1. The Case for Unified Data
2. Federated Data: Maintaining Flexibility and Control
3. Siloed Data: The Necessary Evil
4. The Coexistence of Unified, Federated, and Siloed Data
Marketing technology is vital to the growth of B2B marketing as the field advances quickly, offering the right data, automation, and individualization. However, one of the perennial issues that businesses face is how they can begin to manage data in the context of more complicated environments. The question arises: With the current advancement of Martech, is it possible to say that Martech data is both federated and siloed as well? To the readers’ disbelief, the answer is affirmative. This is not a contradiction, but a perfectly possible and necessary state of affairs as organizations pursue integration, governance, and security in the digital environment.
1. The Case for Unified Data
Another fundamental element that can be seen as a prerequisite for any contemporary martech approach is data unification, which consolidates the information retrieved from various sources. This is tactical because, without a consolidated view of customer data, it is incredibly challenging to target marketing initiatives, gain insights, or leverage artificial intelligence and machine learning to their utmost potential.
According to the Chief Marketing Technologist, it is predicted that by 2024 more businesses will be utilizing CDWs like Snowflake, Databricks, Google BigQuery, and Amazon Redshift. Based on the same survey, 71% of the respondents indicated that they have connected a data warehouse or data lake to the martech technology, and 69% of the companies with the integration have bi-directional data exchange between the data warehouse and applications. This helps integrate information from marketing, sales, product, and customer service so marketers will get a complete view of the customer.
The advantages of consolidating aggregated information are proactively not confined to operational improvements. Cleansed and full data sets feeding into AI models enable organizations to automate multiple tasks, deliver customized client experiences, and forecast future behaviors. This is especially the case when it comes to business-to-business marketing because such customers often take their time to make a purchase, interact with several points of contact, and transact in high-value products or services. Integrated data feeds AI applications like lead scoring, predictive modeling, and micro-moment targeting.
2. Federated Data: Maintaining Flexibility and Control
As it has been stated, the idea of consolidation is imperative, but putting all the eggs into one basket is not always feasible or favorable at the same time. The federated data approach can be seen as the need to keep data related but not fully integrated across those departments while still allowing access. In a federated model, data can be stored in various formats and locations, such as by department, geographic location, or cloud service provider; they are integrated and available using protocols and defined interfaces.
The form is specifically beneficial to multinational B2B businesses involved in selling a wide range of products, targeting a variety of customer segments, or dealing with different regulations. For instance, people in the marketing department can get access to sales details without the need to work it to the central location; this is very useful, especially in sectors that deal with sensitive information like finance and health, where centralization of data can compromise the organization’s security and lead to violations of law. For example, this relative autonomy enables data to remain localized to specific departments more defensively. This facilitates organizations to maintain and achieve broader plans while allowing departments to maintain control over their data more assertively.
Federated data architectures are also advantageous when it comes to implementing new technologies in an existing Martech ecosystem. With the continuous addition of a new set of tools such as AI and personalization platforms, as well as automation solutions by B2B companies, the federated systems come in handy in accommodating the new developments in the data structure without necessarily requiring the overhaul of an entire structure. This is especially important as the use of AI increases in martech, and many of these solutions implement federated learning, where AI models are trained across distributed datasets.
3. Siloed Data: The Necessary Evil
Despite numerous efforts put into breaking data silos, there exist cases where data silos are actually desirable. Some data has to be kept separate due to outdated mainframes, security considerations, and compliance demands. For instance, data that requires protection due to financial, legal, or Personally Identifiable Information (PII) concerns might have to be separated to meet required compliance like GDPR or CCPA.
However, integration becomes a considerable challenge with the presence of legacy data. Legacy systems become massive data stores over time, and many of these are locked away in old systems that are hard to update. Combining these historical datasets into a single or federated model could be expensive or even impossible, and in some cases, the data may not be valuable anymore for the current business goals. Hence, it becomes critical for businesses to determine whether or not data should remain segmented or if integrating them would be profitable in the long run.
Another great benefit of data silos is cybersecurity, or the protection of data and sensitive information from unauthorized access and malicious parties. Especially given the rising threat of digitization and hostile parties, fragmented data remains a valuable security measure due to its ability to minimize contact points. Data that is housed in isolated and enclosed areas and is well guarded against external threats is less likely to be exposed to risk, making it mandatory for most establishments.
4. The Coexistence of Unified, Federated, and Siloed Data
It is important to note that integrated, federated, as well as separated data models are not mutually exclusive but rather can and often should co-exist in many organizations. First, unified data works well for marketing as well as AI integrated with other departments; federated data provides flexibility and control for different departments; and lastly, isolated data provides security and meets the compliance criteria.
For instance, an enterprise might integrate all the data on customer interactions and sales in a single data warehouse to get a holistic view of the customer. It could also share its product data in a federated manner across regions to market differently while keeping financial data siloed to meet different regulatory requirements on data access.
This multi-layered approach allows organizations to adapt to the evolving martech landscape while maintaining the necessary safeguards for data security and compliance. As AI, automation, and real-time data processing become increasingly integrated into B2B marketing strategies, the ability to manage data across these different paradigms will become even more critical.
Hence, when considering data in B2B martech in 2024, it is not a one-size-fits-all scenario. There is no doubt that modern marketing requires businesses to be united and federated while at the same time being carefully siloed. Centralized data contributes to enhanced AI and automation; decentralized data enables adaptability; isolated data guarantees the security of data. With this full-spectrum approach, B2B companies can maximize the value of their martech investments and achieve the dual objectives of data-driven marketing and compliance in a rapidly evolving digital environment.
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