What is data strategy?
Table of Contents Show Hide
Definition of data strategy
Data strategy refers to a company’s vision for how data will be used to help achieve the company’s business goals.
Executive view
Data strategy enables companies to analyse progress and make better business decisions, but it also helps to create a thriving data environment where it can be easily shared and accessed, safely stored and standardised, and viewed as a central resource to all departments. While it can be beneficial for a company to outsource data science skills, we recommend building up data skills within your team by upskilling and recruiting where possible as part of your data strategy.
Data strategy helps businesses:
solve business problems
enhance day-to-day operations
advance the overall functioning
improve decision making
Business function leader view
Data strategy helps teams to work towards a common goal while ensuring a standardised way of working with data.
You may need this service if:
your business has poor data management among departments causing data-related issues and incompatibility between projects.
your business suffers from data silos due to friction between departments or technological factors, which prevents teams from seeing the bigger picture.
If you want to measure the performance of your data strategy, you should set KPIs that are in line with your business strategy and associated KPIs. Your data strategy should always be linked to your business priorities and may include a detailed AI strategy section.
Technical view
Data strategy provides a clear, actionable plan to make data more accessible by relevant departments while ensuring that it is stored efficiently and securely. Technical teams should take the lead in setting a standardised way of working with data and work with executive teams to ensure that data is being used to meet business goals.
Data strategy helps deliver:
- improved data modelling and database structure.
- business intelligence and AI adoption.
Get this service if you encounter:
a lack of cohesion between departments when it comes to using data.
ad hoc or reactive requests for data insights, which can lead to inefficiencies.
Key criteria to consider are:
What tools should the organisation implement to store data securely and efficiently?
How should business intelligence be incorporated into the strategy and which tools will be required?
Will your database structure need to be changed to meet the data strategy?
What are the data needs of all departments and how can the data strategy meet them?
FAQs
What does data strategy mean for a metals manufacturer?
A metals manufacturer’s data lives in more places than most businesses realise: the ERP holds orders and finance, mill certificates arrive as PDFs from suppliers, production records live on the shop floor (sometimes on paper, sometimes in machine logs), and inventory is tracked partly in the ERP and partly on a spreadsheet someone maintains because the ERP is too slow to reflect reality. A data strategy for this kind of business starts with an honest inventory of where the data actually lives — not where it’s supposed to live — before deciding how to connect it.
The highest-value early moves are usually the ones that close a specific, painful gap: linking mill certificates to inventory by heat number so traceability doesn’t depend on someone finding the right PDF; getting real-time (not overnight-batch) visibility of available stock so sales teams stop quoting material that’s already been used.