Find your path to a data and AI-driven enterprise

Today, data holds limitless potential. In addition to improving operational efficiency and saving costs, organizations can monetize their data and leverage it to increase profits.

Examples include Coca-Cola, which is using its data for intelligent revenue growth management, including the formulation of segmentation and pricing strategies, portfolio and packaging mix, promotions, etc. In fact, it is estimated that with this smart prioritization model, they achieved 9% revenue growth in Q1 2023 alone.

However, to take full advantage of data, organizations will need to make significant changes to their data collection, storage, retrieval, and governance processes. Moreover, with the advent of generative AI, the power of data has multiplied. At this point, we come to the point of asking a crucial question: do organizations have the right databases in place to be able to scale, productize, and derive value from their GenAI initiatives?

To answer this and other questions, as well as get you thinking about the current state of your enterprise data, we look at the keys to achieving a fully data-driven enterprise.

According to McKinsey, by 2030, many companies will be approaching “data ubiquity”. This means that employees will not only have the latest data at their fingertips, but that data will also be embedded in systems, processes, channels, interactions, and decision points that drive automated actions.

Examples, such as quantum sensing technologies, will generate more accurate, real-time data on the performance of products from automobiles to medical devices. This data, analyzed by applied AI capabilities, will be used to recommend and make targeted software updates.

Some companies are already embracing this vision, but in many organizations, few people understand what data they really need to make better decisions or understand the capabilities of data for better outcomes.

To realize these advanced technology visions, data leaders must drive the organization to prioritize data and AI in decision-making. This involves making data easier to use (by creating standards and tools that make it easier to access data), easier to track (by providing transparency into models), and easier to trust (by protecting data with protocols and security measures).

In addition, they must also adopt an “everything, everywhere, at once” mentality (like the movie). This will ensure that data across the enterprise is shared and used appropriately, such as clearly defining and communicating data structures so that teams understand the standards required for a given data set and establishing clear business rules.

Sixty-five percent of those surveyed by McKinsey say their organizations regularly use AI in at least one business function, up from one-third the previous year. This is due to two main characteristics of the latest technologies: their ease of use and rapid proliferation.

The problem with this mass adoption is that many organizations are using the same tools or developing similar capabilities, which results in not generating much of a competitive advantage. To change this and stand out, there must be a clear focus on the data strategies that can generate a competitive advantage:

• Customization of models with proprietary data: The power of LLM and SLM models lies in the company’s ability to train them with its own data sets and adapt them through rapid and specific engineering.
• Data, AI, and system integration: Value comes from the efficiency with which companies combine and integrate data and technologies. Integrating AI use cases can generate differentiating capabilities.
• Double down on high-value data products: Most of the value a company can derive from data will come from between five and 15 data products.

The excitement around next-generation AI has meant that data leaders no longer have to dictate the value of data, but struggle to manage demand. To achieve the scale needed to operate data-driven businesses in 2030, data leaders will need an approach that accelerates the impact of use cases while pursuing scalability through an architecture that supports the enterprise. To achieve this, data leaders must build “capability pathways,” which are bundled technology components that enable capabilities that can be used for multiple use cases.

A decentralized approach will make it difficult to create capability paths that can be used across the enterprise, while a more centralized approach requires greater investment in governance and monitoring capabilities. The choice of hyperscaler, with its integrated toolset and capabilities, will also influence the development of capability paths.

Artificial intelligence has unlocked 90% of the unstructured data that enterprises have been missing. But this amount of data can greatly enrich companies’ capabilities, especially when combined or integrated with other data sources. But the scale and variety of unstructured data is a geometrically more complex problem. By definition, they are less consistent, less available, and more difficult to prepare and clean, which is made even more complex by the scale of the data.

To create value from this unstructured data, more effort and time must be invested, as cleansing and tagging tasks must be carried out, focus must be placed on privacy and biases, rising costs of storage and cloud networks, or conversion processes must be taken into account. Therefore, investment must be made in developing new capabilities, such as natural language processing, as well as continuously testing and recalibrating LLMs as models and corresponding data sources are updated.

The ability of companies to make data one of their pillars will depend, to a large extent, on leadership. To do so, companies must find leaders who are competent in three main areas:

• Governance and compliance
• Engineering and architecture
• Business value, with a focus on driving revenue, growth, and efficiency from data

It will be very difficult to find a profile that meets all of the above, but good data leaders can supplement their teams with people who possess the right mix of skills or create an operational committee that represents each capability area.

It’s important to have a good roadmap for transforming your business into a data and AI-driven enterprise, but these are some of the steps that will help you do it:

• Create your Customer 360: Imagine a unified view of every customer interaction, across all channels. This 360 perspective will be the foundation of a customer-centric AI strategy. By centering customer data, you gain a deeper understanding of customer needs and preferences, allowing you to personalize experiences and strengthen relationships.
• Unify data in the cloud: As we have been saying throughout the article, data is the engine of artificial intelligence, but the dispersion of siloed data across departments hinders its effectiveness. A data cloud removes these barriers and creates a single source of accurate and up-to-date information.
• Rely on AI assistants: AI assistants, such as Copilot, are a great ally in enterprises, as they integrate seamlessly with workflows. It allows empowering teams with intelligent automation and real-time information, and can become a very powerful weapon in decision-making.
• Data visualization: This is key to harnessing the power of AI, as it turns complex data sets into clear and compelling visuals. This allows you to identify patterns, trends, and connections that might go unnoticed in the raw data and can be integrated with existing workflows.
• Connect the team with a digital workspace: Collaboration is crucial in a data and AI-driven enterprise. Once AI-generated information is obtained, teams can come together to make decisive decisions on what is generated, so it is critical to have the right working platform for each case.

By following these steps, you can set yourself on the right path to build and transform your business into a data-driven company with the latest technology.

By applying the best practices and solutions mentioned above, companies can effectively address data-related pain points by considering them holistically and applying a comprehensive approach that integrates data management, analytics, and process improvements.

At Plain Concepts, we help you formalize the strategy that best suits you and its subsequent technological implementation. Our advanced analytics services will help you unleash the full potential of your data and turn it into actionable information, identifying patterns and trends that can shape your decisions and drive your business.

Our goal is to approach the challenge of digital and data strategy from a business prism with which you will be able to profit, using a structured framework according to your needs.

With this approach, we define the necessary digital and data strategy through a process of immersion, maturity, and consolidation, working on the generation of short-term benefits that give credibility to this strategy.

1. We assess the company’s data maturity level.
2. We identify critical data to manage, control, and exploit.
3. We establish objective use cases, focused on generating medium-term benefits, and design the initiatives to implement them.
4. We generate interest and commitment in your team through training on the importance and potential of data-driven management.

If you want to start turning your data into actionable information with the latest data architecture, storage, and processing technologies, contact our experts and start your transformation now!