Organizations everywhere are racing to generate, collect, and leverage tremendous amounts of data. As a result, data is being created at an incomprehensible rate: The World Economic Forum estimated that 44 zettabytes of data existed in 2020. (A zettabyte is 1,000 bytes to the seventh power—meaning one zettabyte has 21 zeros.) If this estimate is correct, we now have more bytes of data than stars in the observable universe.
In this era of Big Data, the sheer volume of a company’s information often creates difficulties in analyzing and operationalizing it to guide better decision making. To compound the problem, most enterprises’ data comes from various sources in many formats, both structured and unstructured, and is often rife with errors and anomalies. No wonder that, according to global consultancy McKinsey, only 1% of data is ever analyzed.
Businesses that successfully determine how to use their data will come out ahead. Companies, therefore, have engaged in a talent war to hire data analysts and data scientists to help tame their data and extract valuable insights.
Data analysts collect, process, and apply statistical algorithms to structured data to yield insights and improve decision making. These analysts collaborate with company leaders to identify business-critical questions that can be answered through data analysis, acquire that data from various sources, and then clean and reorganize it before analyzing it to draw conclusions. Those conclusions ideally lead to actionable insights and drive better business decisions.
Data scientists, meanwhile, don’t focus on answering predetermined questions, but instead explore trends and disconnected sources of data to find new patterns. While data analysts collect and curate meaningful insights related to existing problems, data scientists spend their time predicting the future and determining what issues will arise based on past problems.
They design predictive analytics models and machine learning algorithms to mine huge data sets, develop tools to monitor data accuracy, and build data visualization tools, dashboards, and reports.
While debate rages about which role is more useful, the reality is that organizations need both to make sense of their massive troves of data, because data scientists create new ways of capturing and analyzing data so that it can be used by the data analyst.
As companies continue to collect ever-larger amounts of data, however, these traditional analytics solutions can go only so far. A major problem is the expense of such jobs and the shortage of talent in both categories. The McKinsey Global Institute predicts a shortage of about a quarter-million data scientists in the U.S. alone by 2024, so even companies that can afford to hire strong data teams may not be able to staff them.
Moreover, to save time and ensure that every decision is data-driven, it’s critical that all executives, regardless of their skill set, easily glean insight from their organization’s data.
Therefore, the real issue is not whether to hire data analysts or data scientists. Rather, it’s how to supercharge and augment the work of both functions so that error-free, business-boosting results can be created instantly in a digestible format that doesn’t require experts to translate.
The solution to these challenges is augmented analytics. By incorporating artificial intelligence and machine learning, augmented analytics removes barriers and human error to generate deep insights that aid decision-making without requiring time-intensive efforts by data professionals.
This way, organizations can more easily create faster insights and take action to improve their business while allowing their data analysts and scientists to focus on more complex problems without interruption.
By choosing augmentation, executives gain a superior alternative to automation that uses machines to detect anomalies and increase speed. Augmented analytics does not replace but rather assists human reasoning—which remains a critical component of business decisions.
If you’re curious about augmented analytics, read on. In this guide, we’ll explore augmented analytics processes and benefits, the most effective use cases, and why it can generate revenue-boosting results for executives across industries.
The global advisory firm Gartner coined the term “augmented analytics” in 2017 to describe data analytics that incorporates machine learning and artificial intelligence techniques.
Gartner specifically defines augmented analytics as “the use of enabling technologies such as machine learning (ML) and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms.”
By leveraging ML/AI, augmented analytics automates data preparation, insight discovery, and transforms how analytics content is developed, consumed, and shared—helping business users explore and analyze data more effectively and efficiently.
Augmented analysis allows companies to automate the previously laborious process of mining data and communicating insights throughout their organization. Augmented analytics process data much faster than a team of data analysts and scientists possibly could, while eliminating human bias and error.
The resulting answers are comprehensive, unbiased, and completely accurate—and easy to understand. By helping teams better leverage and better understand their data, augmented analytics can expand an organization’s understanding of their customers, products, and future possibilities.
Because augmented analytics automates the manual, time-consuming parts of analysis and uses all of the data businesses collect to deliver more comprehensive insights to the business, it has four core benefits for a business:
By quickly uniting data from multiple sources and applying ML-based recommendations, augmented analytics enables faster access to insights derived from massive amounts of structured and unstructured data.
When it comes to data preparation, augmented analytics replaces the traditional, time-intensive process in which database administrators collect and carefully ready data for integration. On the insight discovery side, augmented analytics allows business users to get specific answers to their questions directly and in real time, without waiting for the help of a data expert.
What’s more, the automated nature of augmented analytics means that it’s “always on,” as ML technologies can work around-the-clock to learn and create faster insights continuously.
Additionally, these technologies will identify and auto-analyze every anomaly so that analysts no longer spend time poring over data to identify variations.
And rather than having to test every data combination manually, analysts can rely on ML to automatically detect hidden correlations and relationships. This promotes increased productivity, efficiency, and smart decision-making across the organization, ultimately improving business results.
As organizations rush to leverage data to make faster and better-informed business decisions, their single-minded focus on generating more data can, ironically, lead to inefficiency and underutilization. In fact, research suggests that enterprises tap less than 70% of their data for analysis.
Whether that data is overlooked because it’s siloed, because it’s spread across too many sources, or because data workloads are not appropriately provisioned, ignoring so much information increases the risk of making suboptimal decisions and missing new opportunities.
Augmented analytics helps companies fix this problem and utilize all their data by introducing automated root-case analysis. Rather than relying on data analysts to efficiently and thoughtfully process ever-increasing reams of data, this analysis automatically tests every bit of data to identify the factors that contribute to a KPI as they shift over time. This helps businesses quickly understand what has changed and why, while also pointing decision-makers to areas for further investigation. These easy-to-understand insights, pulled from a broad swath of organizational data, allows people to collaborate and act on the data–which, in turn, means that companies can finally begin reaping the rewards of their data investments.
Though augmented analytics was originally intended to assist data analysts, businesses are tapping it to directly help end users who lack specialized training in data science or analysis.
These non-technical users can ask questions and, as Gartner explains, receive machine-generated data stories driven by automated findings based on ongoing monitoring of data relevant to their job function.
Analytics platforms clearly communicate data stories, which enables the adoption of actionable insights across the organization. As a result, stakeholders across a company can access self-service data solutions to find the answers that matter most.
As businesses continue to collect massive amounts of data, employees’ ability to read, analyze and work with data grows all the more imperative. Augmented analytics platforms promote data literacy by removing the complications that previously required a data analyst or scientist to translate.
By automatically surfacing insights and working in natural language, augmented analysis helps users of all backgrounds gain confidence exploring data, searching for insights, and visualizing and communicating those insights easily.
Augmented analytics is a great decision intelligence tool because it improves business decisions and can enhance ROI in several ways far beyond what today’s BI visualization tools can accomplish. First, augmented analytics platforms automate and streamline routine tasks such as cleaning and preparing data, finding patterns, auto-generating code, and creating visualizations.
Second, they create relevant suggestions based on a user’s intent and previous behaviors, helping to uncover key drivers and explanations for business behaviors that would otherwise remain unsurfaced.
And finally, because augmented analytics responds to a user’s plain-language questions with clear answers, results can be easily disseminated to encourage insight-based decision making that creates better results while reducing costs.
Augmented analytics tools reduce the route, repetitive part of analysis by automatically pointing out patterns in data. They use machine learning, natural language processing (NLP) and generation (NLG). These technologies allow organizations to analyze data through spoken commands, and then receive automated answers and insights in natural language.
To fully understand this definition, it’s important to delve into the meaning of machine learning and semantic analysis.
First, let’s look at machine learning. According to IBM, machine learning is a branch of artificial intelligence that focuses on using data and algorithms to imitate the way humans learn.
ML’s algorithms can adapt to learn from data without being given explicit rules-based programming—and, like humans, they get better at doing the work as they gain more experience.
The next critical component of augmented analysis is semantic analysis.
Semantic analysis uses statistical modeling and machine learning to deconstruct human languages into a format that computers can understand. This process analyzes a sentence’s grammatical structure to identify the most relevant elements and the relationships between individual words in a specific context.
Semantic analysis enables natural language processing and natural language generation. Semantic analysis algorithms allow machines to understand the correct meaning of words in given contexts and make accurate predictions based on past observations. This allows NLP-enabled augmented analytics platforms to analyze data through simple spoken commands. NLG capabilities then provide automated explanations of those analyses in natural language.
Together, NLP and NLG allow non-technical executives to communicate with their organization’s data, ask questions, and receive easily understandable answers. This accelerates insights and leads to better, faster decision making that improves business results and helps organizations stay a step ahead in a brutally competitive global marketplace.
Every organization, from small businesses to enterprise companies, can benefit from empowering its people to ask questions across all its data.
Whether a business needs to improve customer satisfaction and retention, manage its inventory, or optimize marketing campaigns, the real time insight afforded by augmented analytics allows stakeholders to make fast, data-driven decisions.
Augmented analytics can serve business users across a variety of industries. For example, it can help:
Augmented analytics can assist across many use cases. It can help:
Augmented analytics can help nearly every business understand and act on their data faster, leading to better decisions and more growth. By offering deep, automated analysis of complex data, augmentation provides instant insights that used to take analysts days, weeks or months to identify.
It’s essential to implement a handful of best practices to get the most out of augmented analytics tools:
Within a company, executives often have competing priorities and different opinions about achieving business outcomes that lead to applying data to problems in an inconsistent and piecemeal manner. To fully harness the power of augmentation, organizations should create a data committee staffed by senior leadership across business units.
This group should build consensus and transparency around data by standardizing data-driven definitions and metrics, promote consistency in data strategy, and ensure that data-enabled successes are communicated throughout the organization.
Start by picking an important KPI—for example, customer acquisition cost (CAC) or average order value (AOV)—and use augmented analytics to understand changes and drive decision-making quickly. Once you’ve notched a few successes, you can begin to build analyses that support every KPI you use to manage your business.
Because augmented analysis is fueled by powerful cloud-based engines, optimal datasets for this kind of analysis look quite different than those used by clunky, legacy BI tools. To build datasets that yield the most actionable insights, it’s critical to get granular and ensure that every row in a table ties directly to a unit of data.
Moreover, because new cloud-based data tools can handle richer and wider data than their desktop BI predecessors, you no longer have to aggregate and simplify data. More details lead to more interesting, actionable insights, so it’s best to flatten data and include as many factors as possible in an analysis.
Finally, augmented analytics works best when you build datasets that support your main KPIs. That’s because these key indicators remain consistent, so your data collection will follow suit. The more consistent and automated your data, the less work it takes to maintain and the more opportunity you have to dive deeper.
As the industry’s first decision intelligence engine using augmented analytics to analyze millions of dimensions and trends in cloud-scale data in seconds, Sisu helps teams understand their data and act quickly to solve important business problems. Uncovering clear stories from the signal and noise of data is our mission at Sisu.
Our engine’s personalized model improves as each user interacts with it, providing ever-more contextualized insights and enabling complex diagnostic analysis without SQL or data modeling.
This allows clients to query large transaction-level data sets nearly instantly, helping businesses zero in on the highest-impact KPIs and understand why these dimensions are changing.
We’d love to help you. Reach out to our team to get started surfacing meaningful insights faster.