It is an inevitable fact that every business that is taking strategic steps today based on data is growing faster and earning better than its competitors. In the beginning, these organizations used spreadsheets with ad hoc data, but the systems evolved, and they invested in a centralized data repository that was used to report the data in spreadsheets. In the recent past, these organizations have used self-service analytics tools to improve their understanding of the data. Of course, evolution never ends. So, the technique that we are extracting juice from the data to get a competitive advantage and is becoming commonplace across organizations of all sizes.
Having enough data in the BI tools that we are using these days already gives us the ability to run our data analysis. I am not arguing that obtaining expertise in using these tools is easy. When used effectively, the current breed of BI Products can help a lot to unfold the data that we possess. Even if an organization has all the expertise needed and is exploiting every benefit from the vast amounts of business information it has access to, what happens when human abilities not enough to understand the patterns in the data anymore. What if we overlook opportunities in different places because we did not expect and potential there based on our past experiences, or still worse, our hidden biases.
At the end of the day, we will still need reports and dashboards and spreadsheets, but we also need something else to tell us which report we should use and what we should start to focus on among the thousands of metrics spread across hundreds of reports. At this point, Autonomous Analytics emerges as our companion and will guide us on the journey to create contextual information from the available data.
What is “Autonomous Analytics”?
Autonomous Analytics is a hybrid concept that analyzes your data automatically and continuously to identify potential risks or opportunities and recommends the best approaches to engage with it for easy decision making. They can also talk with you and give you the answers that you need with the help of Natural Language Processing. It is the latest phase in BI’s evolution process and merges human abilities and the disciplines of AI, ML, BI. Contrary to traditional BI, you do not need to set thresholds, KPIs, or monitor them continuously. Autonomous Analytics detects the relevant KPIs, conducts anomaly analysis, sets thresholds, and configures and raises alerts by itself. It presents outcomes of automated analysis in the best layout to the best user.
How Does an “Autonomous Analytics” Solution work?
Autonomous Analytics thrives on data, and you need to feed them with all the available data sources. Once the Autonomous Analytics Engine has access to data, it runs different machine learning models that are available within the application. Some Autonomous Analytics solutions apply a special combination of supervised and unsupervised machine learning models. In contrast, some others let in-house data scientists adjust the existing models or add their own.
They continuously train the ML models in the background and learn data trends, seasonality, outliers, and normality for the data behaviours. This process develops new thresholds without human help, creates alerts, runs “what-if” analysis, and delivers forecasts.
Autonomous Analytics can interact with us in different ways. We can integrate them into our existing BI solutions and can ask questions by typing or verbally. Our Autonomous Analytics integrated BI solution can answer our questions, supported by reasons, by showing us a dashboard with tables and visuals which can help us track incidents. Also, when an anomaly is detected on any component of the system, they can raise alerts, send emails that include root cause analysis reports along with recommendations that include different approaches for risk mitigation along with correlated metrics. Similarly, when they detect an opportunity, they can raise an alert in an alternative format, suitable to the topic in question showing potential actions and outcomes and projecting some future state scenarios.
Owing to the examples above, Autonomous Analytics separates itself from traditional BI. It acts differently than BI because it automates what people do in the BI process and makes it much faster. If you are a BI tool user and notice an anomaly in a critical KPI and are not an expert on the subject, finding the root cause can take a long time, and this delay can be very harmful to business. The worst scenario is that maybe the anomaly disappears, leaving behind its damage, and we never find the root cause. Autonomous Analytics technologies try to create a story with all the correlated anomalies that point to the real cause.
Which Industries Can Use Autonomous Analytics?
Autonomous Analytics has a wide variety of applications and can be used across multiple sectors and industries. ECommerce, Finance, Healthcare, Gaming, Retail all have a use case for its usage. Soon enough, any business that generates a piece of data can find a use case to implement Autonomous Analytics. Sales, Marketing, Operations, IT etc. Almost all the departments are currently using BI tools. So, they are a candidate for an autonomous analytics integration to their current BI structure. However, building a solution in the house is not relatively easy. A team and process that can develop a working Autonomous Analytics Solution may be very expensive. Thankfully, many companies are specializing in this subject and offering their services. The most prominent companies working in this space are Microsoft, Anodot, Oracle, Numerify, Outlier, SAP and Moogsoft. It’s worth mentioning that this list is not exhaustive, and there are many other brands focusing on Autonomous Analytics as an offering. Their services can be leveraged by enterprises of different sizes across different industries, who believe that Autonomous Analytics can help their businesses and employees.
Why We Need Autonomous Analytics?
We are swimming in an ocean of data every day. Even if small companies want to run a proper analysis task or monitor their business performance, the number of combinations of metrics can easily reach thousands. This complex process can result in missed opportunities or detecting problems after it has negatively impacted the businesses. (You can find an excellent example from the blog post in Anodot titled: “We’re Only Human, After All: How Can Effective AI Metrics Monitoring Uncover the Opportunities You’re Missing” written by Ira Cohen.) Every company can benefit from Autonomous Analytics approaches to support their decision-making processes when it is impossible to identify a root cause with just the human eye.
According to Gartner “by 2023, 90% of the world’s top 500 companies will have converged analytics governance into broader data and analytics governance initiatives” (Gartner, “Worlds Collide as Augmented Analytics Draws Analytics, BI and Data Science Together,” Carlie Idoine, 10 March 2020. Gartner subscription required.). In my opinion, this is another reason why companies need this approach. It enables any organization to stay ahead of its competition, and any head start can get you an advantage.
Conclusion
You don’t need to start a project which can take years to complete and replace all of your current systems, but you can start from the highest-ranking KPIs based on their ROI potential and incrementally replace your legacy structure.
Investing time and money into the current traditional BI system should not prevent companies from trying new approaches. Sunk cost fallacy is the last trap to be caught in when the topic is your data. We should understand that there is no constant and eternal technological solution for all the analytics needs of an organization. Change is happening every day in the data analytics industry. Innovations become outdated, extremely fast, but they are bringing more benefits to the individuals and organizations that need them. If the companies need to perform better, centralizing their data or adopting a self-service approach won’t be enough. They will also need to take further steps like Autonomous Analytics solutions.
