What is data?

Data refers to raw and unprocessed information that can be processed and analyzed to extract useful insights and knowledge. Data can take many forms, including text, numbers, images, audio, and video, and can be collected and stored in various ways, including databases, spreadsheets, and cloud storage systems.

Data is used in a wide range of applications, including business intelligence, scientific research, government and policy analysis, and more. The processing and analysis of data often involves statistical and computational methods and is crucial for making informed decisions and improving outcomes in various domains.

In the context of technology, data has become an increasingly valuable asset, as it provides organizations with a wealth of information about their customers, operations, and markets. The ability to collect, store, process, and analyze large amounts of data has led to the development of new technologies, such as artificial intelligence and machine learning, which are transforming many industries and impacting society in many ways.

How does online data compare to offline data?

Online data refers to data that is collected and stored through the internet and other digital networks, while offline data refers to data that is collected and stored through traditional, non-digital means, such as paper records, face-to-face interactions, and telephone surveys.

Here are some of the main differences between online and offline data:

  1. Volume: Online data is often generated in much larger volumes than offline data, as it is generated by a wide range of devices and systems connected to the internet, including smartphones, laptops, sensors, and other Internet of Things (IoT) devices.
  2. Speed of Collection: Online data can be collected and stored in real-time, while offline data collection is often slower, requiring manual entry and processing.
  3. Accuracy: The accuracy of online data can be affected by factors such as user behavior, technical errors, and data privacy issues, while offline data can be more accurate as it is often collected through more controlled and structured processes.
  4. Cost: The cost of collecting and storing online data is often lower compared to offline data, as digital technologies have made it easier to collect, store, and analyze large amounts of data at scale.
  5. Versatility: Online data is often more versatile and can be analyzed and processed in many different ways, while offline data is often limited to the specific forms in which it was collected.

Both online and offline data have their own strengths and weaknesses, and the choice between the two will depend on a number of factors, including the type of data you need to collect, your budget, and the accuracy and speed of data collection you require. Both types of data can be useful in making informed decisions and driving business outcomes, and often a combination of both online and offline data is used to gain a comprehensive understanding of a given situation.

What is the difference between deterministic data and probabilistic data?

Deterministic data and probabilistic data are two different types of data used for consumer targeting and measurement in the digital advertising industry.

Deterministic data refers to data that is tied directly to a specific individual or device, such as an email address or a device ID. This type of data is considered highly accurate because it provides a direct match between a user and their behavior, making it ideal for personalized targeting and measurement.

Probabilistic data, on the other hand, refers to data that is based on statistical modeling and prediction, rather than a direct match. This type of data is used to target and measure anonymous users who can’t be identified through deterministic means, such as IP addresses or device types. While probabilistic data is less accurate than deterministic data, it can still provide valuable insights and is often used in combination with deterministic data to provide a more comprehensive view of consumer behavior.

One Media Connect primarily focuses its data relationships and partnership around deterministic data sources. With a United States focus, One Media Connect’s integrated systems has B2B scale with >328M e-mails (44M at least 1 personal e-mail to 1 work e-mail), >95M mobile Ad ID, >140M phone numbers and >210M physical address.

Deterministic data is highly accurate, while probabilistic data is less accurate but still provides valuable insights-with One Media Connect primarily leveraging deterministic data sources. Both types of data are used in the digital advertising industry to inform targeting and measurement strategies.

How much has data grown over the past 10 years?

Data has grown significantly over the past 10 years, driven by the rapid expansion of technology and the growing importance of data-driven decision making across many industries. The amount of data generated by individuals, organizations, and devices has grown at an unprecedented pace, leading to the creation of massive data stores and the development of new technologies to process and analyze that data.

According to industry estimates, the amount of data generated globally has increased from a few exabytes (1 exabyte = 1 billion gigabytes) in the early 2010s to several zettabytes (1 zettabyte = 1 million exabytes) in the early 2020s. This growth has been fueled by the widespread adoption of mobile devices, the Internet of Things (IoT), cloud computing, and other technologies that have led to a proliferation of data-generating devices and systems.

The growth of data has also led to the development of new technologies and platforms, such as big data analytics, machine learning, and artificial intelligence, which are increasingly being used to process and analyze large and complex data sets. These technologies are driving innovation and growth across many industries, from retail and finance to healthcare and transportation, and are expected to continue to play a major role in the years to come.

When should you apply data in your media plan?

Data should be applied throughout the entire media planning process, from setting objectives and defining target audiences to selecting channels and measuring results. Here are some of the key stages where data can be particularly useful:

  1. Objectives: Data can be used to identify consumer needs, preferences, and behaviors, which can help set clear and measurable media planning objectives.
  2. Target Audience: Data can help define and segment target audiences, allowing you to create more targeted and effective media plans.
  3. Channel Selection: Data can be used to assess the reach and effectiveness of different media channels, helping you make informed decisions about which channels to include in your media plan.
  4. Media Planning: Data can be used to optimize media plans by allocating budget and scheduling ad placements to reach specific target audiences at specific times.
  5. Measurement and Optimization: Data can be used to measure the effectiveness of media plans, including the reach and engagement of target audiences, and to optimize plans based on insights from that data.

By applying data to your media plan, you can ensure that you are making informed decisions and maximizing the effectiveness of your media spend. Data can also help you continuously improve your media plans over time, as you can use insights from previous campaigns to inform future strategies.

Want to learn more about how to effectively leverage data in your targeting, marketing and advertising? E-mail