Data is the lifeblood of search.
The remarkable development of AI and the introduction of generative AI are based on data.
However, the success of an innovation, a product or a technological advance depends on the quality of this data. When combining generative AI, search, and content marketing, leveraging the right data is critical.
Data volumes are exploding, and the IDC predicts that the size of global data will reach 175 zettabytes by 2025.
This is double the amount produced last year, signaling a clear growth trend. I actually predict more!
To adapt to this data boom, professionals using generative AI must evaluate their data sources and identify the most valuable capabilities for the future.
Poor vs. quality data
The damaging impact of bad data on businesses is undeniable.
Poor quality data is the leading cause of operational disruptions, inaccurate insights and poor decisions.
According to a 2021 Gartner report, organizations incur an average annual cost of $12.9 million due to poor data quality.
Historically, data quality efforts have focused primarily on structured data in relational databases.
However, with the advent of big data systems, cloud computing, and unstructured data types such as text and sensor data, marketers are facing new challenges. Managing data quality across cloud systems has become essential.
Data has never been more important in search and content marketing. However, 57% of marketers misinterpret data, leading to costly mistakes.
This may be due to the data coming from different data sources and the associated problems of processing large data sets at scale
What is quality data?
Quality data combines crucial factors such as accuracy, connectivity, completeness and reliability.
The accuracy of the data you use determines search success – it’s important to ensure executives, content, digital, product, marketing and sales departments are armed with accurate information.
Reliable data leads to increasingly intelligent search decisions that impact business performance.
Additionally, quality data management plays a central role in the connection between SEO and content marketing performance.
In addition to accuracy, several other dimensions contribute to good data quality, including:
- completeness: Records should contain all necessary data elements.
- consistency: Data values across different systems or datasets should not conflict.
- uniqueness: Duplicate records should be avoided in databases and data warehouses.
- Actuality: Data should be updated regularly to remain current and readily available.
- validity: The data should contain the expected values and follow the correct structure.
- conformity: Data should follow the standardized formats established by your organization.
By meeting these factors, data sets become reliable and trustworthy and align with data governance efforts to ensure consistent and effective data use across organizations.
Data, search and generative AI
A combination of people and machines creates a data and content marketing battlefield where quality and connectivity are critical to success.
The introduction of AI tools, machine learning applications, real-time data streaming, and complex data pipelines have further complicated the data quality process.
Compliance with data protection and data protection laws such as GDPR and CCPA has increased the demand for accurate and consistent data.
As the volume of global data grows exponentially, SEO is simultaneously changing as consumer demands evolve, and search engines are responding to these changes by creating new experiences and experimenting with integrating AI into search engine results pages (SERPs). .
Therefore, marketers need to carefully consider their approach to data, technical SEO, and generative AI results.
Data inputs and generative AI outputs
The quality of generative AI outputs depends on the quality and connectivity of the data that feeds them.
Many of you will have experienced this, especially in the early days of generative AI and ChatGPT, Bing AI and Google Bard.
This is why we are seeing more and more rapid engineering and fine-tuning of data from large language models (LLM).
Generative AI has been the subject of much discussion, along with tools like ChatGPT and Google Search Generative Experiences (SGE).
Generative AI based on high-quality data analysis is already saving SEO professionals time and efficiency.
Generative AI can help SEO and content marketers complete repetitive tasks faster and more accurately.
Over 98% of our customers save valuable time creating SEO titles and descriptions with BrightEdge Copilot (disclosure: my company).
However, the value of high-quality data that feeds generative AI goes beyond time savings.
By leveraging high-quality data, marketers can improve their understanding of consumer and conversational intent (the key to generative AI results in the SERPS) and understand datasets by incorporating external industry classification data, ultimately reducing processing times.
Additionally, generative AI can create training and synthetic datasets to support the further development of AI and machine learning models.
However, this evolution requires marketers to adapt and ensure how they handle data
- Quality and connectivity of data: AI outputs are only as good as the inputs. Make sure the sources you use are complete and combine historical data with real-time data. Avoid multiple disparate data sources that provide an incomplete picture of your consumer behavior to avoid GIGO – Garbage In, Garbage Out.
- Integration into the corporate data strategy: Generative AI should be considered an integral part of the data strategy. Make sure to include it from the start and align it with your broader marketing goals for your business.
- Proactive challenge management: Proactively address security, bias, and accuracy challenges specific to generative AI. Assessing and mitigating these risks is critical to successful implementation and future compliance issues.
- Focus on analytics cycle components: Initial adoption of generative AI should target specific components of your marketing campaigns and specific use cases. Continuously test outputs to ensure applications work and guarantee success, especially when producing outputs at scale.
- Prioritize business impact: Prioritize programs that have measurable business impact on your campaigns. Ensure that any technologies you use are tried and tested, and that innovations in generative AI are validated and supported by fundamental, high-quality, high-fidelity data sets.
Getting ahead in SEO with data
When looking at how AI impacts SEO, it’s important to remember that every website has human and machine visitors: people looking for relevant content that answers their questions and needs, and search engine spiders or bots that provide technical content analyze.
Data processing has become essential for evaluating website content and informing digital strategies.
SEO marketers today are inundated with incremental data additions that can be overwhelming to decipher. We are fortunate that AI and automation in SEO are not new and automated technologies can reduce manual data effort and improve business decision making, such as:
- Collecting and structuring large amounts of data to generate smaller, more valuable and actionable insights.
- Improve tasks such as data classification, tagging and cleaning.
- Online research, site audit and intent modeling.
- Gain valuable insights into how consumers interact with search engines.
This also helps marketers who lack the necessary degrees or experience in data science to do so effectively.
Marketers who use data correctly can adapt to changing consumer expectations, keep up with granular search changes, and meet Google standards.
Leveraging a combination of unique knowledge and high-precision data (property) is critical to remaining competitive and ensuring that AI applications thrive on solid data foundations.
Marketers can harness the power of data to gain meaningful insights from the noise.
For example, retail marketers can uncover the problem of duplicate content, while banking marketers can focus on concise content. Tailored best practices and industry-specific problem solutions give marketers a competitive advantage.
This all helps create better and faster search experiences.
Many SEO professionals still do not fully leverage the value of data due to its overwhelming complexity. However, with the help of advanced AI, these hidden insights can be uncovered and understood.
By harnessing the power of AI-first technologies, marketers can optimize their content for maximum impact across multiple digital channels and adapt to changing technologies and consumer behavior.
As companies advance their generative AI strategies, it’s important to remember that the success of applications depends on the data that feeds them.
Ensure quality and connecting data are at the heart of your AI roadmap. Without them, success will be limited.
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