Notes about Cutting-Edge Technologies and Everything

  Strategic Planning Assumptions Through 2022, manual data management tasks will be reduced by 45% through the addition of machine learning and automated service-level management. By 2023, AI-enabled automation in data management and integration will reduce the need for IT specialists by 20%.  Read report >>>

  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...

Metadata management is a core aspect of an organization’s ability to manage its data and information assets. The term “metadata” describes the various facets of an information asset that can improve its usability throughout its life cycle. Metadata and its uses go far beyond technical matters. Metadata is used as a reference for business-oriented and technical projects, and lays the foundations for describing, inventorying and understanding data for multiple use cases. Use-case examples include data governance, security and risk, data analysis and data value. The market for metadata management solutions is complex because these solutions are not all identical in scope or capability. Vendors include companies with one or more of the following functional capabilities in their stand-alone metadata management products (not all vendors offer all these capabilities, and not all vendor solutions offer these capabilities in one product): Metadata repositories — Used to document and manage meta...

Key Takeaways  Alation, Collibra, Alex Solutions, And IBM Lead The Pack Forrester’s research uncovered a market in which Alation, Collibra, Alex Solutions, and IBM are Leaders; data.world, Informatica, Io-Tahoe, and Hitachi Vantara are Strong Performers; and Infogix and erwin are Contenders.  Collaboration, Lineage, And Data Variety Are Key Differentiators As metadata and business glossary technology becomes outdated and less effective, improved machine learning will dictate which providers lead the pack. Vendors that can provide scaleout collaboration, offer detailed data lineage, and interpret any type of data will position themselves to successfully deliver contextualized, trusted, accessible data to their customers. Read full report >>>

Data produced by applications, devices, or humans must be processed before it is consumed. By definition, a data pipeline represents the flow of data between two or more systems. It is a set of instructions that determine how and when to move data between these systems. My last blog conveyed how connectivity is foundational to a data platform. In this blog, I will describe the different data processing pipelines that leverage different capabilities of the data platform, such as connectivity and data engines for processing. There are many data processing pipelines. One may: “Integrate” data from multiple sources Perform data quality checks or standardize data Apply data security-related transformations, which include masking, anonymizing, or encryption Match, merge, master, and do entity resolution Share data with partners and customers in the required format, such as HL7 Consumers or “targets” of data pipelines may include: Data warehouses like ...

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...

Key Takeaways Talend, Denodo Technologies, Oracle, IBM, And Paxata Lead The Pack Forrester's research uncovered a market in which Talend, Denodo Technologies, Oracle, IBM, and Paxata are Leaders; Hortonworks, Cambridge Semantics, SAP, Trifacta, Cloudera, and Syncsort are Strong Performers; and Podium Data, TIBCO Software, Informatica, and Hitachi Vantara are Contenders. EA Pros Are Looking To Support Multiple Use Cases With Big Data Fabric The big data fabric market is growing because more EA pros see big data fabric as critical for their enterprise big data strategy. Scale, Performance, AI/Machine Learning, And Use-Case Support Are Key Differentiators The Leaders we identified support a broader set of use cases, enhanced AI and machine learning capabilities, and offer good scalability features. ...

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...

The Forrester Wave™: Big Data Fabric, Q4 2016   Details >>   (the link provided by Informatica here )