It’s time to think about your Data Supply Chain

Let’s start by defining a data supply chain, as a process of data entering an organization, undergoing transformation and standardization, and coming out as information ready to be consumed by the organization.

The data supply chain, or as we call it the data manufacturing pipeline, starts from various sources like websites, social networks, mobile apps, enterprise applications, customer interactions and more. Then moves into a big data architecture to manage and process the volume, variety, and velocity of data.

data supply chain

 

Benefits of Data Supply Chain

We’ve talked a lot about the benefits big data can offer companies, from improved reporting to a better bottom line with Customer 360. So the benefits listed out below are nothing new, the change is the context. This is the key to success; moving from thinking about data as a thing to manage to thinking about data as an asset to be used.

In the context of a data supply chain, data is created, gathered, imported and mixed together. Each step of the way as data flows and transforms, it acquires incremental value. The end results are valuable insights not previously achievable with siloed data. A few benefits:

  • Operational efficiencies
  • Improve business agility
  • Reduce data latency
  • Easy to accommodate new data sources
  • Improve data quality and meet customer expectations
  • Process data faster
  • Increase company revenue by helping make better decisions
  • Enhance customer relationships

Getting Started

Big Data projects can be rather daunting, but with the right expertise and tools your company can achieve results. However, rather than focusing about the biggest obstacles of taking on this sort of digital transformation, we recommend starting off looking for smaller victories to build momentum. Here’s a plan to unleashing the value of your data by treating it as a supply chain:

Short-term:

  • Begin by mapping out your company’s unique data supply chain.
  • Make sure you have a data governance framework to address data quality, metadata management and master data management.
  • Take an inventory of your data, beginning with your most frequently accessed and time-relevant data.
  • Focus on under-utilized and unstructured data, and start looking for external data sources to complement existing data.
  • Develop and enforce a well-structured reference architecture to support the movement and consumption of data.

First Year:

  • Begin looking for ROI opportunities; building the data supply chain to drive those outcomes.
  • Begin to simplify and democratize access to trusted data, using reference architecture as a blueprint.
  • Target a proof of concept on a targeted business-function problem and then iterate. Repeat other such opportunities over the next year, putting the most successful into production and moving all the while towards total digital transformation.

Unfortunately building your data supply chain isn’t easy, as each company has their own set of components and needs. But it is one of the most rewarding journeys companies can make in their transformation to become truly data-driven.