Overcoming challenges to become a data mature organisation

AI Magazine looks at how to overcome challenges to achieve data maturity so businesses can increase their ROI in this increasingly data-driven world

Data is at the forefront of many business leaders’ minds. In the digital economy it is imperative that businesses are able to process and analyse data to gain insights into customer and business trends to drive efficiency, competitiveness and profitability.

To achieve this, organisations need to reach data maturity to really drive decision-making. If data is not properly arranged then it cannot be analysed and unpicked to the full extent. Data maturity is measured by how well the data can be processed, analysed and utilised to drive profitable decisions - the more an organisation values its data, the more mature it is.

In today’s business landscape, it is important for businesses to reach data maturity, particularly as it better equips them to identify opportunities and threats. For example, a data mature business can leverage the power of predictive analytics to anticipate what will happen in the future as well as what the best actions are for them to take.

Shifting business culture to reach data maturity

For a business to become data mature, the culture of that organisation needs to shift to reflect the new priorities of the business - data. With this new priority, organisations should value agility and needs to be aware that failure is necessary before real innovation or growth is possible.

Experimentation is necessary as when data is used or analysed, multiple analysis is often needed before valuable insight is gained. On top of this, businesses needed to become results orientated rather than process or output orientated. By focusing on outcomes, businesses are better prepared to deal with the data at hand.

To ensure this mindset is adopted throughout the company, employees at all levels should be encouraged to analyse and utilise data to support their own roles. Employee mindset should also be curious and willing to challenge assumptions with data.

Data maturity and the importance of leadership support 

For this shift in culture to be successful, it is imperative that there is sufficient support from the leadership team within an organisation - leaders need to model behaviours around using data to make decisions.  

On top of this, business leaders should recognise and reinforce employee behaviours reflective of data use. By celebrating an employee's good use of data, the leadership team can motivate others to adopt the company’s data-driven culture.

Although it is important that leaders are concerned about how data insight can drive revenue, they should also look at different ways types of data within the organisation can be utilised to improve the organisation and make it more productive.

Creating accessible data 

For a business to achieve data maturity, it is essential that data is accessible. For many organisations, data is siloed or inaccessible as it is unavailable for one reason or another. Without accessible data, businesses cannot begin to generate insights to drive decision-making.

Leaders must task staff to organise data so it is clean, high-quality and in a central location. By creating data that is easily accessible, employees will be able to use data to the best of their ability to perform their tasks. It also enables teams to collaborate more effectively and share data from different functional areas.

Aligning leaders and data professionals

Within every data-driven business are a number of data professionals. In some cases, more often in less data mature organisations, the data teams and business teams are not well aligned leading to unnecessary frustrations.

Without this alignment, businesses won’t gain as much from their data as they should. To overcome this, data teams need to share a common language and framework around data.

It is equally important that business leaders take an active role in designing, monitoring and sharing insights from data projects.


Featured Articles

Toshiba Takes Another Step to Ushering in Embodied AI

Toshiba's Cambridge Research Lab has announced two breakthroughs in Embodied AI alongside a new group to renew focus on the tech

Why AWS is Investing $230m in Credits for Gen AI Startups

Amazon is investing US$230m in AWS cloud credits to entice Gen AI startups to get onboard with using its cloud services

How Retrieval Augmented Generation (RAG) Enhances Gen AI

RAG is a technique that promises to improve the way Gen AI fetches answers and provide business with a more reliable use case for client-facing uses

Synechron’s Prag Jaodekar on the UK's AI Regulation Journey

AI Strategy

LGBTQ+ in AI: Vivienne Ming and the Human Power of AI

Machine Learning

Samsung’s AI-Era Vision Coincides With its New Chip Tech

AI Strategy