As AI continues to evolve and integrates into various sectors, business applications, and different use cases, more and more companies are looking to leverage the power of AI in their daily flow of work.
However, good AI starts with good data. The foundation of any successful AI initiative lies in its underlying data strategy. So, building a robust data foundation is essential not only for AI implementation but also for achieving tangible business outcomes.
This blog post will help you prepare your data for the age of AI. It will show you how to build a solid data foundation for AI by aligning your data strategies with business goals, ensuring data maturity, prioritising data governance, harmonising and unifying your data, securing it, and finally, turning it into actionable insights with AI.
Lots of ground to cover… So let’s get started.
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Step 1:
Align Data Strategy with Business Goals
A big challenge in many organisations is the misalignment between data strategy & the overall business goals. Research from Salesforce shows that 41% of business leaders today recognise only partial or no alignment between the two.
Understanding the full potential and value data + AI can deliver to the business, starts by fully understanding what it is that the business is looking to achieve.
1.1 Get clear on the business goals & objectives
Successful data strategies start with clear business goals. Engage both business and IT stakeholders to define and prioritise business objectives that could be diluted down to tangible data or AI use cases. For example:
Business Goal: Revenue Growth
Data & AI Use Case: Use data & AI-driven insights to proactively identify and target high-value customer segments with new cross- and upsell opportunities, leading to increased sales and revenue.
1.2 Identify your KPIs & Key Metrics
Identify key performance indicators (KPIs) that directly measure progress towards your business goals. Are you achieving your goal or not? This doesn’t only sharpen the focus on what truly matters, it also creates more transparency on the potential value the data & AI project is generating. Let’s look back at our example:
Business Goal: Revenue Growth
Data & AI Use Case: Use data & AI-driven insights to proactively identify and target high-value customer segments with new cross- and upsell opportunities, leading to increased sales and revenue.
Key Performance Indicator: Win rate %: measuring the percentage of existing customers who are converting to won deals in cross- & upsell opportunities.
1.3 Assess your data landscape
What does your data estate look like? What systems, tools, platforms and budget do you have available to meet your goals, and track the progress of your data + AI? Investments may be needed in advanced technologies like Salesforce or data analytics platforms like Data Cloud or Snowflake to effectively track KPIs and achieve goals.
1.4 Build a winning data culture
Building a winning data culture is essential for any organisation that wants to use data to inform its decision making and drive business value. Cultivate a data-driven culture where team members are skilled and accustomed to using data effectively. Consider additional training and change management initiatives to enhance data literacy across the organisation.
Step 2:
Data Maturity comes first
There’s no way around it. If you want to leverage AI in your organisation, your level of data maturity needs to be at a certain level. As said before; Great AI starts with great data.
Achieving the highest level of Data Maturity is never a one-shot goal. It means continuously aligning data strategies closely with business goals, and finding a right balance between the 3 key pillars of data maturity.
a) Trusted data: Your organisation must not only be able to trust the data quality, management and security. The trust people have in data is also very reliant on the culture of how the data is perceived.
b) People: Your team and people should know how to work with data, putting the emphasis on elevating your team’s data skills. You can have various training programs both on the technical as well as the functional side.
c) Technology: What tools and platforms are available in your organisation to accelerate the data journey of your business and its people? Where does the data live? How can you bring it all together to get closer to that single source of truth
A mature data approach allows organisations to anticipate and meet customer needs proactively, offering personalised experiences while ensuring robust security and trust protocols to protect organisational and customer data. Develop a collaborative data strategy that fosters a data-savvy culture and emphasises data security to prevent breaches and ensure compliance.
Do our free data maturity audit to establish a baseline and understand your data maturity.
Step 3:
Prepare your data for AI
Data quality is key. Improving data quality is crucial for reaching data maturity and successful AI implementation, so where do you start?
3.1 Focus on data quality
Start by assessing and measuring your current level of data quality. Conduct interviews with stakeholders, data owners and consumers to figure out what challenges they face when working with data and how often these challenges occur.
Based on that information, you can define a benchmark of “good quality” data.
Then, start cleaning data at the base level.
Initially, set up a data lake to store all raw data. This untouched repository ensures that you can always access the original data for troubleshooting. Following this, create a staging area that mirrors the raw data structure but includes essential cleaning processes like renaming columns, deduplicating records, removing incorrect or irrelevant rows, and excluding unnecessary columns. This stage provides a clean, reliable dataset for further processing and analysis, although further refinement into a structured reporting layer is beyond this article's scope.
3.2 Prioritise data governance
Effective data governance involves understanding the data needs of various stakeholders and establishing specific criteria for data quality, including completeness, timeliness, and accuracy. The core elements of any successful data governance strategy are:
a) Improved Data Quality: When it comes to the data lifecycle of your business, moving from ingestion, to processing, to data activation, 86% of IT leaders agree that low quality input will result in low quality output.
b) Managed Data Sources: Data source management is a key aspect of data governance, which refers to the process of identifying, categorising, and managing the various sources of data that an organisation uses, and ensuring it is accurate and up-to-date.
Implement standardised processes for data management and utilise AI tools for data cleansing and validation. Regularly update governance strategies to keep pace with business evolution and educate employees on data handling requirements.
c) Architect your tech stack for data + AI readiness: A well-thought-out data and technology architecture not only paves the way for AI adoption but also brings resilience to handle massive amounts of data, diverse workflows, and extensive user engagement without sacrificing performance.
When organisations overlook the importance of establishing a solid infrastructure, they risk running into challenges like struggling with new tool integrations, decreasing customer retention, and incurring higher costs due to technical debt.
Furthermore, legacy data architectures, often characterised by siloed systems and unreliable data processes, can stymie data governance efforts. Developing a streamlined and interconnected data architecture is crucial for dismantling these silos, fostering a data-driven culture, and effectively implementing a robust data governance framework.
This approach is essential for maintaining a competitive edge in a rapidly evolving digital landscape.
d) Assign ownership: It's crucial for data governance initiatives to be embraced across the entire organisation, but appointing data stewards brings a focused accountability to data ownership, quality, and security, ensuring the initiatives remain on track. Data stewards are invaluable not just in the early stages of implementation but also as the project evolves—they oversee the monitoring, measuring, and reporting that bolster the development of robust data policies and processes, helping to weave these efforts into the fabric of the organisation's operations.
Step 4:
Data security & privacy
Safety first!
Practising good data hygiene is crucial as security threats, primarily stemming from human error, pose a major barrier to data management. Implement key security measures like data encryption, identity and access management, multi-factor authentication, and robust backup and recovery processes to keep sensitive information secure and maintain service reliability.
Step 5:
Harmonise & unify your data
5.1 Remove your data silos
The average enterprise uses over 1,000 applications, creating complex data ecosystems and silos. Data silos occur naturally over time, mirroring organisational structures, but they prevent teams from making informed and timely decisions and having a full view on the business.
This creates fragmented organisations and prevents departments from making data driven decisions together, collaborating effectively on projects, or sharing in a clear and common goal.
Bridging data silo gaps is crucial to increase data clarity, and building a sturdy foundation for AI and data activation.
5.2 Harmonise & unify your data
So, for businesses today, it’s not just about how much data you have, but how effectively you use it. This is especially true in a time where AI-powered innovations are built on that sturdy data foundation mentioned above.
The question now is: “How do I remove my data silos?” In other words: How do you harmonise and unify your data in a single source of truth?
The answer can be different depending on your use case and current tech environment. But the basic idea is that you’ll need at least a great data lake or data lakehouse to store your data in and a cloud data platform like Snowflake or Salesforce Data Cloud to bring all your data together in one place.
Both solutions can even work together. Through zero-copy integrations, Salesforce Data Cloud for example can drive actions and workflows inside of your CRM and other Customer 360 apps with data from any external data lake or warehouse.
Step 6:
Turn data into action with AI
With a strong, secure data foundation, businesses can start to activate that data to leverage AI to transform data into actionable insights, driving better decision-making and more effective strategies.
Data activation ensures that all your unified data is securely integrated into your different business apps like the CRM system for sales, marketing platform for marketing teams, etc. This allows your teams to effectively activate this data within workflows and applications that are directly engaging with customers, ultimately shaping their customer experience.
Sounds a bit fluffy? Let’s make it more tangible with 2 short examples…
6.1 Data + AI Activation for Sales
From better customer engagement, increased sales efficiency and shortened sales cycles - Sales departments are a great place to start with data + AI activation. Data-informed sales tactics can generate higher conversion rates and more satisfied customers. That, ofcourse, leads to higher revenues. Let’s look at a simple example:
Use Case example - AI-driven deal & account plans
Leveraging AI, your sales team utilises data-driven insights and predictive analytics to enhance customer engagement effectively. They can swiftly and accurately identify customer segments and their unique needs, analyse patterns in customer behaviour, service interactions, and purchasing histories to anticipate future needs.
Additionally, AI helps in generating personalised recommendations for products and services that sales teams can offer, allowing them to customise their strategies to optimise cross-selling and up-selling opportunities.
6.2 Data + AI Activation for Marketing teams
Marketers often lack real-time data access, affecting their ability to effectively manage their marketing pipelines and conversion rates. This issue hinders their capacity to segment audiences properly and build highly personalised customer journeys at scale.
Use case example - Highly personalised customer journeys driven by AI
By unlocking and leveraging data, marketers can personalise web experiences and create connected customer journeys. This includes prioritising audiences for tailored offers and using AI-driven insights for strategic campaign adjustments.
With organised data, AI tools can be utilised to identify high-value customer segments, predict their responses to promotions, and suggest next best actions for personalised web engagement.
Marketers can then segment and prioritise their audiences to avoid oversaturation, ensuring personalised content delivery and integration across preferred customer channels. This also includes syncing data across systems like POS and ERP, and enabling sales and marketing teams with notifications to tailor their follow-ups effectively.
Conclusion
As we wrap up this exploration into building a robust foundation for Data + AI, it's clear that the path forward is paved with strategic intent and thoughtful data management. Throughout this post, we've uncovered the pivotal steps necessary to align your organisation's data strategy with its broader business goals, emphasising the importance of data maturity and governance. By integrating these elements, your business can leverage AI not just as a tool, but as a transformative force that drives superior outcomes.
Embarking on this journey requires a commitment to ongoing refinement and adaptation. Cultivating a data-driven culture is essential, empowering your team to harness data and AI effectively. As you continue to enhance your data infrastructure and practices, you'll not only meet but exceed the dynamic needs of your market, ensuring your business remains at the forefront of innovation and efficiency. Let's move forward with the confidence that our data is not just a resource but a strategic asset that will fuel growth and innovation in the AI-driven landscape.
