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Research Report: Constellation’s Research Outlook For 2011

Published on January 4, 2011 by R "Ray" Wang

Organizations Seek Measurable Results In Disruptive Tech, Next Gen Business, And Legacy Optimization Projects For 2011

Credits: Hugh MacLeod

Enterprise leaders seek pragmatic, creative, and disruptive solutions that achieve both profitability and market differentiation. Cutting through the hype and buzz of the latest consumer tech innovations and disruptive technologies, Constellation Research expects business value to reemerge as the common operating principle that resonates among leading marketing, technology, operations, human resource, and finance executives. As a result, Constellation expects organizations to face three main challenges: (see Figure 1.):

Navigating disruptive technologies. Innovative leaders must quickly assess which disruptive technologies show promise for their organizations. The link back to business strategy will drive what to adopt, when to adopt, why to adopt, and how to adopt. Expect leading organizations to reinvest in research budgets and internal processes that inform, disseminate, and prepare their organizations for an increasing pace in technology adoption.
Designing next generation business models. Disruptive technologies on their own will not provide the market leading advantages required for success. Leaders must identify where these technologies can create differentiation through new business models, grow new profit pools via new experiences, and deliver market efficiencies that save money and time. Organizations will also have to learn how to fail fast, and move on to the next set of emerging ideas.
Funding innovation through legacy optimization. Leaders can expect budgets to remain from flat to incremental growth in 2011. As a result, much of the disruptive technology and next generation business models must be funded through optimizing existing investments. Leaders not only must reduce the cost of existing investments, but also, leverage existing infrastructure to achieve the greatest amount of business value.

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Research Report: Rethink Your Next Generation Business Intelligence Strategy

Published on June 28, 2010 by R "Ray" Wang

BI Solutions Must Address The Information Management Matrix

A confluence of changing business requirements and on-going vendor consolidation leads many organizations to rethink their business intelligence (BI) strategies. Many buyers face decisions to move beyond departmental solutions or to stay with an integrated apps based BI solution. Meanwhile, some buyers must decide whether or not an integrated platform provides the right balance between business impact and cost of technology. Additionally, most organizations seek support for new data types and new deployment options. As BI continues to evolve from fragmented and historical reporting to pervasive, predictive, and real-time decision support, an organization’s success increasingly depends on the support for a expanding information matrix of (see Figure 1):

New and traditional data types. A proliferation of data types from social, machine to machine, and mobile sources add new data types to traditional transactional data. Examples include content, geo-spatial, hardware data points, location based, machine data, metrics, mobile, physical data points, process, RFID’s, search, sentiment, streaming data, social, text, and web.
Visualization and reporting paradigms. Users expect more than the traditional charts, gauges, and dials. Web 2.0 innovations show how Rich Internet Applications (RIA) through tools such as AJAX, Adobe Flash and Microsoft Silverlight can create interactive BI experiences. New and old paradigms include ad-hoc query builders, business performance management (BPM) systems, dashboards, production reports, scorecards, and advanced visualizations. New visualization types include matrix charts, network diagrams, bubble charts, tree maps, word trees, tag clouds, phrase nets, and others.
Approaches and styles. Analytical techniques continue to improve as data volumes explode. New and traditional approaches include advanced analytics, business activity monitoring (BAM), BI workspace, decision support systems, low latency BI, meta data generated BI apps, non-modeled exploration and in-memory analytics, scenario analysis, and OLAP.
Deployment options. With data coming from so many different sources, users are seeking new deployment options. Common solutions in the BI portfolio include BI appliances, BI in the Cloud, BI specific DBMS, Mobile BI, open source BI, on-premises packaged BI apps, private BI clouds, and SaaS based BI.

Figure 1. The Information Management Matrix Drives Next Gen BI

Explosion In Semi-Structured And Unstructured Data Challenges Existing Solutions

Along with new business requirements, the old world of structured data must make way for a plethora of new data types in unstructured data. More importantly, solutions must support a growing number of industry vertical standards in semi-structured data. Unfortunately, no single vendor can support all the data types that fit into the following three categories (see Figure 2):

Structured data. Structured data remains the most understood type of data. Traditional sources comprise of data in transactional systems such as ERP, CRM, SCM and other database management systems. Solutions conduct analysis via OLAP and traditional apps centric and database centric BI systems.
Semi-structured data. Common examples include flat files in record format, RSS feeds, XML documents, and data in spreadsheets. Industry-specific XML data standards and cross-enterprise data-exchange standards such as ACORD, EDI, HL7, NACHA, and SWIFT will continue to grow as BI goes vertical. On the document print stream front, new systems should support ASP, Met code, and PCL.
Unstructured data. Sources include natural-language text from e-mail, blogs, SMS, social networking sites, text fields, audio, video, and images. Unstructured data represents up to 80% of today’s data sources. Enterprises are challenged in discovering and organizing unstructured data for the real-time delivery of information to the right user.

Figure 2. Data Types Fall Into Three Main Categories