In the digital era, data is the lifeblood of businesses; data piling up from various customer or operational touch-points have to be efficiently collected and managed for a business to thrive and prosper. In short, Enterprise Data Management (EDM) impacts all core business functions like HR, CRM, ERP, or Supply Chain. In such a scenario, it is only natural that Data Strategy or Data Architecture will play key roles in running a business efficiently.
Definitions: Data Architecture & Data Strategy
Data Architecture defines how data is acquired, stored, processed, distributed, and consumed. On the other hand, the term Data Strategy implies the overall vision and underlying framework of an organization’s data-centric capabilities and activities. In that sense, Data Strategy is the umbrella term, which comprises all significant data-related policies and principles, such Data Governance, Data Stewardship, Master Data Management (MDM), Big Data management, and so on. Data Management vs. Data Strategy: a Framework for Business Success reveals why a solid Data Strategy is needed for maximum business success.
The overall goal of an organization’s Data Strategy and its subordinate activities are mitigating risks, improving Data Quality, streamlining business processes while reducing operating cost, developing and executing advanced Analytics for business gain, generating ROI from data-centric initiatives, leveraging and monetizing data assets, complying with regulatory policies, preventing data breaches or cyberattacks, and enabling new products or services.
Thus collectively, an organization’s Data Strategy and Data Architecture play key roles in running the business efficiently.
The New Complexities of Enterprise Data Management
Currently, Data Management activity is probably the most important differentiator for long-range sustenance and market success. Here are the reasons for data increasingly assuming such a significant role in global businesses:
- Most businesses rely on data-driven IT systems for acquiring transactional, operational, performance, customer behavior, and all other types of data affecting daily business processes.
- The meteoric rise in volumes (petabytes) and types of data (social, mobile, sensor, web) have necessitated the use of highly sophisticated, AI-enabled technologies and tools for Data Management and Analytics.
- For competitive market intelligence, businesses need immediate access to actionable insights facilitated through advanced IT systems.
- Cross-functional data requirements to develop instantaneous marketing plans and programs need sophisticated tools and expert skills.
- The prevalence of regulatory policies, especially those in verticals, inhibit the use and application of business data in many ways.
In such a complex marketplace, industry sectors have taken a “data stance” that is most suitable for them. The Data Strategy not only sets the blueprint for managing data, but also measures how the data is directly responsible for the ROIs. Data Needs A Strategy – Who Can Help Create One? gives a clear view of the widespread impact of Data Strategy in a business.
Data Strategy: The Catch-All Solution for Cross-Function Performance
Data arrives from both “live” and “dead” data channels, and it is not easy to collect, organize, standardize, and manage this avalanche of data flow. Data Strategy provides the basic blueprint for data storage architectures and its internal components. The relationship between the different components of data storage is pre-defined in the Data Strategy guides.
Why Organizations Need a Data Strategy offers the perspective of a seasoned industry leader, Stephen Lahanas, the Vice President & IT Architect of Semantech Inc. With years of experience behind him, Lahanas states that:
“A Data Strategy is not a list of generic principles or obvious statements (such as ‘Data is an Enterprise Asset’). A Data Strategy is not merely the top-level vision either, it can expand into critical data domains such as Business Intelligence and eventually represent a family of strategies.”
According to Lahanas, Data Architects make good candidates for developing enterprise Data Strategy, as these individuals have a deep understanding of data system capabilities.
Data Architecture: Is it the Beginning of Data Governance?
According to Data Governance vs. Data Architecture, the problem of visualizing Data Architecture is quite to similar to that in The Elephant and the Six Blind Men. Each business person has a unique view of the role of Data Architecture, and a few use the terms “Data Architecture” and Data Governance” interchangeably. However, Data Architecture is just one component in the overall Data Governance framework.
Data Governance includes not only Data Architecture, but also operational technologies, processes, people, and organizational culture.
Data Strategy and Data Architecture: A Closer Look
According to Peter Drucker, information is “Data endowed with relevance and purpose.”
Every business has to collect, store, organize, and process vast amounts of inflowing raw data before that data can transform into usable information. Raw data has limited value to businesses, while “information” or insights” flashed through marketing dashboards have tremendous benefits for not only the C-Suite executives but all ground-level managers and staff. Thus, data performs some defensive actions when it shields itself from breaches and corruption, and some offensive actions when it delivers actionable insights or increased revenue.
Data Architecture as a Part of Data Strategy
Data Architecture probably defines and maps out the blueprint for collecting and transforming raw data into information through an end-to-end cycle of data storage and data movement activities. In that sense, Data Architecture simply maps out the data-navigation paths in the whole Data Governance framework. This is explained in a HRB post titled What’s Your Data Strategy?
In sharp contrast, Data Strategy certainly defines and maps out “data storage locations,” but it does much more. The organizational Data Strategy lays out the foundation for “identifying, accessing, sharing, understanding, and using” data. The SAS Institute whitepaper The 5 Essential Components of a Data Strategy offers a clearer understanding of Data Strategy. This paper also distinguishes “data” that is managed outside of application processes, not merely as a byproduct of such processes.
Data Architecture Enables Better Governance in Overall Data Strategy
The SAS white paper provides insight into the role of Data Architecture in the overall Data Strategy, suggesting that the governance and compliance requirements of business data are far better managed through solid Data Architectures. Data security, privacy, and operational best practices are realized through the underlying Data Architecture, which, in a way, initiates the Data Governance process.
Data Management is not just a collection of IT platforms, technologies, and tools. The article suggests that if every organization had realized 20 years ago that a firm Data Strategy could enable better Data Management for business profitability, these organizations would be at a different stage of success today.
The article further states that for businesses to reap the maximum rewards from their data assets, they need to understand metadata, develop strong policies for Data Integration (taxonomy and referencing), provision cross-functional access to single truth of data, comply with all applicable regulations, and finally, establish governance policies for ensuring data practices.
Without Data Architecture, Advanced IT Technologies Cannot be Used
The “core enabler” of modern business processing is a huge collection of highly advanced IT technologies such as Big Data, IoT, Cloud, AI, and ML. It is not possible for any organization to realize the fruits of advanced IT technologies without a Data Architecture in place first.
All Business explains why PwC’s leading thinkers have identified Data Architecture to be the core success enabler in any AI-powered, modern IT system. In a recent conference, PWC staff demonstrated why Data Architectures are needed for organizations to achieve the full benefits of advanced AI technologies like reinforcement learning or agent-based modeling.
According to Mark Paich, director at PwC, Data Management technologies control the success of AI systems, which in turn, assure competitive advantages in business. “Beyond talent, data is probably the most important ingredient for delivering an AI solution.”
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