When Artificial Intelligence Meets Data Analytics
If you are from an organization that strives to function in a highly-technological environment, then it is crucial that you know the relation of big data and artificial intelligence: the latter depends heavily on the former for success, while also helping organizations unlock the potential in their data stores in ways that were previously cumbersome or impossible. Leveraging well-managed and presented data can improve organizations big-time. The problem is, handling data is stressful due to a variety of reasons.
Data Analytics is the process of making sense of and transforming data into useful knowledge. This process is composed of many stages and phases, and while there are software or tools that exist to assist, data-wrangling – the exhaustive process of cleaning and organizing data – is still rarely addressed. Obviously, practical data analytics is painful, and a helping hand in the form of automation through artificial intelligence can make a huge difference in this field.
To revolutionize the speed and efficiency with which data can be transformed into useful knowledge is the goal of The Alan Turing Institute’s Artificial Intelligence for Data Analytics project, otherwise known as AIDA. According to the initiative, it aims to combine multidisciplinary work from machine learning, semantic technologies, and programming languages to: (1) Build AI assistants for individual tasks, (2) Build an open-source platform and integrate the assistants into the platform; and (3) Provide exemplar use cases of real-world data wrangling. It also aims to solve some data engineering challenges such as. (a) data organisation (data parsing, integration, dictionary, and transformation); (b) data quality (canonicalisation, missing data, anomaly detection); and (c) feature engineering.
Data analytics required a lot of effort but with the help of AI, not only did it speed up the process but also allowed depth in making sense of data in the past.
AI-related initiatives like AIDA fuel better opportunities in insights and knowledge production since it is creating new methods in analyzing data, and data analytics has become less labor-intensive. Data analytics required a lot of effort but with the help of AI, not only did it speed up the process but also allowed depth in making sense of data in the past. In fact, AI is now deemed promising as it thrives in different kinds of industries.
AI in Action
AI and machine learning are powerful levers when it comes to big data. Together with the power of human intuition, they are critical to helping businesses have a more holistic view of all of that data. It revolutionizes the way you get rules, decisions, and predictions done which entail the increase of the potential to dramatically improve the productivity of data scientists, analysts, and researchers benefiting governments and organizations because it will allow faster delivery of insights and decision-making.
A recent study from the Organisation for Economic Co-operation and Development (OECD) (2020) encourages the insurance sector to prepare incorporating AI in their specific context. For instance, having more data leads to improved predictive analytics, enabling pricing that is better suited to expected risk. And since insurance is based on predicting how risk is realised, having access to big data has the potential to transform the entire insurance production process.
Payers and providers of care, and life sciences companies have started employing several types of AI in various categories such as diagnosis and treatment, patient engagement, recommendations, and administrative practices (Future Healthc, 2019). It will take many years before AI completely erases humans in medical domains, but at the moment, it has made a promising impact in the medical field: 1) Algorithms are already outperforming radiologists at spotting malignant tumours, and guiding researchers in how to construct cohorts for costly clinical trials; 2) Machine Learning is deemed to have the primary capability behind development of precision medicine; and 3) AI-based capabilities are deemed effective in personalising and contextualising care by, for example, sending messaging alerts with relevant and targeted content that provoke actions at moments.
With AI in data analytics, data-driven governments are reaping a more efficient and convenient delivery of public services, and better-informed policymaking with predictive analytics, policy simulations, and real-time early warning systems because the use of technologies allows them to observe their citizens and physical environment with unprecedented data density and analyse these observations (European Liberal Forum, 2019).
While AI is yet to be explored, it has been actively changing and making a big difference not just in the field of data analytics, but also in the market as a whole.
By bringing the fusion of AI and Data Analytics, Croyten can assist you to ensure that your organization can potentially reap the benefits this advancement is opening. While AI is yet to be explored, it has been actively changing and making a big difference not just in the field of data analytics, but also in the market as a whole. Thanks to Artificial Intelligence, new products are developed which are better than before, and the opportunity of autonomy it offers saves businesses huge amounts of time, leading to quicker decisions gleaned from data.
Data is the new oil, they say. If so, data analytics is the vehicle that processes this oil, and artificial intelligence plays the role of an upgraded machine system. Combine them altogether and they can make your organization stand out from the rest.