Have you ever thought how long it takes you to ingest, process and analyze data to generate insights that make sense? And how much money could be saved if your analytics processes were way faster and error-free? Way too much. At the strategically crucial moment, when there is so much at stake, the decisions you make and the speed at which you drive them can make or break your business. No third option. In today’s fast-paced and highly-competitive operating environment, waiting days or in a worst-case scenario, even weeks for mission-critical insights is simply prohibitive. The solution: AI-powered advanced analytics.

Stop working for analytics – make it work for you

Traditional BI and data analytics solutions. far too many organizations have in place today do not work for the new normal, although they offer a whole lot of essential capabilities. For instance, cleaning up raw data so that it can be properly analyzed by BI tools still remains a manual process, which naturally eats up too much time. On top of that, this opens up the possibility of human error, which only slows down the whole process and creates a roadblock to receiving timely and relevant insights.
What’s more, data is being generated in large volumes at what seems like a faster rate than ever.
Data sources may number in the hundreds and it takes a good deal of time for enterprise software development companies to merge multiple sources and bring petabytes of siloed data together into a single view to leverage them for insights. Beyond that, there is always a risk of human bias when it comes to the analytics process. The point is that traditional BI systems respond exactly to the queries business users make. Such systems surface only those insights that decision-makers are querying, leaving no room for hidden insights and unexpected results. The result: bad business decisions and missed market opportunities. To top it all, traditional BI and analytics solutions lack self-service capabilities compelling business users to depend on IT specialists for data ingestion, analysis and insight generation. This heavily undermines effective and timely decision making. Such tools aren’t capable of scaling to organize and analyze volumes of unstructured data growing at breakneck speed. And it seems that businesses are working for analytics rather than the other way around. So why waste precious time and resources if everything can be done automatically, flawlessly and at a much faster rate? AI-assisted augmented analytics is here to change the game. Once and for all. Here is how.
AI powered augmented analytics

Advanced analytics capabilities

On a larger scale, augmented analytics is designed to automate and accelerate the process of insight generation. More fundamentally, modern data analytics companies harnesses machine learning automation to revolutionize the user experience across the entire BI process from data ingestion to analysis to insights visualization. So, what are those key revolutionary augmented analytics capabilities that help businesses speed up the flow from raw data to meaningful insight to informed decisions?
  • Complete automation, accuracy and unbiased analysis. Unlike traditional BI tools, augmented analytics platforms are capable of handling multiple disparate data sources – live data streams, Excel spreadsheets, cloud storage, in-house databases, numerous apps and more. They automatically sift through a company’s data, prepare it, analyze it and transform this data into analytical insights that make business sense to stakeholders - all at increasingly rapid speeds. Simply put, AI engines do much of the heavy-lifting, thereby supercharging the entire analytics process. Alongside a high degree of automation, advanced analytics algorithms help extract highly reliable and accurate real-time insights that underpin confident decision-making. Moreover, being free of human biases, AI systems help draw hidden and unobvious yet critical insights that business users might not even be aware they need by identifying and suggesting all possible relationships in data that lie under the surface.
  • Data democratization and self-service analytics. The ability to swiftly adjust and effectively react to shifting market conditions rests largely on immediate data exploration and instant data analysis by stakeholders or key decision-makers (read non-tech users). By harnessing the power of ML, NLP and NLG algorithms and providing unparalleled ease of use, self-service analytics systems reduce a company’s dependence on data analysts, enabling non-IT roles to interact with data directly without assistance from data experts. The ability to query the system in a conversational manner makes data accessible to more people in the organization with no data science skills and experience. Besides, hiring expert data scientists to perform simple mechanical tasks like cleaning up data or running routine reports is both counterproductive and cost-prohibitive. So, along with accelerating time-to-insight, self-service BI solutions make a big difference in saving company resources, while also freeing up valuable time data scientists might spend on high-leverage activities.
  • Predictive and prescriptive analysis. Being just data-driven is no longer enough. Companies need to adopt a forward-thinking mindset and approach to make strategic decisions that will work best for them not only now but also in the future.
Equipped with predictive capabilities, next-gen AI-assisted analytics solutions offer a glimpse into the future rather than just giving insights into what happened.
They provide an opportunity to perform predictive analysis on large volumes of data in real-time, thereby allowing business users to make confident, future-oriented decisions at the point of need. Leveraging statistical and machine-learning algorithms, augmented analytics has proven critical in making educated guesses, predicting future events or outcomes, forecasting upcoming trends and getting instantaneous and reliable recommendations on what steps to take next.

Key Takeaways

Augmented analytics is well on its way to revolutionizing the BI landscape. By combining Artificial Intelligence, Machine Learning, and Natural Language Processing, it sets the stage for real-time drill-down data exploration and analysis at speed and scale. Through augmenting human intelligence across the entire analytics life-cycle and democratizing data science, advanced analytics allow making smart, confident and informed decisions at the speed of business. No manual intervention and hence errors. No dependency on tech experts. No decision-making biases. No more second-guessing. Augmented analytics is going to do away with this. Once and for all.