Data Management Best Practices

Instructor: Temitayo Odugbesan

Temitayo has 11+ years Industrial Experience in Information Technology and has a master's degree in Computer Science.

In this lesson, we will learn what data management is all about, data life-cycle and the best practices to ensure during and after data design and implementation.

What Is Data Management?

Data management is the complete set of activities involved in every aspect of data handling. The primary goal of proper data management is to ensure that data sets can be understood by anyone handling it - whether it's a scientist, your colleague, or your drinking buddy who has nothing to do with your project.

Data sets must make sense today and in years to come. Good data management practices, ensure effective planning, creation, organization, management and preservation of data.

Say you own a popular leisure center in Texas and decide to hold a sporting competition at an arena for 50,000 people. This event is predicted to be a big hit. Admission to the venue as a participant is based on prior registration for any of the events.

With such a sizeable number of competitors expected, creating a small database to help manage the event registration process would be best. Better make sure to adhere to data management best practices! Let's take a look a them.

Six Best Practices for Data Management

Data management best practices are actions or activities observed during the initial design of data sets, gathering of data, and management of data through to its archival stage. Let's consider six of them:

1. Make A Plan

In planning data collection consider the following questions:

  • What types of data will you be collecting?
  • Where will you store the data, or what application will you use?
  • How will the data be analyzed, monitored, and stored?
  • What technology and infrastructure will you need?

In planning the Texas sporting competition, analyze and compile all the different data sets you will be collecting. This could include:

  • Competitors details
  • Sporting events
  • Judges
  • Coordinators
  • Time keepers
  • Technical support
  • Technical equipment
  • Ushers

Decide on a detailed format as to how data samples will be collected and analyzed. Include descriptive documentation of the methods used and relevant information.

Include details of the data types characterizing the data. For example, data types of images could be .jpeg, .png, or .tiff. Decide whether a relational database application is needed like MS Access or SQL.

Make a plan of how data would be shared among team members. You might include a short and long term data preservation plan (such as daily, weekly, or even monthly backups).

2. Data Collection

One way of regularizing data collection among multiple collectors is the use of templates to ensure uniformity in the data sets, description, and labels used. Each row must represent a complete record and each column a defined parameter.

Below is a sample spreadsheet template created to manage the data collection process for the Texas competition.

Texas Sporting Events Registration Sheet

Name (All caps) DOB (mm-dd-yyyy) SSN (9 digits) WT (kg) Height (cm) Sporting Event Competing Level (Junior or Senior)
JOHN BULL 01-12-79 999999999 78 175 8-Balls Pool Senior
ALLEN DOE 23-07-75 888888888 64 160 8-Balls Pool Senior

Key Description
DOB Date of Birth
SSN Social Security Number
WT Body Weight

In the above template, you will notice that in each column, the expected data sets are specified to ensure uniformity in the records. For example, the Name must be in all caps, date of birth using the standardized American date format etc.

Data should be kept in a similar group with proper descriptive file names. Any abbreviations used must be properly documented. Keep all raw data, however imperfect it may seem.

3. Data Integrity Checks

Conduct regular quality checks on data collections, data entries as well as analysis. This ensures consistency and accuracy. Identify and note down any potential problems so that it can be resolved.

4. Data Documentation

Comprehensive data documentation is to be employed. This is referred to as your 'metadata' in this project. Metadata is the description of the data-sets, their contexts, dates collected, and how they were collected. It's data that describes the data! It's the key to future usability.

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