24 August 2022
This is the third article in our Qlik Active Intelligence (QAI) series, where we explore how this new approach to data processing can modernise your data analytics. We’ve covered active versus passive intelligence and the history of BI, but how does it all come together in a real-world setting?
The way businesses process and analyse data has changed drastically over the past decade. For one, the advent of social media initiated what we refer to as ‘Big Data,’ or vast amounts of data that cannot be processed by humans alone. Big Data has changed the way information is collected, stored, and analysed.
In fact, the passive method has been the norm since its inception in the 1950s. And this passive analysis served a singular purpose: to provide users with insights that support their decisions using historical analytics. But the days of relying on outdated data and time-consuming information dissection have long since come to an end. Now, with obstacles to business success including competitors and ever-savvy customers – there is a greater need to have near-instant insights at your fingertips.
How do you extract near real-time insights with an ocean of data? Automate your processes and incorporate some form of machine learning (even just a few IF statements). That’s where QAI comes into play.
By implementing automated workflows, your organisation can drastically cut down on the amount of time that users need to wait for insights. Instead of a human analysing the data and coming up with the results, systems can process the information as it’s collected. This helps to uncover insights in the now, such as a real-time overview of your finances.
It may just be the future of data analytics as a whole. The more of the ‘grunt work’ of data collection and dissection that we can automate and unload onto machines, the more time we have to discover real, meaningful insights.
Let’s look at a few examples of QAI working in the real world and helping organisations better understand their data.
How clean is your data? How relevant is the information that you are analysing and how long does it take to realise the raw data that you use? Those are the questions that iA American Warranty Group needed to answer.
In an interview with Qlik, iA American shared how they reaped many benefits from implementing QAI, such as focusing on drawing better decisions from better data.
With the adoption of QAI and curating the data, iA American Warranty Group was also able to increase productivity by five times, deliver informative insights within three months, and reduce the data readiness project span by ten times. Cutting down on unnecessarily messy data was a large leap forward for the organisation – made possible by examining and redefining how their data pipelines functioned.
According to Rob O’Neill, the head of information at the University Hospitals of Morecambe Bay NHS Foundation:
[…]data overload and the inability to extract data insights is the third highest barrier to digital transformation.
The term ‘analysis paralysis’ – or how humans struggle to understand and process information when there is too much of it or too many options – was hindering their analysis. This can reduce the efficacy of business decision-making, removing thought agility from the organisation and its employees.
This is where machine learning and intelligence can strengthen the relationship between users and raw information. QAI’s use of automatic processes and assisted data workflows have the potential to feed information into learning systems that are able to sort, identify, and process data at a significantly faster rate than humans. It can, in turn, make and handle instantaneous decisions that benefit the company. For example, a programme can read thousands of eCommerce credit card transactions at once and pick up any anomalies – like the usage of stolen details – faster than a human ever could.
These automated pipelines help to enhance the flow of key insights so that providers can focus on what’s important, like improving patient care.
Much like any analytics system, the visualisation side of QAI is also critical in supporting a user’s business decisions. By presenting insights in a visually easy-to-digest way, users can take action faster and understand outcomes better. Vizlib compatible with Qlik Sense, Saas, and QAI.
Vizlib’s full range of products for Qlik Sense is fully compatible with QAI and Qlik SaaS. Combining new data workflows with Vizlib Flow Chart, one of the many extensions that make up Vizlib Library, will enable you to clearly and easily visualise and identify bottlenecks and drop-offs in your data. This is vital for organisations that deal with the production of goods.
Another popular Vizlib Library extension is Ridgeline Plot, which provides a simple way to see trends and distribution over time. Overlapping mountain or ridge shapes makes visual comparison quick and easy as they are placed on separate lines in the visualisation. By using Vizlib Ridgeline Plot with QAI, you can actually compare historical data with current trends without having to wait.
With the right systems in place, instantaneous data analytics will be your company’s biggest asset. Are you ready to modernise your organisation’s analytics and reap the real-world benefits?
Vizlib’s feature-rich data visualisation products will help you get started on your data storytelling journey.
Got a technical question? Our Help Centre has heaps of helpful guides, articles, FAQs and more. Access Help Centre