Last year, Gartner predicted that composable data and analytics would be a trend-setter. At the time, it stated that composable analytics would enable a new collaboration model between citizen developers and business analysts. Fast forward to the present, and the notion of composable analytics, specifically at an application level, is still very much key to business success.
Of course, the low-code development approach associated with going ‘composable’ is hardly new. What has changed is the level at which these pre-built components will be used to create new, business-friendly solutions. Beyond technology teams, mainstream employees will rely on these composable applications to quickly build the things they need to integrate with existing workflows. The benefit? Specialised developers can remain focused on more complex programming.
Furthermore, composable data, analytics, and applications will see innovation happening more rapidly across all business spheres. As the development process is no longer limited to technology teams, iterations can happen faster at the coal face of the organisation, with business users taking charge of their specific analytical needs.
One of the biggest stumbling blocks to turning data insights into action is managing decision-making. Many leaders do not have access to actionable insights when the decision needs to be made.
The model needs to be redesigned to consider decisioning first. In this way, those who must act can do so with the confidence that it is based on real-time insights. Remember, part of this process is to refocus on creating hyper-personalised experiences for customers.
The availability of more customer data means the process can be injected with relevant information on the wants and needs of an organisation's most important stakeholder group. By understanding their customers better, executives can give them what they want in real-time and as personalised as possible.
By now, many companies are seeing the potential Internet of Things (IoT) devices can have on their operational environments and sensors and smart devices are taking concepts around edge computing to a new level. Especially in manufacturing, mining, and healthcare, South African entities can benefit the most by analysing equipment at the edge to ensure they remain efficient.
With this comes the ability to do predictive maintenance to minimise downtime and service disruptions in remote areas. And when combined with the new decision-making process and composable applications, the efficiency gains to be had can be significant.
By incorporating edge data into their processes, executives can optimise the entire value chain across any environment.
From remote to distributed, and now hybrid, business processes and operational systems have irrevocably changed. Today, companies and their employees access data from various sources, using virtually any device from any geographic location. This puts pressure on businesses to rethink how to work and ensure optimal service delivery requirements.
More than ever, data analytic tools will become instrumental in providing the insights needed to make the most relevant decisions that will help shape the hybrid environment. Along with this comes the need to consider automation enriched by sophisticated artificial intelligence and machine learning algorithms. Consider how this automation can increase efficiencies from IoT and edge data, composable applications, and a radically restructured decision-making process
Suddenly, companies can rapidly transform into insights-driven organisations that leverage their human resources for strategic projects while automation takes care of repetitive, administrative-heavy functions. But to move from data to insights to action, businesses must turn to what can be considered hyperconverged analytics. Essentially, these tools integrate data science, streaming capabilities, and visual analytics.
Organisations must start thinking differently about their data, systems, decision-making processes, and how they use automation. This is the year where all these digital transformation and analytics aspects come to a head. The resultant disruption can either be positive or negative depending on the willingness of business and technology leaders to change.
