In today’s organization, the myriad of analytics and permutations of dashboards challenge workers’ ability to take contextual actions efficiently. Unfortunately, conventional wisdom for investing in analytics does not recognize the benefits of empowering the workforce to understand the situation, examine options and work together to make the best possible decision.
Pyramid Analytics Expands Decision Intelligence Across the Organization
Posted by
Mark Smith on Dec 30, 2022 3:00:00 AM
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Topics: business intelligence, Analytics, Data, Digital Technology, AI and Machine Learning, Digital Business, Analytics and Data, Analytic Data Platforms
The Data Catalog is Indispensable for Good Data Governance
Posted by
Matt Aslett on Sep 21, 2022 3:15:00 AM
The data catalog has become an integral component of organizational data strategies over the past decade, serving as a conduit for good data governance and facilitating self-service analytics initiatives. The data catalog has become so important, in fact, that it is easy to forget that just 10 years ago it did not exist in terms of a standalone product category. Metadata-based data management functionality has had a role to play within products for data governance and business intelligence for much longer than that, of course, but the emergence of the data catalog as a product category provided a platform for metadata-based data inventory and discovery that could span an entire organization, serving multiple departments, use cases and initiatives.
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Topics: business intelligence, Data Governance, Data Management, Data, data operations, Analytics and Data
TigerGraph Promotes Graph Database for Data Science with ML Workbench
Posted by
Matt Aslett on Jul 14, 2022 3:00:00 AM
I recently wrote about the growing range of use cases for which NoSQL databases can be considered, given increased breadth and depth of functionality available from providers of the various non-relational data platforms. As I noted, one category of NoSQL databases — graph databases — are inherently suitable for use cases that rely on relationships, such as social media, fraud detection and recommendation engines, since the graph data model represents the entities and values and also the relationships between them. The native representation of relationships can also be significant in surfacing “features” for use in machine learning modeling. There has been a concerted effort in recent years by graph database providers, including TigerGraph, to encourage and facilitate the use of graph databases by data scientists to support the development, testing and deployment of machine learning models.
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Topics: business intelligence, Analytics, Cloud Computing, Data, Digital Technology, AI and Machine Learning, data platforms, Analytics & Data
I previously explained how the data lakehouse is one of two primary approaches being adopted to deliver what I have called a hydroanalytic data platform. Hydroanalytics involves the combination of data warehouse and data lake functionality to enable and accelerate analysis of data in cloud storage services. The term data lakehouse has been rapidly adopted by several vendors in recent years to describe an environment in which data warehousing functionality is integrated into the data lake environment, rather than coexisting alongside. One of the vendors that has embraced the data lakehouse concept and terminology is Dremio, which recently launched the general availability of its Dremio Cloud data lakehouse platform.
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Topics: business intelligence, Analytics, Data, data lakes, data platforms
Oracle Positions to Address Any and All Data Platform Needs
Posted by
Matt Aslett on May 5, 2022 3:00:00 AM
I recently described how the operational data platforms sector is in a state of flux. There are multiple trends at play, including the increasing need for hybrid and multicloud data platforms, the evolution of NoSQL database functionality and applicable use-cases, and the drivers for hybrid data processing. The past decade has seen significant change in the emergence of new vendors, data models and architectures as well as new deployment and consumption approaches. As organizations adopted strategies to address these new options, a few things remained constant – one being the influence and importance of Oracle. The company’s database business continues to be a core focus of innovation, evolution and differentiation, even as it expanded its portfolio to address cloud applications and infrastructure.
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Topics: business intelligence, Analytics, Data Integration, Data, AI and Machine Learning, data platforms
Improving the State of Analytics in Organizations
Posted by
David Menninger on Feb 24, 2022 3:00:00 AM
Despite all the advances organizations have made with respect to analytics, our most recent research shows the majority of the workforce in the majority of organizations are not using analytics and business intelligence (BI). Less than one-quarter (23%) report that one-half or more of their workforce is using analytics and BI. This is a problem. It means organizations are not enabling their workforce to perform at peak efficiency and effectiveness. It means the workforce in many organizations does not have access to the same information by which they are being measured. It means organizations must find other ways to communicate with, and manage, the workforce.
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Topics: Sales, business intelligence, embedded analytics, Analytics, Data, Sales Performance Management, Digital Technology, Digital Commerce, natural language processing, Subscription Management, partner management, Revenue Management, Sales Engagement, Collaborative & Conversational Computing
Data Platforms Landscape Divided Between Analytic and Operational
Posted by
Matt Aslett on Dec 14, 2021 3:00:00 AM
As I noted when joining Ventana Research, the range of options faced by organizations in relation to data processing and analytics can be bewildering. When it comes to data platforms, however, there is one fundamental consideration that comes before all others: Is the workload primarily operational or analytic? Although most database products can be used for operational or analytic workloads, the market has been segmented between products targeting operational workloads, and those targeting analytic workloads for almost as long as there has been a database market.
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Topics: business intelligence, Analytics, Data, data lakes, AI and Machine Learning, data operations, data platforms
Organizations have become more agile and responsive, in part, as a result of being more agile with their information technology. Adopting a DevOps approach to application deployment has allowed organizations to deploy new and revised applications more quickly. DataOps is enabling organizations to be more agile in their data processes. As organizations are embracing artificial intelligence (AI) and machine learning (ML), they are recognizing the need to adopt MLOps. The same desire for agility suggests that organizations need to adopt AnalyticOps.
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Topics: business intelligence, Analytics, Data Governance, Data, Digital Technology, data operations, data platforms
Sisu Optimizes Analytics with Machine Learning for Actions & Decisions
Posted by
David Menninger on Sep 23, 2021 3:00:00 AM
Sisu Data is an analytics platform for structured data that uses machine learning and statistical analysis to automatically monitor changes in data sets and surface explanations. It can prioritize facts based on their impact and provide a detailed, interpretable context to refine and support conclusions. The product features fact boards, annotations and the ability to share facts and analysis across teams. Data teams and analysts start by creating common definitions of key performance indicators, which Sisu then utilizes to automatically test thousands of hypotheses to identify differences between groups.
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Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data, AI and Machine Learning
The Market and Value Index for Collaborative Analytics and Data
Posted by
David Menninger on Jul 23, 2021 3:00:00 AM
I am happy to share insights gleaned from our latest Value Index research, an assessment of how well vendors’ offerings meet buyers’ requirements. The 2021 Ventana Research Value Index: Collaborative Analytics and Data is the distillation of a year of market and product research by Ventana Research. See our prior post for a description of our methodology and included vendors.
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Topics: business intelligence, Analytics, Collaboration, Data Preparation, Information Management, Data, Digital Technology, natural language processing, mobile computing
The Value Index for Vendors Providing Analytics and Data on Mobile Devices
Posted by
David Menninger on Jul 19, 2021 3:00:00 AM
I am happy to share insights gleaned from our latest Value Index research, an assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Mobile Analytics and Data 2021 is the distillation of a year of market and product research by Ventana Research. See our prior post for a description of our methodology and which vendors are included.
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Topics: business intelligence, Analytics, Collaboration, Data Governance, Information Management, Data, Digital Technology, natural language processing, mobile computing
TIBCO Rated with Merit in 2021 Value Index in Analytics and Data
Posted by
David Menninger on Jul 9, 2021 3:00:00 AM
We are happy to share some insights about TIBCO Spotfire drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, AI and Machine Learning
Sisense is Vendor of Merit for Analytics and Data Value Index
Posted by
David Menninger on Jul 8, 2021 3:00:00 AM
We are happy to share some insights about Sisense drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
Infor Gets Assurance Rating for Analytics and Data Value Index
Posted by
David Menninger on Jul 7, 2021 3:00:00 AM
We are happy to share some insights about Infor Birst drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Preparation, Data, Information Management (IM), natural language processing, AI and Machine Learning
SAP Excels in Value Index for Analytics with its Customer Experience
Posted by
David Menninger on Jun 22, 2021 3:00:00 AM
We are happy to share some insights about SAP drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Data, natural language processing, AI and Machine Learning
Qlik is Exemplary and Value Index Leader in Customer Experience for 2021 Value Index for Analytics and Data
Posted by
David Menninger on Jun 8, 2021 3:00:00 AM
We are happy to share some insights about Qlik drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Data, Information Management (IM), natural language processing, AI and Machine Learning
TIBCO Information Builders is Named an Innovative Vendor in 2021 Value Index
Posted by
David Menninger on Jun 4, 2021 3:00:00 AM
We are happy to share some insights about Information Builders’ WebFOCUS Business Intelligence and Analytics Platform drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
MicroStrategy Earns Value Index Rating of Exemplary in Analytics and Data
Posted by
David Menninger on Jun 3, 2021 3:00:00 AM
We are happy to share some insights about MicroStrategy drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.
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Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, natural language processing, AI and Machine Learning
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 process. But there are many other steps in the process as well, such as coordinating the handoff between data scientists and IT or software engineers for deployment to production. This can potentially slow down the entire data-to-insights process. End-to-end platforms for AI offer the promise of simplifying these processes, allowing teams that work with data to improve organizational results.
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Topics: business intelligence, Analytics, Collaboration, Data Governance, Data Preparation, Data, AI and Machine Learning
Process Mining: Improve Execution and Operations with Analytics
Posted by
David Menninger on Apr 12, 2021 3:00:00 AM
Process-mining software isn’t exactly new, but it’s also not widely known in the software technology market. The discipline has been around for at least a decade, but is generating more interest these days with both specialist vendors and major enterprise software vendors offering process-mining products and services. We assert that through 2022, 1 in 4 organizations will look to streamline their operations by exploring process mining.
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Topics: business intelligence, Analytics, Digital Technology, AI and Machine Learning
Informatica Continues to Evolve Data Management Platform
Posted by
David Menninger on Apr 8, 2021 3:00:00 AM
Organizations are accelerating their digital transformation and looking for innovative ways to engage with customers in this new digital era of data management. The goal is to understand how to manage the growing volume of data in real time, across all sources and platforms, and use it to inform, streamline and transform internal operations. Over the years, the adoption of cloud computing has gained momentum with more and more organizations trying to make use of applications, data, analytics and self-service business intelligence (BI) tools running on top of cloud-computing infrastructure in order to improve efficiency. However, cloud adoption means living with a mix of on-premises and multiple cloud-based systems in a hybrid computing environment. The challenge is to ensure that processes, applications and data can still be integrated across cloud and on-premises systems. Our research shows that organizations still have a significant requirement for on-premises data management but also have a growing requirement for cloud-based capabilities.
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Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Information Management, Internet of Things, Data, natural language processing, AI and 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
Organizations are always looking to improve their ability to use data and AI to gain meaningful and actionable insights into their operations, services and customer needs. But unlocking value from data requires multiple analytics workloads, data science tools and machine learning algorithms to run against the same diverse data sets. Organizations still struggle with limited data visibility and insufficient insights, which are often caused by a multitude of reasons such as analytic workloads running independently, data spread across multiple data centers, data governance, etc. In our ongoing benchmark research project, we are researching the ways in which organizations work with big data and the challenges they face.
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Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Data, Information Management (IM), data lakes, AI and Machine Learning
Why Collaboration Matters in Analytic Processes
Posted by
David Menninger on Oct 29, 2020 4:00:00 AM
Every organization performing analytics with multiple employees needs to collaborate. They should be collaborating in the analytics process and in communicating the results of those analyses. As I continue my evaluation of analytics and data vendors, I have to admit some disappointment at the level of collaborative capabilities some analytics vendors provide. To be fair, the level of capabilities vary widely, but I expected collaborative capabilities to be more uniformly available as a standard feature in analytics technologies by now. I had anticipated that three-quarters of analytics vendors would include collaboration capabilities. More than half the vendors I have evaluated support some comments and discussion in their products, only a few have incorporated social recognition and wall posting as part of their collaborative capabilities. So, what impact does a lack of analytics collaboration have on organizations undergoing digital transformation?
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Topics: business intelligence, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Data, Digital Technology, collaborative computing
Blockchains Pose Problems of Persistence and Trust
Posted by
Robert Kugel on Jan 15, 2020 6:00:00 AM
I’ve written before about blockchain’s significant potential. A lot of the current discussion on the topic centers on cryptocurrencies and financial trading platforms, both of which are already in operation. However, my focus is on its applicability to business generally, especially in B2B commerce, where I believe there is significant potential for it to serve as a universal data connector. There’s also a great deal of potential for blockchain to provide individuals with greater power in managing their identity and greasing the wheels of trade. That noted, those designing and planning to implement commerce-related blockchains must address fundamental issues if blockchain technology is to achieve its potential.
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Topics: Sales, Human Capital Management, business intelligence, Collaboration, Internet of Things, Data, Product Information Management, Digital Commerce, Enterprise Resource Planning, blockchain, candidate engagement, collaborative computing, continuous supply chain
Sage Intacct recently hosted its annual user group meeting, Advantage, and earlier this year met with industry analysts. Both meetings shed light on how the company is addressing two key opportunities. One is building a robust offering to address rapidly evolving technology requirements for the Office of Finance. The other is broadening the scope of its offering to address the financial management and administration needs of its customers.
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Topics: Office of Finance, business intelligence, Financial Performance Management, ERP and Continuous Accounting, robotic finance, Predictive Planning, AI and Machine Learning, revenue and lease accounting
A 360-Degree View of the Customer Journey Can Provide Customer Experience Insights
Posted by
Mark Smith on Nov 7, 2019 7:00:00 AM
For interactions with customers to go well, organizations must manage an ever-increasing array of engagement channels. Our research finds that organizations expect to see interaction volumes increase on all channels, especially digital ones such as text-based messaging, chat, mobile and social apps. Unfortunately, the systems that manage these channels are typically disparate and uncoordinated and may not use the same underlying technology. This makes it difficult for organizations to coordinate customer interactions consistently and provide the best possible customer experience.
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Topics: Customer Experience, Voice of the Customer, business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Internet of Things, Contact Center, Data, Digital Technology, Digital Commerce, blockchain, natural language processing, data lakes, Intelligent CX, AI and Machine Learning, Subscription Management, agent management, extended reality
Today’s intense competition requires that companies know as much as they can about their customers in order to anticipate their needs and deliver a superior customer experience. However, many organizations struggle to do this well. Implementing initiatives to improve customer value across any department or process involving customers requires both in-depth visibility into current operations and excellent metrics.
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Topics: Customer Experience, Voice of the Customer, business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Internet of Things, Contact Center, Data, Digital Commerce, blockchain, natural language processing, data lakes, Intelligent CX, Conversational Computing, AI and Machine Learning, collaborative computing, mobile computing, Subscription Management, agent management, extended reality
Historical Databases: Becoming a Thing of the Past?
Posted by
David Menninger on Aug 13, 2019 7:00:00 AM
Organizations’ use of data and information is evolving as the amount of data and the frequency with which that data is collected increase. Data now streams into organizations from myriad sources, among them social media feeds and internet-of-things devices. These seemingly ever-increasing volumes of devices and data streams offer both challenges and opportunities to capture information about a business and improve its operations.
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Topics: business intelligence, embedded analytics, Analytics, Collaboration, Data Lake, Information Management, Internet of Things, Data, Digital Technology, AI and Machine Learning
Technology to Improve the Engagement and Experience of Employees
Posted by
Ventana Research on Jul 24, 2019 7:00:00 AM
It’s no secret that employees are overwhelmed. They’re having to use an array of systems and enterprise tools in the flow of work and deal with an explosion of email messages and other communications requiring some response or action and mountains of content to consume and retain. On top of these time demands, employees must try to keep up with a staggering amount of organizational change.
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Topics: Human Capital Management, business intelligence, Learning Management, Information Management, Workforce Management, Digital Technology, natural language processing, AI and Machine Learning
The Internet of Things: Real-Time Data and Analytics Enable Business Innovation
Posted by
David Menninger on May 20, 2019 6:00:00 AM
The emerging internet of things (IoT) is an extension of digital connectivity to devices and sensors in homes, businesses, vehicles and potentially almost anywhere. This innovation means that virtually any appropriately designed device can generate and transmit data about its operations, which can facilitate monitoring and a range of automatic functions. To do this IoT requires a set of event-centered information and analytic processes that enable people to use that event information to make optimal decisions and take act effectively.
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Topics: business intelligence, embedded analytics, Analytics, Collaboration, Internet of Things, Data, Information Management (IM), Digital Technology, data lakes, AI and Machine Learning
I am happy to offer some insights on Yellowfin drawn from our latest Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Analytics and Business Intelligence 2019 is the distillation of a year of market and product research efforts by Ventana Research. We utilized a structured research methodology that includes evaluation categories designed to reflect the breadth of the real-world criteria incorporated in a request for proposal (RFP) and vendor selection process for analytics and business intelligence. We evaluated Yellowfin and 14 other vendors in seven categories, five relevant to the product (adaptability, capability, manageability, reliability and usability) and two related to the vendor (TCO/ROI and vendor validation). To arrive at the Value Index rating for a given vendor, we weighted each category to reflect its relative importance in an RFP process, with the weightings based on our experience and data derived from our benchmark research on analytics and business intelligence.
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Topics: Data Science, Mobile Technology, business intelligence, Analytics, Cloud Computing, Collaboration, Yellowfin, Digital Technology
Information Builders Earns Top Analytics and BI Honors
Posted by
David Menninger on Feb 6, 2019 6:00:00 AM
I am happy to offer some insights on Information Builders drawn from our latest Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Analytics and Business Intelligence 2019 is the distillation of a year of market and product research efforts by Ventana Research. We utilized a structured research methodology that includes evaluation categories designed to reflect the breadth of the real-world criteria incorporated in a request for proposal (RFP) and vendor selection process for analytics and business intelligence software. We evaluated Information Builders and 14 other vendors in seven categories, five relevant to the product (adaptability, capability, manageability, reliability and usability) and two related to the vendor (TCO/ROI and vendor validation). To arrive at the Value Index rating for a given vendor, we weighted each category to reflect its relative importance in an RFP process, with the weightings based on our experience and data derived from our benchmark research on analytics and business intelligence.
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Topics: Data Science, business intelligence, Mobile Intelligence, Analytics, Cloud Computing, Collaboration, Digital Technology
How Do Analytics and Business Intelligence Vendors Stack Up?
Posted by
David Menninger on Feb 4, 2019 6:00:00 AM
I am happy to share some insights gleaned from our latest Value Index research, which provides an analytic representation of our assessment of how well vendors’ offerings meet buyers’ requirements. The Ventana Research Value Index: Analytics and Business Intelligence 2019 is the distillation of a year of market and product research efforts by Ventana Research. Drawing on our benchmark research, we apply a structured research methodology built on evaluation categories that are designed to reflect the real-world criteria incorporated in a request for proposal to vendors in analytics and business intelligence. Using this methodology, we evaluated vendor submissions in seven categories, five relevant to the product (adaptability, capability, manageability, reliability and usability) and two related to the vendor (TCO/ROI and vendor validation). This research-based index is the first such evaluation to assess the full business value of analytics and business intelligence software. You can learn more about our Value Index as an effective vendor selection and RFI/RFP tool at https://www.ventanaresearch.com/value-indexes/inclusion.
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Topics: Data Science, business intelligence, Mobile Intelligence, Analytics, Cloud Computing, Collaboration, Digital Technology
Analytics and Business Intelligence for a Data-Driven World
Posted by
David Menninger on Jan 29, 2019 6:00:00 AM
Ventana Research provides unique insight into the analytics and business intelligence (BI) industry. This is important, as its processes and technology play an instrumental role in enabling an organization’s business units and IT to utilize its data in both tactical and strategic ways to perform optimally. To accomplish this, organizations must provide technology that can access the data, generate and apply insights from analytics, communicate the results and support collaboration as needed.
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Topics: Data Science, business intelligence, Mobile Intelligence, Analytics, Cloud Computing, Collaboration, Digital Technology
Was accounting ever cool? Well, yes, in a nerdy sort of way. Double-entry bookkeeping, codified in the 15th century by Fra Luca Pacioli, a Franciscan friar and pal of Leonardo Da Vinci, was essential for the expansion of trade and the creation of the modern corporation. Bookkeeping and accounting were as important to economic development as two other financial inventions – insurance and fractional reserve banking. Double-entry bookkeeping is an elegant system, simple yet powerful. It supports the accurate recording of transactions and the economic condition of a business as well as analyses of its performance. That’s cool.
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Topics: Big Data, Office of Finance, Continuous Planning, business intelligence, Analytics, Financial Performance Management, Enterprise Resource Planning, ERP and Continuous Accounting
I recently attended SAS Institute’s analyst relations conference. There the company provided updates on its financial performance and its Viya platform and a glimpse into some of its future plans.
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Topics: Big Data, Data Science, Mobile Technology, business intelligence, Analytics, Cloud Computing, Collaboration, Data Governance, Data Integration, Data Preparation, Internet of Things, Information Optimization, Machine Learning and Cognitive Computing, Machine Learning Digital Technology
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