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Ventana Research’s Data Lakes Dynamics Insights research illustrates that while data lakes are fulfilling their promise of enabling organizations to economically store and process large volumes of raw data, data lake environments continue to evolve. Data lakes were initially based primarily on Apache Hadoop deployed on-premises but are now increasingly based on cloud object storage. Adopters are...

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


As I recently pointed out, process mining has emerged as a pivotal technology for data-driven organizations to discover, monitor and improve processes through use of real-time event data, transactional data and log files. With recent advancements, process mining has become more efficient at discovering insights in complex processes using algorithms and visualizations. Organizations use it to...

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Topics: Analytics, Business Intelligence, Process Mining, Streaming Analytics, AI and Machine Learning


Earlier this year I described the growing use-cases for hybrid data processing. Although it is anticipated that the majority of database workloads will continue to be served by specialist data platforms targeting operational and analytic workloads respectively, there is increased demand for intelligent operational applications infused with the results of analytic processes, such as...

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Topics: Business Intelligence, Cloud Computing, Data, Streaming Data Events, analytic data platforms, AI and Machine Learning


A predictive finance department is one that can command technology to be more forward-looking and action-oriented while still fulfilling its core role of handling the financial elements of its organization including accounting, treasury and corporate finance. Beyond just automating rote tasks, technology also facilitates a shift toward becoming a predictive finance organization. Greater amounts...

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


Process mining is defined as the analysis of application telemetry including log files, transaction data and other instrumentation to understand and improve operational processes. Log data provides an abundance of information about what operations are occurring, the sequences involved in the processes, how long the processes are taking and whether or not the processes are completed successfully....

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Topics: Analytics, Business Intelligence, Process Mining, AI and Machine Learning


Organizations are collecting data from multiple data sources and a variety of systems to enrich their analytics and business intelligence (BI). But collecting data is only half of the equation. As the data grows, it becomes challenging to find the right data at the right time. Many organizations can’t take full advantage of their data lakes because they don’t know what data actually exists. Also,...

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


Kinaxis recently announced it has acquired a Netherlands-based company, MPO, a cloud-based software offering that orchestrates multiparty supply chain execution. The combination is designed to enable Kinaxis to extend its concurrent planning platform to handle core elements of supply chain execution. Kinaxis acquired all the shares of MPO for approximately US$45 million, with some of the final...

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Topics: Business Intelligence, Business Planning, Operations & Supply Chain, Enterprise Resource Planning, continuous supply chain, AI and Machine Learning


I have written about vendor efforts to use artificial intelligence (AI) and advanced analytics in their applications targeted at sales and revenue teams to improve focus and prioritize activities, both for pipeline management as well as individual opportunities. Since then, vendors have continued to innovate, and there have been more releases showcasing efforts to aid sales and revenue. And with...

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Topics: AI and Machine Learning


Business intelligence has evolved. It now includes a spectrum of analytics, one of the most promising of which has been described as augmented intelligence. Some organizations have used the term to describe the practical reality that artificial intelligence with machine learning is not replacing human intelligence, but augmenting it. The term also represents the application of AI/ML to make...

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Topics: Analytics, Business Intelligence, natural language processing, Collaborative & Conversational Computing, Analytics and Data, AI and Machine Learning


Organizations do not live in a vacuum and things happening outside their walls have a direct impact on how they perform. So, it is essential for them to incorporate external data in their forecasting, planning and budgeting, especially for predictive analytics and machine learning (ML) to support artificial intelligence (AI). I use the term external data to include any information about the world...

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Topics: Office of Finance, Business Planning, Financial Performance Management, AI and Machine Learning


Zoho presented analysts with a deep look at its strategy and roadmap at its July analyst conference, describing how it intends to meld its many business applications together through integration at the level of the platform. The company, which is privately owned and funded, has generally sought to build its own tools rather than buy or partner. This approach has allowed the firm to create a suite...

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Topics: Customer Experience, Voice of the Customer, Data, AI and Machine Learning


Organizations are managing and analyzing large datasets every day, identifying patterns and generating insights to inform decisions. This can provide numerous benefits for an organization, such as improved operational efficiency, cost optimization, fraud detection, competitive advantage and enhanced business processes. By bringing the right, actionable data to the right user, organizations can...

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


The analytics and business intelligence market landscape continues to grow as more organizations seek robust tools and capabilities to visualize and better understand data. BI systems are used to perform data analysis, identify market trends and opportunities and streamline business processes. They can collect and combine data from internal and external systems to present a holistic view.

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


Anaplan offers a cloud-based business planning platform that incorporates a modeling and calculation engine. The tool makes it relatively easy to add or expand the scope of plans that can be connected and monitored on a single platform. This Integrated Business Planning (IBP) approach enables organizations to use the software for financial planning or budgeting, sales, supply chain, workforce,...

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Topics: Office of Finance, Continuous Planning, Business Intelligence, Business Planning, Financial Performance Management, continuous supply chain, AI and Machine Learning


I recently explained how emerging application requirements were expanding the range of use cases for NoSQL databases, increasing adoption based on the availability of enhanced functionality. These intelligent applications require a close relationship between operational data platforms and the output of data science and machine learning projects. This ensures that machine learning and predictive...

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Topics: Business Intelligence, Data, analytic data platforms, AI and Machine Learning


I often use the term “analytics” to refer to a broad set of capabilities, deliberately broader than business intelligence. In this Perspective, I’d like to share what decision-makers should consider as they evaluate the range of analytics requirements for their organization.

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Topics: Business Intelligence, natural language processing, Streaming Analytics, Analytics and Data, AI and Machine Learning


Organizations are collecting vast amounts of data every day, utilizing business intelligence software and data visualization to gain insights and identify patterns and errors in the data. Making sense of these patterns can enable an organization to gain an edge in the marketplace and plan more strategically.

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


When joining Ventana Research, I noted that the need to be more data-driven has become a mantra among large and small organizations alike. Data-driven organizations stand to gain competitive advantage, responding faster to worker and customer demands for more innovative, data-rich applications and personalized experiences. Being data-driven is clearly something to aspire to. However, it is also a...

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


OneStream offers a platform designed to serve the needs of accounting and financial planning and analysis organizations. The software handles financial close and consolidation, planning and budgeting, analysis and reporting. For me, the most significant announcement at the company’s recent user conference was the unveiling of its Sensible ML (Machine Learning) offering, which is in limited...

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Topics: Business Planning, Financial Performance Management, ERP and Continuous Accounting, AI and Machine Learning


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...

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Topics: business intelligence, Analytics, Cloud Computing, Data, Digital Technology, Analytics and Data, AI and Machine Learning


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