The right data climate for value from day one

Yet, many enterprises still think that to fully reap data’s benefits rather than simply produce or consume it, they must have completed a digital transformation. We’ve seen otherwise: organizations can extract value from data from day one, regardless of where they are in an IT transformation journey. Small, highly specific business-owned use cases are steps toward delivering long-term enterprise-wide business goals. Although immediate immersion in advanced analytics is an option, simple statistics also yield insights to improve everyday processes. Today’s state-of-the-art technology can, to name a few examples, help identify consumer trends to develop competitive digital business services; forecast credit risk to make sustainable investments; and analyze factories to optimize output.

What matters most is that the data is unlocked and available – and that the data is quality data. Data quality is standardly measured along six dimensions: accuracy, completeness, consistency, timeliness, uniqueness, validity. We believe all add up to data resilience. And importantly, resilient data provides a fertile ground to solve business-critical challenges.

Pivoting from silos, getting actionable insights
With high-quality data, organizations have not just facts but actionable insights at their disposal. But having quality data requires unlocking the data in the first place. Unlocking is crucial because it generates cross-domain data, which delivers quicker, higher-quality insights. Available right away or in real time, these insights can make all the difference in decision-making.

Unlocking data also permits the same body of data to be used across different contexts, being continuously revisited and repurposed. This integrated information produces more novel, fit-for-purpose digital business services. Together with critical thinking – human judgement continues to prevail – data thus enables enterprises to pursue operational stability and business progress at the same time.

To unlock the data, there must be a readiness to democratize it. Making data available and accessible to everyone in an organization, regardless of their technical skill level, allows all employees to make data-driven decisions. Like most business best practices, having a democratic data culture requires pivoting away from knowledge silos and enhancing collaboration among colleagues. That often inspires a change in outlook, if not entire culture, to set such exchanges free within the enterprise. Silos can be finessed by gathering data not along the lines of particular tech functionalities or applications, but in terms of business challenges. Analyzed more holistically, the data then offers insights that translate into competitive advantages.

An orchestration team for domain knowledge sharing
Modern-day organizations that have outgrown the model of a single data team may already have multiple data teams spread across multiple business domains. Logically, decentralization brings experts close to the data they know how to interpret and embeds them among the colleagues who rely on the data projects they’ve designed. To ensure that these insights don’t stay siloed within a single domain, however, enterprises can profit from having their own data and analytics capability. In this role, the organization maintains a centralized hub connecting data teams and facilitating the sharing of data sources as well as all the necessary extraction and analysis tools, knowledge, and support. This orchestration team can also establish best practices for data governance, ensuring that sensitive data is protected and that data is used ethically and compliantly.

The benefits of having an orchestration team for domain knowledge sharing are numerous. By breaking down data silos and encouraging cross-domain collaboration, enterprises can gain a more comprehensive understanding of their own business operations as well as their customers’ behaviors. This can lead to more accurate predictions and better-informed decision-making. Additionally, a hub for data access and analysis permits the organization to streamline its data workflows and reduce duplication of effort. That can save time and resources while increasing the overall efficiency of data projects within and across domains.

An open ecosystem for data sharing
Enterprises can scale data-sharing practices to span not just their entire organization, but the entire business ecosystem in which they operate. Although the inclination has long been to hold data close, external data sharing can be done securely. Yet, to leverage the potentially unprecedented business outcomes of a collaborative data ecosystem, there must be data governance. This means that each member of the ecosystem has in place policies, systems, and guidelines for collecting, storing, analyzing, and using data. A governance framework defines data ownership, access, security, and privacy guidelines, as well as includes a process for resolving disputes and enforcing compliance. Rules are rigorous but not so vast that they overwhelm users and hamstring actual data usage.

Well-governed data creates a hospitable environment in which all data producers and consumers can trust in what they’re sharing. Trust is critical in any ecosystem with different organizations, especially when dealing with sensitive or proprietary information. One way to build trust is to use technologies that protect the data while allowing it to be shared between organizations, such as data cleanrooms, encryption, and tokenization. Other options include hiring third parties to verify quality and security as well as drawing data-sharing agreements to establish expectations and responsibilities among organizations. Interorganizational transparency and communication about data practices and usage are also fundamental for building trust.

Applying artificial intelligence, boosting human happiness
The more that business users work with data scientists, analysts, and engineers, the more that organizations can tap data’s potential. When quality data is democratized and well governed, its users may make more timely decisions and take more effective actions. Often, this leads to improved employee happiness because contributing to the success of the enterprise feels validating. Ideally, employees are empowered to use self-service analytics, designing their own digital business services to best serve their domain-specific needs.

Meanwhile, the boom of artificial intelligence (AI) is yielding a dizzying number of ways to learn from and use data. Machine learning, in particular, can surface previously hidden correlations between the data and activate diverse data models. In outsourcing routine computation and correlation work to a machine, humans get more time and brainpower to think about the data, challenge analytics, and apply insights creatively – potentially leading to disruptive innovations. This is all the more urgent during an age of global crises and economic turbulence, with enterprises pressured to secure assets, achieve breakneck time to market, and attain slim margins. Data can translate these aims into insights, decisions, and actions regardless of where an organization is on its digital transformation. And like soil is to a garden, with the right care and climate control, it can provide fertile ground to keep the business thriving.