Augmented Analytics: The Future of Data Interpretation and Intelligence

"Without big data, you are blind and deaf and in the midst of a freeway," stated Geoffrey Moore, a prominent American management thinker and consultant.

Data is one of the newest and arguably most sensitive building pieces that the business world has ever encountered. It is, nevertheless, one that necessitates sensible treatment - otherwise, its very existence is worthless. It is insufficient to simply have data. The data must lead to some useful information in order to make sense of it and make it useful to enterprises.

The Process of Data Interpretation

The foundation of any business activity in today's world is based on accurate data insights. To reach adequate information and take necessary business actions, the data is first collected from a number of diverse sources such as web analytics, social media trends, and market surveys.

The collected data is then processed through a five-step flow of analytics to arrive at resourceful insights at the end. The steps of the process are -

1. Data Collection

There are certain sectors whose services predominantly include a collection of large scale data such as banking, finance, insurance, healthcare, hospitality, transportation, and trade. In this stage, data flows freely between industries to reach their respective vantage points.

2. Pre-processing

Pre-processing is a key stage before the analysis takes place to transform the raw collected data into a meaningful format. It is a data mining technique that makes sense to the inconsistent, incomplete data, cuts down the noise and irrelevancy, and gives them a shape from which further deductions can be made.

3. Analysis

Analyzing the processed data includes cleansing, modeling, and evaluating it to arrive at answers to certain questions. But to do that accurately, problems to which solutions are being sought must be determined and questions are to be framed definitively.

4. Generate Insights

Once the perusal is over, the analysis of the data leads to certain conclusions. These insights are precisely the answer to questions which were posed in the earlier stage and for which the data was particularly processed.

5. Determine Actions

In the final stage of the procedure, potential business actions are drawn up and suggested to the authoritative body based on the derived insights. Upon accurate processing and analysis of data, the said actions are expected to result in the optimization of business and achieve goals set by the company.

Challenges in Data Analytics

Despite the very streamlined process, the domain of data analytics has been facing quite a few challenges of late. The reason behind it is the complexity and layers involved in each stage. The journey from collection to action is quite a tedious one, and the process itself is quite time and labor-intensive in nature. A number of expert personnel, such as data collectors, data scientists, and data analysts have to come together and collaborate to successfully execute it.

Mining, cleansing, and pre-processing of the data takes up more than 70% of the time and effort of the specialists involved. Quite a large part of it, like data selection, setting parameters, discovering patterns and inferring insights are manual.

As a result, data scientists are impractically expensive to hire, and even then optimization becomes quite an impossibility. That is where Augmented Analytics steps in.

Augmented Analytics: The Automated Future

The challenges faced by data analytics can be combated with the automation of the process flow, which also facilitates a much faster and unbiased one. Augmented Analytics is basically the use of Machine Learning and Natural Language Processing to understand, interpret, and interact with large data sets at a much faster rate and less redundancy.