The Essentials of Data Analysis

Pharma Data Analytics

With the predictive analytics software becoming increasingly easier to use, banking and financial services teams are adopting Data Analytics to help in multiple areas. One of them is how to gain customer insight and the second one is to carry out predictive modeling against a combination of internal and external data for risk management.

What is Data Analytics?

Data Analytics deals with understanding and deriving meaning and insights out of data. But that’s not where it stops. Insights enable businesses to understand what happened and also why it happened. The “Why” will empower customers to take the right course of action and change strategies/execution in order to maximize growth and revenue.

At Incedo we understand that the tangible business benefits which (https://www.incedoinc.com/lifesciences) Pharma Data Analytics can offer to our clients in the Financial and Banking services. Apart from the fact that it is important to provide accurate data to management and regulators, insights and trends uncovered using Data Analytics can give customers an edge by predicting what will happen in the future. Insights like : “How our customers behave, how our competitor’s customers behave, how this behavior changes over time, what factors will influence this change” - these are sample, critical indicators which will help in product and pricing strategy among other decisions. But before we can run Analysis on Data, it is important that we understand which data can provide this value, who will need this information, how frequently will they need this information, and what are the right models to use.

Let’s look at some key skills for a Data Analyst: • Understanding the Data • Visualization expertise • Business domain expertise • Technical Skills

To efficiently deal with underlying data, it is important to understand the data being analyzed completely. Proficient business domain knowledge, visualization expertise and technical skills are equally crucial. The Data Analytics team at Incedo Inc.

At Incedo Inc. there are dedicated teams in the Data Analytics area who have sound knowledge of data in the Banking and Financial industry. This vertical at Incedo is called BI & Analytics. Through this practice Incedo Inc. is helping various banking and financial service providers in the area of Feasibility Study, (https://www.incedoinc.com/biandanalytics) BI & Analytics, Business Analysis, Profile & Cluster Analysis, Meta-Data Extraction, Data Collection & Validation, Predictive Modeling & Evaluation, building & viewing predictive reports.

Besides domain expertise, Analytics requires the right technical skills to work and analyze data (which come from various channels, at various speeds/forms). A sound knowledge of the right software tools and technologies for Data Analytics are critical. The Data Analytics team at Incedo Inc. has a multi-node clustered Hadoop Lab to facilitate us to prototype any solution for our customers in the shortest possible time. Apart from this, Incedo Inc. has team members skilled on Informatica, Ab-Initio, Teradata and Netezza as well.

The promise of achieving significant, measurable business value from big data can only be realized if organizations put into place an information foundation that supports the rapidly growing volume, variety and velocity of data. At Incedo, a team of Data Scientists focus on getting the right modes of crawling the different social media channels for data. These teams also ensure we have the right models and infrastructure to be able to utilize this data as a BAU activity.

Incedo is working with one of the top 10 online trading firms in India having a daily transaction value of more than USD 1 Billion is engaged with Incedo to build a Big-data based Portfolio Recommendation Engine. Incedo Inc. is using Hadoop, MapReduce, Pig, Hbase, Oozie, R and Java to build this solution for them. Models are being built around the Co-relation of news to specific stocks in phase I. Phase II will forge ahead with Sentimental Analysis using NLP (Natural Language Processing) for ranking news items & incorporating recommendations. The sentimental analysis will help in understanding the trends and users reactions to various products, investment trends and views around investments which will help influence the firm’s product strategy.

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