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...

  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...

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 >>>

DataOps techniques can provide a more agile and collaborative approach to building and managing data pipelines. The pandemic has accelerated the need for data and analytics leaders to deliver data and analytics insight faster, with higher quality and resiliency in the face of constant change. Organizations need to make better-informed and faster decisions with a focus on automation, real-time risk assessment and mitigation, continuous value delivery and agility. The point of DataOps is to change how people collaborate around data and how it is used in the organization  As a result, data and analytics leaders are increasingly applying DataOps techniques that provide a more agile and collaborative approach to building and managing data pipelines. What is DataOps? Gartner defines DataOps as a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and data consumers across an organization. “The poi...

Digital society expects its citizens to “speak data.” Unless data and analytics  leaders treat information as the new second language of business,  government and communities, they will not be able to deliver the  competitive advantage and agility demanded by their enterprises. Key Challenges ■ Poor data literacy is the second highest inhibitor to progress, as reported by respondents to Gartner’s third annual Chief Data Oficer Survey, behind culture change and just ahead of lack of t alent and skills. ■ An information language barrier exists across business units and IT functions, rooted in ineffective communication across a wide range of diverse stakeholders. As a result, data and analytics leaders struggle to get their message across and information assets go underutilized. ■ Although academic and professional programs are beginning to address the disparity in talent  and skills, in many cases they reinforce the information language barrier with narrow content...

AIOps is an emerging technology and addresses something I’m a big fan of – improving IT Operations.  So I asked fellow Gartner analyst Colin Fletcher for a guest blog on the topic… Roughly three years ago, it was looking like we were going to see many enterprise IT operations leaders put themselves in the precarious role of “ the cobbler’s children ” by forgoing investment in Artificial Intelligence (AI) to help them do their work better, faster, and cheaper. We were hearing from many IT ops leaders building incredibly sophisticated Big Data and Advanced Analytics systems for business stakeholders, but were themselves using rudimentary, reactive red/yellow/green lights and manual steps to help run the infrastructure required to keep those same systems up and running. Further, we’re all now familiar in our personal lives with dynamic recommendations from online retailers, search providers, virtual personal assistants, and entertainment services, Talk about a paradox! Now I...

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...

More than $4 trillion in goods are shipped globally each year. The 80% of those goods carried via ocean shipping creates a lot of paperwork. Required trade documentation to process and administer all the goods is approximately one-fifth of the actual physical transportation costs. Last year, a logistics business and a large technology company developed a joint global trade digitalization platform built using blockchain technology. It will enable them to establish a shared, immutable record of all transactions and provide all disparate partners access to that information at any time. Although the distributed, immutable, encrypted nature of blockchain solutions can help with such business issues, blockchain can achieve much more than that. Large companies looking to explore new disruptive business opportunities need to think beyond efficiency gains. And to do so, they need real blockchain solutions. Full article >>>

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 ...

Gartner defines a data management solution for analytics (DMSA) as a complete software system that supports and manages data in one or many file management systems, most commonly a database or multiple databases. These management systems include specific optimization strategies designed for supporting analytical processing — including, but not limited to, relational processing, nonrelational processing (such as graph processing), and machine learning or programming languages such as Python or R. Data is not necessarily stored in a relational structure, and can use multiple data models — relational, XML, JavaScript Object Notation (JSON), key-value, graph, geospatial and others. Our definition also states that: A DMSA is a system for storing, accessing, processing and delivering data intended for one or more of the four primary use cases Gartner identifies that support analytics (see Note 1). A DMSA is not a specific class or type of technology; it is a use case. A DMSA ma...

The Five Use Cases and 15 Critical Capabilities of an Analytics and BI Platform We define and assess product capabilities across the following five use cases: Agile, centralized BI provisioning: Supports an agile IT-enabled workflow, from data to centrally delivered and managed analytic content, using the platform’s self-contained data management capabilities. Decentralized analytics: Supports a workflow from data to self-service analytics, and includes analytics for individual business units and users. Governed data discovery: Supports a workflow from data to self-service analytics to system of record (SOR), IT-managed content with governance, reusability and promotability of user-generated content to certified data and analytics content. OEM or embedded analytics: Supports a workflow from data to embedded BI content in a process or application. Extranet deployment: Supports a workflow similar to agile, centralized BI provisioning for the external customer or, in the pu...

The critical need for information governance continues to drive a diversified market for information stewardship solutions that support it. Data and analytics leaders must assess the capabilities these solutions offer to select vendors that will best suit their needs. Key Findings Policy setting in information governance programs is still so different and inconsistent that no market of offerings is forming as yet. Furthermore, policy enforcement in information stewardship initiatives is conforming to a market, but now across a wider set of use cases. Information stewardship applications available in the market do not yet fully support the information steward's wider role and tasks. Growth in the market for information stewardship applications is being disrupted by new technology capabilities in adjacent markets, such as data quality and metadata management, and new regulatory requirements, such as GDPR. Recommendations For data and analytics leaders working with dat...

Because data is the most valuable resource in the digital business era, collecting it using only a centralized management approach is no longer viable. Data and analytics leaders need to take an aggressive approach that creates an appropriate balance between data collection and data connection. Key Challenges Data is distributed between cloud and premises, and hybrid deployments are becoming the default approach. The scale and pace of creation of data, as well as the need to harness it in real time, make it impossible to always collect data and then process it for a single value proposition or use case. As organizations prioritize operational efficiency and analytics, these two forces are making organizations rethink their data management strategies and investments. Data governance and regulatory requirements need to span all use cases and data distribution is further challenging centralized data governance approaches. Deploying different data management ...

The hype around data science and machine learning has increased from already high levels in the past year. Data and analytics leaders should use this Hype Cycle to understand technologies generating excitement and inflated expectations, as well as significant movements in adoption and maturity. The Hype Cycle The Peak of Inflated Expectations is crowded and the Trough of Disillusionment remains sparse, though several highly hyped technologies are beginning to hear the first disillusioned rumblings from the market. In general, the faster a technology moves from the innovation trigger to the peak, the faster the technology moves into the trough as organizations quickly see it as just another passing fad. This Hype Cycle is especially relevant to data and analytics leaders, chief data officers, and heads of data science teams who are implementing machine-learning programs and looking to understand the next-generation innovations. Technology provider product marketers and strategists...

Modern analytics and business intelligence platforms represent mainstream buying, with deployments increasingly cloud-based. Data and analytics leaders are upgrading traditional solutions as well as expanding portfolios with new vendors as the market innovates on ease of use and augmented analytics. Details >>   (Provided by Looker here )

Data science and machine-learning platforms enable organizations to take an end-to-end approach to building and deploying data science models. This Magic Quadrant evaluates 16 vendors to help you identify the right one for your organization's needs. Details >> (Provided by Alteryx here )

CIOs in utilities and other asset-intensive organizations can use this research to support the development of enterprise APM strategies. APM is a key element of the foundational technology that can help their organizations achieve higher levels of operational reliability, safety and efficiency. Key Findings Asset performance management (APM) solutions are widening in scope and decreasing in deployment cost due to market acceptance, increasing competition and maturation of enabling technologies such as advanced analytics, algorithms, cloud and the Internet of Things (IoT). As APM solutions mature and cloud deployment increases, asset management will become a more collaborative process. Activities will be shared among asset owners, operators, service providers and OEMs. Asset management strategies are beginning to shift from preventive to predictive — driven by innovation in enabling technologies and streamlined access to consistent operational technology (OT) data resulting from I...

The analytics center of excellence has a new mandate for making the entire organization proficient in generating and leveraging automated insights. Data and analytics leaders should include a broad spectrum of organizational, project, data, educational and technological capabilities in their ACE. Key Challenges Data and analytics leaders often struggle to define, establish and communicate the range of analytics capabilities their teams can offer the organization. IT leaders such as CIOs regularly contend that they want to "get out of the report writing business" and expand the notion of analytics from BI application development to enable the entire organization to benefit from data and analytics. Tactical business intelligence competency centers (BICCs), having formed within and emerged from IT organizations, are too limited in scope and technology-focused to provide broad-spectrum analytic enablement. Leading enterprises in most industries generally are more successf...

Data preparation — the most time-consuming task in analytics and BI — is evolving from a self-service activity to an enterprise imperative. We profile 28 data preparation tools for data and analytics leaders to consider to accelerate agile data preparation for a range of distributed content authors. Overview Key Findings The market for data preparation has now evolved from tools supporting only self-service use cases into platforms that enable data and analytics teams to build agile and searchable datasets at an enterprise scale for distributed content authors. Most vendor offerings support data profiling, data exploration, transformation, modeling and curation, and metadata support. More than 80% of the vendors surveyed embed some data cataloging features and offer varying degrees of machine-learning capabilities. The market is crowded with a range of choices, from stand-alone specialists to vendors that embed data preparation as a capability into analyti...