The number of data sources an average company uses to operate effectively now exceeds 400 (and counting.)
Up to 90% of the information companies need for quality analytics is unstructured, consisting of sensor readings, customer emails, PDF files, visuals, and other data assets that cannot be analyzed in bulk on a whim.
According to a recent 1PATH survey, 56% of SMBs spend as much as $25,000 annually on data analytics tools — and end up wishing the solutions were easier to use (49%) and readily available to all employees regardless of their tech expertise (45% of the participants).
All in all, traditional approaches to data analytics are starting to hit their limits.
And here’s where augmented analytics comes in handy.
What Is Augmented Analytics Anyway?
Augmented analytics is an approach that uses AI technologies to source data, prepare it for algorithmic analysis, produce insights, and visualize the findings in a visually appealing, intuitive way.
In particular, artificial intelligence can take over manual and repetitive tasks that traditionally slow down analytics processes, such as cleaning and blending data, identifying outliers, and performing statistical analysis.
Augmented analytics tools help users uncover insights from voluminous data sets even without specialized knowledge and tech skills, thus democratizing access to operational data and empowering better-informed decision-making.
This approach stands in sharp contrast with traditional analytics, where data analysts collect and prepare data for software-based analysis manually and produce custom reports about very specific tasks and issues, one at a time. As a result, traditional data analytics solutions cater to the needs of a single department or business unit and cannot be used at scale.
But the differences do not end here.
In augmented analytics, AI algorithms identify patterns and relationships in data, suggesting insights, trends, and correlations that might not be apparent to humans. These findings often stretch far beyond the tasks you expect the software to perform.
For instance, your Power BI dashboard can draw your attention to a decline in sales during the past month, determine its likely cause, and even nudge your managers to double their efforts mid-month to avoid similar situations in the future.
What traditional analytics can’t do is predict sales volumes based on historical and real-time data or help you devise an effective marketing strategy to meet your revenue targets. Although, retail business intelligence can help.
That’s where augmented analytics solutions excel.
What’s Wrong with Data Analytics as We Know It?
One of the reasons to address an artificial intelligence (AI) consulting company and give your data systems a smart overhaul is that traditional vendors have been slow in adapting to the new normal.
This “new normal” spans a staggering data growth rate (+63% monthly in large enterprises), the aftermaths of the COVID-19 pandemic, workforce shortages, and rising operational expenses.
Modern businesses need fast, efficient, and error-free data analytics solutions that scavenge information across a company’s IT infrastructure with little to no human assistance, interpret structured and unstructured data, and empower employees with actionable insights.
And the fact that prominent data platforms, such as Tableau, Salesforce, and SAP, infuse their tools with AI capabilities only supports our argument: the future of data analytics is increasingly augmented.
Top 3 Augmented Analytics Benefits You Can’t Ignore
On a broader scale, augmented analytics benefits revolve around speeding up or completely automating the insight generation process.
More fundamentally, data analytics providers turn to AI to take user experience to the next level, simplifying the process of obtaining meaningful insights from operational data — and putting these insights into action.
Below you’ll find a rundown of augmented analytics benefits that stem from these revolutionary capabilities:
- Increased automation. As we told you earlier, augmented analytics solutions consume all kinds of data no matter the format, from CRM entries to streaming data from IoT devices, which sits in data lakes. Once you train AI algorithms to process particular data types (and opt for a more advanced AI approach, such as unsupervised and reinforcement machine learning), augmented analytics solutions will dig into operational data independently, allowing your employees to focus on creative and strategic work. Some augmented analytics examples include Google’s decision to use deep neural networks to devise a more effective framework for power management in data centers. The algorithms studied historical information from temperature, pressure, and pump speed sensors and recommended a pathway that helped Google reduce data center cooling expenses by 40%.
- Self-service analytics. Data democratization is the cornerstone of augmented analytics, meaning decision-makers at every level, from the C-Suite to managers working on the factory floor, should be able to use data analytics solutions without requesting help from an IT department — and waiting for reports for weeks on end. AI algorithms, including those with natural language processing and generation capabilities, help users seamlessly interact with augmented analytics software and generate complex reports by themselves, often in real time. This way, your company can be less reliant on data analysts, reduce analytics costs, and streamline time to insight — i.e., a performance indicator that measures how quickly your big data can be transformed into smart recommendations.
- Predictive and prescriptive analysis. There are four types of analytics you can perform on operational data. These include descriptive, diagnostic, predictive, and prescriptive analytics. Returning to the sales department example we’ve given earlier, traditional analysis techniques can merely explain what is happening and why. Augmented analytics, on the other hand, can give you practical tips for dealing with scenarios that are likely to occur in the future, thus improving your company’s resilience. For instance, H&M, one of the world’s leading fashion retailers, taps into AI to predict sales volumes across its 5,000 stores and always keep popular items in stock.
Summing It Up
In times of recession, becoming a data-driven business is key to outpacing your rivals and hitting revenue targets.
However, various data analytics solutions serve different purposes and are not created equal.
Considering the growing amount of operational data and the pressure to reduce operating costs, employing an army of data analysts is not feasible for most companies. And descriptive and diagnostic reports, which sufficed in the past, can no longer ensure the desired operational intelligence.
Augmented analytics emerges as a silver-bullet solution to these daunting problems. By automating data aggregation, preparation, and processing tasks, AI-infused tools can help your company pinpoint inefficiencies in business processes and brace yourself for future challenges while dramatically reducing IT expenses.
