Augmented Analytics, Enabling Analytics-Driven Organization
Artificial Intelligence, Business Intelligence and Big Data, Machine Learning

Augmented Analytics, Enabling Analytics-Driven Organization

In the past, we’ve talked about building an analytics-driven organization where analytics is not just about using the tools and having big data infrastructure. Rather, it is an organization culture of data-driven decision-making that starts with looking at relevant data—everything that tells what is happening (hindsight). Then, applying the right techniques to understand why it has happened (insights) and finally, predicting the future (foresight) and make better business decisions.

Let’s try to understand this with the help of an example. Say, you’re trying to figure out how many customers stopped doing business with you, why they did so, and how you can minimize the situation. So where do you start? Ideally, by looking at the data over the last few quarters. Analyzing this data will help you identify the root causes for customer churn and suggest strategies to reduce the churn rate. This is predictive analytics.

However, this process isn’t as simple as it may sound. From data preparation, finding patterns in data, building a predictive model, and sharing and deployment of insights — it involves many complex implementations and requires specialist data science skills to get value out of this process. Not just this; the current approach to handle data and extract insights doesn’t cut it today’s world where data is growing at a lightening pace. Why is that?

Dependence on Manual Tasks

While there are many traditional BI and analytics tools allow businesses organize data and visualize the data with pretty charts, searching and derive insights about a specific business KPI or behavior is primarily done manually, with smaller data and is quite laborious and time consuming. Second the analysis often starts with human driven hypothesis and doesn’t surface underlying trends or new emerging hypothesis that the data may suggest. Therefore, it’s limited primarily to validate human assumptions out of experience or intuition.  Simply put, with traditional BI and analytics tools, It’s not possible to search or glean insights in today’s complex and multi-dimensional business environment with large variety of data. On the other hand pure play machine intelligence has capability to search thru large and complex data and glean insights, but often outputs black box models. Business has to make decision without any rationale of why the model doing what it’s doing or have any human input.

Augmented analytics takes advantage of the cognitive intelligence of humans and learning capabilities of machines and the output insights needs in business context while speeding up the entire insight generation process.

What is Augmented Analytics?

Gartner defines it as “a next-generation data and analytics paradigm that uses machine learning to automate data preparation, insight discovery and insight sharing for a broad range of business users, operational workers and citizen data scientists.”

In other words, Augmented Analytics automates machine language and data science steps involved in an advanced analytics process and puts it into the hands of business people who are guiding these machines in terms of right insights in right business context, while validating human or machine generated hypothesis along the way.

Augmented Analytics Puts Data and Analytics at the Center of Your Organization

More relevant insights with business context. Applying a range of algorithms and machine learning abilities, Augmented Analytics platforms identify false or less relevant insights, reduce the risk of missing important insights in the data, explain actionable findings to users, and even optimize resulting decisions and actions.

Speed to Insight. Augmented Analytics can reduce time spent in data discovery and exploration, helping business users get better insights and augmenting their analysis with the help of machine-learning algorithms.

Insights across the organization. Augmented Analytics empowers all employees with data insights, when and where they need it. It expands the reach of data insights beyond citizen data scientists and into the hands of operational workers who can leverage those insights to contribute towards business transformation.

Removes the skill constraints. Data scientists are high in demand and expensive resources to hire. Using AI algorithms and advanced machine learning, Augmented Analytics automates data management and insight generation in an organization, allowing them to rely less on data scientists. Thus, businesses can to do more with the people they already have.

At Intuceo, we help our clients achieve this by bringing man, machine, and collective intelligence together. We help minimize the complexity and skills constraints associated with building a predictive model, data preparation, insight generation — all of which is automated and made available for subject matter experts (SME).

Further, SMEs enhance and improve the insights using their expertise and experience. Ultimately, the insights are shared across the organization and are subject to further reviews and modifications through collective knowledge. This helps to democratize analytics and accelerate analytics adoption and culture within a company.

Conclusion

As the future of business intelligence and predictive analytics, Augmented Analytics is already on its way to transform the entire analytics workflow and the way in which enterprises will access data and work on insights.

Harness the benefits of Augmented Analytics and take your business intelligence to the next level with Intuceo® Augmented Analytics Platform. Learn more about how it can empower your business analysts and subject matter experts with a cloud based, self-service model and speed up your journey in building analytics driven organization.

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