In our previous blog—Five reasons to move to Modern Data Analytics—we discussed the need for organisations to make informed decisions from their data, quickly. Modern analytics tools enable organisations to quickly extract, transform, and visualise their data, and we outlined five reasons to adopt these tools.
At Professional Advantage, our mission is "Helping organisation achieve more with technology". This statement rings true in regard to data as our goal is to enable an organisation to use their data more effectively to gain better and quicker insights for growth and profitability.
To become an organisation that is using its data effectively, it must go on a journey to identify business problems, invest in the right technology, and deliver insightful analytics with an acceptable return on investment. As part of this journey, it is essential to see where you have been from a data maturity perspective; where you currently are; and where you plan to go. By using a data maturity model and self-assessing their current stage, organisations can understand the bigger picture of what it looks like to progress through the stages of data maturity.
The below diagram shows four common stages within the data maturity model. Within each stage, it is important to see that the larger the business impact (Y axis), the greater the required business investment (X axis).
1. Data Aware stage
This first stage of data maturity is characterised by an organisation currently producing ad hoc reports with the goal of standardised reporting. The data sources are internal only, mostly static, and siloed to each team. Furthermore, the reporting is periodic and historical with a significant manual effort each reporting cycle to transform and present the data. Excel is generally the tool of choice, giving this stage the term "Excel Hell!". Overall, the data performs a descriptive function (what happened?) and there can be trust issues with reporting.
For organisations wanting to move beyond this stage, they should focus on automating the extraction and transformation of their data, building a centralised data store, data modelling, and building standardised dashboards and reporting.
2. Data Capable stage
Organisations in the Data Capable stage have reached a proficient level where reporting and dashboards are standardised. Investments have been made in implementing analytics tools and centralising the data management. There is consolidation of multiple data sources into a centralised data store and multiple teams throughout the organisation access the data, increasing transparency and trust. Business insights from data are emerging as data takes on a diagnostic function (why did this happen?), but there may be increasing concerns about data quality.
Moving beyond this stage will require organisations to focus on data integration and quality and create an organisational data strategy to optimise data processes and adopt new and advanced analytics technologies. It is common for these to be beyond the capabilities of current IT functions.
3. Data Adept stage
This stage is characterised by organisations who are using data to make critical business decisions for key initiatives. Business-ready data is available over the entire organisation and is managed by a dedicated data team. Parts of the organisation may see data as a competitive differentiator and tasks the data team with implementing new technologies that increase effectiveness and advanced analytical insights. Data science, machine learning, and streaming analytics enable data to form a predictive analytics function (what will happen?).
An organisation is already at high functioning data level and would need to focus on building advanced data analytics capabilities and be seen as a leader within its industry to move to the next stage.
4. Data Driven stage
This is the final maturity stage and is characterised by a data culture driving the organisation. Sophisticated insights and continuous innovation are critical to success, and data is seen as a significant operational area with high functioning governance and compliance. Self-service analytics and data literacy are adopted by all employees within the organisation and are readily shared with customers and other parties. Ultimately, data takes on a prescriptive function (how can we make this happen?) and is used to find new ways to adapt to the uncertainty of the future.
Data is one of the most valuable assets available to an organisation. The data maturity model and its four stages give an organisation a bigger picture of how data can be used more effectively; not just as a source of operational reporting but as a way to empower new insights and innovations to influence decision-making for the future. The good news is that now is the right time to start on the journey of data maturity. There is no single or immediate path, especially as the amount of data and analytical tools continue to evolve.
We hope this blog has set you on the path of data maturity. Enabling data to be at the centre of your organisation allows you to drive strategy, inform decision-making, and uncover competitive insights.
