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Analytics vendors are increasingly providing embedded analytic solutions based on inside applications that are real-time to receive the next level of business intelligence for companies.
FREMONT, CA: Business analytics–associated with steep learning curves and significant investments in infrastructure–is an increasingly powerful instrument for enterprises nowadays. Using data to drive better decision-making has been well established. However, the traditional approach based on reporting and analysis tools depends on expert applications and highly trained staff. Organisations are, therefore, building teams of data scientists to amass data, manage tools, and build queries.
This leads to congestion in the flow of information, as business centres rely on specialist teams to interrogate the data and report back. Although there has been improvement in reporting tools over time, such as the transition from spreadsheets to visual dashboards, there is still a wider gap between data and decision-makers. Companies also find it challenging to handle multiple sets of data sources. Consequently, vendors have developed embedded analytics to bring users closer to the data and lead to faster and more accurate decision-making.
Embedded analytics provides functionality within existing enterprise software and web applications. Users no longer need to change into another application like a dashboard or BI tool to view data. Instead, analytics suppliers offer application programming interfaces (APIs) to connect their tools to a host application. Embedded analytics allows mobile and remote workers access to decision support insights and potential data. Platforms with embedded analytics built in enable users to view visualisations and dive deep into real-time data.
Embedded Analytics to Help Decision-Making
Customer service agents are using embedded analytics to help with decision-making and customise offers to customers. These embedded systems are built to work with live data and data streams even when customers do not need to delve deep into data. Businesses are likely to use the same data to drive multiple analysis tools. The analytics, business development, or finance teams will use their tools to carry out complex queries, and field service or customer service agents might need little more than a red or green traffic light on their screen.
The fundamental idea is that when the traditional reporting process identifies the root cause of a business problem, companies train their software either by formal if-then-else rules or through machine learning to warn as similar situations arise in the future. The old approach creates reports only when there is an investigation in search of poor performers being conducted. Unlike this approach, the new one facilitates a view within the home screen or dashboard, constantly alerting the worst performers or deteriorating ones and instigating a formal workflow to record the actions that companies have initiated. This helps in contacting suppliers to find out what they are doing to fix the problems.
Such alerting assists businesses in accelerating decision-making by improving access to the data that the organisations possess. Businesses are required to move faster to respond more quickly to issues. This is also an evolution of the technology to make embedded alert-up analytics easier to deliver.