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How To Speed Up Your Data Projects

Data-led projects are rarely delivered on time. Project Teams build in contingencies and utilise agile approaches, but continue to face delays. Resolving this issue is increasingly urgent as companies seek to become data-driven and convert data to business value.

In this article, we’ll explore a powerful approach to reduce data project delays. We’ll identify the different stakeholders in data projects, and how to utilise the data supply chain approach to manage these stakeholders. 

Data project delays are caused by three common issues.

First, is data availability. Data may exist within the business, but is not often readily accessible for data projects. It may be locked in a system, stored in a data warehouse, or depend on business teams for access. Extracting data technically, or building a process to source data manually from business teams, can be a labourious task. 

Second, is data quality. Data that is in systems, or received from business teams is often collected with a different purpose in mind. As a result, it can be missing values, or in different formats to the one required for a data project. Communicating with the teams who supplied the data to remediate these issues is often time consuming.

Third, is communication. Most organisations have multiple parties involved in data management. Business departments are responsible for data creation, and the usage of data in decision making. Technical teams are responsible for integrating and extracting data from systems. Data teams are responsible for the creation of data solutions. The interface between these teams is often poor, and accountability can be unclear, which slows down the process of accessing and quality assuring data. 

When you improve the interface between these parties, and the management controls in place, it’s possible to accelerate data delivery times by an average of 94%. We know because we’ve done this successfully across eight different industries.

Manage data like a supply chain. 

The data supply chain approach (as coined in this HBR article), adopts many of the best practices from the modern supply chain and applies these to the way that we manage data. 

In particular, this method defines a process with clear responsibilities for each party involved, by treating the ‘data assets’ within an organisation as a component part that will be used across multiple ‘data products’ and solutions within that company. 

It ensures that suppliers of data (often the business team) are aware of their responsibilities regarding data sharing, and makes the requirements for data clear, so that many quality issues can be addressed at the source. 

It also enables technical teams to be clear on what data components are needed, and build data pipelines to make useful data available to the business as a whole, rather than requesting for data to be extracted on a per-project basis.

Finally, it means that data that is defined as an ‘asset’ within the business can be sourced and quality-assured in advance of its use in data projects, and stored in ready-made information layers, which can be used to power multiple data solutions within the business. 

This approach moves the business from a position where there is a dependency on the technical team and business team to source and move data on a per-project basis. Instead, the business has access to a clean, project-agnostic data table that is available as a component for use in any future data project.

Steps to implement the data supply chain approach:

  1. Identify the ‘data assets’ that are useful components for data projects in your business, and the sources the data is extracted from, using our Executive Data Framework.
  2. Nominate suppliers that are responsible for the creation of this data, and therefore for remediating quality issues. 
  3. Define and establish measurements, such as data quality, supplier performance, and time elapsed between data receipt and incorporation into a data product. 
  4. Establish a process to test that data meets requirements and remediate data issues.
  5. Define accountability between suppliers, technical teams and data teams for different parts of the process.  
  6. Use the measurements in Step 4 to determine performance along this process and identify areas for improvement. 

What’s Next?

Our expert team specialises in establishing the data supply chain approach in medium and large organisations. Contact us to find out more about how we can help your team.