Ensure the quality of your data

PBT Group thought leadership

Data is all around us. In the digital landscape, it has become the currency to do business and remain relevant. But what of its quality and how best to effectively manage it? After all, if your underlying foundation is not sound, there is very little hope of transforming business processes successfully.

On its own, data means very little. This is even more so the case today where data sources have increased exponentially thanks to the likes of cloud computing, mobility, social networking, and the Internet of Things. It is no longer just a case of receiving data from email, customer forms on websites and call centre agents. Companies are inundated with data coming into the business from all sides.

Involving business

And, unlike in the past, this is not just a technology discussion. With terms like structured and unstructured data becoming part of boardroom discussions with all C-suite executives contributing viewpoints, companies need to find a way to effectively manage their data and ensure its quality.

Considering this, Data Quality Management and Master Data Management are two singularly important concepts that should be part of any business strategy in the digital world. Like one litre of oil contaminating one million litres of fresh water, dirty data can significantly negatively impact its effective analysis inside an organisation.

Data Quality Management ensures that the data reflects and represents real-life truths comprehensively and consistently throughout complete data sets. Master Data Management enables us to manage the contextual data relating to the key data entities to set standards, ensure consistencies and increase confident interpretation of the data trends and analytics.

Embracing new technology

While the data coming in to the organisation needs to be closely scrutinised for its quality, just because it is considered ‘good’ in January does not mean it will continue to be relevant to the business by the time December comes.

Fortunately, machine-learning provides deeper data analysis capabilities, that can overcome data quality gaps in historic datasets. Even though many companies still rely on data scientists or analysts to accurately prepare and interrogate the information stored in back-end systems, these newer technologies provide agility and speed to deliver insights to more people inside the organisation.

On the one hand, this means different departments can take more nuanced insights from the data than just receiving a high-level overview. On the other hand, it creates an enabling environment for the company to break the traditional silo approach of data analysis.

Continually evolving

Irrespective of the technology or approach used, decision-makers across the organisation need to remember that neither Data Quality Management and Master Data Management are ‘fire-and-forget’ solutions.

For these approaches to work effectively, the company must continually review and update its internal best practice to ensure aspects around data stay aligned to the corporate strategy. The importance of data is only going to increase as organisations strive to find that competitive advantage in a connected world. The differentiation will come in terms of who manages their data best and who can ensure its quality for business success.

 

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