Does your organization struggle with massive data challenges—volume, variety, and velocity? Modern businesses face increasing complexity as they strive to manage data effectively while keeping up with technological advancements and digital transformation.
Your organization is experiencing massive data challenges. Large data volume, variety, and velocity, if not properly managed, may result in unreliable data and unfavorable output. Business transformation shapes the modern data landscape and organizations face a growing number of challenges when it comes to adopting modern technologies.
Data architecture is a cornerstone of significant business transformation (such as digital transformation, mergers and acquisitions, and emergence of new business models and innovations). Businesses could ease their transformation with sound data architecture.
But data architecture involves many moving pieces. Pieces requiring coordination to extract the greatest value from data, and data architects are at the center of this turmoil. They must be able to translate high-level business requirements into specific instructions for data consumers using complex data models. In general, the primary objective of data architecture is the standardization of data for the benefit of the organization.
Data architecture is largely dependent on the human element and can be viewed as the bridge between defining strategy and its implementation. Data architects must account for the constantly proliferating data and application complexity, more demanding needs from the business, an ever-increasing number of data sources, and a growing need to integrate components to ensure that performance isn’t compromised. Fundamentally, the role of a data architect is to understand the data in an organization at a “reasonable level of abstraction”.
And yet, there isn’t always a clearly defined data architect role, yet the responsibilities must be filled to get maximum value from data.
Data architecture needs to evolve with the changing business landscape. As a result, the organization’s architecture must be flexible and responsive to changing business needs.
Data architecture is not just about models. Viewing data architecture as just technical data modeling can lead to structurally unsound data that does not serve the business.
Data is used differently across the layers of an organization’s data architecture, and the capabilities needed to optimize use of data change with it. Architecting and managing data from source to warehousing to presentation requires different tactics for optimal use.
We propose a three-phase practical approach in a Data Architecture Roadmap Deck that will help you build custom and modernized data architecture:
A data architecture roadmap is a series of policies that govern every part of how an organization manages its data, from collection and storage to transformation and consumption.
The Data Architecture Roadmap Deck is a step-by-step document that walks you through our three-phase methodology for optimizing data architecture because the pains of a stale and outdated data architecture can be felt throughout an organization, from data workers under pressure to produce reports with tighter deadlines to executives who do not receive data in time to make effective decisions.
As organizations strive to become more data-driven, and as worldwide data creation is projected to increase to more than 180 zettabytes by the New Year (Statista, 2021), it is imperative to better manage data for its effective use. And, arriving at the understanding that data can be the driving force of your organization is just the beginning.
This challenge is only compounded by the speed at which the data is expected to move. The faster rates and increased complexity of data is a growing concern.
There are additional obstacles to overcome in facing the challenges of today’s data landscape. From greater amounts to different types, to questionable quality, “data at rest” presents a growing challenge to building sound architectures.
The journey to becoming a data-driven organization requires well-defined and structured data management practices, and attention to strategic elements that guide your architecture to mitigate the limitations that derive from these challenges and leverage the most possible value from your data.
Data debt is the accumulated cost that is associated with the sub-optimal governance of data assets in an enterprise, like technical debt.
Think of data debt this way: all the time your organization spends on non-value-add activities such as cleaning and massaging data, discussing and disagreeing on the proper definition of business data terms. These are examples of the cost of fixing things instead of gathering value when your data architecture is not efficient.
Optimizing data architecture requires a tactical approach, not a passive one. The demanding task of optimization requires the ability to heavily prioritize. After you have identified why, determine how to use our prebuilt roadmap to address the four common drivers.
Source: Info-Tech Research Group
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