In a rapidly digitised and competitive business landscape, the need to adopt a mature approach to data management is growing; businesses that don’t will be left behind. Gero Renker explains the three pillars of data maturity which can help to create a ‘data driven’ business.
While high quality data is integral to good decision making and business growth, many businesses are still holding onto outdated systems, processes and cultural habits that undermine their ability to be ‘data driven’. Many businesses are still falling short of achieving data maturity. Typically, this is due to a fragmentation of data across departments and a lack of appropriate governance processes and IT infrastructure. Some businesses continue to operate in siloes or have a culture that doesn’t contribute to a successful data maturity journey.
For businesses to become data driven, the barriers rooted in people, processes and technology need to be addressed.To achieve this, organisations should aim to meet three pillars of data maturity: data acquisition, data assurance and data value. Assessing how well an organisation performs within these pillars can be the starting point to defining initiatives for improvement.
Pillar 1: data acquisition
Traditionally, the primary method of acquiring digital information has been to manually type data into computer applications. Business operations have relied upon spreadsheets and email communication to collect and share data which has made room for the double-entry of data and errors. However, in the digital age, the potential channels for data acquisition have multiplied.
Electronic data interchange has given way to technologies such as cloud platforms, intelligent devices and robotic process automation that allow businesses to harvest data more efficiently. How well a business is able to exploit these channels impacts the quantity and quality of data it can acquire. For example, organisations that maximise the use of web portals can improve data exchange and optimise online collaboration.
Developments in cloud technology and artificial intelligence (AI) are also delivering tools to improve data acquisition. They enable greater connectivity and integration, collaboration across organisational boundaries and provide flexible and scalable enterprise data stores. AI can also help to extract data from unstructured content by recognising specific patterns and relationships in the information provided. Equally, cloud-based, low-code technology platforms are allowing applications to be created faster and for less cost to enable business users to capture and process information more efficiently.
Pillar 2: data assurance
Data ownership brings with it the need to secure and assure the data. This includes the physical protection of data via cyber security controls, encryption and disaster recovery strategies. Owning data also requires businesses to assure data accuracy, consistency and completeness using measures such as meta data management, data quality profiling, audit trails and approval workflows.
Data timestamps can help to identify data records that are stale and require refreshing. If there are gaps in the consistent user adoption of processes and applications, data quality will suffer. Strong governance is therefore key and people in governance roles need to be equipped with the right tools to identify non-compliance easily.
Pillar 3: data value
Once rich, good quality data becomes available, it needs to be applied to deliver value to the business. This will provide insights to optimise supply chains, improve customer service, control project delivery and support a multitude of other scenarios that rely on data for better decision making. Extracting this value is a maturity journey in itself.
At a basic level, businesses will use data to understand the current status of their operations. As data maturity grows, historical data can be used to see trends over time and understand if the business is moving in the right direction to deliver its strategy. Advanced data analytics capabilities will bring the potential for new insights, by sharing previously siloed data across the business and creating new connections. Once sufficient historical data is available, businesses can use it to predict the future. With AI and machine learning, patterns can be recognised and applied to calculate likely outcomes, such as risk scenarios.
People, process and technology
While modern business intelligence and analytical tools are an essential foundation for the consumption and exploration of data, businesses will need to encourage a greater understanding of using data appropriately and the questions it needs to answer to deliver business benefits. As such, people and culture are at the very heart of the data maturity journey. The focus of the workforce should be on building knowledge, taking ownership and driving consistency of process in terms of data acquisition, assurance and exploration.
Senior sponsorship is key in fostering a collective commitment to data maturity. Businesses may choose to appoint a board-level executive, such as a chief data officer (CDO), to take responsibility for executing data initiatives. The CDO ensures data is relevant, accurate and channelled to the right areas of the business. It is their duty to monitor and prompt workers to exercise a high level of data governance and stay abreast of the latest technologies that will empower this.
As the business world becomes increasingly datacentric, businesses that implement the three pillars of data maturity and shape a culture to bolster them will be a stronger match against competitors. Quality data is a cornerstone for success and businesses that take it seriously are more likely to reap future rewards.
Gero Renker is a director at Program Framework. Established in 2005, the consultancy firm and Microsoft partner specialises in project portfolio and risk management and deploys its range of cloud-based products for organisations in the public and private sector.
- Data Science
