Digital Transformation has become a key objective for many organisations. A study from Gartner found that 87% of senior business leaders believe that digitisation is a priority for their organisation. The study also found that 91% of organisations are already involved in some form of digital initiative.

Despite this, it is estimated that between 75% (Inc.com) and 97% (Gartner) of the data that organisations collect sits unused. In addition, Gartner estimates that 87% of organisations have “low BI and Analytics Maturity”.

For organisations engaged in digital transformation, a clear, comprehensive data strategy has become essential. In this blog post, we discuss data strategy, its impacts, and how adopting a data strategy can help organisations to utilise their data more effectively and transform their data from an inconvenience into an asset.

What is data strategy?

Data strategy is a business process broadly concerned with the acquisition, management and application of data within an organisation. It supports organisations by establishing standards and procedures that ensure that the quality, access, and use of their data aligns with their broader business objectives.

• Acquisition
  Organisations of all sizes are collecting data each and every day. Data acquisition refers to the process by which organisations collect this data and the format in which it is collected. In addition, organisations may acquire data from third parties through the use of an API or data connector.
• Management
  Data management is the way by which data is stored, organised, and maintained. It is broadly concerned with the quality and accuracy of a dataset through the use of functions such as deduplication, enrichment, and augmentation.
• Application
  Application is the way by which end-users within an organisation access and utilise data. In addition to ensuring that the correct users have access to the correct data, application also relates to the way by which users drive value from their data. Examples of application include data visualisation and machine learning.

How can a data strategy impact your business?

An effective data strategy can support an organisation in a variety of ways. Not least of all, adoption of a data strategy is the first step towards transforming data from something that a business simply collects, into a critical asset, capable of driving value and informing decisions.

Adopting a data strategy embeds a holistic, standardised approach to the way by which an organisation uses its data, helping to eliminate data siloes and ensure visibility of critical insights across an organisation.

Considerations

When developing a data strategy, organisations should consider the following:

• What are your business objectives?
  What is it that you specifically want to achieve and how can your data help you to achieve it? Examples might include addressing operational inefficiencies, understanding customer behaviour, or identifying new market opportunities.
• Who are your end-users?
  Another important consideration in any data strategy is the end-user. What tasks are they performing and what insights do they need? How can data help them to perform their role more effectively and what tools do they need? For example, can data visualisation help them to identify anomalous customer behaviour?

• What form does your data take?
  Data can come in a variety of forms. Structured data is highly organised and standardised within a defined format whereas Unstructured data has no defined structure and tends to be more qualitative in nature. Examples of structured data might include names and addresses while examples of unstructured data might include text and documents.
• Where is your data stored?
  Identify what data your business has access to and how does that data align with your business objectives? It is also important to consider where your data is stored. If your internal data isn’t appropriate, can a third-party help to achieve your business objectives?
• What quality is the data?
  It is important to consider the quality and completeness of your data. Taking steps to first validate and refine the quality, accuracy, and consistency of a dataset helps to eliminate uncertainty and provides the foundation for effective business decision-making. It is also important to consider how your data might change over time.

Experts in data

Analytics Engines offer end-to-end guidance, consultation, and support in response to your data challenge. We are experts in data, and we work in close collaboration with you and your team to understand your specific objectives and challenges. We develop solutions that provide your business experts with the resources they need to make critical business decisions more effectively.

If you’d like to develop a strategy within your organisation, arrange a no-obligation introductory call with one of our data experts using the form below.