The Data-Driven Enterprise of 2025

In defining the new data-driven enterprise, seven distinctive characteristics come to the forefront. Many companies are already showcasing some of these traits, while others are embarking on this transformative journey. The ones progressing rapidly are poised to reap the maximum benefits from data-driven capabilities.

For instance, companies currently deriving 20 percent of their Earnings Before Interest and Taxes (EBIT) from artificial intelligence (AI) are more inclined to adopt data practices aligned with these defining characteristics.

?This guide is tailored to assist executives in comprehending these traits of the emerging data-driven enterprise and the opportunities they unlock. It also serves as a valuable resource for delving deeper into how to integrate these principles into your organization's DNA. Embrace the future; it's data-driven and waiting to be harnessed!

Characteristic 1:

Data Integration in Every Decision, Interaction, and Process

Today, data-driven approaches within organizations tend to be sporadic. These approaches, whether involving predictive systems or AI-driven automation, often leave untapped potential on the table, resulting in inefficiencies. Many business challenges still follow traditional problem-solving methods, stretching over months or even years.

?Fast forward to 2025, and we witness a remarkable shift. Nearly all employees seamlessly incorporate data into their daily work routines. Instead of relying on lengthy, multi-year roadmaps to address issues, they are empowered to explore innovative data techniques that can resolve challenges within hours, days, or weeks. Organizations become adept at making better decisions and automating routine tasks, liberating employees to concentrate on more "human" aspects like innovation, collaboration, and communication.

This data-driven culture fuels an ongoing journey toward improved performance, creating distinctive experiences for both customers and employees. It also paves the way for the development of sophisticated, cutting-edge applications that were previously beyond reach. The future is marked by a data-driven transformation that elevates the possibilities of innovation.

Characteristic 2:

Real-Time Data Processing and Delivery

Today, only a fraction of data from connected devices undergoes real-time ingestion, processing, querying, and analysis. This limitation is primarily attributed to legacy technology structures, the complexities associated with adopting more contemporary architectural components, and the substantial computational demands of intensive real-time processing tasks. Companies often find themselves at a crossroads, having to choose between speed and computational intensity, which can, in turn, delay more intricate analyses and hinder the implementation of real-time applications.

?Fast forward to 2025, and we witness a remarkable transformation. Extensive networks of interconnected devices efficiently collect and transmit data and insights, often in real time. The process of data generation, processing, analysis, and visualization for end-users undergoes a profound makeover, driven by the adoption of cutting-edge technologies like kappa or lambda architectures tailored for real-time analysis. These innovations result in faster and more robust insights. Moreover, even the most advanced analytics become accessible to a wider range of organizations, thanks to the declining costs of cloud computing and the emergence of more potent "in-memory" data tools, such as Redis and Memcached.

Collectively, these advancements open the door to a multitude of advanced use cases, enabling the seamless delivery of insights to customers, employees, and partners. The future is all about real-time data excellence, empowering organizations to thrive in the fast-paced digital landscape.

Characteristic 3:

Flexibility in Data Storage for Ready-to-Use Data?

Today, despite the data explosion driven by unstructured or semi-structured data, the majority of usable data remains organized in a structured manner using relational database tools. Data engineers invest significant time manually delving into datasets, establishing connections between them, and merging them together. They are often tasked with transforming data from its inherent, unstructured state into a structured format using manual, customized processes. These procedures are not only time-consuming but also lack scalability and are prone to errors.

Fast forward to 2025, and we witness a fundamental shift in data practices. Data practitioners increasingly harness a diverse range of database types, including time-series databases, graph databases, and NoSQL databases. This diversification facilitates more adaptable approaches to data organization. Consequently, teams can now query and comprehend relationships between unstructured and semi-structured data with greater ease and speed. This acceleration in data understanding fuels the development of new AI-driven capabilities and unlocks the potential for uncovering novel relationships within the data, driving innovation.

The fusion of these versatile data storage solutions with advancements in real-time technology and architecture empowers organizations to craft data products, such as "customer 360" data platforms and digital twins—real-time-enabled data models representing physical entities like manufacturing facilities, supply chains, or even the human body. This capability paves the way for sophisticated simulations and the exploration of "what-if" scenarios, leveraging conventional machine learning techniques and more advanced methodologies like reinforcement learning.

In essence, the future of data storage is all about adaptability and agility, enabling organizations to extract maximum value and insights from their data resources.

Characteristic 4:

Treating Data as a Product

Today, within organizations, the data function, when separate from IT, typically manages data through top-down standards, rules, and controls. Unfortunately, data often lacks a true "owner" responsible for ensuring its currency and readiness for diverse applications. Data sets tend to be stored, sometimes duplicated, in sprawling, isolated, and often expensive environments. This setup poses a challenge for users within the organization, such as data scientists seeking data for building analytical models. They struggle to swiftly locate, access, and integrate the data they require.

Fast forward to 2025, and a transformative shift occurs. Data assets are treated as products, regardless of their intended users—be it internal teams or external customers. These data products boast dedicated teams, or "squads," entrusted with embedding data security, evolving data engineering (including data transformation and continuous integration of new data sources), and introducing self-service access and analytics tools. The evolution of data products follows an agile approach, adapting to consumer needs using DataOps principles (akin to DevOps for data) and continuous integration and delivery processes and tools.

In unison, these data products furnish flexible data solutions that can be repeatedly employed to address diverse business challenges. They also slash the time and costs associated with deploying new AI-driven capabilities. This strategic shift heralds an era where data is not just an asset but a dynamic and responsive product, integral to an organization's agility and innovation.

Characteristic 5:

Empowering the Chief Data Officer for Value Creation

In the present landscape, Chief Data Officers (CDOs) and their teams predominantly operate as cost centers. Their primary responsibility centers around developing and enforcing policies, standards, and procedures aimed at data management and quality assurance.

Fast forward to 2025, and a transformation of the CDO role emerges. CDOs and their teams transition into a fully-fledged business unit endowed with profit-and-loss responsibilities. In close collaboration with business teams, they take on the mantle of envisioning innovative ways to harness data's potential. This includes formulating a comprehensive enterprise data strategy, seamlessly embedding it within the overarching business strategy. Additionally, they nurture fresh streams of revenue by capitalizing on data services and fostering data sharing.

This paradigm shift propels the CDO into the role of a value generator rather than a cost center, forging a data-driven future that's not only efficient but also profitable.

Characteristic 6:

Data Ecosystem Evolution

In the present landscape, data often resides in silos, even within organizations. Although there's a growing trend of data-sharing agreements with external partners and competitors, such collaborations remain uncommon and are frequently constrained in scope.

?Fast forward to 2025, and a significant transformation unfolds. Large, intricate organizations leverage data-sharing platforms to foster collaboration on data-centric initiatives, both internally and across organizational boundaries. Data-driven enterprises actively engage in a data economy that facilitates the amalgamation of data resources, yielding richer insights for all participants.

Data marketplaces come to the fore, enabling the exchange, sharing, and augmentation of data. This empowerment allows companies to craft truly distinctive and proprietary data products, extracting invaluable insights. In this new landscape, barriers to data exchange and amalgamation are significantly dismantled, uniting diverse data sources in a manner where the resultant value surpasses the sum of its individual components.

?Characteristic 7:

Data Governance Transformation

In the contemporary landscape, data security and privacy are frequently perceived as compliance matters. They are driven by emerging regulatory data-protection mandates, as well as consumers' growing awareness of the extent to which their information is gathered and utilized. Often, data security and privacy measures are either inadequate or uniform, lacking customization for specific datasets. Providing employees with secure data access is a labor-intensive, error-prone, and time-consuming process. Manual data-resilience processes hinder the swift and complete recovery of data, posing risks of prolonged data outages that impact employee efficiency.

Fast forward to 2025, and a significant shift in organizational mindset emerges. Data privacy, ethics, and security are now considered core competencies. This shift is propelled by evolving regulatory expectations, such as the Virginia Consumer Data Protection Act (VCDPA), General Data Protection Regulation (GDPR), and California Consumer Privacy Act (CCPA). It's further reinforced by heightened consumer awareness of data rights and the escalating stakes associated with security breaches. Self-service provisioning portals take center stage, automating data provisioning through predefined scripts, offering secure and near real-time access to data, significantly enhancing user productivity.

Automated, nearly continuous backup procedures ensure data resiliency. Rapid recovery processes swiftly reestablish and retrieve the "last good copy" of data within minutes, rather than days or weeks, minimizing risks associated with technological glitches. AI tools come into play, effectively managing data by automating tasks such as identifying, correcting, and remediating data-quality issues. These collective efforts empower organizations to instill greater trust in both their data and the methods employed to manage it, ultimately expediting the adoption of innovative data-driven services.