Data-driven enterprises use data collected from all activities of the organisation to support better decision making and timely support of operational activities.
Data that is useful to the broader organisation can often end up locked away in specific applications or within a specific part of the organisation. In addition, data is often more likely to deliver better insights when combined into a broader set with additional data from other sources. Fragmented data is a major obstacle to realising effective organisation-wide insights.
A growing sprawl of unmanaged data leads to problems with how discoverable and usable the data is, along with issues around accuracy, traceability, and incorrect interpretation. Healthy data assets are made possible by adopting practical data governance foundations and principles. Poor data quality is also a significant obstacle to realising effective insights.
Removing or streamlining the manual processing and management of data for reporting and analysis also reduces the associated cost and workforce effort. Furthermore, it reduces the cycle time of having the right data availability when it is needed, while tightening the ongoing feedback loop between insights and action.
Cloud-based providers deliver capabilities in analytics, AI, and machine learning that can be turned on and off as needed, for much less then what it would cost to build and deploy the same technology on-premises. They also greatly speed up and simplify implementing these capabilities, while providing the flexibility needed for an organisation’s changing needs.
The full range of infrastructure, solutions, models, and data can grow in size and complexity as the organisation progresses from ad-hoc trials to the full capability of being a data-driven organisation. The ongoing deployment, enhancement, and support of these assets needs to align with how other technologies essential to the organisation are managed and operated.
Making the right decisions when they matter is an essential part of improving and streamlining an organisation’s activities. Data-driven organisations support their decision making by correctly utilising the vast amount of data that their organisation handles.
Removing the delays in capturing, organising, and assessing data means that a data-driven organisation can address challenges closer to when they are occurring, rather than waiting for delays with end-of-period reporting and similar historical models of assessment and decision making.
With better distribution and organisation of data achieved, there are a range of technology platforms and analytical models that can be utilised to simulate, optimise, and predict specific scenarios that are challenging the organisation. Cloud platforms and dedicated analytics tools allow this to occur without impacting core applications and critical processes.
Using all of the above, the organisation can remain competitive through ongoing innovation, adaptation, growth, and rapid response to disruption.
One of the main challenges to be overcome in delivering better data-driven insights is the transition from data being segregated and isolated in system and activity specific silos, to data being coordinated into enterprise-wide repositories where it can be used in aggregate to provide a more complete picture.
That’s why the additional practices which complement the adoption of data and insight technologies are very important, they will help the organisation’s workforce and processes transition into a way of working where the delivery of timely, accurate, and well managed data becomes a responsibility for everyone across the enterprise. Understanding how data from one organisational unit provides value to other parts of the organisation is an essential part of the journey to data sharing maturity.
As these practices are used when delivering many other types of solutions, such as Customer Experience and Digital Transformation, we sometimes refer to them as Shared Practices.
An effective strategy will combine an understanding of the organisation’s current data related assets alongside a consideration of the challenges and priorities specific to that organisation. It will also provide a roadmap and change management plan to help the organisation’s workforce and processes adapt to the outcome of seeing data as a valuable and shared enterprise asset. Our Shared Practices which are used for many different types of transformation support the development and execution of a successful strategy.
There is a very wide range of technologies that an organisation can make use of, including application integration, data warehouses, data lakes, IoT (Internet of things) data collection, data publishing frameworks, visualisation tools, reporting repositories, statistics tools, and analytics platforms. Aligning the selection of technology to the organisation’s strategy and overall priorities will deliver a more coherent and consistent outcome then pursuing many different ad-hoc and uncoordinated options.
Getting the right foundation is a critical layer in supporting data and insight outcomes for the organisation. It’s the less visible data engineering work which aggregates and distributes data, getting it out of silos to where it can be of the most benefit. This is also the work that will automate the flow of data as much as possible, while also looking to improve data quality through verification, cross-checking, and traceability of the data’s lineage (how data has moved through multiple systems and processes over time).
While the data foundations provide the raw input for better insights, it’s the increasing availability and accessibility of more recent tools that are providing organisations with more ways to make use of their enterprise data. In addition to the more contemporary tools which do user-piloted analysis, the technologies which deliver automated optimisation/prediction and continuous decision support are growing more sophisticated alongside improvements in machine learning, generative AI, and similar approaches.
Similar to the more recent tools for analytics, the more recent tools for presenting data are taking organisations from historical data table reports that need manual interpretation to visual dashboards and front ends. Presenting the data in a visual form which helps to highlight the interpretation and understanding of that data, makes sharing the information more effective and streamlines the decision making around what actions need to be taken. These tools also provide important self-service and ad-hoc investigation of data to help identify new types of insights.
The most obvious application of data governance is to help manage data quality alongside the correct definitions and interpretation of data, as these are the outcomes will ensure that the data insights are not incorrect or misleading. This management is not just process based, as algorithms and analysis platforms can be used to continually check for data discrepancies and bias. A less obvious, but equally important consideration, is to establish the incentives around data being shared and the controls to manage the appropriate, risk-mitigated, access and use of the organisation’s data.
While an organisation’s core systems will provide a solid foundation of data collection, they are often reliant on a lot of manual data management and do not fully cover data collection across all of the organisation’s activities. Solutions based around Intelligent automation can streamline and reduce manual data entry and provide better traceability across processes. Likewise, solutions for better Workplace experience and Customer experience can also provide other types of data that captures the activities taking place across an organisation. Finally, smart devices and similar Iot (Internet of Things) technology also provides further useful sources of automated data capture.
The team from Diversus bring their experience and expertise from helping many other organisations improve both their underlying data capability and the sophistication of their tools which use that data. Our clients can now make their decisions based on richer insights and better evidence through improvements across expanded data collection, reliable data engineering, effective governance, accessible visualisation tools, and broader capability with analytics and AI.