Overview
At Apto we see data through the lens of our data lifecycle, in our data discovery process we break down the data layer of your organisation into a tangible map ready for strategic development. Within this we look at data through data sources, collection & ingestion, data processing, data storage, data consumption and data governance. But this is only one piece of the puzzle. Even in today’s complex environments, your data, its lifecycle, its insights, and its impact underpins the people, processes, and technologies within your organisation. In our Data Discovery process we aim to combine all of this into one state of affairs, a strategic document to drive business decisions and technological development.
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.
Services included:
At a higher level, in a shorter time frame, we can also adapt our Data Discovery to look at specific tools and teams. Apto’s approach to this technical scoping would follow 3 high-level themes, each providing a key component to understanding the SIEM or ITOPS platform and its data: Data into, Data at rest, and Data Outside of your SIEM/ITOPS, operational monitoring platform, or MELT solution.
Data into focuses on the utility of the data ingested into the platform, its licencing impact, and the suitability of technical actions in governing this.
Data at Rest looks to establish required data retention policies, and the benefits and drawbacks of the different storage options available in the given platform.
Data Outside would provide an overview of compatible storage beyond the platform, aiming to reduce the cost of storing unused data and large volumes of data by looking at cheaper alternatives beyond the given platform.
Next Steps
After our Foundational Data Discovery service, the outputs and next steps are bespoke to your data landscape, organisational ambitions, and present technologies but our clients tend to fall into the following initiatives:
- Cost savings and data reductions for the explored tools via strategic engineering work.
- Defining an open informational model for the explored data to establish log conformity via our advanced data strategy service.
- LDaaS our structured approach for logging data.
- A breadth and depth development of the Discovery work via repeated processes with new team, data sources, and tools.
Key Deliverables
Data Landscape Mapping: A graphical representation of data and tools, along with high level idea of data residency (detailing where your data resides geographically).
Data Catalogue: Written detail on each data source and tool.
Tool Heath Checks: A report outlining the condition of each tool and the ways to improve its state.
Value Evaluations: A measured set of criteria that each tool is functioning against, and where this adds value against its use case and intended purpose.
User Group Definitions: Data driven insights into tool use and working habits.
Outcomes and benefits
- This piece of work creates a robust foundation to enable informed decision making about your data, tangibly representing the data layer of your organisation.
- This outline underpins every aspect of your environment and creates a basis to understand and improve your data landscape.
- The analysis allows for a by-tool definition of suitability, utilisation, management, and duplication – outlining dependencies and areas for data and workflow improvements.
- The facilitates tool optimisation and metrics for tool improvement and success.
See how we can build your digital capability,
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