Matt Aslett's Analyst Perspectives

Tamr Directs Data Integrity

Posted by Matt Aslett on Feb 8, 2023 3:00:00 AM

Organizations across various industries collect multiple types of data from disparate systems to answer key business questions and deliver personalized experiences for customers. The expanding volume of data increases complexity, and data management becomes a challenge if the process is manual and rules-based. There can be numerous siloed, incomplete and outdated data sources that result in inaccurate results. Organizations must also deal with concurrent errors – from customers to products to suppliers – to create a complete view of the data. Many vendors, including Tamr, have turned to artificial intelligence and machine learning to overcome the challenges associated with maintaining data quality amid the growing volume and variety of data. I assert that by 2026, more than three-quarters of organizations’ data management processes will be enhanced with artificial intelligence and machine learning to increase automation, accuracy, agility and speed.

Read More

Topics: Data Governance, Data Management, Data, data operations, analytic data platforms

SnapLogic Promotes Intelligent Automation for All

Posted by Matt Aslett on Jan 31, 2023 3:00:00 AM

Despite the emphasis on organizations being more data-driven and making an increasing proportion of business decisions based on data and analytics, it remains the case that some of the most fundamental questions about an organization are difficult to answer using data and analytics. Ostensibly simple questions such as, “how many customers does the organization have?” can be fiendishly difficult to answer, especially for organizations with multiple business entities, regions, departments and applications. Increasing volumes and sources of data can hinder, rather than help. Only 1 in 5 participants (20%) in Ventana Research’s Analytics and Data Benchmark research are very confident in their organization’s ability to analyze the overall quantity of data. This is a perennial issue that data and application integration vendors, such as SnapLogic, are aiming to address – increasingly through automation and products for business users as well as data management professionals.

Read More

Topics: Cloud Computing, Data Management, Data, data operations, AI & Machine Learning, Analytics & Data

2023 Market Agenda for Data: Accelerating Data Agility

Posted by Matt Aslett on Jan 18, 2023 3:00:00 AM

Ventana Research recently announced its 2023 Market Agenda for Data, continuing the guidance we have offered for two decades to help organizations derive optimal value and improve business outcomes.

Read More

Topics: Cloud Computing, Data Governance, Data Management, Data, Digital Technology, data operations, Analytics & Data, Streaming Data & Events, analytic data platforms, Operational Data Platforms

Acceldata Enables Data Observability

Posted by Matt Aslett on Jan 10, 2023 3:00:00 AM

Data observability is a hot topic and trend. I have written about the importance of data observability for ensuring healthy data pipelines, and have covered multiple vendors with data observability capabilities, offered both as standalone and part of a larger data engineering system. Data observability software provides an environment that takes advantage of machine learning and DataOps to automate the monitoring of data quality and reliability. The term has been adopted by multiple vendors across the industry, and while they all have key functionality in common – including collecting and measuring metrics related to data quality and data lineage – there is also room for differentiation. A prime example is Acceldata, which takes a position that data observability requires monitoring not only data and data pipelines but also the underlying data processing compute infrastructure as well as data access and usage.

Read More

Topics: Cloud Computing, Data Management, Data, Digital Technology, data operations

InterSystems Transforming Organizations with Cloud Smart Data Fabric

Posted by Matt Aslett on Dec 27, 2022 3:00:00 AM

The shift from on-premises server infrastructure to cloud-based and software-as-a-service (SaaS) models has had a profound impact on the data and analytics architecture of many organizations in recent years. More than one-half of participants (59%) in Ventana Research’s Analytics and Data Benchmark research are deploying data and analytics workloads in the cloud, and a further 30% plan to do so. Customer demand for cloud-based consumption models has also had a significant impact on the products and services that are available from data and analytics vendors. Data platform providers, both operational and analytic, have had to adapt to changing customer demand. The initial response — making existing products available for deployment on cloud infrastructure — only scratched the surface in terms of responding to emerging expectations. We now see the next generation of products, designed specifically to deliver innovation by taking advantage of cloud-native architecture, being brought to market both by emerging startups, and established vendors, including InterSystems.

Read More

Topics: business intelligence, Cloud Computing, Data Management, Data, natural language processing, data operations, AI & Machine Learning, Analytics & Data, analytic data platforms, Operational Data Platforms

Monte Carlo Bets on the Future of Data Observability

Posted by Matt Aslett on Dec 13, 2022 3:00:00 AM

Earlier this year, I wrote about the increasing importance of data observability, an emerging product category that takes advantage of machine learning (ML) and Data Operations (DataOps) to automate the monitoring of data used for analytics projects to ensure its quality and lineage. Monitoring the quality and lineage of data is nothing new. Manual tools exist to ensure that it is complete, valid and consistent, as well as relevant and free from duplication. Data observability vendors, including Monte Carlo Data, have emerged in recent years with the goal of increasing the productivity of data teams and improving organizations’ trust in data using automation and artificial intelligence and machine learning (AI/ML).

Read More

Topics: business intelligence, Cloud Computing, Data Management, Data, data operations

Teradata Goes Cloud Native with VantageCloud Lake

Posted by Matt Aslett on Dec 1, 2022 3:00:00 AM

Organizations are increasingly utilizing cloud object storage as the foundation for analytic initiatives. There are multiple advantages to this approach, not least of which is enabling organizations to keep higher volumes of data relatively inexpensively, increasing the amount of data queried in analytics initiatives. I assert that by 2024, 6 in ten organizations will use cloud-based technology as the primary analytics data platform, making it easier to adopt and scale operations as necessary.

Read More

Topics: Teradata, Data Governance, Data Management, Data, analytic data platforms, operational data plaftforms, Object storage, vantage platforms

The Arguments For, and Against, In-Database Machine Learning

Posted by Matt Aslett on Nov 23, 2022 3:00:00 AM

Almost all organizations are investing in data science, or planning to, as they seek to encourage experimentation and exploration to identify new business challenges and opportunities as part of the drive toward creating a more data-driven culture. My colleague, David Menninger, has written about how organizations using artificial intelligence and machine learning (AI/ML) report gaining competitive advantage, improving customer experiences, responding faster to opportunities and threats, and improving the bottom line with increased sales and lower costs. One-quarter of participants (25%) in Ventana Research’s Analytics and Data Benchmark Research are already using AI/ML, while more than one-third (34%) plan to do so in the next year, and more than one-quarter (28%) plan to do so eventually. As organizations adopt data science and expand their analytics initiatives, they face no shortage of options for AI/ML capabilities. Understanding which is the most appropriate approach to take could be the difference between success and failure. The cloud providers all offer services, including general-purpose ML environments, as well as dedicated services for specific use cases, such as image detection or language translation. Software vendors also provide a range of products, both on-premises and in the cloud, including general-purpose ML platforms and specialist applications. Meanwhile, analytic data platform providers are increasingly adding ML capabilities to their offerings to provide additional value to customers and differentiate themselves from their competitors. There is no simple answer as to which is the best approach, but it is worth weighing the relative benefits and challenges. Looking at the options from the perspective of our analytic data platform expertise, the key choice is between AI/ML capabilities provided on a standalone basis or integrated into a larger data platform.

Read More

Topics: Data Governance, Data Management, Data, data operations, AI & Machine Learning, Analytics & Data, analytic data platforms

Databricks Lakehouse Platform Maximizes Analytical Value

Posted by Matt Aslett on Nov 16, 2022 3:00:00 AM

I have previously written about growing interest in the data lakehouse as one of the design patterns for delivering hydroanalytics analysis of data in a data lake. Many organizations have invested in data lakes as a relatively inexpensive way of storing large volumes of data from multiple enterprise applications and workloads, especially semi- and unstructured data that is unsuitable for storing and processing in a data warehouse. However, early data lake projects lacked structured data management and processing functionality to support multiple business intelligence efforts as well as data science and even operational applications.

Read More

Topics: Business Intelligence, Data Governance, Data Management, Data, AI & Machine Learning, Streaming Data & Events, analytic data platforms

IBM’s Cloud Pak for Data Builds a Foundation for Data Fabric

Posted by Matt Aslett on Nov 8, 2022 3:03:00 AM

I have written recently about the similarities and differences between data mesh and data fabric. The two are potentially complementary. Data mesh is an organizational and cultural approach to data ownership, access and governance. Data fabric is a technical approach to automating data management and data governance in a distributed architecture. There are various definitions of data fabric, but key elements include a data catalog for metadata-driven data governance and self-service, agile data integration.

Read More

Topics: business intelligence, Cloud Computing, Data Governance, Data Management, Data, data operations, AI & Machine Learning, operational data plaftforms

Content not found