AI-powered augmented analytics: capabilities and benefits for business

Over-reliance on IT specialists. Traditional BI and data analytics tools often lack easy-to-use self-service capabilities. To generate reports, business users thus need to enlist the help of internal IT departments, waiting for actionable insights for weeks on end.

Manual data preparation. When working with traditional BI and data analytics solutions, data scientists need to collect and clean up raw data manually. This eats up too much time and opens up the possibility of human error.

Data consolidation challenges. Data sources may number in the hundreds—the average number of data sources used by enterprises has already reached 400. It takes a good deal of time and effort for enterprises to merge multiple sources and bring terabytes of siloed data together into a single view to leverage them for insights in traditional BI tools.

Business decisions based on guesswork rather than data. Conventional BI systems respond exactly to the queries business users make. Being retrospective, these systems analyze past data in static formats, such as dashboards, charts, and graphs, and surface only those insights that decision-makers are querying, leaving no room for hidden insights, smart predictions, or unexpected results.

Inability to analyze unstructured data. BI and data analytics tools lacking AI capabilities only work with structured data, which represents roughly 10% of all the data any company has. To draw insights from unstructured data, such as emails, documents, visual content, and sensor readings, you need to either structure it manually or spice up your system with AI.

Owing to easy-to-navigate interfaces and strong analytics capabilities, self-service BI tools help non-tech users create ad hoc reports with minimal or no assistance from a technical person.

Self-service BI solutions allow users to ask follow-up questions and explore the insights manually for more personalized results.

Unlike traditional BI tools, augmented analytics platforms are capable of handling multiple data sources—live data streams, Excel spreadsheets, cloud storage, in-house databases, numerous apps, and more—thereby making it possible to process structured and unstructured data. Some AI and machine-based analytics technologies can automatically identify certain attributes of data, such as phone numbers or email addresses. Furthermore, one of the key benefits of augmented analytics is the ability to provide users with AI-driven recommendations. Automated recommendations span data discovery, cleansing, and analysis. For example, an augmented analytics system can recommend which datasets to include in analyses and suggest other datasets that might be useful for getting the results users expect. Likewise, powerful analytics algorithms can index and group related words during data preparation, thereby reducing the manual labor of searching for fields and values. Simply put, AI engines do much of the heavy lifting by automating manual and labor-intensive tasks, thus supercharging the entire analytics process and accelerating insight to action.

However, in the context of business process automation, we need to reemphasize that augmented analytics does NOT replace people—it is designed to augment human efforts by combining the power of automation and human expertise.

Alongside a high degree of automation, advanced analytics algorithms help extract highly reliable and accurate real-time insights to underpin confident and unbiased decision-making. Unlike humans, AI systems can 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. Looking under every tree and automatically querying billions of data points, ML models help humans build confidence and avoid faulty assumptions and biases in their conclusions that are typical of manual forecasting.

There is one caveat, though: while AI has gone far in accuracy, a machine learning model can’t be 100% accurate. To achieve a higher level of confidence and make sure the results aren’t due to chance, the estimates may require additional validation and verification on the part of the user.

Your company’s key decision-makers are frequently non-technical users. Your organization will be unable to respond quickly to market changes unless these non-technical users can explore, analyze, and act on your operational data. Augmented analytics systems that use ML, NLP, and natural language generation (NLG) technologies reduce the need for data analysts by allowing non-IT personnel to interact with data directly through conversational queries and receive results in the form of simple visualizations or text summaries.

Furthermore, these systems free up data scientists‘ time for more complex tasks by eliminating the need for them to participate in basic data preparation and routine reporting.

However, putting insight at the fingertips of anyone who needs it raises data security and privacy concerns; we’ll be discussing this in more detail in the augmented analytics challenges section.

Simply being data-driven is not enough anymore. Businesses must adopt a forward-thinking mindset in order to make strategic, future-oriented decisions. Next-generation AI-augmented analytics extend traditional insights by providing real-time predictive analysis. This allows business users to make informed decisions quickly by employing statistical and machine-learning algorithms to understand causes, predict outcomes, and identify trends. Augmented analytics provide instant, reliable recommendations for the next steps, allowing for informed, future-focused decision-making.

If you’re unsure how to start with a report, simply ask Copilot to recommend content or create a report based on your instructions. For example, you might say, “Show me how sales of different products and categories vary by region and the impact of discounts,” and Copilot will generate relevant report pages with visualizations for you.

For any report you have, Copilot can produce a clear text summary that makes the data easy to understand. Just ask it to summarize a whole report, specific pages, or even particular charts or graphs.

These are unexpected spikes and dips in the time-series data— i.e., any values that fall outside of the expected or normal range. Anomalies are scored and categorized as “significant” (based on the score) and/or “recent” (based on timing). For example, there was a consistent growth in sales from January to September. However, in September, the sales suddenly declined by 70%. Copilot can detect and flag this unusual drop, prompting further investigation into potential causes such as supply chain disruptions or shifting consumer preferences.

Power BI has an algorithm that determines whether a prolonged increase or decrease in time-series data is significant. If found to be significant, it is classified as a trend. The trends are then categorized as Long, Steep, Recent, or Trend Reversal. For example, the augmented analytics solution might highlight that sales of a particular product have been steadily increasing over the past quarter, suggesting growing market interest or effective marketing strategies.

Depending on whether KPI analysis includes a target, a Power BI algorithm either calculates the variance of the current KPI value to its target or compares the current value itself to other data segments. For instance, Copilot can compare the financial performance of different products or services, identifying bestsellers and underperformers. It can calculate the average revenue, cost, and profit for each product category, giving insights into which categories are most profitable or cost-intensive. This insight can then guide strategic planning, inventory management, and promotional efforts.

Inflated expectations of what machines can actually do can lead to wasted investment. Augmented analytics lacks human qualities like intuition, creative thinking, emotional intelligence, and common sense, which are critical for data analysis. It operates within predefined limits and relies on the data provided, emphasizing the importance of human oversight in drawing insights.

The belief that machine learning models can achieve perfect accuracy is unrealistic. Errors and inaccuracies are inevitable because these models cannot fully understand complex systems. This misunderstanding can lead to misplaced trust in AI’s capabilities and potentially flawed decisions.

Effective data and model management are essential. Poor-quality data results in inaccurate predictions and recommendations, underscoring the importance of good data practices.

While augmented analytics promotes data accessibility, it also raises data privacy and security concerns. Balancing user autonomy with data integrity and security is a critical challenge. Trading control for availability is prohibitive.

Trying to implement augmented analytics across an entire organization at once can lead to massive disruption and resistance. Overambitious rollouts make no sense.
A phased, considered approach is more likely to succeed.

Do not overestimate the powers of augmented analytics, shifting the focus from human intelligence to machine intelligence. Keep in mind that people are your main asset and must remain such, as machines are unable to replace the human brain’s power to create, improvise, consider and interpret context, and think critically. With this in mind, fostering data literacy and the development of essential critical thinking skills across your organization should be your top priorities. The way your employees and machines interact determines how efficient the results will be. Teach your people to think outside the box and beyond the numbers, and incentivize them to develop those skills that are harder for machines to handle. Make sure your employees have a precise understanding of how AI works and how it can be used best to serve human interests. You may try building immersive training programs like Bloomberg, Guardian, and Adobe did to help employees across all business roles learn how to leverage data analytics tools and predictive analytics to capitalize on business opportunities.

Set realistic expectations for machine learning and AI—uncertainty exists no matter what. It’s important to recognize that ML models can’t achieve a 100% accuracy level. Nor can they identify all data sets and formats. Deciding whether the data output is accurate and statistically significant is usually a human’s prerogative. Bear in mind that a fair share of data discovery work will anyways fall on the shoulders of your human users. This includes validation of data outputs, analysis of complex data sets, or data transformations. By having a realistic understanding of a machine model’s capabilities, you can turn its constraints into opportunities.

Make sure you train ML models using high-quality, unbiased, and error-free data and regularly update the algorithms as your data changes. Note that AI is as unbiased as the data it has been trained on. See to it that you have solid data management practices in place that empower your users with accurate and reliable insights derived from well-managed data assets.

In the pursuit of giving more users access to augmented analytics tools, it’s mission-critical that you also remember to provide efficient data security. Establishing an effective and clear framework for sustainable data governance within your organization is pivotal to upholding data privacy, security, and compliance while enabling hassle-free access to meaningful data across departments. This will allow you to drive maximum value from your data in a controlled and compliant manner.

Start small; think big. It helps to break down your big data analytics initiative into feasible implementations and consider the end-user experience every step of the way. That is to say, it’s a good practice to start with a certain department or a use case that has high business value and incrementally tune the system based on user feedback while also having a long-term strategy in place. Once you’ve implemented augmented analytics on a small scale and have got end-user buy-in, you may then extend it to broader use cases throughout the organization. It’s critical that you start small to prove the value before scaling the system company-wide.