Many marketing teams are exploring ideas to integrate big data tools into their MarTech stack to enhance operations.
Businesses today generate a large amount of unstructured data that can have valuable customer insights to enhance the customer experience. Leveraging big data analytics tools into the MarTech stack will enable businesses to get a competitive edge by improving overall operations and offering a holistic view of marketing operations. However, CMOs should understand that it can be a daunting task for them to integrate the big data tools in their MarTech stack because integrating them in the MarTech stack can be a complex, intricate, and lengthy process with a tremendous impact on the operations.
It can be daunting because it is more challenging than consolidating an organization’s structured operational data in the data repository. Integrating big data tools in the MarTech stack can be tricky because it needs the extraction of information from multiple sources, which can be structured, unstructured, or semi-structured. Making them compatible, accessing them whenever required, and analyzing them can be complex tasks.
Here are a few strategies that businesses can use to integrate big data tools in the MarTech stack to improve their efficiency:
Identify all Marketing Data Sources
The surge in channels that gather structured and unstructured information about users has made it even more challenging for modern marketers to gather, store and process data effectively. Marketers today have various paid channels, multiple marketing vendors, dense advertisement networks, and other organic channels to gather information. Multiple platforms in the market enable businesses to gather data from various sources and segment the customer base to understand the difference between potential users and customers. Integrating the right big data tools in the MarTech stack will enable businesses to gather data from all the available channels and consolidate it in one repository.
However, the CMOs and CDOs must understand all the available channels and select the right big data tools to gather data from these sources.
Marketing data is growing exponentially, and It has become difficult for businesses to adapt to the pace because big data is scaling much faster than the industry itself.
Integrate Big Data Analytics Tools to Make the Most Out Of It
Gather, Store, and Process Data
it is essential to integrate the correct big data and data analytics tools that help marketing teams gather, store, analyze, and process data efficiently in real-time. Gathering data for analytics will differ from organization to organization based on their presales goals. Businesses need to implement the right big data tools that help them to gather, analyze, process, and store structured and unstructured data from all the data channels. One of the crucial aspects that CDOs and CIOs need to consider is that raw or unstructured data is too diverse and intricate, which can have valuable customer insights that can revolutionize marketing campaigns.
Once businesses gather and save data, they need to organize it effectively to get more accurate results on all the analytical requests. Many CDOs overwhelmed with the data size can leverage the batch data processing approach to streamline the analytics process. Businesses can leverage the batch processing approach when they have a longer turnaround time between gathering and evaluating data. CDOS can also consider looking at Stream processing for gathering and analyzing small data batches simultaneously, reducing the time between collection and analysis. Stream big data processing is an effective way to make strategic data-driven decisions quickly. However, while considering the stream processing approach, it is crucial to understand that it is an intricate and expensive process.
Clean and Analyze Data
Just gathering and storing big data will not be effective for organizations; businesses must clean and analyze the data to make the most out of the data gathered. Irrespective of the volume of the data, it will always require scrubbing to enhance its quality and get accurate results. Black or dirty data will mislead the marketing campaigns and will not help accomplish the business goals. It is time-consuming to gather, clean, and transform big data into a usable format. Following are a few ways that businesses can leverage for big data analysis:
- Data mining is a data analytics approach that helps the DataOps teams to segment data through large datasets to determine patterns and correlations by spotting abnormalities and generating various data clusters.
- Businesses can leverage the predictive big data analytics approach to utilize their organization’s historical data to make accurate future forecasts to determine upcoming risks to mitigate and opportunities to seize.
- Deep learning is another big data analytics strategy that imitates human learning patterns by leveraging Artificial Intelligence (AI) and Machine Learning (ML) tools to identify complex and abstract data patterns.
Embrace Tools in the MarTech Stack to Ensure Seamless Big Data Integration
Today’s data can have multiple sources because of the multiple channels that generate data. Businesses must work on unstructured data to get valuable insights into customer needs. Irrespective of the channel or type of data, it is crucial for businesses to ensure it is uniform in nature. Marketing teams need to embrace the right tools in their tech stack to ensure seamless data integration to make the most out of the data gathered. CMOs should consider integrating the right big data tools in the MarTech stack that help them to gather unstructured data, structure it, store it, and analyze it to make strategic changes in their operations. Businesses today need more effective big data tools than extract, transform, and load (ETL) tools. CMOs with effective workflows and tools set to ensure seamless data integration can get a holistic view of the customer journey to make strategic decisions to enhance the customer experience.
Enterprises considering the strategies mentioned above will be able to effectively embrace big data in the MarTech stack to make the most out of data gathered throughout all the channels.