Complex Event Analysis - Report

Complex Event Analysis - REPORT

Supporting narratives:

• momentum (Read more)
• MVOTs result from the business-specific transformation of data into information.data imbued with "relevance and purpose." Thus, as various groups within units or functions transform, label, and report data, they create distinct, controlled versions of the truth that, when queried, yield consistent, customized responses according to the groups'predetermined requirements.
  Consider how a supplier might classify its clients Bayer and Apple according to industry
• High Level Abstractions:
• (ssot,yield)
• (ssot,various)

• momentum (Read more)
• The challenge for CDOs and the rest of the C-suite is to establish the appropriate trade-offs between defense and offense and to ensure the best balance in support of the company's overall strategy.
  Decisions about these trade-offs are rooted in the fundamental dichotomy between standardizing data and keeping it more flexible
• High Level Abstractions:
• (c-suite,offense,standardizing)
• (cdos,offense,standardizing)
• (defense,offense,trade-offs)
• (offense,standardizing,trade-offs)
• (offense,trade-offs)
• Inferred entity relationships (6)

  (defense,offense,standardizing) [inferred]

  (defense,offense,real-time) [inferred]

  (defense,offense,services) [inferred]

  (defense,offense,smarts) [inferred]

  (offense,trade-offs) [inferred]

  (offense,standardizing,trade-offs) [inferred]

• momentum (Read more)
• Few if any data-management frameworks are as business-focused as ours: It not only promotes the efficient use of data and allocation of resources but also helps companies design their data-management activities to support their overall strategy.
  Data defense and offense are differentiated by distinct business objectives and the activities designed to address them
• High Level Abstractions:
• (data-management,defense,offense)
• (data-management,offense)
• Inferred entity relationships (6)

  (defense,offense,standardizing) [inferred]

  (defense,offense,real-time) [inferred]

  (defense,offense,services) [inferred]

  (data-management,defense) [inferred]

  (defense,offense,smarts) [inferred]

  (defense,offense,trade-offs) [inferred]

• momentum (Read more)
• The more flexible data is.that is, the more readily it can be transformed or interpreted to meet specific business needs.the more useful it is in offense. Balancing offense and defense, then, requires balancing data control and flexibility, as we will describe.
  Single Source, Multiple Versions
  Before we explore the framework, it's important to distinguish between information and data and to differentiate information architecture from data architecture
• High Level Abstractions:
• (balancing,defense,offense)
• (balancing,offense)
• Inferred entity relationships (6)

  (defense,offense,standardizing) [inferred]

  (defense,offense,real-time) [inferred]

  (defense,offense,services) [inferred]

  (defense,offense,smarts) [inferred]

  (balancing,defense) [inferred]

  (defense,offense,trade-offs) [inferred]

• momentum (Read more)
• The challenge for CDOs and the rest of the C-suite is to establish the appropriate trade-offs between defense and offense and to ensure the best balance in support of the company's overall strategy.
  Decisions about these trade-offs are rooted in the fundamental dichotomy between standardizing data and keeping it more flexible.
• High Level Abstractions:
• (defense,offense,standardizing)
• (dichotomy,offense,standardizing)
• Inferred entity relationships (5)

  (defense,offense,real-time) [inferred]

  (defense,offense,services) [inferred]

  (defense,offense,smarts) [inferred]

  (offense,standardizing,trade-offs) [inferred]

  (defense,offense,trade-offs) [inferred]

• challenge (Read more)
• In short, multiple versions of the truth, derived from a common SSOT, support superior decision making.
  A company's position on the offense-defense spectrum is rarely static.
  At a global asset management company we studied, the marketing and finance departments both produced monthly reports on television ad spending.MVOTs derived from a common SSOT. Marketing, interested in analyzing advertising effectiveness, reported on spending after ads had aired
• High Level Abstractions:
• (spending,ssot)

• challenge (Read more)
• Defensive efforts also ensure the integrity of data flowing through a company's internal systems by identifying, standardizing, and governing authoritative data sources, such as fundamental customer and supplier information or sales data, in a "single source of truth." Data offense focuses on supporting business objectives such as increasing revenue, profitability, and customer satisfaction.
• High Level Abstractions:
• (offense,revenue)
• (offense,revenue,standardizing)
• Inferred entity relationships (2)

  (offense,revenue) [inferred]

  (offense,revenue,standardizing) [inferred]

• WIP (Read more)
• Automated synchronization programs connect SSOT and MVOTs data, with nightly "exception handling" to identify and address data-integrity issues such as inconsistent customer profiles.
  Although the SSOT-MVOTs model is conceptually straightforward, it requires robust data controls, standards, governance, and technology
• High Level Abstractions:
• (mvots,ssot)
• (mvots,synchronization)
• (mvots,ssot-mvots)
• (model,mvots)

• WIP (Read more)
• However, the idea that a single source can feed multiple versions of the truth (such as revenue figures that differ according to users'needs) is not well understood, commonly articulated, or, in general, properly executed.
  The key innovation of our framework is this: It requires flexible data and information architectures that permit both single and multiple versions of the truth to support a defensive-offensive approach to data strategy.
  OK. Let's parse that.
  The SSOT is a logical, often virtual and cloud-based repository that contains one authoritative copy of all crucial data, such as customer, supplier, and product details
• High Level Abstractions:
• (defensive-offensive,revenue,ssot)
• (revenue,ssot,users)
• (ssot,users)
• (cloud-based,revenue,ssot)
• (parse,revenue,ssot)
• Inferred entity relationships (3)

  (revenue,ssot,unchanging) [inferred]

  (revenue,ssot,users) [inferred]

  (ssot,users) [inferred]

• WIP (Read more)
• Thus, for example, revenue is reported, customers are defined, and products are classified in a single, unchanging, agreed-upon way within the SSOT.
  Not having an SSOT can lead to chaos
• High Level Abstractions:
• (revenue,ssot,unchanging)
• (ssot,unchanging)
• (agreed-upon,revenue,ssot)
• Inferred entity relationships (3)

  (ssot,unchanging) [inferred]

  (revenue,ssot,unchanging) [inferred]

  (revenue,ssot,users) [inferred]

• WIP (Read more)
• However, the idea that a single source can feed multiple versions of the truth (such as revenue figures that differ according to users'needs) is not well understood, commonly articulated, or, in general, properly executed.
  The key innovation of our framework is this: It requires flexible data and information architectures that permit both single and multiple versions of the truth to support a defensive-offensive approach to data strategy.
  OK. Let's parse that.
  The SSOT is a logical, often virtual and cloud-based repository that contains one authoritative copy of all crucial data, such as customer, supplier, and product details.
• High Level Abstractions:
• (product,revenue,ssot)
• Inferred entity relationships (4)

  (product,revenue,subscription) [inferred]

  (product,revenue,spending) [inferred]

  (revenue,ssot,unchanging) [inferred]

  (revenue,ssot,users) [inferred]

• WIP (Read more)
• Similarly, Bayer might be more usefully classified as a drug or a pesticide company for the purposes of competitive analysis
• High Level Abstractions:
• (ssot,usefully)

• WIP (Read more)
• (An exception would be data fraud protection, in which seconds count and real-time analytics smarts are critical.) Every company needs both offense and defense to succeed, but getting the balance right is tricky
• High Level Abstractions:
• (offense,smarts)
• (defense,offense,smarts)
• (defense,offense,real-time)
• Inferred entity relationships (7)

  (defense,offense,standardizing) [inferred]

  (defense,offense,real-time) [inferred]

  (defense,offense,services) [inferred]

  (offense,smarts) [inferred]

  (offense,real-time) [inferred]

  (defense,offense,smarts) [inferred]

  (defense,offense,trade-offs) [inferred]

• WIP (Read more)
• But for many others it's wiser to favor one or the other.
  Some company or environmental factors may influence the direction of data strategy: Strong regulation in an industry (financial services or health care, for example) would move the organization toward defense; strong competition for customers would shift it toward offense
• High Level Abstractions:
• (defense,offense,services)
• (offense,services)
• Inferred entity relationships (5)

  (defense,offense,standardizing) [inferred]

  (defense,offense,real-time) [inferred]

  (defense,offense,smarts) [inferred]

  (offense,services) [inferred]

  (defense,offense,trade-offs) [inferred]

• WIP (Read more)
• In our experience, a more flexible and realistic approach to data and information architectures involves both a single source of truth (SSOT) and multiple versions of the truth (MVOTs). The SSOT works at the data level; MVOTs support the management of information.
  In the organizations we've studied, the concept of a single version of truth.for example, one inviolable primary source of revenue data.is fully grasped and accepted by IT and across the business
• High Level Abstractions:
• (mvots,the_ssot)

• WIP (Read more)
• The SSOT works at the data level; MVOTs support the management of information.
  In the organizations we've studied, the concept of a single version of truth.for example, one inviolable primary source of revenue data.is fully grasped and accepted by IT and across the business.
• High Level Abstractions:
• (mvots,revenue)

• WIP (Read more)
• But such broad industry classifications may be of little use to sales, for example, where a more practical version of the truth would classify Apple as a mobile phone or a laptop company, depending on which division sales was interacting with
• High Level Abstractions:
• (mvots,phone)

Target rule match count: 43.0 Challenge: 0.03 Momentum: 0.19 WIP: 0.29