Notes about Cutting-Edge Technologies and Everything

  Today’s analytics and BI platforms are augmented throughout and enable users to compose low/no-code workflows and applications. Cloud ecosystems and alignment with digital workplace tools are key selection factors. This research helps data and analytics leaders plan for and select these platforms. Analytics and business intelligence (ABI) platforms enable less technical users, including businesspeople, to model, analyze, explore, share and manage data, and collaborate and share findings, enabled by IT and augmented by artificial intelligence (AI). ABI platforms may optionally include the ability to create, modify or enrich a semantic model including business rules. Today’s ABI platforms have an emphasis on visual self-service for end users, augmented by AI to deliver automated insights. Increasingly, the focus of augmentation is shifting from the analyst persona to the consumer or decision maker. To achieve this, automated insights must not only be statistically relevant, but the...

This report assesses 20 vendors of platforms that data scientists and others can use to source data, build models and operationalize machine learning. It will help them make the right choice from a crowded field in a maturing DSML platform market that continues to show rapid product development. Market Definition/Description Gartner  defines a data science and machine learning (DSML) platform as a core product and supporting portfolio of coherently integrated products, components, libraries and frameworks (including proprietary, partner-sourced and open-source). Its primary users are data science professionals, including expert data scientists, citizen data scientists, data engineers, application developers and machine learning (ML) specialists. The core product and supporting portfolio: Are sufficiently well-integrated to provide a consistent “look and feel.” Create a user experience in which all components are reasonably interoperable in support of an analytics pipeline. The...

From the first glance, you can see that there are seven key Subject Areas where the Subject domains are located. These are: Data Data and System Design Technology, Governance Data Quality Security Related Capabilities. You can see that the difference in approaches to define the key Domains are rather big. It is not the purpose of this article to deliver a detailed analysis, but there is one striking observation I would like to share: the Subject domains and deliverables of these domains are being mixed with one another.  For example, let us have a look at Data governance. The domain ‘Data governance’ exists in four different models. Some other domains like ‘Data management strategy’, that appears in three models, is considered as a deliverable of Data Governance domain in other models, for example in DAMA model. Such a big difference of opinions on key Subject domains is rather confusing. Subject domain dimensions Subject domain dimensions are characteristics of (sub-) domains. It ...

  Data integration tools address a wide range of use cases that rely on key data delivery capabilities. This research helps data and analytics leaders identify vendors’ relative strengths across these capabilities and select the right tool in support of their data management solutions. Key Findings All data integration tool vendors were rated as “meeting/exceeding expectations” for their support for bulk/batch data movement and streaming data integration. However, support for other data delivery styles (data virtualization and data replication, for example) is less consistently delivered across the range of products evaluated. Active metadata is now critical as organizations continue to focus on metadata-driven optimization and automation of integration flows. The cohort of products in this evaluation averaged 3.3 out of a possible 5.0. While adequate, these capabilities must improve. Data virtualization has become less prominent as a data integration delivery style, with 30% of su...

Why Read This Report In our 30-criterion evaluation of value stream management (VSM) providers, we identified the 11 most significant ones — Atlassian, Blueprint, CloudBees, ConnectALL, Digital.ai, GitLab, IBM, Plutora, ServiceNow, Targetprocess, and Tasktop — and researched, analyzed, and scored them. This report shows how each provider measures up and helps application development and delivery (AD&D) professionals select the right one for their needs. Strong interest in VSM is driven primarily by three roles: 1) product owners and/or program managers who need data to help drive strategies, set priorities, and unlock team potential; 2) development leaders who use VSM to create connected, automated, and self-governed CI/CD pipelines with observability for improving and accelerating the pace of delivery; and 3) release engineers who use VSM for governance, compliance, and upstream observability to manage risk. (see endnote 3) To serve these roles effectively, customers should look f...

According to Gartner: “By 2022, organizations using multiple go-to-market approaches for digital commerce will outperform noncommerce organizations by 30 percentage points in sales growth." Covid-19 has forced many brands to accelerate their digital-first commerce strategies, sooner than they had planned, in an effort to keep up with customer demands and drive revenue.    Whether your brand was an early adopter, or is still struggling to implement a robust digital commerce strategy, the Gartner Predicts 2021:  COVID-19 Drives Accelerated Shift to Digital and Commerce Model Evolution report will help you prepare for 2021. We believe, in this report you will discover:  5 key digital commerce predictions for 2021 and beyond The market implications of these predictions  How you can embrace new market realities to propel your business Get the report >>>

Our analyses confirm that data is in far worse shape than most managers realize — and than we feared — and carry enormous implications for managers everywhere: On average, 47% of newly-created data records have at least one critical (e.g., work-impacting) error.  A full quarter of the scores in our sample are below 30% and half are below 57%. In today’s business world, work and data are inextricably tied to one another. No manager can claim that his area is functioning properly in the face of data quality issues. It is hard to see how businesses can survive, never mind thrive, under such conditions. Only 3% of the DQ scores in our study can be rated “acceptable” using the loosest-possible standard.  We often ask managers (both in these classes and in consulting engagements) how good their data needs to be. While a fine-grained answer depends on their uses of the data, how much an error costs them, and other company- and department-specific considerations, none has ever thought...

Expert data scientists and other professionals working in data science roles require capabilities to source data, build models and operationalize machine learning insights. Significant vendor growth, product development and myriad competing visions reflect a healthy market that is maturing rapidly. This Magic Quadrant evaluates vendors of data science and machine learning (DSML) platforms. Gartner defines a DSML platform as a core product and supporting portfolio of coherently integrated products, components, libraries and frameworks (including proprietary, partner and open source). Its primary users are data science professionals. These include expert data scientists, citizen data scientists, data engineers and machine learning (ML) engineers/specialists. Coherent integration means that the core product and supporting portfolio provide a consistent “look and feel” and create a user experience where all components are reasonably interoperable in support of an analytics pipel...

While traditional data warehouses often took years to build, deploy, and reap benefits from, today's organizations want simple, agile, integrated, cost-effective, and highly automated solutions to support insights. In addition, traditional architectures are failing to meet new business requirements, especially around high-speed data streaming, real-time analytics, large volumes of messy and complex data sets, and self-service. As a result, firms are revisiting their data architectures, looking for ways to modernize to support new requirements. DMA is a modern architecture that minimizes the complexity of messy data and hides heterogeneity by embodying a trusted model and integrated policies and by adapting to changing business requirements. It leverages metadata, in-memory, and distributed data repositories, running on-premises or in the cloud, to deliver scalable and integrated analytics. Adoption of DMA will grow further as enterprise architects look at overcoming data challeng...

This quarter: Investments - record breaking third quarter for investments in data & analytics companies. M&A - The Hadoop market consolidates quickly and data science/AI companies add portfolio capabilities by acquisition. B2B software brand Idera has bought WhereScape. WhereScape develops and markets automation software for modern Data Warehouses deployed in in the cloud or on premise. Idera, a parent company for several database, development, and testing software companies announced to integrate WhereScape in their Database Tools unit. Other software providers in the same Idera unit are AquaFold featuring an IDE for visual database queries and Webyog featuring MySQL monitoring and management tools. With the acquisition of WhereScape, Idera improves its capabilities for empowering data professionals regarding DevOps use cases in complex data environments. WhereScape was a very visible player in the Data & Analytics ecosystem. It will be interesting to watch whether Idera...

Key Takeaways Software AG, IBM, Microsoft, Google, And TIBCO Software Lead The Pack Forrester's research uncovered a market in which Software AG, IBM, Microsoft, Google, and TIBCO Software are Leaders; Cloudera, SAS, Amazon Web Services, and Impetus are Strong Performers; and EsperTech and Alibaba are Contenders. Analytics Prowess, Scalability, And Deployment Freedom Are Key Differentiators Depth and breadth of analytics types on streaming data are critical. But that is all for naught if streaming analytics vendors cannot also scale to handle potentially huge volumes of streaming data. Also, it's critical that streaming analytics can be deployed where it is most needed, such as on-premises, in the cloud, and/or at the edge. Read report >>>

The Gartner Hype Cycle highlights the 29 emerging technologies CIOs should experiment with over the next year. Today, companies detect insurance fraud using a combination of claim analysis, computer programs and private investigators. The FBI estimates the total cost of non-healthcare-related insurance fraud to be around $40 billion per year. But a maturing emerging technology called emotion artificial intelligence (AI) might make it possible to detect insurance fraud based on audio analysis of the caller. Some technologies will provide “superhuman capabilities” In addition to catching fraud, this technology can improve customer experience by tracking happiness, more accurately directing callers, enabling better diagnostics for dementia, detecting distracted drivers, and even adapting education to a student’s current emotional state. Though still relatively new, emotion AI is one of 21 new technologies added to the Gartner Hype Cycle for Emerging Technologies, 2019. Original article ...

We discovered that machine learning engineer job postings had the highest percentage of AI and machine learning keywords this year (as they did in 2018). Machine learning engineers develop devices and software that use predictive technology, such as Apple’s Siri or weather-forecasting apps. They ensure machine learning algorithms have the data that needs to be processed and analyze huge amounts of real-time data to make machine learning models more accurate. While machine learning engineer jobs still have the largest number of postings containing the relevant keywords, in 2018, they composed a greater percentage of these postings (94.2%, versus 75% in 2019). Many of the jobs requiring AI skills on 2019’s top 10 were nowhere to be found on 2018’s list — such as deep learning engineer, appearing for the first time in second place. Deep learning engineers develop programming systems that mimic brain functions, among other tasks. These engineers are key players in three rapidly growing fie...