Managed data pipeline

We start with why data is central to value and follow up with exactly where and how data makes your organization stronger through insights, process optimization, and digital products.

Then we follow through with a comprehensive solution that covers the critical success factors for becoming data-driven:

A managed data platform, business driven data pipelines for self service, and a sustaining data and analytics operating model.

We take the best ideas and practices of Data Mesh, Data Fabric, Modern Data Platform, and Operating Model for organizing, discovering, securing, and governing your data and apply them to your specific needs.

Apiphani implements the data solution that’s right for your business.

Apiphani takes responsibility and accountability for the Pipeline source connect, data product build, and data discovery and delivery.

Your organization sets priorities for what to build and establishes the Data Domain and Center of Excellence for discovery, usage, and governance using apiphani’s best practices model.

Apiphani typically focuses on building a minimum viable product (MVP) for data analysis and visualization to speed development and drive adoption, with branded design guidelines and best practices for self service development.

How much the MVP is iterated beyond that is up to you.

Building your own data platform and data pipelines requires highly specialized resources, takes time to develop, and is at risk of being suboptimal. Apiphani already has a high performance, cohesive team in place for this purpose.

Furthermore, we estimate that companies spend 20% more in year 1 building their first set of Data Products and then up to 40% more in subsequent years enhancing and maintaining data products.

It’s not just about getting the Data Pipeline built; it’s about maintaining it after launch to ensure it continues to be useful.

We also continuously improve and optimize our data platform with high value-to-cost components, including apiphani proprietary tools.

We recommend taking three progressive steps, with each step providing clear objectives and outcomes to help evaluate how and whether to move to the next step:

1. Identify a data set that apiphani can put into the pipeline to quickly demonstrate the power and value of our services, at no cost to you.
2. Complete apiphani’s proprietary strategic data planning template so that we can together establish the best scope, objectives, and outcomes for the first engagement.
3. Identify and build two or three showcases that demonstrate value, create momentum, and build a foundation of reusable assets.

First and foremost, adoption is not something that takes place only after the data products are built and ready for general use. Adoption must be part of the planning process from the beginning.

There are two parts to adoption that we address head on, from the start:

1) Redirecting shadow, siloed data and analytics capabilities to the apiphani Pipeline at every step.
2) Ensuring the data products are valuable, accessible, and – most importantly – designed as part of how the business operates.

The apiphani approach sees adoption as central to success and is built into what we do from the very start.

For example, ensure the Data Pipeline is on the C-Suite radar; onboard a topflight client business and technical team; provide active, clear roles for setting priorities; assign and train data product owners trained to think like product owners … and more.

Data pipelines are the connected modern data platform tools that move data from source systems at whatever pace is needed, automatically taking care of quality, commingling, analysis, and presentation for role-specific use by data consumers.

Data pipelines absorb data complexity so the data consumer has easy-to-understand, accessible, and reliable data.

Data pipelines transform raw data into actionable formats.

A modern, third-generation data catalog is a cornerstone to successfully building and using data pipelines for a company. Everything that is built from the ground up is recorded and active in a third-generation data catalog.

From the consumer end, the catalog is the central tool for data discovery, understanding, and self-service.

Data catalog usage and adoption is a natural fallout when the catalog is built into both data product development and discovery processes.