Written by Debra Williams
In the data world, you have options. Companies are no longer restricted to data lake or a data hub. It's about looking at all the technical arrows we have and then drawing out the best one for the client.
How do we decide what is the best data option? Henry Ford, an early automobile manufacturer, is attributed to saying, "If I had asked people what they wanted, they would have said faster horses." Most people know how to describe a problem. What they don't always know is how to think about a solution outside of traditional methods available.
Think beyond technical stacks to deliver the greatest value
One of the reasons why I am so interested in the data and artificial intelligent (AI) platform evolution is because of how important it is for businesses to rethink how we talk about and look at data. Most technical engineers talk in terms of platform stacks, and this perspective is often what drives how data is organized and accessed.
Moving beyond the infrastructure platform stack, businesses need to think about new emerging data ecosystems. Market drivers today require organizations to translate data ecosystems into what we are introducing as a "data value chain." This is the data chain unique to each business that links different domains, from sales and product development to operations, so organizations achieve widespread value.
Market drivers triggering a data transformation
How people work and live are creating new challenges with every platform leap from dta warehouses and data lakes to data hubs. For example, with content consumption moving to mobile from desktop computers and data coming from connected devices, it's more important than ever organizations unity the data journey to include a vast array of new types of data.
The ability to stay locally connected and pull data from decentralized sources is another market driver influencing how data is collected, stored, nd used. With the help of Microsoft Edge, emergency professionals, for example, can drive an ambulance in a war-torn country, and even if connectivity is down, the #Edge will still help them do their job with cloud like capabilities closer to the data where it is needed.
Conversely, things like data sovereignty, security, privacy and trust are forcing centralized governance and a controlled agenda. The push and pull factors of each could create a stalemate. However, we see a path forward if business and technical leaders can take a step back, be open to new ideas and stop thinking about faster horses.
Harnessing the data value chain
The core concept of the data value chain revolves around driving business value realization, cost reduction and new revenue streams. The data value chain represents data modelled on product thinking from across the Data Mesh used to connect distributed data across domains and physical locations. The construct is designed to connect business areas through an adaptive, contextual meta-data abstraction (the emerging data stack), formed incrementally through a roadmap of use cases, thereby uncovering multi-tiered solutions, and optimize service experiences.
The data value chain combines historical archives with real-time data to discover and realize holistic enterprise-wide use cases. These use cases can be designed to help business with demand forecasting in parallel to predictive maintenance while fueling efficiency.
A powerful organic approach to design the data journey
The data value chain is very powerful and helps contextualize content throughout the enterprise. When we were working with the product development team for one client, the input received from product development did not include data on what to build from sales and marketing.
None of the data points connected. Using the value chain, the adaptive data context organically bubbles up within each business domain to reflect an end-to-end knowledge graph that can be easily consumed to create an end-to-end solution.
By 2020, more than half of major business systems will incorporate continuous intelligence that uses real-time context data to improve decision-making.
A transparent, leaner data supply chain
As you can imagine, this type of modeling has a huge potential impact on the supply chain. Most supply chains are built around cost and efficiency but not what could go wrong along the way. The data value chain increases supply chain transparency from the very start all the way to the end consumer. The result is more just-in-time, lean supply chains for greater resiliency and without the need for a complex technology like Blockchain.
The technology value levers we use include our partner ecosystem with Microsoft and Databricks as well as machine learning and deep learning, business knowledge graphs, 2D/3D simulations, cost and usage data and many more. With these tools and our own assets, we create an end-to-end view that connects data and systems and helps reduce costs.
Rethinking your data in a new and different way
The data literacy of an organization exposes how much the data and AI architecture have fallen behind other platform thinking. The data value chain transcends beyond traditional failure modes, embracing loosely coupled, distributed approaches and moves past isolated data ownership.
The data value chain lifts the context of the data away from the technology stack that stores it. The construct combines the business value with contextual data to create an end-to-end narrative that organically bubbles up within each business domain.
One of the greatest advantages is that the data value chain delivers an end-to-end approach increasing an organization’s ability to view information from the lens of the consumer for the greatest positive impact possible.
Find out more about how to power your data and advanced analytics and help your business rethink their data for the greatest value.
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