Artificial Intelligence has been making headlines recently for a variety of reasons. From helping people avoid paying parking tickets to winning a photography competition, the benefits of AI are wide-reaching. Embracing this technology now could improve your business and give you a competitive advantage.
"Research reveals that a combination of AI and Big Data technologies can automate almost 80% of all physical work, 70% of data processing, and 64% of data collection tasks." (Forbes)
At first glance, this statement may seem like a cause for concern for data analysts worried that their jobs are at stake. However, it is more realistic to argue that AI will support data analysts rather than replace them - or perhaps the data analysts who use AI will replace those that do not. AI presents huge and far-reaching opportunities for improvements in efficiency and accuracy in data analysis, guided by the experts already working in the industry.
Intelligent automation can provide predictive analytics to inform business decisions and learn to detect patterns that aid data analysts in providing better insights. To do these things AI needs a lot of data and sometimes the sets it pulls from contain what’s known as “bad data”, which needs to be cleansed.
Data cleansing is the process of identifying and correcting bad data in order to make informative insights. Data analysts often encounter bad data from legacy systems this could include duplicate data, formatting issues or missing information. Poor data can lead to poor insights, goal failure and higher operation costs. Bad data can also impact the future use of AI as without large quantities of high-quality data it cannot function. The greater the quantity and quality of the dataset, the better the performance efficacy of the AI. Bad data can be a significant obstacle to success in the analytics industry.
Data cleansing is an essential part of business automation and customer data improvement; however, it is a time-consuming task. This process is further complicated by the ever-increasing datasets that analysts can struggle to keep up with. It’s often stated that the vast majority of time spent by a data analyst is on data cleansing rather than analysing and identifying insights; this time could instead be spent performing meaningful analysis. Fortunately, at Wyser, we implement AI and machine learning solutions to make data cleansing easier, faster, and more precise.
Firstly, machine learning algorithms can uncover flaws in large datasets and provide predictive analysis. One of the benefits of this is, unlike human-driven systems, the more data a machine learning system has access to regardless of scale, the more accurate its predictions are. Another advantage of machine learning-based algorithms is that they continue to learn and improve over time. Because deep learning improves machine learning-based software over time, data cleaning becomes faster even while it is being fed in, speeding up the entire data delivery process.
Does this mean that AI will replace your data analysts? Not necessarily because efficient data cleaning frees them up to focus on value-add tasks like making and delivering insights. AI is a means to support rather than to replace, and the same is true for the presentation of findings in more user-friendly ways. Data democratisation is made easier by utilising a variety of innovative AI-based methods such as Virtual Reality presentation.
At Wyser, we recognise that high-performing businesses are those using AI and identify that greater levels of efficiency in data analysis are made possible by these new technological advances. Laszlo, our AI-driven advice software, is just one solution we provide to improve customer data. Laszlo helps advisers save time while producing stronger core data, such as management information and analysis. The more Laszlo is utilised, the better it becomes at giving insights and projecting trends across your organisation.
If you would like to know more about AI and how we can help solve your business challenges, please get in touch.