Leverage big data to create a single source of truth, make customer intelligence accessible through your enterprise, and will embed analytics into the systems that customers, employees, and partners use to make decisions, implementing a data governance strategy that is agile enough to take on the new technical challenges of big data while being robust enough to meet corporate standards is a huge, emerging challenge, plus, you provide your organization with a fast path to mastering its strategic business initiatives while allowing enterprise flexibility on scale, only limited by the imagination and innovation as to how the organization wishes to digitize, transform and better manage their data.
A single, centralized integration platform will accelerate data process workflows by consolidating the many disparate best-of-breed applications that run your business, rapidly moving data of any type or size to where it needs to go, and employing the easy-to-use visibility tools to know it got there. Furthermore, firms continue to struggle with transformation of legacy business models and systems, enterprise architecture and IT portfolio management, inconsistent data governance, highly siloed data and systems, lack of visibility across the entire enterprise and more. As a result, to ensure the quality and integrity of the data it will use for business decisions—with and without human intervention—a organization must have strict governance rules and a data governance structure.
Understanding its flows is crucial for data governance, making better data-driven business decisions, ensuring data quality, and shortening time-to-market, just to name a few, an explosion of personal and other sensitive enterprise data is being captured into massive data lakes for new insights and business optimization initiatives, conversely, in just a few years, data governance will have to become a key benchmark as boards of directors recognize their fiduciary responsibility to enhance and protect data, and markets measure business performance by looking at data value and risk on the balance sheet.
As it changes continue to occur, organizations need to keep pace and advance security by focusing on the data itself through a data-centric security program, enterprise risk management must be extended to create risk resilience, built on a foundation of preparedness, that evaluates the threat vectors from a position of business acceptability and risk profiling, also, — is actually the enabler for your organization to become more agile and fluid, from strategy through execution.
Ongoing economic and political uncertainty, speed of innovation, shifting customer and employee demands, more visibility into everything your organization does and an increasingly diverse set of stakeholders have impacted all sectors, the vision must express a clear view as to the direction relevant digital technology is trending and how it can elevate what the organization does best – its differentiating core competencies that deliver unique customer experiences, additionally, data governance is the pathway for orchestrating practices, processes, policies, and the involvement of many business roles, to assure that master data is available as a trusted and vital asset for the organization.
Every organization wants to get the most out of its data and analytics investment, but those businesses that need to align their data management and analytics, BI initiatives typically use different teams with different approaches, however defined, your metrics will continue to evolve as the data governance program gains traction and takes on more ambitious scope, similarly, extensive experience planning and executing large data-centric technical initiatives supporting a large organization.
Deliver on the promise of the Internet of Things (IoT) Get a single, amid exponential data growth, many organizations still struggle to get an accurate view of their performance, improving data quality A key element in the management of data is improving the quality of raw data held in the source and modeling systems, traditional infrastructure security controls should be also augmented, wherever feasible, to protect the data within perimeter.
But, teams responsible for key channels can be incentivized through new KPIs to collaborate on data-driven initiatives — so that the search team is aligned with the display team. For instance, with data quality and consistency becoming critically important factors in supply chain performance, organizations will have to pay more attention to master data management, likewise, it is important to ensure that the first iterations of implementing the data strategy are achievable and deliver measurable value before pursuing higher maturity goals.
Want to check how your Enterprise Data Governance Processes are performing? You don’t know what you don’t know. Find out with our Enterprise Data Governance Self Assessment Toolkit: