Many companies have embarked on digital transformation journeys to shift their focus towards becoming data-driven organisations. Fundamental to this is having a data strategy in place that will enable the business to better deal with the complexities of the rapidly expanding world of data.
By Andreas Bartsch, head of innovation and services at PBT Group
Thanks to the Internet, social media, the introduction of cloud services providers, mobile devices, and the growth of the Fourth Industrial Revolution (4IR), the number of data sources have become virtually limitless. Today, unstructured data has become a major ingredient of the data landscape.
If these disparate data sources are to become useful within a company’s decision support system, data must be collected, integrated, managed, and made available. Fundamentally, a data strategy is a plan designed to improve the ways a company acquires, manages, and shares data.
What does it entail?
Importantly, the data strategy must support the strategic goals of the organisation to ensure that it delivers real business outcomes and ROI.
Typically, it consists of five components:
* Identify: Identify data and understand its meaning
* Process: Move and combine data to provide a unified, consistent view
* Store: Persist data where it is easily accessible
* Provision: Package the data for re-use with appropriate business rules and guidelines
* Govern: Establish and communicate information governance policies.
Regardless of industry, every organisation operates in highly regulated environments. It is therefore essential that organisational data assets are managed and protected throughout the entire data lifecycle – from where data is created to where it is used for reporting and analytics, and when it is archived or destroyed.
Standardised policies, standards, and processes are essential components of a data strategy. These not only establish trust in the quality of data used for analytics and decision-making, but also ensure that data management processes are consistent across all organisational divisions. It is this consistent and focused management of data throughout its lifecycle that becomes the foundation of establishing high quality, accessible data for analytics.
The typical data strategy contains the data vision, defines the business context, success, and capabilities. It describes the data principles and provides context around the value of information, the management thereof, standardisation, re-useability, accessibility, and sharing of such information. It will also define the data architecture and supporting aspects like the conceptual, logical, and physical data models.
Furthermore, the data strategy will shape the business glossary or data dictionary. In here, data governance principles and their implementation must be comprehensively described. Consideration must be given to the data lifecycle management, master data management, and data quality. The data strategy must also contemplate the organisational impact related to its human capital and culture.
A clear strategy around analytics is essential as data only has value if it is used. A successful data strategy cannot be developed in isolation. It must be designed and developed in collaboration with business and IT. Its establishment and execution require ongoing organisational change management.
Keeping it relevant
While it is a complex undertaking, compiling a data strategy is an essential component to best position a business on its journey in becoming a data driven organisation. Although a base strategy should be in place and agreed upon by all stakeholders from the start, it is important to note that such a strategy will continuously evolve.
The focus must be on achieving ongoing business value. This can be accomplished by starting with what is termed a Minimum Viable Product, followed by an iterative approach of implementing prioritised use cases that address real business needs.
As the data platform expands, new technologies will emerge, regulatory requirements might change, usage will increase, data volumes will continue to grow, additional data sources will have to be included, and business demand will escalate. All these factors must be aligned with the data strategy. Where necessary, amendments or refinements must be made using a proper change management process.
For organisations to remain successful, putting in place a modern data platform becomes non-negotiable. Data integration and data management are essential enablers to artificial intelligence (AI) and machine learning (ML). In a constantly evolving digital world, a supporting data strategy must therefore be able to mature, evolve, and remain relevant.