Data Analytics

People analytics is the practice of collecting and analyzing data on the people who make up a company’s workforce in order to gain insights to improve how the company operates.

The six steps of the data analysis process are: ask, prepare, process, analyze, share, and act. These six steps apply to any data analysis.

Collecting and using data ethically is one of the responsibilities of a data analyst. Then the data was cleaned up to make sure it was complete, correct, and relevant, and uploaded to an internal data warehouse for an additional layer of security.

The analysts asked questions to define both the issue to be solved and what would equal a successful result. Next, they prepared by building a timeline and collecting data with employee surveys that were designed to be inclusive. They processed the data by cleaning it to make sure it was complete, correct, relevant, and free of errors and outliers. They analyzed the clean employee survey data. Then the analysts shared their findings and recommendations with team leaders. Afterward, leadership acted on the results and focused on improving key areas.

Decision Intelligence is a combination of applied data science and the social and managerial sciences. It is all about harnessing the power and beauty of data.

A data analyst is an explorer, a detective, and an artist all rolled into one. Analytics is a quest for inspiration.

Now, data science, the discipline of making data useful, is an umbrella term that encompasses three disciplines: machine learning, statistics, and analytics.

Performance is the excellence of machine learning and AI engineers.

These are separated by how many decisions we know we want to make before we begin with them. If we want to make a few important decisions under uncertainty, that is statistics. If we want to automate, in other words, make many, many, many decisions under uncertainty, that is machine learning and AI. But what if we don't know how many decisions we want to make before we begin? What if what we're looking for is inspiration? We want to encounter our unknowns. We want to understand. Now, this is analytics.

Data ecosystems are made up of various elements that interact with each other in order to produce, manage, store, organize, analyze, and share data. These elements include hardware and software tools and the people who use them.

Data science is defined as creating new ways of modeling and understanding the unknown by using raw data. Here's a good way to think about it: Data scientists create new questions using data, while analysts find answers to existing questions by creating insights from data sources.

Data analysis is the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making. Data analytics, in its simplest terms, is the science of data. It's a very broad concept that encompasses everything from the job of managing and using data to describing the tools and methods that data workers use each and every day.

Data-driven decision-making is defined as using facts to guide business strategy.

1. Ask questions and define the problem.

2. Prepare data by collecting and storing the information.

3. Process the data by cleaning and checking the information.

4. Analyze the data to find patterns, relationships, and trends.

5. Share data with your audience.

6. Act on the data and use the analysis results.

Data + business knowledge = mystery solved

It is time to enter the data analysis life cycle—the process of going from data to decision. Data goes through several phases as it gets created, consumed, tested, processed, and reused.

1. Ask: Business Challenge/Objective/Question

2. Prepare: Data generation, collection, storage, and data management

3. Process: Data cleaning and data integrity

4. Analyze: Data exploration, visualization, and analysis

5. Share: Communicating and interpreting results

6. Act: Putting your insights to work to solve the problem

EMC Corporation's data analytics life cycle is cyclical, with six steps:

1. Discovery

2. Pre-processing data

3. Model planning

4. Model building

5. Communicate results

6. Operationalize

SAS' iterative life cycle

An iterative life cycle was created by a company called SAS, a leading data analytics solutions provider. It can be used to produce repeatable, reliable, and predictive results.

1. Ask

2. Prepare

3. Explore

4. Model

5. Implement

6. Act

7. Evaluate

Project-based data analytics life cycle

A project-based data analytics life cycle has five simple steps:

1. Identifying the problem

2. Designing data requirements

3. Pre-processing data

4. Data analysis

5. Data visualizing

Big data analytics life cycle

Authors Thomas Erl, Wajid Khattak, and Paul Buhler proposed a big data analytics life cycle in their book, Big Data Fundamentals: Concepts, Drivers, and Techniques. Their life cycle suggests phases divided into nine steps:

1. Business case evaluation

2. Data identification

3. Data acquisition and filtering

4. Data extraction

5. Data validation and cleaning

6. Data aggregation and representation

7. Data analysis

8. Data visualization

9. Utilization of analysis results

Data life cycle based on research

One final data life cycle informed by Harvard University research has eight phases:

1. Generation

2. Collection

3. Processing

4. Storage

5. Management

6. Analysis

7. Visualization

8. Interpretation