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vr_Big_Data_Analytics_02_defining_big_data_analyticsTeradata continues to expand its information management and analytics technology for big data to meet growing demand. My analysis last year discussed Teradata’s approach to big data in the context of its distributed computing and data architecture. I recently got an update on the company’s strategy and products at the annual Teradata analyst summit. Our big data analytics research finds that a broad approach to big data is wise: Three-quarters of organizations want analytics to access data from all sources and not just one specific to big data. This inclusive approach is what Teradata as designed its architectural and technological approach in managing the access, storage and use of data and analytics.

Teradata has advanced its data warehouse appliance and database technologies to unify in-memory and distributed computing with Hadoop, other databases and NoSQL in one architecture; this enables it to move to center stage of the big data market. Teradata Intelligent Memory provides optimal accessibility to data based on usage characteristics for DBAs, analysts and business users consuming data from Teradata’s Unified Data Architecture (UDA). Teradata also introduced QueryGrid technology, which virtualizes distributed access to and processing of data across many sources, including the Teradata range of appliances, Teradata Aster technology, Hadoop through its SQL-H, other databases including Oracle’s and data sources including the SAS, Perl, Python and even R languages. Teradata can provide push-down processing of getting data and analytics processed through parallel execution in its UDA including data from Hadoop. Teradata QueryGrid data virtualization layer can dynamically access data and compute analytics as needed making it versatile to meet a broadening scope of big data needs.

Teradata has embraced Hadoop through a strategic relationship with Hortonworks. Its commercial distribution, Teradata Open Distribution for Hadoop (TDH) 2.1, and originates from Hortonworks. It recently announced Teradata Portfolio for Hadoop 2, which has many components. There is also a new Teradata Appliance for Hadoop; this is its fourth-generation machine and includes previously integrated and configured software with the hardware and services. Teradata has embraced and integrated Hadoop into its UDA to ensure it is a unified part of its product portfolio that is essential as Hadoop is still maturing and is not ready to operate in a fully managed and scalable environment.

Teradata has enhanced its existing portfolio of workload-specific appliances. It includes the Integrated Big Data Platform 1700, which handles up to 234 petabytes, the Integrated Data Warehouses 2750 for up to 21 petabytes for scalable data warehousing and the 6750 for balanced active data warehousing. Each appliance is configured for enterprise-class needs, works in a multisystem environment and supports balancing and shifting of workloads with high availability and disaster recovery. They are available in a variety of ratios including disks, arrays and nodes, which makes them uniquely focused for enterprise use. The appliances run version 15 of the Teradata database with Teradata Intelligent Memory and interoperate through integrated workload management. In a virtual data warehouse the appliances can provide maximum compute power, capacity and concurrent user potential for heavy work such as connecting to Hadoop and Teradata Aster. UDA enables distributed management and operations of workload-specific platforms to use data assets efficiently. Teradata Unity now is more robust in moving and loading data, and Ecosystem Manager now supports monitoring of Aster and Hadoop systems across the entire range of data managed by Teradata.

Teradata is entering the market for legacy SAP applications with Teradata Analytics for SAP, which provides integration and data models across lines of business to use logical data from SAP applications more efficiently. Teradata acquired this product from a small company in last year; it uses an approach common among data integration technologies today and can make data readily available through new access points to SAP HANA. The product can help organizations that have not committed to SAP and its technology roadmap, which proposes using SAP HANA to streamline processing of data and analytics from business applications such as CRM and ERP. For others that are moving to SAP, Teradata Analytics for SAP can provide interim support for existing SAP applications.

Teradata continues to advance JavaScript Object Notation (JSON) integration for support of document-oriented databases that are schemaless and semistructured. JSON has become a critical tool as more applications need to store and access data efficiently. NoSQL databases have become more popular recently: 25 percent of organizations in our big data analytics research are using them today, 20 percent  plan to use them within two years, and another 23 percent are evaluating NoSQL. With this focus Teradata provides for its customers application and operational support beyond just supporting data for analytic purposes.

Teradata continues expansion of its Aster Discovery Platform to process analytics for discovery and exploration and also advances visualization and interactivity with analytics, which could encroach on partners that provide advanced analytics capabilities like discovery and exploration. Organizations looking for analytic discovery tools should consider this technology overlap. Teradata provides a broad and integrated big data platform and architecture with advanced resource management to process data and analytics efficiently. In addition it provides archiving, auditing and compliance support for enterprises. It can support a range of data refining tasks including fast data landing and staging, lower workload concurrency, and multistructured and file-based data.

Teradata efforts are also supported in what I call a big data or data warehouse as a service and is called Teradata Cloud. Its approach is can operate across and be accessed from a multitenant environment where it makes its portfolio of Teradata, Aster and Hadoop available in what they call cloud compute units. This can be used in a variety of cloud computing approaches including public, private, hybrid and for backup and discovery needs. It has gained brand name customers like BevMo and Netflix who have been public references on their support of Teradata Cloud. Utilizing this cloud computing approach eliminates the need for placing Teradata appliances in the data center while providing maximum value from the technology. Teradata advancements in cloud computing comes at a perfect time where our information optimization research finds that a quarter of organizations now prefer a cloud computing approach with eight percent prefer it to be hosted by a supplier in a specific private cloud approach.

vr_Info_Optimization_10_reasons_to_change_information_availabilityWhat makes Teradata’s direction unique is moving beyond its own appliances to embrace the enterprise architecture and existing data sources; this makes it more inclusive in access than other big data approaches like those from Hadoop providers and in-memory approaches that focus more on themselves than their customers’ actual needs. Data architectures have become more complex with Hadoop, in-memory, NoSQL and appliances all in the mix. Teradata has gathered this broad range of database technology into a unified approach while integrating its products directly with those of other vendors. This inclusive approach is timely as organizations are changing how they make information available, and our information optimization benchmark research finds improving operational efficiency (for 67%) and gaining a competitive advantage (63%) to be the top two reasons for doing that. Teradata’s approach to big data helps broaden data architectures, which will help organizations in the long run. If you have not considered Teradata and its UDA and new QueryGrid technologies for your enterprise architecture, I recommend looking at them.

Regards,

Mark Smith

CEO & Chief Research Officer

Our latest benchmark research into the market for location analytics software finds significant demand for location-related technology that can improve business outcomes and generateVentanaResearch_LocationAnalytics relevant information for various types of users. (Location analytics is an extension of business analytics that can enhance the sophistication of data and processes by adding a geographic context.)  My last analyst perspective on this topic discussed the business value of insights based on geography and what organizations are doing to advance their efforts here. Our research also shows, however, that most still lack satisfaction and confidence in using the technology. Just 12 percent of all participants said they are very satisfied with the location information and analytics available in their organization. Further analysis shows that satisfaction increases with use of a dedicated application for location analytics: 71 percent of those are satisfied or very satisfied, substantially more than those using location analytics within a BI tool (22%); findings are similar for both B2B and B2C use. We find similar levels of confidence in the quality of location information: 15 percent of those using a dedicated application are very confident in their location analytics. Confidence in the reliability of such information is essential to more organizations adopting location analytics.

vr_LA_driving_change_in_location_analyticsOne cause of limited satisfaction and confidence appears to be the difficulty of analyzing information that has a location context. Two-thirds of organizations said doing so requires significant effort or some effort, and 17 percent said that is very difficult or they cannot do it. Thus it is not surprising that about three in fiveorganizations plan to change the way they use location information in the next 12 to 18 months. For more than 40 percent each, that change is driven by efforts to improve processes: a new initiative to improve information and decision-making (51%), a need to improve business-to-business planning and collaboration (50%), the desire to promote operational efficiency (49%) and as part of a wider analytics and business intelligence initiative (44%). Participants with IT titles most often identified as the driver a new initiative improving information and decision-making (61%), as did those from the services (69%) and government (63%) industry sectors; those working in lines of business insisted more on seeking change to improve B2B planning and collaboration (54%). The need for improvement shows that organizations recognize a potentially important role for location analytics in various business processes, from information use to decision-making.

A range of technologies can be used for location analytics, vr_LA_dedicated_technology_provides_satisfactionbut not all options work equally well. Today nearly half (49%) of organizations use spreadsheets heavily for analyzing information that includes location data; significantly fewer use other tools heavily – custom applications (36%), analytic or BI tools (34%) and a geographic information system (GIS, 23%). Many organizations use business applications heavily for analyzing this type of information, most often customer relationship management (CRM, 28%), supply chain management (16%) and enterprise asset management (14%) systems. Yet heavy users of a GIS or a dedicated application are the ones most often very satisfied (49%), and heavy users of spread­sheets are very satisfied least often (16%). Among those saying that the use of location analytics has im­proved their results, spreadsheet users ranked last (35%), far behind users of a GIS (55%) and analytic or BI tools (49%). Organizations that use a dedicated tool for location analytics (49%) are the most satisfied significantly more than those that use only spreadsheets (16%).

A look at the capabilities necessary for effective location analytics indicates why tools designed for the purpose get better results. More than three in five organizations said three basic capabilities are important: geographic representation of data, visual metrics associated with locations on a map, and selecting and analyzing locations on a map. One-half to one-third said interacting with maps and locations for further analysis, determining distance and drive time, and adding layers to maps are important. All of these basic capabilities are the building blocks for conducting specific analytics that can identify or recommend actions from the mashup of data about a location or to provide insights to guide decisions based on location-specific indicators.

Another technology approach used most frequently is business intelligence (BI). These tools are designed for reporting, creating dashboards and general access to analytic information such as metrics. BI tools and processes are established in both IT departments and lines of business, and location information can further enhance BI efforts. Nearly half (48%) of participants in this research ranked business intelligence interfaces as the most important to integrate with other enterprise software; custom interfaces was a distant second at only 13 percent. IT participants (55%) put BI first more often than did those in business (44%), and manufacturing (55%) ranked it higher than other industries. BI also is the application most often integrated with location analytics (45%), even more so in the largest companies by number of employees (56%) and by annual revenue (65%). In terms of planning and developing a strategy to use location analytics with other systems, most intend to integrate it with marketing automation (33%), sales force automation (30%) and enterprise content management (also 30%).

However, the research also finds impediments in using BI and location analytics together. Almost half  (46%) of participating organizations said that integrating the two requires significant effort; another 16 percent said doing that is very difficult and requires substantial time or that they have no practical way to do it. On a positive note, integration of these two technologies has advanced significantly in the last several years, and it is easier to exchange data and add it to presentations. In addition, organizations that use business intelligence to conduct location analytics reported benefits, particularly improving the customer experience (21%) and gaining competitive advantage (20%). More than three in five companies that use BI with location analytics are very satisfied (17%) or satisfied (44%) with theinformation and analytics they have available. Thus the research clearly shows that integrating location information into business intelligence can deliver value.

Looking at location information in a broader sense we find many organizations using consumer mapping to plot data quickly, predominantly free software such as from Google (which 45% use) and Microsoft (31%). The research also reveals that while almost one-third (31%) have used these for enterprise needs, only 8 percent are very satisfied with them. Like personal productivity tools, these tools can help in individual tasks like driving instructions and plotting locations for quick geographic placement, but they lack task support and operational or specific analytical context that requires secure, integrated access to enterprise systems. Free and easy access makes them attractive, but they do not provide enough capabilities for skilled workers to use in complex business tasks.

As deployments grow, so does the need to integrate and adapt location analytics to other technologies. For example, one in five research participants said mobile technology is critical for improving location analytics, as did smaller numbers for cloud computing (15%), big data (15%) and collaboration (8%). Ways of deploying location analytics also are changing, as more organizations realize that buying and installing the software on-premises (which 35% prefer) is not the only approach; nearly as many (33%) want to access it on demand through software as a service (SaaS). Very large companies by number of employees (44%) and annual revenue (39%) have the strongest bias for on-demand deployment, as does manufacturing (43%) among industry sectors. Exploiting the full potential of big data investments, whether representing machine data or customer locations, is a prime example of where location analytics can help use data effectively. The research strongly suggests that location analytics will have a place in evolving business technology environments and that broader use of innovative technology will extend the value of this investment also.

vr_LA_location_analytics_requires_experiencesHowever an organization deploys location analytics, the research shows that experience in using it is critical to success. Half of participating organizations have deployed location-focused technology, and the percentage is highest among very large companies by number of employees (56%) and annual revenue (67%). Almost two-thirds (62%) of all companies that have the most experience said location analytics has helped improve results significantly; among those who are somewhat experienced just 23 percent said this.

Organizations of course expect to realize important benefits from software investments. The top five benefits being sought from location analytics are to improve the customer experience and customer satisfaction; gain competitive advantage; improve access to and value of existing information; improve organizational alignment and coordination; and deliver products and services faster. Organizations that use a dedicated technology focus most on gaining competitive advantage (21%) and delivering products and services faster (16%). Investment in a dedicated tool for location analytics can increase the value of an organization’s information and analytics, which improves with experience in using the technology. We recommend that organizations develop a location-specific component in their agenda for analytics. If you want to learn more on the value and potential of technology in location analytics our community is available to help with more depth in best practices and insights on this topic.

Regards,

Mark Smith

CEO & Chief Research Officer

Mark Smith – Twitter

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