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6 min read
Oana Mirea and Bojan Sapunov

In data-driven organisations, having information is just the start. Companies need to equip their teams with the knowledge, resources, support and intent necessary to turn that access into action and create a data-centred mindset that permeates down from the top of the company.


We asked two of our data experts to share their views on what it takes to create a data-driven organisation. Gain first-hand insights from Senior Data Analysis Consultant Oana Mirea and Data Architect Bojan Sapunov.


1. Data culture


Building and nourishing data culture inside an organisation isn’t easy. The companies that pull it off embed data at every level and within every process, using it to drive decisions, optimise strategies and uncover value.


That commitment must start at the top with management. Initiatives around data need to be promoted, advocated and cascaded throughout the entire organisation to maximise effectiveness and use. In many organisations, data initiatives start in a small silo and then struggle to be adopted further.


Another must-have for data cultures is awareness. Individuals need to be aware of data and the value it brings. Most people use data on a day-to-day basis and can bring valuable insights that can contribute to data initiatives.


Another important aspect of data-driven organisations is the ability to leverage data to drive decision-making, though it is important to remember the difference between driving a decision and using data to validate it. In a culture that calls data a core value, companies must stay prepared for unexpected results – initial theories might not always be backed up by the data, and this should be embraced as part of the process. When promoting a data culture, encourage people to explore data and use it as a reliable partner.


2. Data literacy


In a data-driven organisation, fostering data literacy among all employees is a perpetually relevant process. It is not only essential for data professionals to receive training in interpreting and understanding data, but also for data consumers, data producers and executives. Employees at all levels of the organisation must possess the ability to comprehend data insights, patterns and trends, as well as effectively communicate, present and visualise them to ensure comprehension across the board. This ensures that the organisation can make informed decisions based on data rather than relying solely on intuition.


Based on a survey conducted by New Vantage Partners among data executives in 116 Fortune 1000 companies, a mere 2% of respondents ranked ’data literacy’ as their top investment priority. It is crucial for data executives to recognise the significance of investing in data training for all employees who interact with data. By mastering the art of storytelling with top-quality data, employees can effectively convey insights and enable the organisation to transition from intuition-based to data-supported decision-making.


3. Data tools and technologies


People play a crucial role in establishing data-driven organisations. However, to fully leverage the potential of a data-driven culture and data literacy, cutting-edge technology and tools are essential.


Data is often referred to as the new oil, and what is needed is a powerful engine to extract, refine and harness it in a smarter and faster manner than ever before. Fortunately, there are new trending technologies that analyse and share the data faster, categorise and contextualise it, while finding patterns and trends not obvious to humans. The following technologies and tools can unlock the full potential of a data culture and data literacy, enabling organisations to make data-driven decisions faster and smarter:


Cloud-based data platforms: Solutions such as Snowflake, Databricks, Synapse, Redshift and RDS offer virtually limitless storage and processing resources. By 2024, it’s projected that a staggering 75% of all data workloads will be managed by the cloud. They provide excellent means for data sharing, which further empowers data democratisation and productivity.


Mature analytics and business intelligence (BI) tools: With the increasing demand for high-quality data, self-service or democratised analytics is becoming a coveted goal for data-driven organisations of the future. The industry is rapidly evolving around cloud architecture for data transformation, storing and on-demand analytics platforms. According to Gartner, organisations that have a shared ontology, semantics and well-established data governance and data-sharing practices will outperform those that do not.


Artificial intelligence (AI) and machine learning (ML): Currently, an estimated 90% of data is unstructured. AI and ML technologies are expected to enable business users to analyse unstructured data faster and smarter, uncovering new patterns and trends. Integration of AI with reporting and analytical tools is expected to mature further, including natural language translation into SQL queries for data transformation.


Metadata-driven data fabric architecture: These setups facilitate end-to-end integration and processing of data from physically and logically diverse sources such as on-premise hardware, multiple clouds, edge computing, social media and mobile applications. Metadata contextualises the data, which enables analysts to derive meaningful insights from it. By understanding the relationship of data with other types of data, analysts can gain a more holistic view of the business. Ultimately, the insights derived from metadata can lead to informed decisions and actions that help unlock the full potential of the data.


4. Data strategy


A sound data strategy empowers companies to have unified, governed data that can be accessed and extracted in a secure manner. This sounds simple enough. However, building such a strategy requires careful planning and consideration from multiple angles. To start, one needs to look at the business objectives and ask questions. How can those goals be facilitated using data in the current landscape? What data is available compared to what data would be needed to reach the business goals? How can current processes be changed or enriched to accommodate the data goals? What tech stacks and skillsets are needed within the organisation?


When creating your data strategy, you should not only look at the data platforms, but at the entire company landscape and how it will be impacted in its entirety. Data is generated from all over the organisation, and a comprehensive data strategy needs to cover all areas of the business.


Furthermore, to have a strong data strategy, a data governance framework needs to be in place. This will allow specialised roles such as data owners and data stewards to define processes, standards and policies around data. By implementing such a framework, an organisation can ensure that the data is consistent throughout, both in terms of meaning and use.


When setting the data strategy, one other element that should be considered is the data quality. Having reliable, high-quality data is at the core of a data-driven organisation. Historical data, for instance, can be an issue in terms of data quality. One could spend much effort trying to ’clean’ old data, so the benefits of using it need to be weighed against the effort. Whichever way this decision goes, quality is key to having reliable data, and it needs to be present in all data generation activities. This will lead to higher confidence in data usage and data-driven decision-making.


When looking at the data landscape inside an organisation, trust is essential, both internally and externally. That is why the data strategy also needs to include security standards. Data privacy and security have been a hot topic over the last years and will continue to have the spotlight as businesses collect and store sensitive data. As data threats grow, organisations need to invest in keeping it safe, an investment that is ultimately in the trust of the clients that their data is secure.


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