16 September 2022
As inflation rises throughout much of the world’s leading economies, the current global economic outlook remains uncertain.
Businesses are facing extreme challenges due to a combination of economic factors such as rising resource costs and supply chain bottlenecks, as well as societal shifts and heightened customer expectations.
As such, perhaps now more than ever, businesses are in need of real-time data analytics to help them take informed action and better understand how market trends and complications could impact their organisation.
The term ‘Business Intelligence’ (BI) can be traced back to 1865 where it was first used by writer Richard Miller Devens in his book, the Cyclopaedia of Commercial and Business Anecdotes.
The origins behind what we refer to as BI today however, stems from a 1958 journal by IBM computer scientist Hans Peter Luhn, named ‘A Business Intelligence System’. Defined by Luhn as an “automatic system […] developed to disseminate information”, the advancement of technology and in particular, the internet has seen this concept fully realised to become what we now refer to as BI.
Today, BI can best be defined by the American data-driven marketing company TechTarget, which stated that BI is “a technology-driven process for analyzing data and delivering actionable information that helps executives, managers and workers make informed business decisions.”
BI is vital to companies making informed decisions. According to a study by Deloitte, companies with CEOs that spearhead data-driven decisions are 77% more likely to achieve their business goals. The report also showed that they are also 59% more likely to derive actionable insights from analytics results.
Traditional BI or ‘passive intelligence’ refers to the original method of conducting BI. Typically, in years gone by, traditional BI would consist of a stream of data scientists painstakingly sifting through complex data in order to generate analysis reports.
In recent times, the proliferation of easier-to-use data analytics tools (and later, data dashboards) has allowed business analysts to produce more timely insights and reports.
Traditional BI data dashboards give end users the ability to source historic data themselves, rather than having to rely on an external third party such as data scientists or contracted business analysts. However, it’s important to note that while these dashboards do make data querying easier, they are not designed to support real-time decision making due to their reliance on historical data.
Should a business wish to take an informed and immediate reaction to market trends, then their decisions must be based on the most accurate and up-to-date data available. Organizations using an ‘active’ approach to BI will be able to see data on what is currently happening in their market, as opposed to trends that have come and gone.
Based on the concept of continuous intelligence, Active Intelligence (AI) is a dynamic and new approach to BI that can modernise your data analytics and make it faster than ever for an organisation to comprehend and react to market trends.
With traditional BI or passive intelligence, organisations are able to review past data trends to understand why certain events occurred, and make assumptions about what might happen in the future. However, with AI, organisations can merge historical data with brand new data to analyse and gather insights in real time.
The ‘active’ approach of AI provides businesses with an advantage over their competitors as it allows them to pivot to market changes and make data-led decisions in real time.
As highlighted previously, traditional BI is primarily designed to inform a business of past information, rather than compel the company into taking any future action.
AI however is designed to promote and facilitate immediate actions from the data provided. An organisation applying Active Intelligence for example will be able to accurately react to market trends in real time and identify the best way to implement an informed action.
AI is based on a continuous stream of intelligence and data in real-time. The use of real-time data analytics allows companies to better understand their particular market as it is today – not as it was days, weeks, months or years ago.
Organisations using AI are able to immediately respond to trends in the marketplace and adjust their messaging or products accordingly. For example, during the pandemic the American ecommerce website Etsy relied on data that showed an increased demand for face masks to prompt its’ sellers with the appropriate skills and resources to create face masks in order to meet this market need. Real-time analytics allowed Etsy to analyse market trends and boost their business once sellers unanimously answered their call, resulting in record stock prices for Etsy in the following months.
AI closes the gap between what’s happening in your business right now and what the information and insights are telling you. As a result of real-time analysis, companies using AI can enjoy 76% improved operational efficiency as well as a 75% and 74% revenue and profit increase respectively.
Forecasting is also dramatically improved when using AI. By using accurate, up-to-date real time information, your forecasts are more accurate than ever before, allowing you to make complex decisions comfortably.
Lastly, due to the real-time nature of AI, businesses are able to have more faith that the actions they take are reflective of the current market conditions. With passive intelligence, it’s often hard to gauge whether the market is still being affected by trends that were popular in weeks gone by. Yet, with AI, you can accurately see which topics and trends have mindshare amongst your customers.
In order to efficiently reap the benefits of AI you must first ensure that your workforce is prepared. Upskilling, and in particular, promoting data literacy is vital to the successful implementation of an AI solution such as Qlik Active Intelligence.
We have provided a detailed report named “Data Literacy: The Upskilling Evolution” which outlines the new skills and roles that will be an essential component to enabling your workforce to become AI ready. Some of the key points highlighted in the report are as follows:
Our report finds that despite c-level executives maintaining a higher data literacy their confidence in their data is still lacking. 45% frequently make decisions based on their instincts rather than data-led insights.
85% of C-level executives spoke to believe that being data literate will someday be as important as computer literacy is today.
Our report states that only currently 1 in 10 employees in finance, marketing and HR teams are offered the required data literacy skill training despite 70% of staff requiring these skills on a daily basis.
In a digital and globally interconnected environment, modern teams need real-time data more than ever in order to rapidly change course, and steer their business in the right direction.
As highlighted by Paul Asquith, Insight and Reporting Manager at Samsung Electronics UK: “Having real-time data is absolutely critical”. If you’re a retailer or other customer-facing business, gone are the days where you can simply close your business down at 5pm and resume working the following day. Instead, modern business owners need to have 24/7 productivity and visibility, ensuring that customers can always find and use their services no matter where they are in the world.
The modern consumer has little patience for poor response time, or being unable to access vital services due to maintenance work. As such, it’s important to make use of real-time analytics to understand the times your business is interacted with by customers so any emergency maintenance work on your channels or upgrades to your logistics can be carried out with minimal interruption.
Modern data teams understand, therefore, that traditional BI is incapable of handling today’s data-driven demands. The importance of having up-to-date, real-time data cannot be understated. Harnessing real-time analytics allows teams to make informed decisions that are backed by data rather than instinct.
Vizlib’s feature-rich data visualisation products will help you get started on your data storytelling journey.
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