Understanding Logical Data Model in Plumb5

An EDM (Enterprise Data Model) is the starting point for all data system designs. It falls in the realm of information architecture. The model can be compared with an architectural blueprint is to a building; it draws up visualization, as well act as a framework supporting planning, building and implementation of data systems. This helps in planning your data integration and takes away the data silos. With this, the unified data present the “single version of the truth” for the advantage of all. It minimizes data redundancy, disparity, and errors; core to data quality, consistency, and accuracy.

The efficiency of a good EDM lies in the design of its logical data model. The logical data model defines the relationship between subject areas like people, places, things, events and associated rules. Using taxonomies, data can be organized  in a hierarchical fashion as part of the master data management environment

Plumb5 developed its own model, designed to primarily achieve (1) fetch results in a minimum number of steps (shortest path to responses) and (2) incorporation of computational rules and states to perform like a finite state machine.

The EDM design in Plumb5 not only creates an absolute relationship among data entities but also incorporates a comprehensive meta data management with rules in order to compute in real-time.

Plumb5 patented methodology enables scoring and computing data on the fly which helps in transiting between decisions and states. If a specific decision pattern is met, the automation triggers to record a new response.

This allows Plumb5 to deliver its primary goal of personalization in real-time by incorporating current session data to the overall computation to drive high relevance and context in customer communication.

Explaining LDM Model
The design incorporates data entities that encompass both raw and synthesized data entities in order to compute and retrieve responses on the fly.

ldm2

Design Utilities

  • The Customer Key is upheld as the primary entity of the structure – which allows quick identification of a person at any channel and allows quick retrieval of customer insights at any given situation,
  • The Customer maintains a relationship of every unique product type with categories as attributes. This allows computing individual customers intent on an individual product which is crucial for personalization
  • The Product can be broken down to 4 other entities: Customer States, Behavior, Product States and Price. The Customer State defines the states (VAPR), scores and any unique segmentation definitions that are related to the product of a particular customer. VAPR stands for Viewed, AddedtoCart, Purchase and Reject. The customer-product relationship is in one of the four states.
  • The Behavior entity is one of the core entities. The Behavior entity covers all data coming from channels, which can be further broken down by sources and intent states. The intent states are a calculation of scores assigned to stages (VIRST).
    VIRST tags stand for Viewed /Interacted /Responded /Sentiment /Transaction, which is used to classify the type of behavioral states and can be used to understand the depth of intent. Parallelly, data is stacked by time-series which allow the machine to update patterns for decision matching.
  • The Product States comprises of inventory and delivery data and is aligned to feed real-time availability for both customer facing and operation intelligence systems
  • You can avoid the product segment entity and hook up the inventory and delivery entities directly to the product, but this was created, in case product level insights could be deduced and reused by other entities
  • The Inventory data can be further classified based on procurement type which could be self-manufacturing or sourced through a vendor
  • In case of manufacturing, you can add materials or other relevant data attributes that can be used for operational and price deductions
  • The Price entity linked to the product is broken by actual price (which is deduced by expense feeds) and selling price (collected from channel data) to understand margin, discount and customer revenues.
  • The price entity is a derivative of all the inputs which is used to deduce NPV, Product Pricing and Change in margins, propose attractive offers, all in real-time
  • The Actual Price is further broken down into Fixed and Dynamic entities which derives its numbers from People, Place, S&M, GA and other costs documented

Areas of Applications: The design can be applied to configure solutions on various aspects of business functions.

  1. MDM Solution
    The design enables to extract all unique data attributes in a hierarchical fashion which can help in creating simpler and effective master data management
  2. Real-time Operational and Business Intelligence
    The design allows the business user to query any entity types and analyze the data breadth of each entity. Using entity and attributional tags, businesses can enable enterprise data search easily. The tags can be served to bot frameworks to enable real-time dialogues
  3. Customer Data and 1:1 Marketing
    The purpose of the design is modeled to achieve customer marketing in real-time. The platform which allows users to configure rules uses the model to dynamically pick content or messages to suit a specific customer who satisfies a particular rule or state. The response gathered from this targeted delivery is collected to synthesize output for next action. This will enable the machine to decide and automate next actions using pattern or score matching.
  4. IoT data entities for Emerging Device Scenario
    The design designates two different areas for data from IoT devices. Devices like wearables collecting customer data are mapped under behaviors and product related IoT data like usage/delivery/availability data is mapped to product entities.
  5. Pattern Detection, Decisions, and State Automata
    The design allows analysts or data stewards to extract data in its entirety. Learning models require absolute data sets to avoid inaccuracy in learning and decision-making. The design accommodates rules to generate patterns over specific data entities like customer behavior, product usage, change in KPIs and more. Cognitive functions like object or sentiment extraction or bot conversation can readily get/post tags allowing cognitive computing.

Business optimization starts with a great data foundation 

Most small and medium businesses invest on many touch-point applications and end up creating a silo environment. Without a proper EDM, they need to keep spending on IT resources to merge or unite silos to suit a specific need of the hour. This could be causing redundancies in data integration and finally end up spending on a data warehouse.

Using a platform with EDM presets will save the business from creating unwanted silos and make sure their data foundation is in place at all times of the business journey.

Small and medium businesses which cannot afford expensive data modelers can get started with a generic EDM template and allow a consultant to modify the template based on their requirement.

Plumb5 EDM design serves as a ready base for businesses investing in next generation AI environment to automate their processes.

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