The recent Gartner Business Intelligence & Analytics Summit drew more than 2,000 attendees to Las Vegas to hear and share the latest on business intelligence, analytics, and data management. As a leader in advancing the Industrial Internet, GE Software was at the center of the action.

Product Marketing Director Venkat Eswara presented a keynote entitled “Machine Analytics Driving Industrial Outcomes at GE” (see abstract at right). Lothar Schubert, platform software product marketing leader at GE Software, joined the closing keynote panel alongside representatives from Caesars Entertainment, Cisco, Gartner, and Schneider. In addition to sharing GE’s vision and experience to date, the GE Software team fanned out to network, listen, and learn. Here are some of the key themes that emerged:

#1. Disruption starts with data discovery

Data discovery starts with the understanding of “why” existing tools in BI and analytics are not sufficient in terms of usability, speed, and relevance. The core need for discovery is around intuitive interfaces and fast iterations. The merging of styles for data discovery around interactive virtualization, search, data flow, and smart will be disruptive:

  • Interactive Visualization – The current dominant data discovery type in the market with vendors such as QlikView, Tableau, Datameter, Platfora, IBM, SAS, etc.
  • Search – Ability to index, search, and correlate structured and unstructured data sources. Limited number of vendors in this space, such as DataRPM, Squirro, Oracle.
  • Data Flow – Blending of data with interactive visualization tools and advanced analytics.  Vendors such as SAS, Alteryx, and Lavastorm are getting early traction.
  • Smart – Natural language processing with advanced data correlation, simplified visualizations, and cloud-based discovery models such as IBM Watson.
Lothar Schubert, platform software product marketing leader
Lothar Schubert, platform software product marketing leader

 

#2. Rise of the “chief data officer”

With regulations around managing the reliability and traceability of the business’s data and the need for collaboration among IT and lines of business, significant changes are underway.  A new role, chief data officer (CDO), is emerging and IT’s function is moving from “gatekeeper” to “traffic controller.”

Key trends:

  • Boards are directing some CEOs to appoint an information executive to drive information investments, data management, and reduce risk.
  • IT and digital governance are being redesigned to embrace revenue-dependent risk taking.
  • Total control of data is not giving way to open access. Instead, companies are focusing on getting the right information to the right people at the right time.
  • Tiered promotion framework and agility are needed to promote data discovery and to close the gap between IT and businesses:
    • Systems of record such as reports and dashboards that are more structured and repeatable
    • Systems of differentiation such as data discovery that are more configurable and autonomous
    • Systems of innovation such as data mining and advanced analytics that are more dynamic and ad hoc.

#3. Business analytics in the cloud

Big data analytics is gaining momentum towards cloud-based discovery, delivery, and consumption models. Projections suggest that by 2016, 25% of net-new business analytics deployments will be in the form of subscription to cloud analytics platforms or application services. By 2016, more than 5% of the total BI, analytics, and performance management market will be driven by cloud.

Businesses need to adopt the following principles as best practices for cloud-based analytics models:

  1. Technology
    • Evaluate and select the use cases and deployment scenarios that best fit the needs of the business. Start with a limited scope project before moving the entire BI platform to the cloud.
    • Consider technical options like the logical data warehouse to bridge the gap between traditional BI and cloud.
  2. Processes
    • Move customer-facing processes and workloads that are already outside the firewall to cloud.
    • Update governance processes to embrace scalability on demand and prevent risks such as security and privacy issues.
    • Prototype with cloud environments to shorten BI development life cycles.
  3. People
    • Empower users with self-service capabilities by leveraging cloud-based sandbox environments.
    • Extend BI and analytics constituencies to customer and partners by introducing cloud-based models.
    • Complement and expand skills by using Analytics as a Service (AaaS).

#4. Driving customer value with transparent business model and customer facing analytics

Embracing transparency is key to deepen customer relationships, to increase trust, to share insights, and to attract and retain customers.

Five categories of customer-facing analytics that companies need to look at when enabling business outcomes for their customers:

  1. Self-service: Detailed data about their activities
  2. Comparative benchmarks: Compare their data and performance measures with their peers
  3. Personalized experiences: Based on analytical modeling that suggests optimal recommendations
  4. Product tracking: Provide a continuous stream of information throughout the product life cycle
  5. Monetization: Provide data that is viewed as a trusted and valuable source of information

#5. Vendors are still trying to figure out their big data analytics and BI positioning

Until 2016, big data confusion will inhibit spending on BI and analytics to single-digit growth. Vendors are more focused on selling the stack vs. building an ecosystem. A key challenge for vendors is to find ways to address dark data, data types that are currently not usable by existing tools, and fill the gaps with big data stores as data warehouses are reductive and current systems are lousy.

In 2014, half of new license spend in BI will be driven by data discovery requirements.  The majority of BI vendors will make data discovery their prime BI platform offering, shifting from reporting centric to analysis centric BI. As a result, existing vendors will lose relevance in OLAP, dashboards, and ad hoc query with a shift to geospatial and location intelligence, embedded advanced analytics, and support for big data sources.

Bottom line: the data layer is not yet well defined and opportunities abound for vendors to position themselves for differentiation and a land grab.

Keynote Abstract:
“Machine Analytics Driving Industrial Outcomes at GE”

General Electric (GE) is one of the world’s leading provider of industrial equipment and machinery - moving, powering, and healing the world. GE provides services to industrial clients worldwide, minimizing unplanned downtime for assets and driving operational efficiencies. Data management and advanced analytics are core to GE’s recent success in delivering superior software-based services to customers across aviation, power generation, oil & gas, healthcare, and transportation.

GE shared real-world case studies demonstrating tangible operational benefits - ranging from fuel savings to improving productivity to reducing unscheduled maintenance to enhancing on-time performance — by tightly integrating machines, networked sensors, industrial-strength data, and software to enable intelligent insights and affect measurable outcomes.