It’s no secret that data is king right now. Organizations from across sectors have begun to benefit from data, content and information sharing that are relevant to their market segment on an unforeseen scale. In order to use data to make informed decisions, professionals in finance, healthcare, legal, education, research and scientists need reliable solutions that unite information and analytics.
Today’s advanced technology platforms help these experts and their organizations gain customer insights, streamline workflows, and manage risk, as well as share and analyze large amounts of content. The platforms providing this content and analytics must pay close attention to their supporting technologies, especially when done on a global scale. This means, amongst other things, investing in AIOps.
In essence, AIOps is the ability to automate production environment of AI solutions and IT operations, monitor the performance and quality of AI solutions, and retrain and test AI models. This approach has never been more important than right now: 40% of respondents to this AIOps Exchange survey are flooded with more than one million events each day, so it’s no surprise that 68% of them have AIOps projects underway.
Adopting AIOps is critical for software-driven businesses. It can help drive innovation, scalable solutions, and a more consistent IT architecture. AIOps is also paramount to driving problem resolution in complex IT environments, as teams can respond proactively to any upcoming obstacles or challenges.
With regards to information-based analytics platforms specifically, AIOps-dedicated teams can help these companies not only drive efficiencies through automation of data processing and security, but also ensure business users have the capability to enforce ethics and compliance through their AI models.
Challenges for information-based analytics platforms
The market for information-based analytics platforms is on a consistent growth trajectory, with the built-in tools providing analytical resources and data to professionals from across the spectrum. However, the level of scaling presents a number of challenges, including the not-so-simple act of managing the sheer volume of data coming in, and then securely processing it.
Data privacy legislation like GDPR means platforms must leave no stone unturned when it comes to protecting personal and sensitive data. And when it comes to people’s health records and reports, legislation like HIPAA must be kept central to any decisions around managing and analyzing that data to avoid unauthorized access or manipulation of patient information. This is especially vital given the vulnerability of healthcare data during the pandemic. Over nine million records were exposed in a single breach in September 2020 - one of many which took place throughout the year.
For those in the legal profession, the information they access through these platforms must be up-to-date to ensure regulatory compliance, especially given the dynamic nature of changing legislation. It’s important to note that 45% of law firms use legal analytics platforms, with the most cited use case being legal research.
Here’s where AIOps comes in.
AIOps allows scaling and consistent delivery of actionable insights
Given the huge amount of structured and unstructured data coming in, it is important for these platforms to automate processes to ensure that data quality isn’t sacrificed, and that it can be distributed in a secure and efficient way.
Financial professionals rely on concise, actionable data from authenticated, reliable sources, which ultimately helps predict outcomes of investment decisions. Information analytical platforms must be able to provide this capability and guarantee trusted, accurate, near real-time data, while also making it easy to consume through an enhanced data visualization experience.
In medical field, clinical decision makers including physicians, medical researchers, educational institutions, pharmacists, and specialists depend on scalable data platforms. These platforms must not only provide access to actionable datasets, but also facilitate collaboration between stakeholders when groundbreaking or novel results must be shared.
Each of these industry use cases, and many more, can benefit from AIOps which can help them automate the production environment of their solution, and thus ensure data quality and allow for scaling of the solutions. For example, teams can use AIOps to monitor model accuracy and attempt to re-train the model to improve it. AI solutions can also aggregate, combine and extract useful information from otherwise vast and unstructured data.
By adopting AIOps, teams can aggregate the ever-increasing volumes of data, sift out performance issues or bad quality data, and make informed decisions to resolve these problems. Ultimately, this leads to platforms being more able to better service their customers and users, which is the main reason for AIOps adoption amongst 50% of the AIOps Exchange survey respondents.
AIOps to ensure up-to-date, ethical and compliant AI models
Information-based analytics platforms can also employ AIOps to keep their AI algorithms up to date. Even if an algorithm was updated one year ago, that doesn’t mean that it’s “safe” to use today. This is because as new data comes in, such as new legislation or demographic changes, teams can’t know for sure how the existing model will respond.
AI models also inherently allow for bias based on data they are being trained on, so platforms must monitor the data’s quality and adjust for any biases being shown. This can help mitigate ethical issues around preferences for certain groups over others. In the legal world and medical research in particular, the potential for bias in analytics can have serious real-life implications.
For example, a 2019 study conducted by researchers from the University of California Berkeley uncovered racial bias in a predictive analytics platform that was being used to refer high-risk patients to care management programs. White patients were being referred at a higher rate than less healthy black patients, as the algorithm assessed the cost of healthcare spent on the patient to determine their risk.
Adopting AIOps can help here: Teams can leverage reinforcement learning to account for necessary changes to AI models, and A/B test models in parallel and compare quality and results.
Information-based analytics has been growing, and IT engineering teams within those companies need to scale up for the level of data processing, securing, monitoring, testing, and everything else that comes along with dealing with such huge volumes of data. With a solid team dedicated to AIOps, these platforms can not only meet the needs of their users today, but ensure that they’re ready to scale for tomorrow.
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