Information management strategy
Companies are accumulating data at unprecedented rates. The variety of data poses a major challenge to any IT organization.
The data lake model is displacing the data warehouse in the modern data-driven enterprise. Organizations understand that true data insight comes from looking across types and sources of data and understanding the information that it provides and then using that information to obtain new insights.
But the data lake is just the data part of the information management problem.
The variety of data that a lake is expected to contain means it’s best suited to being stored in a raw format. Raw data mirrors that of the system that feeds it. Lakes are designed to be analyzed and queried by programmers in an effort to find information.
Data lakes are optimized for high volume writes of variable data types from multiple sources.
While the native format is one that preserves as much of the data as possible, it still needs to be connected and understood by people as information. People have different demands of information compared to system demands for data.
The kinds of core questions one might ask of information.
- What? What is the business context for this information?
- Who? Which customer or account is referenced or affected? And what user, employee or system made the data?
- How? What type of transaction or event happened?
- When? When did the actions occur?
This implies a blend of raw transactional data to establish some human context. Context is supplied by adding meta-data in order to connect key orgranizational concepts to data. Information is Data with Context.
An information management strategy requires two key components:
- The data lake: raw data storage
- The lake shore: information storage and retrieval
The lake shore is where data from a lake is stored as information so that data analysts, business analyst or regular users can query and extract information in the quest for insight. Information is tracked in the business context in a shore and is curated to support building reports, dashboards or data-driven systems of insight.
The lake shore is the go-to service optimized for reading and analyzing information in different ways
FlockData as an information management platform
If you already have a data lake strategy, FlockData can act as a Lake Shore service to turn that data into information and bring it to your users. Don’t store information in the lake; store data in the lake.
A data lake is not a pre-requisite for using FlockData to obtain insights. If you understand the context of your data, it can be integrated directly in to FlockData for analysis. FlockData offers a scalable framework for implementing an end-to-end information management strategy. Our data integration and import tools help you get data from any source system into FlockData as information. The multi-model approach of FlockData gives you access to both data and information allowing organizations to query information in a variety of powerful ways.
FlockData and the lake shore
You analyzed your data and found information. Storing this information in a lake shore gives users and applications API-level access to the data for any purpose you can imagine.
The lake shore is the the human face of data – enabling data scientists, analysts and others to extract subsets of information in order to produce reports, dashboards or integrate into other applications. FlockData has a unique multi-model approach to delivering lake shore functionality:
- Graph index – lets you find data with its connections
- Search index – free text search for any data or meta-data
- Aggregates – real-time warehousing letting you create sums, averages or statistical breakdowns of data
- Audit trail – lets you see how data has changed over time, calculate differentials and build a timeline
With contextual understanding of the data already established, you reduce the time to market for new applications.