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There is a fundamental flaw in information technology, or at least in the way it is most commonly delivered. Most technology systems are developed under the assumption that all people will use the system primarily in the same way. Sure, there are some options built in — perhaps the same action can be initiated by either clicking on a button, selecting a menu item or invoking a keyboard short-cut....

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Topics: Business Intelligence, Data Management, natural language processing, data operations, Analytics and Data, AI and Machine Learning


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...

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Topics: business intelligence, Analytics, Data Integration, Data, AI and Machine Learning


Organizations have been using data virtualization to collect and integrate data from various sources, and in different formats, to create a single source of truth without redundancy or overlap, thus improving and accelerating decision-making giving them a competitive advantage in the market. Our research shows that data virtualization is popular in the big data world. One-quarter (27%) of...

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Topics: embedded analytics, Analytics, Business Intelligence, Streaming Analytics, AI and Machine Learning


I recently wrote about the importance of data pipelines and the role they play in transporting data between the stages of data processing and analytics. Healthy data pipelines are necessary to ensure data is integrated and processed in the sequence required to generate business intelligence. The concept of the data pipeline is nothing new of course, but it is becoming increasingly important as...

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Topics: Analytics, Business Intelligence, Data Governance, Data Integration, Data, Digital Technology, Digital transformation, data lakes, data operations, Streaming Data Events, Analytics and Data, AI and Machine Learning


I have written previously that the world of data and analytics will become more and more centered around real-time, streaming data. Data is created constantly and increasingly is being collected simultaneously. Technology advances now enable organizations to process and analyze information as it is being collected to respond in real time to opportunities and threats. Not all use cases require...

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Topics: business intelligence, Analytics, Internet of Things, Data, Digital Technology, Streaming Analytics, Streaming Data Events, Analytics and Data, AI and Machine Learning


For years, maybe decades, we have heard about the struggles between IT and line-of-business functions. In this perspective, we will look at some of the data from our Analytics and Data Benchmark Research about the roles of IT and line-of-business teams in analytics and data processes. We will also look at some of the disconnects between these two groups. And, by looking at how organizations are...

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Topics: Analytics, Business Intelligence, Data, Digital Technology, Analytics and Data, AI and Machine Learning


Despite widespread and increasing use of the cloud for data and analytics workloads, it has become clear in recent years that, for most organizations, a proportion of data-processing workloads will remain on-premises in centralized data centers or distributed-edge processing infrastructure. As we recently noted, as compute and storage are distributed across a hybrid and multi-cloud architecture,...

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Topics: Analytics, Business Intelligence, Data Governance, Data, data operations, AI and Machine Learning


The various NoSQL databases have become a staple of the data platforms landscape since the term entered the IT industry lexicon in 2009 to describe a new generation of non-relational databases. While NoSQL began as a ragtag collection of loosely affiliated, open-source database projects, several commercial NoSQL database providers are now established as credible alternatives to the various...

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Topics: Analytics, Data, AI and Machine Learning


I recently described the emergence of hydroanalytic data platforms, outlining how the processes involved in generating energy from a lake or reservoir were analogous to those required to generate intelligence from a data lake. I explained how structured data processing and analytics acceleration capabilities are the equivalent of turbines, generators and transformers in a hydroelectric power...

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Topics: Analytics, Data Governance, Data, Digital Technology, data lakes, data operations, Streaming Data Events, AI and Machine Learning


The use of artificial intelligence (AI) using machine learning (ML) will be the single most important trend in business software this decade because it can multiply the investment value of such applications and provide vendors an important source of differentiation to achieve a competitive advantage in what are today very mature software categories. I assert that by 2025, almost all Office of...

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Topics: Office of Finance, embedded analytics, Data Management, Business Planning, Financial Performance Management, ERP and Continuous Accounting, AI and Machine Learning


As I stated when joining Ventana Research, the socioeconomic impacts of the pandemic and its aftereffects have highlighted more than ever the differences between organizations that can turn data into insights and are agile enough to act upon it and those that are incapable of seeing or responding to the need for change. Data-driven organizations stand to gain competitive advantage, responding...

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Topics: Analytics, Business Intelligence, Data Integration, Data, data lakes, data operations, Streaming Data Events, AI and Machine Learning


I recently described how the data platforms landscape will remain divided between analytic and operational workloads for the foreseeable future. Analytic data platforms are designed to store, manage, process and analyze data, enabling organizations to maximize data to operate with greater efficiency, while operational data platforms are designed to store, manage and process data to support...

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Topics: embedded analytics, Analytics, Business Intelligence, Data, Digital Technology, Streaming Data Events, Analytics and Data, AI and Machine Learning


Organizations of all sizes are dealing with exponentially increasing data volume and data sources, which creates challenges such as siloed information, increased technical complexities across various systems and slow reporting of important business metrics. Migrating to the cloud does not solve the problems associated with performing analytics and business intelligence on data stored in disparate...

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Topics: Analytics, Business Intelligence, Data Integration, Data, data lakes, data operations, Streaming Analytics, AI and Machine Learning


Ventana Research recently announced its 2022 Market Agenda for the Office of Finance, continuing the guidance we have offered since 2003 on the practical use of technology for the finance and accounting department. Our insights and best practices aim to enable organizations to operate with agility and resiliency, improving performance and delivering greater value as a strategic partner.

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Topics: Office of Finance, Business Intelligence, Collaboration, Business Planning, Financial Performance Management, ERP and Continuous Accounting, Revenue, blockchain, robotic finance, Predictive Planning, AI and Machine Learning, lease and tax accounting


Ventana Research recently announced its 2022 Market Agenda for the Office of Revenue, continuing the guidance we have offered for nearly two decades to help organizations realize optimal value from applying technology to improve business outcomes. Chief sales and revenue officers and their associated operations teams are experts in their respective fields but may not have the guidance needed to...

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Topics: Sales, Analytics, Internet of Things, Data, Sales Performance Management, Digital Technology, Digital Commerce, Conversational Computing, mobile computing, Subscription Management, extended reality, intelligent sales, partner management, AI and Machine Learning


Organizations today have huge volumes of data across various cloud and on-premises systems which keep growing by the second. To derive value from this data, organizations must query the data regularly and share insights with relevant teams and departments. Automating this process using natural language processing (NLP) and artificial intelligence and machine learning (AI/ML) enables...

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Topics: embedded analytics, Analytics, Business Intelligence, Data Integration, Data, natural language processing, data lakes, data operations, AI and Machine Learning


The internet is a rich source of information and is used by buyers to research new applications and offerings well before ever engaging a vendor and salesperson. Along with massive growth in offerings, this is a major reason why sales teams are facing increasing challenges to successfully sell and attain targets.

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Topics: Sales, AI and Machine Learning


Data lakes have enormous potential as a source of business intelligence. However, many early adopters of data lakes have found that simply storing large amounts of data in a data lake environment is not enough to generate business intelligence from that data. Similarly, lakes and reservoirs have enormous potential as sources of energy. However, simply storing large amounts of water in a lake is...

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Topics: Analytics, Business Intelligence, Cloud Computing, Data Governance, Data Integration, Data, Digital Technology, data lakes, data operations, AI and Machine Learning


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...

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Topics: business intelligence, Analytics, Data, data lakes, data operations, AI and Machine Learning


With the emergence of multiple selling channels and the rise of the subscription model, the need for a unified approach to revenue planning and execution should be a priority for every organization. As I have written about in my analyst perspective Revenue Management: The Opportunity for Innovation and Optimization, this need to unify the approach and focus on alignment across all revenue...

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Topics: Sales, Customer Experience, Sales Performance Management, Subscription Management, AI and Machine Learning


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