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As businesses increasingly rely on technology to drive growth, the importance of data has reached unprecedented levels. In the last decade, the value of data has skyrocketed, and its significance has expanded beyond anyone’s imagination. Today, Artificial Intelligence (AI) and machine learning have emerged as crucial tools to help organizations manage and leverage data to drive better outcomes.
Leading digital-native companies have shown how powerful the combination of AI and data can be, unlocking new products, experiences, and operational efficiencies on an unprecedented scale. These transformative advancements have brought cloud-native services to the forefront, making it possible for businesses of all sizes to take advantage of the power of data.
This is evident from the fact that in 2021, nearly half of the CEOs of the world’s 2,000 largest companies discussed AI during their earnings calls[i]. This shift in the use of data has created a new era of innovation and redefined what success means in the competitive global market.
As businesses embrace this transformation, it is critical to keep in mind that the true value of data is not just in collecting it, but in leveraging it to drive better outcomes. By leveraging AI and cloud-native services, businesses can make informed decisions, streamline operations, and unlock new opportunities that will help them stay ahead of the competition.
“Unlock your organization’s data potential by taking a holistic approach to data management and leveraging modern technologies and practices.” Deploy AI
The complexity conundrum is a significant challenge that many organizations are grappling with in today’s rapidly evolving data landscape. As data volumes continue to grow exponentially, and new data sources and types emerge, it has become increasingly difficult to effectively manage, process, and analyze data.
In addition to the sheer volume and variety of data, organizations also face significant challenges in accessing and utilizing it effectively. Many organizations struggle with real-time data usage, while others have difficulty ensuring interoperability and connectivity across different infrastructure components. Finding and tracing the data needed to answer basic questions about customers, assets, people, and partners can also be a significant challenge.
The complexity conundrum is further compounded by the fact that it is not enough to know where the data resides; organizations must also be able to extract, process, analyze, and distribute insights to the right people or systems at a pace that meets business needs. All of these tasks are heavily reliant on the data platforms, processes, skills, and culture an organization has at its disposal.
Whether the complexity is due to old challenges or exacerbated by new ones, a majority of organizations believe that they are falling short of where they want to be in terms of effectively managing their data. This gap between ambition and reality can be a significant roadblock to digital transformation, as organizations struggle to make sense of their data and leverage it effectively.
To address this challenge, organizations must take a holistic approach that encompasses the full data lifecycle. This means implementing modern data architectures that leverage cloud-native technologies, ensuring interoperability across systems and data sources, and investing in the necessary data skills and culture. By doing so, organizations can turn the complexity conundrum into an opportunity for growth and innovation.
In summary, as the data landscape continues to evolve, organizations must find ways to effectively manage the complexity conundrum. This requires a multifaceted approach that addresses the full data lifecycle and leverages modern technologies and practices. By doing so, organizations can unlock the full potential of their data and gain a competitive edge in the digital age.
The rapid evolution of cloud and edge capabilities over the past few years has brought forth new solutions to overcome the complexity conundrum in the data landscape. Enterprises can now leverage these capabilities to create a profound impact through three strategic objectives: extracting data’s intrinsic value, accelerating its value via pre-built cloud solutions, and creating exponential value with AI.
The value journey of data begins with the first objective of extracting its intrinsic value, which refers to the data’s true worth. Enterprise leaders must recognize the untapped potential of data that is currently being treated below its intrinsic value, like a diamond in the rough.
To achieve this objective, enterprises must break down data silos, eliminate duplication, create trusted data products, reduce the cost of data rework, ensure timely insights and cross-functional use cases, and improve user adoption. This approach enables enterprises to tap into the full range of available data faster and enhances business intelligence and performance.
By unlocking the intrinsic value of data, enterprises can create a competitive edge in today’s digital landscape. The ability to extract the true worth of data will allow enterprises to make informed decisions, drive innovation, and achieve transformative outcomes that were once unimaginable.
This practice involves breaking down data silos and unlocking data that may be trapped in legacy systems. By enabling enterprise-level data graphs and analyzing data in one place, organizations can activate advanced AI/ML, at scale. This can provide insights that were previously hidden or difficult to obtain.
This practice involves capturing, managing, and processing data across the full span of public and private clouds, and network edge. This enables real-time data activation and optimization, helping organizations to make better, faster decisions.
Organizations should establish product attributes for their data and bring proven product development practices, processes, and tools. This includes delivering quality, configurability, and reusability, which can help maximize the value of the data.
Organizations should adopt rigorous approaches that are widely used in software development, and apply them to data management. This includes evolving from DevOps to DataOps and spotlighting autonomous data management. By automating processes and applying best practices, organizations can achieve greater efficiency and reduce the risk of errors.
Organizations should make high-quality data products available to the people who need them when they need them, and in the way they need them. This includes implementing data governance practices to ensure that data is accurate, reliable, and secure.
Organizations should tap into more of data’s intrinsic value by ingesting and sharing data seamlessly and securely with the broader ecosystem. This includes implementing data-sharing practices that are secure and respect privacy regulations.
In addition to these practices, organizations should also ensure that they have the necessary data skills and culture to effectively manage their data. This includes investing in data literacy, creating a data-driven culture, and providing ongoing training and development opportunities.
In today’s complex data landscape, it’s essential for organizations to extract the full potential of their data. The six key practices outlined in this article, including breaking down data silos, expanding data mindsets, treating data as a valuable product, adopting data-as-code best practices, making high-quality data products accessible, and sharing data securely, are crucial to achieving this goal.
However, these practices are just the beginning of the data maturity journey. To truly unlock the power of data, organizations must also embrace more advanced strategic data capabilities, such as building decentralized data meshes and embedding advanced AI throughout the organization.
By waking up their dormant data and extracting its full potential, organizations can gain a competitive edge and drive growth and innovation in the digital age.
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