By Anurag Sanghai, Principal Solution Architect, Intellicus Technologies
The BI and Analytics juggernaut continue to pick up speed as more companies across industries see the benefits of data-driven intelligence. The platforms and tools are now simpler to use with easy and intuitive UI and reduced IT/ coding expertise requirements and implementation timeframes have shortened. Beyond the ease of setup and agile implementation, the products have continuously innovated imbibing enhancements in data science and technological fields. This makes the Analytics and BI tools bring out the higher value and deeper insights from the data every year.
Here are three key trends to watch as modern businesses applying these concepts in 2023:
1. Going beyond Data Types: The growth in the variety of data types that analytics software can process will continue to increase significantly. This rides on the wave of the datafication of almost everything. Data of all types is generated and stored is now sourced from a plethora of devices, IoT, Mobile, social media, websites, POS, transactions, video, multimedia and graphical.
Organisations expect analytics software to make sense of all of this data and provide decision support and business intelligence (BI). Earlier, data required to be collated, converted and prepared to be run through analytics engines as they could handle only certain kinds of structured data. This manual data handling resulted in a lack of trust, besides adding dependencies and delays in the availability of reports.
Analytics software has evolved based on the user’s needs to handle a wide range of data types, including structured data, unstructured data, semi-structured data and even multimedia data. Data preparation is automated and the use of smart OLAP technology adds capability to deal with scale and complexity of data and accelerates performance. Insights are now real-time and will continue to see an increase in the capability of ingesting multiple data sources and streams.
2. Increased reliance on Predictive Business Analytics: There is an increasing trend across organisations of trusting business predictions made by analytics and BI algorithms. This grows out of the development of the capability of analytics platforms in processing the volume, velocity and variety of continuous data streams in real time. These Big Data handling capacities produce a single source of truth that is reliable and actionable.
Descriptive analytics helps users in understanding what is happening with their business, while Diagnostic analytics takes a step forward to find hidden patterns and trends to ascertain root causes or cause-and-effect. Predictive analytics, however, moves beyond extrapolating from historical data trends to forecast the future. It proves to be very useful in business planning and decision support.
Predictive analytics is widely used in the financial services industry to identify future trends, stock movement and fraud prevention. It is also useful in preempting cyber-attacks; scheduling preventive maintenance, logistics optimisation, predictive shipping and product recommendations, customer lifecycle management and digital marketing campaigns.
With increased data and decision veracity, predictive analytics would continue to find new applications in several industries.
3. Explainable Intelligence (XAI) will become integral to Analytics and BI suite: With the integration of ML, BI and analytics, products have benefitted in performing complex multi-variate processes, and discover hidden data patterns and relations beyond what is normally apparent to users. Paired with AI, these tools have the ability to make critical business decisions on their own.
However, it is not always that users shall trust the results and allow decisions to be completely based on them. An increased reliance on algorithmic decision-making needs to be complemented with explainable Intelligence (XAI). XAI is important in BI suites as it allows decision-makers to understand how AI models arrive at their predictions and recommendations. It auto-generates an explanation summarizing why a decision was taken. By citing attributes, accuracy, statistics, and other sufficient collateral XAI explain the need and relevance of a decision.
Without XAI the entire process could well be a cryptic black box with little explanation as to why a particular decision was made. However, businesses cannot completely rely on black-box decision automation, and there are times when it is necessary to justify and document why a particular decision was made. Additionally, explainable intelligence can help in identifying and addressing potential biases in the model; and can also be necessary for organisations to comply with legal regulations.
