Advanced Data Strategy

Overview

A cohesive data approach and engineering strategy is integral from modern organisations dealing with varied tool sets and widespread teams. Without a clear strategy for data engineering organisations are lefts with a vendor defined structure for their data, siloed and locked into tools and the agents deployed to collect it. In this service we work to create a unified framework for your organisational toolset, defining how each application will process or handle its given set of data. By defining your own framework, agnostic to vendor or product, you can decouple reliance and build in data source compatibility and solution scalability on an open global level.

Another aspect to a data strategy is a tooling approach, refining the use of a platform to provide more value and reduce user friction. This alternative pathway to a unified data approach involved a targeted program of engineering developed in partnership with clients using their data discovery output.

Who is it for?

• Organisations modernising a legacy data approach.
• Customers struggling with agent sprawl, data silos, and log non-conformity.
• Enterprises preparing to scale or optimise their data collection strategy.
• Organisations who want to leverage their data across multiple teams, to build a
  data first approach in a common language.

Key Deliverables

Conceptual Data Model: Understanding and recording the different entities, datatypes, and data sources that the final model should cover at a top level, understanding the relations between data entities and the variety of data types.

Entity Attribute Correlation: Similarly to a database schema a data model requires each data entity within the conceptual model to be given the technical detail of its attributes: the variables it holds, how they are formatted, how they should be formatted, what they are called, and any rules that may be applied.

Data flows and Entity Relationships: Supplementing the conceptual model again with the data sources related to each entity and its attributes and defining the relationships between them (one-to-one etc).

Normalisation, Tagging, and Data Integrity Validation: Ensuring each entity focuses on a dedicated attribute set, that objects are tagged uniquely and with relevance, and that modifying an attributes data values is intuitive and non-duplicative.

Outcomes and benefits

1. Data is a central business component, if neglected it will create overheads for system managements, obscured tooling dependencies, and inefficient decisions. By tackling your data head on, from source, using a bespoke strategy you can facilitate a true shift – open format understanding of your data landscape enabling a true dialogue around data driven initiatives within your business.