How to find the path to a data-powered enterprise

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Companies are still struggling to activate data and artificial intelligence.

Companies know they need to activate data enterprise-wide in order to achieve multiple business goals. From responding swiftly to changing consumer expectations and competing with digital giants and new entrants to leveraging the power of new technologies to make better business decisions, activating data is the key. Data means more insight, focus, agility, and flexibility, and the power to provide innovative end-to-end services to customers.

With the speed of business accelerating rapidly, business and technology leaders of traditional organizations need to reassess many deeply held assumptions. Responses based on traditional intuitions are no longer reliable and there is increasing urgency to respond to the market downturn faster by leveraging the power of data.

Scaling data and AI delivers a competitive advantage

The changing dynamics have made it imperative for companies to look towards data and AI to finetune strategic initiatives, lay foundations for growth, and compete more effectively. Data and AI are powerful catalysts for digital success, enabled by the vast increase in computing power, storage capabilities, and the always-connected network of things. There is an opportunity to deploy data and AI for a full range of benefits across the front-, middle-, and back-office as well as the customer, partner, employee, and supply-chain ecosystem.

Exploring a selective set of business problems and experimenting with a few potential use cases, while a great starting point, won’t be enough to deliver a step-change in business value creation

The path to the truly data-powered enterprise is elusive

Even while investment levels in data and AI initiatives are increasing, organizations continue to struggle to become data-powered. Many have yet to forge a supportive culture and a large number are not managing data as a business asset. For many firms, people and process challenges are the biggest barriers in activating data across the enterprise. What explains this disconnect?

Committing to a comprehensive transformation strategy

Activating data across the enterprise means robust data-management discipline, flexible data architectures, well-orchestrated data services (for sharing with internal/external ecosystems), mature business-intelligence platforms and tools, advanced analytics capabilities, and scaled AI solutions. When organizations embark upon this holistic journey to become data-powered, they grapple with technological challenges but cultural resistance, talent, governance, and misaligned operating models can also act as roadblocks. The attempt to scale and activate data faces the same challenges for every company:

  • No alignment on business priorities. The most successful data projects have strong executive sponsors and buy-in.
  • Scattered and siloed data assets. Creating a trusted pipeline of data is essential.
  • Adapting to the new mindset. Employees may resist doing their jobs differently, so they must first understand the opportunity.
  • Building the right talent. Companies may need to upskill or acquire new talent to help drive data transformation.
  • The ability to move data and AI to the heart of decisioning. Making data-powered decisions is a different mindset but it starts with having a trusted data pipeline providing the right information to the business.
  • Issues with data quality. Different groups have their own ways of capturing data. Consistency is required to ensure quality information.

No one step will solve all these challenges. It starts with shaping a competitive strategy and roadmap, to facilitate data-powered innovation across the value chain, growing the data and AI talent needed and, ultimately, establishing a fast-moving, scalable, data-powered culture.

Your successful data and AI scaling journey should focus on four key tenets:

  • A structure and framework to assess the current state and develop a vision and roadmap that is connected to the business strategy
  • An extended ecosystem of talent, capabilities, and domain assets to accelerate your innovation and maximize returns
  • A framework to institutionalized data and AI at enterprise scale with tools and playbook for standardization, repeatability, and knowledge sharing
  • A robust mechanism to continuously upskill and re-skill your talent to keep them engaged.

Building on a strong, industrialized foundation not only ensures that the data and AI fabric are enterprise-grade but also guarantees that it is agile, flexible, scalable, repeatable, reliable, and secure. This is how a company becomes a data-powered enterprise.

You can reach out to the author, Dinand Tinholt, VP at Capgemini Insights and Data, to find out how your business can effectively activate data and AI to become a data-powered enterprise.

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