neighbors for the first time in the middle of the night when your house is burning down.

Expectations Alignment

Check and recheck expectations.

Cultural Adaptation

Think of yourself as an anthropologist sent to study a newly discovered civilization.

Identifying Cultural Norms

Influence

How do people get support? Is it more important to have support of a patron within the senior team or affirmation from peers and direct reports?

Meetings

Are meetings filled with dialogue on hard issues or are they simply forums for publicly ratifying agreements that have been reached in private?

Execution

Which matters more- a deep understanding of processes or knowing the right people?

Conflict

Can people talk openly about difficult issues without fear or retribution?

Recognition

Does the company promote stars or does it encourage team players?

Ends Versus Means

Are there any restrictions on how you achieve results? Does the organization have a well-defined, well-communicated set of values that is reinforced through positive and negative incentives?

Preparing Yourself

Take time to celebrate your move, even informally, with family and friends. Touch base with your informal advisers and counselors and to ask for advice.

Assess Your Vulnerabilities

One way to pinpoint your vulnerabilities is to assess the kinds of problems toward which you naturally gravitate.

Watch Out for Your Strengths

“To a person with a hammer, everything looks like a nail.”

Relearn How to Learn

New challenges and associated fears of incompetence can set up a vicious cycle of denial and defensiveness. Put bluntly, you can decide to learn and adapt, or you can become brittle and fail.

Relearning how to learn can be stressful… if you embrace the need to learn, you can surmount them.

Get Some Help

Engage with HR and your new boss about creating a 90-day transition plan. Ask for help in identifying and connecting with key stakeholders or finding a cultural interpreter.

Closing the Loop

You have to work constantly to ensure that you’re engaging with the real challenges of your new position and not retreating to your comfort zone.

Highlights- The First 90 Days: Introduction

Michael Watkins

The actions you take during your first few months in a new role will largely determine whether you succeed or fail.

“Success or failure during the first few months is a strong predictor of overall success or failure in the job.”

If you’re successful in building credibility and securing early wins, the momentum likely will propel you through the rest of your tenure.

The most dangerous transition can be the one you don’t recognize is happening.

Leaders also are impacted by the transitions of many others around them.

Your goal in every transition is to get as rapidly as possible to the break-even point. This is the point at which you have contributed as much value to your new organization as you have consumed from it.

The goal is the same: to get there as quickly as possible.

Avoiding Transition Traps

  • Sticking with what you know
  • Falling prey to the “action imperative”
  • Setting unrealistic expectations
  • Attempting to do too much
  • Coming in with “the” answer
  • Engaging in the wrong type of learning
  • Neglecting horizontal relationships

Understanding the Fundamental Principles

  • Prepare yourself
  • Accelerate learning
  • Match your strategy to the situation
  • Secure early wins
  • Negotiate success
  • Achieve alignment
  • Build your team
  • Create coalitions
  • Keep your balance
  • Accelerate everyone

Mapping Out Your First 90 Days

Your transition begins the moment you learn you are being considered for a new job.

Use the 90-day period as a planning horizon.

Start planning what you hope to accomplish by specific milestones.

Begin by thinking about your first day in the new job. What do you want to do by the end of that day? Then move to the first week. Then focus on the end of the first month, the second month, and finally the three-month mark.

Hitting the Ground Running

Every new leader needs to quickly become familiar with the new organization, secure early wins, and build supportive coalitions.

Introduction to Data Analysis – Part III

This is a continuation of Chapter 1 summary of Python Data Analytics by Fabio Nelli. Click here for Part I  or here for Part II.

Quantitative and Qualitative Data Analysis

QUANTITATIVE: numerical & categorical, quantitative predictions, more objective conclusion. QUALITATIVE: textual visual audio, qualitative predictions, more subjective conclusion.
Quantitative and Qualitative Data Analysis

Open Data

Introduction to Data Analysis – Part II

This is a continuation of Chapter 1 summary of Python Data Analytics by Fabio Nelli. Click here for Part I.

The Data Analysis Process

Data analysis is nothing more than a sequence of steps:

  1. Problem definition
  2. Data extraction
  3. Data preparation: Cleaning
  4. Data preparation: Transformation
  5. Data exploration and visualization
  6. Predictive modeling
  7. Model validation/test
  8. Deployment: visualization and interpretation of results
  9. Deployment: deployment of solutions
problem, extraction, preparation, exploration & visualization, predictive modeling, model validation , deployment, solution
The Data Analysis Process

Problem Definition

“Data analysis always starts with a problem to be solved.” A study of the system is conducted and is designed to be able to make informed predictions or choices.

“Building a good team is certainly one of the key factors leading to success in data analysis.” Fabio recommends an effective cross-disciplinary team.

Data Extraction

As much as possible, sample data must reflect the real world. In addition to data selection, extracting and using the best data sources is another issue to keep in mind.

Data Preparation

Data preparation comprises of obtaining, cleaning, normalizing, transforming, and optimizing a data set. Although it may seem that data preparation is less problematic, it actually requires the more resources and more time to be completed. Potential problems includes data values that are ambiguous, missing, replicated, or out of range.

Data Exploration/Visualization

Exploring data involves “searching the data in graphical or statistical presentation to find patterns, connections, and relationships. Data visualization is the best tool to highlight possible patterns.”

Summarization is the process where data are reduced without sacrificing important information. Clustering is used to find groups united by a common attributes. Another step of analysis focuses on identification of relationships, trends, and anomalies in the data.Other methods of data mining automatically extract important facts or rules from the data.

Predictive Modeling

Predictive modeling is used to create or choose a statistical model that predicts the probability of a result. The purpose of these models is to make predictions about the data values and to classify new data products.

The models can be divided into three types:

  • Classification models: if the result is categorical
  • Regression models: if the result is numerical
  • Clustering models: if the result is descriptive

Some of the methods include linear regression, logistical regression, classification and regression trees, and k-nearest neighbors.

Some models explain the characteristics of the system under study in a clear and simple way while some models have limited ability to explain the characteristics of systems but still make good predictions.

Model Validation

Validation of the model is the test phase. Data is called the training set when used to build model. It is called validation set when used to validate the model.

Comparing data enables us to evaluate the error and estimate the limits of validity.

This process allows you to numerically evaluate the effectiveness of the model and compare it with other existing models.

Deployment

This is the final step of the analysis process which aims to translate the result into a benefit. Normally, it consists of “writing a report for management or for the customer who requested the analysis.”

In the report, the following topics are discussed:

  • Analysis results
  • Decision deployment
  • Risk analysis
  • Measuring the business impact

We’ll conclude this summary by discussing quantitative/qualitative data analysis and open data sources in part III.

Introduction to Data Analysis – Part I

The following is my attempt to summarize the first chapter of the book, Python Data Analytics by Fabio Nelli.

– E.C. De Dios

According to Merriam-Webster, data is “factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation.” I usually just think of it is as anything that can be recorded or measured.

In the book, Fabio makes the distinction that “data actually are not information” and that “information is actually the result of processing.” He then proclaims that data analysis is the “process of extracting information from raw data.”

Data Analysis

“Data analysis allows you to forecast possible responses of systems and their evolution in time.” Its aim is not the mathematical models themselves but the quality of the its predictive power.

The search for data, their extraction, and preparation are also part of the data analysis process because of their importance in the critical role and influence in the success of the results.

All stages of data analysis employ different techniques of data visualizations. It’s all about the charts!

Knowledge Domains of the Data Analyst

Fabio also points out that data analysis is a multi-disciplinary field and is “well suited to many professional activities. He adds, “a good data analyst must be able to move and act in many different disciplinary areas.”

Not only is it necessary to know other disciplines, it is also imperative that a data analyst know “how to search not only for data, but also for information on how to treat that data.”

Computer Science

Knowledge of information technology is necessary to know how to use the various tools like applications and programming languages which in turn are needed to perform data analysis and visualization.

Mathematics and Statistics

Data analysis requires a lot of complex math. Statistics form the concepts that form the basis of data analysis. Bayesian methods, regression, and clustering are just some of the most commonly used techniques in data analysis.

Machine Learning and Artificial Intelligence

Machine learning analyzes data in order to recognize patterns, cluster, or trends and then extracts useful information in an automated way.

Professional Fields of Application

Better understanding of where the data comes from greatly improves their interpretation. It is good practice to find consultants to whom you can pose the right questions about your data.

Types of Data

Data is divided into two distinct categories:

  • Categorical (nominal and ordinal)
  • Numerical (discrete and continuous)

Categorical data are observations that can be divided into groups or categories. Nominal variables has no intrinsic order while ordinal variables has a predetermined order.

Numerical data are measured observations. Discrete variables can be counted while continuous values assume any value within a defined range.

Next in part II, we will explore the process of data analysis in detail.