We all know inertia doesn’t generate energy - movement does. Why then is the data in many organizations idle or stuck in various places? Part of the reason is that companies adopt data technologies in piecemeal fashion a new tool to analyze customer churn here or an application to forecast sales there. This approach leaves enterprise data vastly underutilized.
Data ecosystems are complex and often littered with data silos, limiting the value that organizations can get out of their own data by making it difficult to access. To unlock the real value, data should flow easily not haphazardly, but managed like the supply chain of a company’s core product through an organization and eventually through its ecosystem of partners, too. According to Accenture’s Technology Vision 2014, building data supply chains will be a major focus for enterprises in every industry in the coming years.
Steps to Build a Data Supply Chain
1. Enabling the data supply chain with a data services platform.
The first step of building a data supply chain begins with utilizing a data service platform that makes all data accessible to those who need it when they need it. Think of this as a platform from which users can dive into an inventory of data directly. It can be a solution provided by large database vendors or a combination of tools from various vendors.
Many newer data platform strategies now depend on opening up each data source separately, but through a common standard access protocol. Increasingly, companies are turning to APIs to achieve this, and they’re often aided by API management platforms.
2. Accelerating data through the supply chain.
The next step in building a data supply chain involves integrating data from multiple sources. Organizations can increase data velocity by going beyond the traditional hot-and-cold approach of distinguishing between frequently needed information stored on high-performing systems and less relevant data kept on slower hardware. They can add further gradations of data temperature to the spectrum and dynamically assign these based on when certain data is needed most. Once that data is accessible to employees and partners at high velocity, companies need to advance the discovery of insights from the data.
3. Advancing data discovery.
Previously, traditional business intelligence methods could answer prescribed business questions, often requiring multiple lengthy steps with data scientists or other analytics professionals running the queries. Today, intuitive and easy-to-use data discovery solutions extend analytics capabilities to business users, helping them to discern the very questions that companies should be asking by uncovering insights in a visually interactive and rapidly iterative manner.
4. Realizing data value.
When companies reach the final stages of the data supply chain, a significant investment has been made in the ingestion, transformation and analysis of data — and now that data is both accessible and sharable, companies have new opportunities to capitalize on its value. To further maximize the value of data, the supply chain should extend beyond the company’s walls to suppliers, partners and customers.
The Next Step: Cognitive Computing
What if machines could be taught to leverage data, learn from it and, with a little guidance, figure out what to do with it? That’s the power of machine learning — which is a major building block of the ultimate long-term solution: cognitive computing. Rather than being programmed for specific tasks, machine learning systems gain knowledge from data as “experience” and then generalize what they’ve learned in upcoming situations.
Currently, data insights may be solving singular business use cases, but they’re not always driving strategic value across the organization. Managing data in the context of a supply chain offers a way to change that.
No organization will master this data supply chain effort in one giant leap. Leading enterprises will start by establishing a data services platform, followed by implementing a single data supply chain for a specific department step by step anchoring the goals of their business in the movement of their data.
Author's note: To read about the benefits and analytics components of an effective data acceleration platform, download Accenture’s recently released analysis, “Data Acceleration: Architecture for the Modern Data Supply Chain.”
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