IN OUR HEADS
April 18, 2023
As more brands are looking for ways to expand their data-driven capabilities, we are dealing with more datasets than ever before. The surge of interest in available data has required fresh thinking around “Big Data” and its role in your MarSci ecosystem.
By definition, Big Data describes a large quantity of data that grows, even exponentially, over time. This data began being categorized by Volume, Velocity, and Veracity, but Big Data is now viewed through the five Vs — Volume, Velocity, Veracity, Value, and Variety.
Unfortunately, we are seeing many companies misinterpreting what Big Data really is, which is causing some major discrepancies and problems with staffing and resource management. In order for Big Data to help your brand move forward, it is important that you have a thorough understanding of what it is and how you can successfully leverage it in the most effective way.
Evolving Big Data in 2022
We witnessed major growth in the amount of information surrounding Big Data in marketing last year. With this information also came a new light on what challenges companies were facing when it came to marketing data management and delivering answers and insight.
There have been undeniable increases in the depth and breadth of datasets given the tracking methods that have evolved alongside artificial intelligence, machine learning, and first- and third-party data vendors in a post-cookie world.
A growing number of clients are also interested in leveraging data-based tools and applications not only to guide budgets but also to answer ad hoc business questions. It is critical that new thinking and approaches toward Big Data also come alongside the major growth in interest in order for it to be successful.
How to Leverage Big Data More Effectively
When it comes to creating a data-driven marketing strategy, there are a few things you can do to improve how effectively you are able to leverage Big Data.
The need for formalized data management has never been greater, as advanced technology makes more and more data available while tracking methods and privacy laws evolve. Although there are excellent off-the-shelf Business Intelligence and Database software tools for managing and using Big Data, they are not universally effective.
For this reason, it is important that you are able to plan, strategize, and invest in strategies that work. Data management and planning are both critically important, regardless of whether you are choosing Big Data vs. Little Data.
Include Your Marketing Data Teams in Your Marketing Plan
It is extremely common to overlook data when implementing a media plan. Marketers generally purchase media in order to achieve a specific goal or a combination of several goals in their data-driven marketing strategy. This can include:
- Raising awareness
- Increasing brand consideration
- Improving conversion rate
- Driving sales
Unfortunately, marketing science teams are often left with gaps in data when media plans are developed without proper attention to tracking and data availability. Sometimes, ads are bought with no tracking in place. Other times, tracking gets corrupted through erroneous trafficking.
Either way, both affect the Variety and possible Veracity of the data that will be used for performance reporting. Given the fact that there are generally multiple buys in any given media plan where these instances occur, the marketing science staff is forced to deal with an overwhelming amount of unstructured data that all needs to be processed within a specific deadline.
Had these buys been preempted with a data strategy, Marketing Sciences would have been able to develop a more strategic approach for handling the buys and reducing the expected Velocity and Variety that is often uncovered during the analysis.
Build Your Data Strategy
Next, it is important to implement a strategy that focuses on what types of data you can expect. This can help you with developing a strategic approach for handling the data.
Create a strategy that answers how data analytics is used in marketing. This includes completing a thorough assessment of each data source. You will want to evaluate the frequency of the data. You will also want to evaluate the structure of the data. Is the information JSON, TXT, EXCEL, or something else? What is the structure of the data? Is it unpivoted or crosstab? Is it string-based, or does it have pre-delimited dimensions?
Review the consistency of the data that is provided. A good deal of marketers and publishers promise consistency of the data, but most of the time, they encounter frequent inconsistencies with the data. If you expect inconsistent data, your marketing and data science resources can devise a process to handle it. The key is to have a process in place that can successfully handle any inconsistent data sources.
Next, evaluate how the data will be delivered. Never assume that an API feed is a water hose of all of the available data from any given resource. This common misconception can lead to costly and time-consuming mistakes.
Finally, strategize known data limitations. Examples include channels with limited tracking such as podcasts or streaming buys, or partners that don’t allow pixels on their sites. These situations can sometimes be resolved with partner data but often lacks Veracity (or Value). Therefore, you may want to consider a third-party survey or utilizing data scientists to model the impact of these channels.
Make the Investment
It is important to make an investment in the proper tools and data management. You should ask yourself if it is more beneficial to hire based on experience with any specific tool or purchase off-the-shelf software that meets your needs.
Evaluate what BI tool requirements are and how they differ from your brand. Resourcing may be heavier on data engineering in order to master a specific tool. You will need to learn what skillset is needed for the full data ecosystem. In this way, identifying Big Data vs. Little Data will help with recruiting and better align your marketing science staff with the correct R&Rs.
