You’ve got a mountain of data… but are you able to extract actionable insights from it?
If your organization is like most businesses today, you’ve got a lot of data, but you’re struggling to extract value from it. Are you still making decisions based on intuition? Are your key indicators scattered across several systems or not yet well defined? If so,business intelligence is the first lever you need to activate to structure your data before considering artificial intelligence, and data enhancement is the first lever you need to activate before considering artificial intelligence.
Too much data, not enough usable information
According to Harvard Business Review, “most companies are data-rich, but information-poor.” Business intelligence enables you to transform this raw data into strategic insights to better understand your operations, identify opportunities and make informed decisions.
Without a reliable, well-structured database, AI becomes a risky bet. Why? Because AI relies on sophisticated algorithms that require clean, consistent and well-organized data, but also data that is well understood and representative of your company’s reality to produce relevant results.
Example:
A company that does not yet accurately measure its sales, acquisition costs or customer retention rate will not be able to benefit from a predictive model capable of optimizing its marketing campaigns.
Making the most of data: understanding before automating
Data enhancement plays a key role in an organization’s data maturity. Here’s how it fits into a progressive approach to AI:
Structuring and enhancing data reliability
- Audit data to assess quality and reliability
- Identifying gaps and inconsistencies
- Setting up efficient collection and organization processes
Visualize and extract insights
- Create dashboards to monitor performance in real time
- Advanced trend and correlation analysis
- Generate data-driven strategic recommendations
Switching to artificial intelligence
- Once the data has been mastered, it becomes possible to integrate predictive models.
- Automating decision-making with AI algorithms
- Continuous optimization based on reliable, well-used data
Example:
A retailer who has set up accurate dashboards to track sales and customer behavior can then leverage AI to predict demand and adjust inventory automatically.
Why is this an essential step?
Adopting AI without a solid analytical foundation often leads to models that are biased, poorly interpretable and difficult for business teams to exploit. In contrast, companies that structure their data with business intelligence first can reap tangible benefits quickly, while paving the way for an effective transition to AI.
Making the most of data: a performance driver even before AI
Even without going as far as artificial intelligence, leveraging data already offers considerable benefits:
- Process optimization: save time and cut costs
- Improved decision-making: greater visibility of performance
- Detecting opportunities and risks: anticipating rather than reacting
Business intelligence is much more than just an intermediate step, it’s a performance gas pedal that enables AI to be adopted under optimal conditions.
Conclusion
AI can transform your business, but it doesn’t work without a reliable, well-operated database. Business intelligence enables you to structure your data, extract actionable insights and lay the foundations for automation and AI.
Want to add value to your data and prepare your organization for AI? Start by adding value to your data.
Lead de la pratique Analytique chez Moov AI, Sophie aide les organisations à transformer leurs données en leviers stratégiques pour une prise de décision éclairée. Avec une expertise pointue en BI et en analytique, elle a généré un impact business direct dans de multiples industries, en alliant performance opérationnelle et amélioration de la compétitivité
