Business

The Third Generation of Qlik’s Method of Business Intelligence

Although data has always been a crucial aspect for companies, the recent pandemic has shown how valuable it can actually be. According to research conducted by West Monroe, 60% of executives in the C-suite are planning for investments in automated, continuous, data-driven management. In addition to this investment, 47% of executives are contemplating the broad availability of the data as well as decentralised decision-making. According to the quarterly pulse survey, 84 percent of executives want to gain insights in data.

Business intelligence (BI) makes use of tools and services that transform data into useful insights that help an organization make strategic and strategic business decision-making. BI tools analyze and access data sets and present the results in summaries, reports charts, dashboards charts and maps that give users detailed information about the status of their business.

Reporting is an integral aspect of business intelligence. The dashboard may be the most well-known tool used in BI. Dashboards are software programs hosted on the internet which automatically collate information into graphs and charts that provide a view of the current situation of the business.

The term”business intelligence” typically also refers to a set of software that provides rapid, simple information about the present state of an organization in light of the available data.

We’re currently in the 3rd generation of Business Intelligence (BI). The previous generation was focused on gathering data and empowering employees to access and utilize data to make better decisions. This generation 3 is going further.

Moving from the second generation to the third generation

Source: qlik.com

The pandemic highlighted certain weaknesses of the second generation. For one, employees cannot be expected to look over data for any changes that need to be addressed. Furthermore, the ability to gain insights and take action rapidly is almost impossible. A more proactive and automated approach is required. Data analysis is a crucial part of every industry right now, you can be working at a grocery store or in NetBet and you would need to process all kinds of insights.

The latest tools, such as artificial intelligence (AI) and machine learning (ML), promise to make the analysis and mining experience even more user-friendly and personal with the goal of assisting in better decision-making processes.

With only 20 percent of the Australian workforce classified as being data-literate, companies must think about the ways that non-specialist users interact with data. Despite the inclusion of these instruments, there’s no guarantee that better decisions can be taken.

A great parallel can be seen by people’s experiences using smart home assistants, such as The Amazon Alexas and Google Homes of the world. They’re easy to use, and have the ability to plug and play. But without the most advanced technology, natural technology for processing languages (NLP) capabilities, they can result in a bit more than hand-free timers which is far from the essential assistant the makers of these devices had in mind.

The same is true for the software for BI. It may provide the most natural method of working however, unless the data is regulated, trusted and constantly informs users when changes in situations require action and it is not able to make informed decisions at critical times.

The significance of data

Source: resagratia.com

Data on its own is of no more or less worth than another item of information. Data gains value when it can lead to results. This means using statistics to help make the best decisions at the right times.

To ensure the realization of the value data could bring for the company, it needs an advanced analytical data-driven pipeline that can connect data to the right chance for actions.

What exactly does that look like? It’s the result of speed and contextual information – the rapid pace of change in our world means that an opportunity could pass quickly. Companies must be able to combine real-time access to other sources both external and internal. When they do, they are able to present the chance in a way that helps users recognize occasions in their business and respond in a timely manner.

In reality, Qlik research with IDC found that nearly half of organizations have added fresh internal data (45 percent) and additional external data (40 percent) as well as new types of data (40 percent) in their data pipelines in the last 12-18 months.

Thus, an intelligent analytics data pipeline that provides real-time access to controlled clean and reliable data can give organizations more confidence in the insights they gain. Fed into a reliable database which will guide the next generation of BI software and consequently will allow them to offer substantial benefits. This is the combination that is set to be the future generation of BI and allow every moment of business to be optimized for active intelligence.

It also allows the use of AI as well as ML to assist users to discover insights, and also in identifying outliers and new developments which require immediate decision. This is only possible through reliable data, which results in people being reassured by their analytics tool to take action in the event that needs them to act and follow the recommendations it gives. They may even be able to delegate some of their operational decisions to the system in the event that a particular scenario occurs, which helps users to lessen the stress of routine decision-making.

Transformation of the process of decision-making

Source: gls.global

This isn’t only about speedier and better operational decision-making, but an enormous shift in how decision-making is made, at every level of the company. Business models of all kinds can be changed using reliable insights which can then be used to influence the methods that operational decisions are taken.

Automated systems can replace routine operational decision-making, allowing an immediate action by the user, and ease some of the burden of making decisions. The attention can shift to more collaborative and strategic decisions that determine the way an organization will react to various scenarios that might take place.

The 3rd generation of BI should be more than just making decision-making easier and more efficient. Being flexible, completely trusted, reliable, collaborative, and more automated is the only way of realizing the power of data in the coming years.

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