- August 6, 2021
- Posted by: Aanchal Iyer
- Category: Big Data Analytics
Technology has advanced in leaps and bounds in the last few years and this has most certainly changed what analytics can achieve today. Analytics has become more easy and powerful to use.
Data analytics has a lot of powerful features; however, it is still not easy to use analytics and data to foresee most of the happenings in an organization. Predictive models need a huge amount of past data and analytical expertise to be created and used, which limits how and what can be deployed. Descriptive analysis needs users to devote more time to generate analytical models that will be used for analyzing data. Analytical models have primarily focussed on a specific unit even though the business problems cut across business units.
The New Generation Analytics
The new generation analytics integrates both context and automation. This form of analytics relies on connections across current information systems, role-based decisions on what decision will be made based on analytics, and AI. Such tools can formulate insights that can be delivered to the top management directly.
Automation in Analytics
Automation in analytics, which is often termed as “augmented analytics” or “smart data discovery” decreases the dependence on human judgment and expertise by automatically pointing out patterns and relationships in data. In most cases, it can even recommend solutions based on automated analysis. Together all these capabilities can change how we analyze and consume data.
Use of Context in Analytics
Traditionally, analytics and data were distinct resources that had to be combined to gain value. This needed special expertise on which data was appropriate for analysis and where it can be found. Also, many analytics lacked the knowledge of the broader context. However, analytics and AI applications too can increasingly offer context. These capabilities are now frequently included by key vendors in their transactional systems offerings, such as Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP).
Besides automatically looking for data and connecting to provide context, the analytical process itself is being increasingly automated by AI. Vendors are adopting augmented analytics which can automatically discover patterns of data and query by using natural language interfaces. In short, AI and analytics are being combined to make analysis easier. These tools are being used increasingly in NLG, NLP, and more traditional technologies. Predictive analysis can recognize and categorize trends and anomalies in data and explain what has caused these anomalies. These new capabilities remove the barricade of time and expertise and make it possible to generate insights and take action to boost an organization’s business. Augmented analytics is now being rolled out by top-most vendors, such as Salesforce and Oracle, but some organizations have already undertaken successful experiments with them.
The potential and capability of these new tools is marvelous. These tools have the potential to provide a significant amount of improved insights efficiently to more people faster. Quantitative professionals and business intelligence analysts will still have crucial tasks to perform, but many will no longer have to offer support and training to amateur data users. The small and mid-sized organizations that could not afford to invest in data scientists will now be able to analyse their data with advanced precision and insights. Now, all that will be required is an appetite for data and a willingness to invest in and implement these new technologies.