Despite best intentions, many organizations still struggle with some fundamental aspects of data processing and analytics. Taking data from operational applications and making it available for analysis is a first step, but data management remains a perennial challenge. Data movement and transformation difficulties can lead to delays and data quality problems that prevent organizations from generating value from data. The inability to govern and integrate data from multiple data sources prevents...
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Topics:
Cloud Computing,
Data Management,
Data,
Digital Technology,
data operations,
Analytics & Data
Maintaining data quality and trust is a perennial data management challenge, often preventing organizations from operating at the speed of business. Recent years have seen the emergence of data observability as a category of DataOps focused on monitoring the quality and reliability of data used for analytics and governance projects and associated data pipelines. There is clear overlap with data quality, which is more established as both a discipline and product category for improving trust in...
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Topics:
Data Management,
Data,
data operations
Organizations increasingly rely on real-time analytics to make informed decisions and stay competitive in today’s data-driven business landscape. As the complexity of data grows with the continuous addition of diverse sources, customers and workers alike expect real-time responsiveness. Accelerated query performance is crucial to process and extract valuable insights from data in a timely manner. Traditional analytics applications are often insufficient for managing the scale, velocity and...
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Topics:
Data Management,
Data,
data operations,
Streaming Data & Events,
Analytic Data Platforms
Data fabric has grown in popularity as organizations struggle to manage data spread across multiple data centers, systems and applications. By providing a technology-driven approach to automating data management and governance across distributed environments, data fabric is attractive to organizations seeking to simplify and standardize data management. I assert that by 2025, more than 6 in 10 organizations will adopt data fabric technologies to facilitate the management and processing of data...
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Topics:
Cloud Computing,
Data Management,
Data,
Digital Technology,
data operations,
Analytics & Data,
Analytic Data Platforms
The Office of Finance can be compared to a numbers factory where the main raw material, data, is transformed into financial statements, management accounting, analyses, forecasts, budgets, regulatory filings, tax returns and all kinds of reports. Data is the strategic raw material of the finance and accounting department. It is the key ingredient in every sale and purchase as well as every transaction of any description. Quality control is essential to achieving high standards of output in any...
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Topics:
Office of Finance,
embedded analytics,
Analytics,
Business Intelligence,
Data Management,
Business Planning,
ERP and Continuous Accounting,
data operations,
digital finance,
AI & Machine Learning,
operational data platforms,
Analytic Data Platforms,
Revenue, Lease and Tax Accounting,
Purchasing/Sourcing/Payments,
Consolidate/Close/Report
Master data management may not attract the same level of excitement as fashionable topics such as DataOps or Data Platforms, but it remains one of the most significant aspects of an organization’s strategic approach to data management. Having trust in data is critical to the ability of an organization to make data-driven business decisions. Along with data quality, MDM enables organizations to ensure data is accurate, complete and consistent to fulfill operational business objectives.
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Topics:
Data Governance,
Data Management,
Data,
data operations
To execute more data-driven business strategies, organizations need linked and comprehensive data that is available in real time. By consistently managing data across siloed systems and ensuring that data definitions are agreed and current, organizations can overcome the challenges presented by data being distributed across an increasingly disparate range of applications and data-processing locations. Maintaining data quality is a perennial data management challenge, often preventing...
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Topics:
Data Management,
Data,
data operations
Data Operations (DataOps) has been part of the lexicon of the data market for almost a decade, with the term used to describe products, practices and processes designed to support agile and continuous delivery of data analytics. DataOps takes inspiration from DevOps, which describes a set of tools, practices and philosophy used to support the continuous delivery of software applications in the face of constant changes. DataOps describes a set of tools, practices and philosophy used to ensure...
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Topics:
Data Governance,
Data Management,
Data,
data operations
As data continues to grow and evolve, organizations seek better tools and technologies to employ data faster and more efficiently. Finding and managing data remains a perennial challenge for most organizations, and is exacerbated by increasing volumes of data and an expanding array of data formats. At the same time, organizations must comply with a growing list of national and regional rules and regulations, such as General Data Protection Regulation and the California Consumer Privacy Act....
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Topics:
Data Governance,
Data Management,
Data,
data operations
I have previously written about the importance of data democratization as a key element of a data-driven agenda. Removing barriers that prevent or delay users from gaining access to data enables it to be treated as a product that is generated and consumed, either internally by employees or externally by partners and customers. This is particularly important for organizations adopting the data mesh approach to data ownership, access and governance. Data mesh is an organizational and cultural...
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Topics:
Cloud Computing,
Data Governance,
Data Management,
Data,
Digital Technology,
data operations,
Analytics & Data