5 Key Components for a Successful Data Strategy

Despite its increasing importance to business success, robust data management continues to be a problem in many organisations. Historically, data has been treated as a byproduct of tech and digital activities, not being treated as an important business asset. As a result of this outdated belief, database management and planning have often been minimal and insufficient to meet evolving business needs and objectives.?

While once treated as an extension of business activity, corporate data has grown in scope and diversity, making it one of the most vital corporate assets to a business. With it, comes an urgent need for businesses to address how they manage their organisational data.?

Although the value and significance of data are being recognised by businesses, many organisations are adjusting and adapting their data strategies for handling corporate data assets.?

It’s imperative for businesses to build data strategies that simplify their management and access to the vast datasets they own, for strategic planning and implementation.?

What is a Data Strategy?

Data is no longer a static byproduct of business processes. It’s a powerful tool that can drive more accurate, strategic business decision-making and plans of action.?

A robust data strategy aims to instil systems and processes that allow easy, efficient access to data resources so they can be reviewed, shared, and moved throughout the organisation with ease. It establishes the methods, processes, and practices that a business uses to manage its data.?

A data strategy creates a data road map that identifies, aligns, and integrates different data management initiatives across an organisation to build on each other and work in tandem to deliver more value.?

The 5 Key Components of a Data Strategy

Identification

Sharing data quickly and seamlessly across an organisation requires that the data in question is clearly and consistently marked and labelled for easy identification. Regardless of whether the data is structured or unstructured, accessing and utilising this data isn’t possible unless the dataset has a defined label and format.?

Naming and labelling of data elements should follow an established, consistent format to prevent confusion and make it easy to identify and share data within a company.?

Storage

As companies continue to produce vast quantities of datasets, it’s become far more common for businesses to share “big data'' within and between organisations. Managing and storing these vast sets of big data has become a key challenge for businesses - one that a good data strategy should address.

Businesses that lack a centrally managed data sharing process force different internal systems to each manage this area individually, with employees and teams making their own data copies. A data strategy should identify how businesses can store their data in such a way that everyone can access it without needing to create copies and duplicates.??

Provision

In earlier years when data sharing within a business wasn’t nearly as common, data was largely organised and stored for the application collecting it, nothing else. However, data sharing today is not an infrequent or uncommon occurrence - data is often shared between multiple systems to power and support multiple business processes at once.?

For this reason, data provision needs to be accessible to the individual accessing and using the data, not just the IT or application developer. With businesses being dependent on seamless data provision processes to support their operations and analytics, it needs to be standardised across different systems.

Processing

Data generated from various applications always emerges in raw format when it’s created. This means it hasn’t been cleaned, extracted, prepared, or transformed so that it's ready to be used by an organisation.?

Processing is the aspect of a data strategy that focuses on cleaning and preparing data for practical application, generally consisting of a series of steps that results in a smaller set of homogeneous data sets that are ready for immediate use.?

Governance

As data sharing processes begin to broaden and refine, it becomes essential for businesses to have strong governance policies overseeing and regulating how the data is managed by all parties - known as data governance within a data strategy.?

The decisions about how data is processed, shared, accessed, and manipulated are all established by a company’s data governance policies. Data governance is often mistakenly thought to only apply to developers and data users when it actually applies to systems, applications, and employees.?

A successful data strategy must include principles of data governance to ensure that data is protected, maintained, and accessible at all times.

Data Strategy is a Work in Progress

A data strategy isn’t meant to be a once-off effort that’s static and fixed - it’s meant to be a work in progress that’s dynamic and adaptable as your organisation expands. The best practice for creating a dynamic data strategy is to identify the long-term as well as short-term goals to be achieved with it.?

This allows organisations to consistently review and evaluate the success of their strategy and make amendments if need be, however, it’s important to delineate who exactly will be responsible for managing its continuous improvement within the business.?

A robust data strategy shouldn’t aim to prevent any unexpected or unforeseen data issues from arising. Rather, it should position businesses to be able to respond swiftly and effectively and implement changes across your data management landscape as your business operations and requirements evolve with time.?