Augmented Analytics - The Future of Data Analytics !

Augmented Analytics automates data insight by utilizing machine learning and natural language to automate data preparation and enable data sharing. This advanced use, manipulation and presentation of data simplifies data to present clear results and provides access to sophisticated tools so business users can make day-to-day decisions with confidence. Users can go beyond opinion and bias to get real insight and act on data quickly and accurately.

Simply put, augmented analytics is the next-gen intelligence delivery model running on mature platforms that automate and integrate activities like cleansing the data and making it analysis ready, detecting data patterns and layers, correlating with business ontology and workflows, creating models around data clusters, and deriving insights from the models for easy digestion by decision makers – all in a plug-and-play format.

Why is all of this important to your organization?

Here are a few reasons you should consider advanced analytics and augmented data preparation for your enterprise:

• These solutions allow the data scientist and IT community to focus on strategic issues and special projects.
• Accessible augmented analytics creates Citizen Data Scientists and improves accountability and empowerment.
• Advances in smart data discovery and other sophisticated techniques and solutions can positively impact ROI and TCO.
• These solutions produce better decisions, more accurate business predictions and measurable analysis of product and service offerings, pricing, financials, production and other aspects of business.
• Augmented data preparation and related tools will improve user adoption, data popularity, social BI integration and data literacy.

Augmented analytics reveals the hidden side of things

Compared to BI, augmented analytics improves overall speed and accuracy, and, because more data can be analyzed, it can potentially reduce data bias. Data discovery and prep are faster as algorithms are applied to data to automatically search for patterns, while features, models and code are auto-selected. Insights are narrated using natural language processes, or visualizations are created to show what's important.

New approaches to business analytics include AI within the analysis process itself. With augmented analytics, for example, you can ask a question concerning a drop in sales to derive insights from areas you normally wouldn't consider.

An augmented analytics engine can automatically go through a company’s data, clean it, analyze it, and convert these insights into action steps for the executives or marketers with little to no supervision from a technical person. Augmented analytics therefore can make analytics accessible to all SMB owners.

So how mature is Augmented Analytics right now?

Short answer, not very mature but it is going to grow VERY fast in the next couple of years.

We can measure the maturity of all augmented algorithms in three stages, with each stage being significantly more advanced than the stage before it.

Stage 1: Data Preparation and Discovery

This is the stage most of the existing augmented analytics technologies are in. Key players include IBM Watson Analytics, Tableau Insights, and Qlik Sense.

At this stage, augmented analytics algorithms serve as a great complement to existing data scientists or analysts, but does not have the ability to completely substitute them.

Here, the algorithm’s primary purpose is to automate boring data preparation tasks such as data cleaning, data labeling, and data collection.

It may be able to detect some correlations and anomalies in the data, but most of these detections are noise, and data scientists still need to parse out real signals manually.

Stage 2: Signal Detection

At this stage, the augmented analytics algorithm can detect true signals in a company’s data with extreme reliability. However, it is unable to connect these discoveries with business situations or business actions.

Assistance is still needed from data analysts or data scientists to convert those discoveries into concrete business insights, but the time they need to spend on each insight is reduced dramatically.

Many companies will likely reach this stage in 2–3 years.

Stage 3: Actionable Insight Generation

This is the stage in which the augmented analytics engine can directly interface with executives in the company with little or no need for input from a business analyst or data scientist.

A large knowledge base of past business cases will be developed to help the augmented analytics systems connect trends in the company’s data to the larger context of the business. It can then offer concrete action steps based on these insights.

More importantly, the system will be able to track the implementation of these actions and provide additional insights on what the company can do better next time to maximize its operational effectiveness.

Here, the augmented analytics engine is not only a substitute for business analysts, but can also do a lot of things current analysts cannot do.

This stage is definitely a significant leap compared with the previous stages. I believe many businesses will start to reach this stage in 5–10 years.

Source-web