Everyone talks about data quality, as they should. Our research shows that improving the quality of information is the top benefit of data preparation activities. Data quality efforts are focused on clean data. Yes, clean data is important. but so is bad data. To be more accurate, the original data as recorded by an organization’s various devices and systems is important.
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Topics:
Data Governance,
Data Preparation,
Data,
Information Management (IM),
data lakes
Machine learning is valuable for organizations, but it can be hard to deploy. Our Machine Learning Dynamic Insights research identifies that not having enough skilled resources and difficulty building and maintaining ML systems are pressing challenges organizations face in applying ML. Traditional ML model development is resource-intensive, requiring significant domain knowledge and time to produce and compare dozens of models. And as the number of ML models grow, their management becomes...
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Topics:
embedded analytics,
Analytics,
Business Intelligence,
Collaboration,
Data Governance,
Data Preparation,
Data,
AI & Machine Learning
The amount of data flowing into organizations is growing exponentially, creating a need to process more data more quickly than ever before. Our Data Preparation Benchmark Research shows that accessing and preparing data continues to be the most time-consuming part of making data available for analysis. This can potentially slow down the organizational functions which depend on the analysis results. Trying to get ahead of the backlog with incremental improvements to existing approaches and...
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Topics:
business intelligence,
embedded analytics,
Analytics,
Collaboration,
Data Governance,
Data Preparation,
Data,
Information Management (IM),
data lakes
Organizations are becoming more and more data-driven and are looking for ways to accelerate the usage of artificial intelligence and machine learning (AI/ML). Developing and deploying AI/ML models can be complicated in many ways, often involving different tools and services to manage these solutions from end to end. Accessing and preparing data is the most common challenge organizations face in this process, and consequently, AI/ML vendors typically incorporate tools to address this part of the...
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Topics:
business intelligence,
Analytics,
Collaboration,
Data Governance,
Data Preparation,
Data,
AI & Machine Learning
Having just completed the 2021 Ventana Research Value Index for Analytics and Data, I want to share some of my observations about how the market has advanced since our assessment two years ago. The analytics software market is quite mature and products from any of the vendors we assess can be used to effectively deliver information to help your organization improve its operations. However, it’s also interesting to see how much the market continues to advance and how much investment vendors...
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Topics:
Big Data,
Key Performance Indictors,
embedded analytics,
exadata,
Analytics,
Business Collaboration,
Business Intelligence,
Collaboration,
Data Preparation,
Digital Technology,
natural language processing,
Conversational Computing,
collaborative computing,
mobile computing,
AI & Machine Learning
Organizations are increasingly using data as a strategic asset, which makes data services critical. Huge volumes of data need to be stored, managed, discovered and analyzed. Cloud computing and storage approaches provide enterprises with various capabilities to store and process their data in third-party data centers. The advent of data platforms previously discussed here are essential for organizations to effectively manage their data assets.
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Topics:
embedded analytics,
Analytics,
Business Intelligence,
Collaboration,
Data Governance,
Data Lake,
Data Preparation,
Data,
AI & Machine Learning,
Microsoft Azure
Since customer data platforms (CDP) emerged in the marketplace about five years ago, there has been debate about what roles they fill, especially within customer service organizations. They were originally developed by small software firms to provide marketing teams with a comprehensive view of customer records. Those records could be scattered throughout an organization, siloed by system and department. CDPs were an attempt to shortcut integration processes that are long, expensive and often...
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Topics:
Customer Experience,
Marketing,
data artisan,
Data Governance,
Data Lake,
Data Preparation,
Data,
Information Management (IM),
intelligent marketing
Ventana Research recently announced its 2021 Market Agenda for data, continuing the guidance we have offered for nearly two decades to help organizations derive optimal value and improve business outcomes.
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Topics:
Data Governance,
Data Preparation,
Information Management,
Data,
data lakes,
Streaming Data,
data operations,
Event Data,
Data catalog,
Event Streams,
Event Stream Processing
The industry is making huge strides with artificial intelligence (AI) and machine learning (ML). There is more data available to analyze. Analytics vendors have made it easier to build and deploy models, and AI/ML is being embedded into many types of applications. Organizations are realizing the value that AI/ML provides and there are now millions of professionals with AI or ML in their title or job description. AI/ML is even being used to make many aspects of itself easier. Organizations that...
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Topics:
Sales,
Customer Experience,
Marketing,
Analytics,
Business Intelligence,
Data Preparation,
Digital Technology,
AI & Machine Learning
Data is becoming more valuable and more important to organizations. At the same time, organizations have become more disciplined about the data on which they rely to ensure it is robust, accurate and governed properly. Without data integrity, organizations cannot trust the information produced by their data processes, and will be discouraged from using that data, resulting in inefficiencies and reduced effectiveness.
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Topics:
business intelligence,
Analytics,
Data Governance,
Data Preparation,
Information Management,
Data,
data lakes