The ever-increasing amount of data makes data analytics and an important constituent of business development. To beat the competition in business, you need to stay abreast with the changes in business analytics. The term “smart data discovery” was introduced formally in 2017 and is reigning as a powerful differentiator across industries. To get clear insights, most organisations are now focusing on building models and integrating data for simplifying it and automating the tasks. This has increased the demand for skilled data scientists considerably in the market. Augmented analytics is the alternative for these data scientists that can iteratively perform the data-to-insight-to-action activities like preparing the data, deciphering data patterns and building models, and distributing and operationalising the data findings. This saves both time and resources used for getting relevant business insights from the data.

Using augmented reality data insights can be automated with the help of machine learning and natural language generation (NLG). Data-based decision making is facilitated faster and more aptly using these insights. Thus, augmented analytics is a new wave of disruption in the data analytics market, and enterprises need to opt for it as the platform capabilities are maturing.

What is the function of augmented analytics?

Various advances have already been made in the field of automated analytics over the years using Ai, NLP, and many other modern computational technologies. However, apart from the current capabilities of simple forecasting, and using data analytics tools for visualising and clustering data, the next-gen augmented analytics, delves much deeper. It provides not only historical reports and dashboards, but also provides automated and actionable predictive and prescriptive guidance.

Many investigations that are complex to solve manually and may be much time consuming are completed quickly using augmented analytics. It cleanses and prepares the data automatically, deciphers the hidden patterns in data and builds models using them. AI algorithms are used for interpreting the data and presenting insights and recommendation for taking suitable action. Enterprises can test their hypothesis and theories as they can interpret their data and access crucial information using varied statistical algorithms. New data sets can be explored, leads can be identified, customer churn can be predicted, results can be analysed, insurance frauds can be identified, and much more can be done with augmented analytics.

Why should enterprises opt for augmented analytics?

Manual or traditional methods of data analytics are:

• Expensive to implement and not cost-effective
• Time-consuming and slow in providing desired results
• Manual dependency is high, such as on data analyst and data scientist
• Human predictions are prone to greater errors
• rather than scientific predictions based on statistical analysis “gut feeling” may be used as a basis for decision-making, thus introducing bias

Business owners are, however, looking for unbiased, accurate, automated, cost-efficient, and less time-consuming predictions and solutions so that they can make sound business decisions. This is facilitated by augmented analytics as it helps enterprises improve their products, services, and other aspects of a business. Rapid smart data insights offered by augmented analytics improve the productivity and analytics and thereby have a positive impact on the ultimate business turnover. Moreover, data scientists can have more time to focus on other important projects and strategic issues.

How is augmented analytics being adopted by enterprises?

Agile centralised business intelligence provisioning

Cloud-based deployments on the basis of all modern analytics and business intelligence platforms. Next-generation augmented analytics has enabled data analytics delivery at a much faster pace across the enterprise while using far fewer resources.

Mutually beneficial business interactions

Organizations are expected to offer access to curated internal and external data to users by providing double business value to those who invest in augmented analytics by 2020. Organizations can share their prepared data through a cloud with other organisations, thereby enriching their analytics and those of others for mutual benefits through seamless, networked integration of data and analytics across their organisations.

Decentralized analytics

Augmentation of the corporate data model has not been made possible with the decentralised teams and individual users without compromising data governance. Common definitions and key metrics forma unified semantic layer that maintains consistency irrespective of the user’s location.

A comprehensible user interface of augmented analytics tools

Easy-to-use tools that support a wide range of analytic workflow capabilities form the basis of augmented analytics in business. Next-generation augmented analytics provide a consumer-grade user interface that is more intuitive, elegant, and user-friendly for the business people and provides them more power.

Governed data discovery

With augmented analytics, it is possible to support the workflow from data to self-service analytics, a system of record, and IT-managed content. This can then be governed, reused, and promoted as certified data and analytics content.

To wrap up

Augmented analytics is bound to transform the business intelligence completely in the coming years. It has already transformed the entire workflow of analytics and the way in which data analysts access data and work on insights. The mainstream adoption of augmented analytics is not far from reality for all enterprises. Thus, modern business intelligence analytics involving automated data preparation, data science platforms, and automated insights will be embedded in the enterprise applications and conversational analytics in the future as a must-have. This is bound to reach beyond data scientists and transform businesses significantly.