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Hit the gas pedal with your data: Unlocking value with a modern supply chain

Treat your data as a “supply chain.” You can unlock hidden value and make your data work harder through your entire organization.

Overview

Our Big Data team and Accenture Labs examined three key data-related challenges: movement, processing and interactivity.

More importantly, we’ve pragmatically explored the full landscape of architectural components to address these challenges.

We have learned that organizations can leverage many different data technology components and combinations to build a modern supply chain that provides “acceleration.”

With data acceleration, you can move data swiftly from its source to where it is needed. You can process data to gain actionable insights quickly. And you can enable faster data connections and query responses from users or applications.

Background

Analysis

Data acceleration can help organizations address three challenges:

  1. Movement
    To extract insights from data, businesses need to harness it from multiple sources without losing any of it, and then deliver it for processing and storage.

    Moving data from its origin to where it is needed can seem like drinking from a fire hose while trying not to lose a single drop. Acceleration enables multiple means to bring data into an infrastructure and ensure it can be referenced quickly.

  2. Processing
    Volume and variety of data requiring processing has ballooned. Companies need to perform calculations on the data, create and execute simulation models, and compare data statistics faster than ever.

    Good analytical technology allows you to “pre-process” incoming data. Better technology combines streaming data with historical (or modeled) data to enable more intelligent decision making.

    Data acceleration supports faster processing by leveraging hardware and software advances for computer clusters, enabling greater efficiency.

  1. Interactivity
    This about usability of the data infrastructure. Traditional solutions have made it easy for people to submit queries to get desired results and insights.

    However, the rise of big data has led to new programming languages that discourage existing users from adopting the systems. Data acceleration supports faster interactivity by enabling users and applications to connect to the infrastructure in universally acceptable ways and by ensuring that query results are delivered quickly.

Recommendations

A supply chain can allow you to accelerate data movement, processing and interactivity. This enables your decision makers to more swiftly capture and act on insights.

Here’s a few tips to begin building a supply chain strategy that supports data acceleration:

  • Inventory your data—Start with your most frequently accessed and time-relevant data, and provide it first access to your platform.

  • Identify inefficient processes—Look for any manual, time-consuming data curation processes, such as tagging or cleansing. These may be candidates for replacement with machine-learning algorithms.

  • Identify data silos—Also, identify corresponding data needs that are currently unmet across the business.

  • Simplify data access—Create a strategy for standardizing data access via the platform. Solutions include traditional middleware and API management, a platform-as-a-service, or a hybrid model.

  • Prioritize individual data supply chains—Prioritizing helps you develop a road map for implementing the data supply chain at scale.

  • Consider external data sources—Look outside your organization for external data sources that can be incorporated to complement existing data and help create more complete insights.

Business leaders must be able to generate insights from enterprise data to gain a competitive advantage. Building a supply chain that supports data acceleration will put your business on the path to data-driven outcomes.

Industry & topics highlighted

Analytics