How Augmented Analytics Unlock Critical Insights in Your Data
Gina Chen, VP Sisu Data moderated a session with Brynne Henn, Director Sisu Data and Regina Karson, WiBD US Board Advisor for an overview session on Augmented Analytics.
Organizations everywhere are racing to generate, collect, and leverage tremendous amounts of data—which can often lead to a talent war between companies to hire data analysts or data scientists to help tame their data and extract valuable insights. But what if we told you that the real issue is not whether to hire analysts…
Augmented Analytics Agenda:
- Use Cases
- And more…
Analytics and the Modern Data Stack
- Extracting value from data while data sources are growing is an obvious challenge for businesses today.
- The scalability and elasticity of cloud data warehouses has provided a foundation for a cloud-first data stack and pipeline.
- This is coupled with what Gartner calls “augmented analytics,” which describes data analytics that incorporates machine learning and artificial intelligence techniques brings us to a company like Sisu.
- Sisu accelerates the exploration of complex data and automates the diagnosis of key performance indicators (KPIs). The Sisu platform’s personalized model for each user improves over time to provide contextualized insights while augmenting the user to achieve complex diagnostic analysis without requiring complex SQL or data modeling.
- With that being said, if you make these changes, you’ll be ahead of the competition. In fact, Gartner just released their Cool Vendor report, which says that by 2023, overall analytics adoption will increase from 35% to 50%, driven by vertical- and domain-specific augmented analytics solutions.
What is Augmented Analytics (What and Why)?
- Gartner specifically defines augmented analytics as “the use of enabling technologies such as machine learning (ML) to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms.”
One of the hallmarks of Augmented Analytics is the use of NLP to make data interactions more intuitive for human beings, to help extract more value out of data.
- Some benefits to Sisu customers include:
- Faster insights: Data preparation and data management consumes much of a data analyst’s or data scientist’s time. By quickly uniting data from multiple sources and applying ML-based recommendations, augmented analytics enables faster access to insights derived from massive amounts of data from any source. Analysts can rely on augmented analytics to find the hidden factors and relationships driving changes for the business.
- Better Decision Making: Businesses have used dashboards and reports to understand what’s happening with their key metrics for decades. A challenge can be that these dashboards are typically static—analysts have to know what to ask and where to look in their data for answers. Data teams can be caught in a vicious, reactive pattern of noticing changes, digging in to diagnose, and repeating the dashboard cycle. Proactive analytics platforms are emerging to break this reactive pattern using anomaly or outlier detection and root-cause analysis. These platforms can use all of the data from disparate sources, locations and formats. Anomaly or outlier analysis detects unusual behavior in a monitored metric/KPI over time. However, it does not explain why something is unusual. On the other hand, root-cause analysis identifies key factors that contribute to a metric/KPI over time. Therefore, it can identify why metrics changed and what action one can take, based on all of the data.
- Democratizing insights: Augmented analytics empowers data analysts and non-technical users to ask questions of their data and get automated answers relevant to their queries.
- Increasing data literacy: 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.
Core Augmented Analytics Techniques
- AI encompasses ML which in turn encompasses NLP and Deep Learning.
- ML’s algorithms can adapt to learn from data without being given explicit rules-based programming—and, like humans, ML algorithms or models get better at doing the work as they gain more experience.
- Semantic analysis is another critical aspect of Augmented 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.
- This allows natural language processing or NLP-enabled augmented analytics platforms to analyze data through simple spoken commands.
- Natural language generation or 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 is an important step in the democratization of data analytics.
Predictive Analytics vs. Augmented Analytics
- Like augmented analytics, predictive analytics is a type of advanced analytics.
- However, instead of answering what changed in a business and why, predictive analytics focuses on identifying patterns in data and determining if those events are likely to happen again.
- Predictive analytics combines historical datasets with statistical modeling, data mining, and machine learning techniques to estimate or forecast future outcomes for a business or organization.
- Many industries and use cases depend on predictive analytics to make critical decisions. It’s an effective tool for modeling customer behavior, assessing risk, building accurate sales forecasts, and more.
How Will Augmented Analytics Help Your Organization
- Accelerating data exploration: With the ability to analyze all your data in seconds, augmented analytics unlocks the speed in data exploration by quickly and comprehensively analyzing millions of dimensions and trends. We’ll talk about this with Samsung.
- Instantly see the factors in your data: Marketing executives identify attractive customer segments and instantly see the factors that impact customer behavior, so they can adjust quickly to build programs that engage customers.
- Get the complete picture: Customer Success teams cull insights from hundreds of potential sources to completely understand their customers, their market, and how their customers use their product or service. We don’t have time to discuss today, but this is one of our top HR apps, and you can visit our website to learn more about them, we’re able to use insights from multiple sources to understand how clients use their product to prevent churn and improve customer experience.
- Sisu use cases, Fractory and Samsung:
- Buy in: To fully harness the power of augmentation, organizations should create a data committee staffed by stakeholders across business units. This group should build consensus and transparency around data by standardizing data-driven definitions and metrics, promoting consistency in data strategy, and ensuring that data-enabled successes are communicated throughout the organization.
- Small wins: Start by picking an important KPI—for example as in the previous use cases, customer acquisition cost (CAC) or average order value (AOV)—and use augmented analytics to understand changes and accelerate decision-making. Once you’ve a few successes, you can begin to build analyses that support every KPI you use to manage your business.
- Build the right datasets: Augmented analytics takes advantage of cloud capabilities to handle richer and wider data sets for effective diagnosis and root cause analysis.
- Anomaly or outlier analysis detects unusual behavior in a monitored metric/KPI over time. However, It does not explain why something is unusual.
- Root-cause analysis identifies key factors that contribute to a metric/KPI over time. Therefore, it can identify why metrics changed and what action one can take based on all of the data. This is in contrast to aggregated data sets that are honed for more static data dashboarding tools.
Bias is another concern with aggregated datasets as choices are being made about what data to include.
Sisu has some great resources, including a guide on designing better datasets for diagnostic analytics and a blog on root cause analysis and outlier detection.
WiBD also has some great resources on our website such as event blogs:
A discussion on bias and ethical AI with a data scientist from Tibco and a Principal Architect from the Ethical AI practice at Salesforce….and a blog on data science project success with Director an Analytics from CUNA Mutual Group who is the liaison between business and data science groups .
Please see this Brighttalk link for video recording and attachments from Sisu Data and Women in Big Data.
Please see this link for the talk slides.
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About the speakers:
Brynne Henn is an experienced content marketer and strategist who believes stories are just data with a soul. As Director of Content Marketing at Sisu, she takes pride in surfacing the right narrative to explain the changing world of data and highlighting how analysts can take a more strategic role in the business.
Regina Karson has a business and technical background professionally and educationally. She has held roles in marketing, business development, sales, and engineering for Fortune 50 to start-up companies. She has held several roles over five years with WiBD and is currently on the US Board of Advisors.