The metamorphosis taking place in the analytics world has been rapid and transformational. The analytics space has moved from business intelligence to the age of big data analytics. These transformations have been affected in the recent years as more and more companies are opting for digitization. The technology, infrastructure and the philosophy of organizations have changed as well. It is now evident, that organizations will have to keep pace with the changes or perish. It has all the more become important to embrace data analytics to develop an agile IT framework and build a strong base for data science.

The Challenge

The question here is not the data but what has to be done with the data. The challenge is to utilize data in such a way so as to address key businesses challenges. In today’s world, if organizations need to succeed and innovate digitally, they have to be prepared to utilize big data effectively. Organizations need to also bring under their ambit areas such an artificial intelligence(AI), Internet of Things (IoT)and data services tools. The need of the hour is to develop an IT framework that is able to analyze data along with identifying the most relevant data, monitor and manage the quality of data and gain an in-depth understanding of the data. Adding new parameters and understanding to the data in hand is more important. The challenge can be broadly mentioned as a) identifying the problem in hand, b) Innovative methods using data analytics to solve the problem.

The way out

The few things that organizations can do to fabricate a strong bond between analytics and business results are:-

• Identifying business goals – Organizations face problem while measuring the return on investment on big data analytics project. The number of variables is multiple on a project related to big data, which becomes a cause for concern for organizations. Confusion rules and often organizations shift from their ultimate goals. Thus, it becomes extremely essential for an organization to define measurable goals that can be monitored right at the beginning. If ROI(return on investment) is calculated based on clearly defined measurable goals, organizations will be clear about their standing.
• Understanding the Uniqueness of the project – Big Data Analytics projects vary from each other, for example, a project based on predictive analytics will be inherently different from a project based on the non-predictive Organizations need to adapt themselves according to the project in hand. Offering unique and best in the breed solutions to clients is the key to staying ahead in a competition.
• Build a bridge – Organizations need to build a bridge between conventional data sources along with endorsing cloud based analytics, Internet of Things to generate both human and machine data analytics. The need of the hour is to continuously evolve their thinking process and keep on correlating unrelated data points to generate unique outcomes.

Apart from opting for the above-mentioned steps, organizations need to also understand that just adopting big data analytics will not solve all problems and drive business benefits. However, organizations will become efficient after adopting data analytics in this digital era. Data Analytics service providers will definitely have an edge over their competitors.

Since its invention back in the 1990s, digitization has diffused its way into every conceivable sector of commerce; specially the service industry. In financial services the rise of digital technologies has dramatically changed the manner in which institutions deliver information to their clients. Zivanta Analytics has helped in data digitization of a leading real estate hedge fund company which has brought more efficiency in their work there by shortening the TAT for their bidding for each deal.

The company’s primary objective is to deploy capital for pools of real estate assets that contain a title, document or compliance issue which impacts the marketability of that asset to the current owner. It utilized an asset based due diligence review which encompassed the legal curative strategy, property value and cash flow analysis data points needed to secure the assets with an appropriate equity position to protect a potential investment risk.

The objective of the company is to acquire real estate pool which came with thousands of scanned collateral documents which told the story about the constituent properties, its lien and title status, default details, assignments and other key variables which determine the risk profile of the property. For taking a price position for bidding for a Deal, the risk profile needs to be determined.

To create the risk profile of these properties, the relevant data had to be culled out from the collateral documents for each property. Zivanta Analytics had been retained by the Hedge fund company to sift through thousands of collateral documents and the key indicators. Zivanta Analytics has also built an analytics engine which uses the key data to build the risk contours of each individual property in a deal.

The company selected a specific deal and had transferred the entire collateral set to Zivanta Analytics in pdf format. Analysts at Zivanta analytics who are trained to look into the real estate collaterals, sorted the documents and looked at the completeness for building the collateral datasets. Accordingly the digitization strategy for a given deal was decided.  The collaterals which could be machine read were sent to the technical data scraping team who wrote codes to digitise. The remaining collaterals which were not machine readable were sent to the data associates who keyed in the data for each property from each collateral using either voice or key board based data entry method. As the data were entered, the data quality team concurrently checked the  data for quality using proprietary checking  tools and analytics.

The cleansed data was then processed by the Zivanta proprietary analytics engine to create a risk profile for each property.  The data could be queried by the hedge fund company experts through a custom interface developed by Zivanta Analytics. A dashboard to help analyze the risk profile was also developed.

With the help of this newly developed process the hedge fund company got quality data on tight deadlines at very economic costs. The risk profile created using the data from the collateral documents helped make an informed decision regarding the bid price for acquiring a deal. Once a Deal is acquired, the  experts  at the company are now using the data set to identify and mitigate the risk associated with each individual property  thereby making the property marketable at prevailing  market rates.

The 80s and the 90s made a revolution in the banking industry when IT systems virtually revamped the whole banking process. The use of the internet  made it a lot easier to assess and evaluate the progress of a bank beforehand. It even had enhanced the service provided by the banks.

Banks always have a lot of information regarding their clients. With this information they can learn newer ways to provide better services. With the insights they have, they can make sure their customers are always provided with resources that are beneficial and not available elsewhere. The use of Big data has now opened up a new way for banks to be more profitable. The very use of Big data is enabling the banking sector answer a simple question in seconds which is not possible without harnessing the power of Big Data.

Big Data is an extensive system that can help banking industries simplify their system and work better. It helps them in developing a sincere clientele. Following is a list of ways in which the banking industry is affected by the use of Big Data:

Customer Segregation:

When a bank is provided with the insight to track and trace the habits of their clients of where and how they spend their money, it becomes easier for them to understand the clients’ needs. Once the results are analysed, clients can be categorized into different segments. This will suggest their banking needs and the marketing campaigns can be drawn up accordingly. This will also promote a healthier customer relationship.

Cross Sell Opportunity Enhancement:

With the use of Big Data, the banking industry can improve their relationships with their clients and understand them better. Big Data provides a more personal assistance and helps banks with detailed information on their clients. This information helps banks come up with schemes that are direct and personalized. These schemes gain maximum attention and are able to make sure they can come up with similar plans for the future. As their ability to analyze and assess the situation increases, their ability to deliver according to the expectations of their clients increases as well.

Efficiency Improvement:

Big Data helps banks avoid all situations that can be embarrassing for them. When a client asks a question that can only be answered by assessing the database, Big Data comes handy. The clients no longer have to wait to be answered because of a system failure. Their queries are solved immediately. By entering the name of the customer, banks are provided with all his important details. This also increases the level of satisfaction received from the customers. It also develops a sincere number of clients that learn to rely on their banks more.

 Fraud Detection:

Internet no doubt has made life simpler; however, it has also made fraud easy. When banks come up with newer ways to stop fraud, people come up with newer ways to commit it. This makes it impossible for banks to be able to trace the fraudulent convicts. The use of Big Data rids them of this problem. It makes it easy for banks to immediately be able to trace a fraud and put an end to it. The installation and use of this system makes it easy for banks to identify where the fraud is being committed. They can stop it immediately without delaying or experiencing a loss.

Risk Management:

With help of Big Data information is easily located on a single large scale platform and it becomes easier to reduce the number of risks. Everything that is needed by the banks becomes available at a central platform. This reduces the chances of them losing any information. It also helps them avoid being ignorant towards a fraud. They can easily detect them and in turn reduce all kinds of risks.

So in a nut shell Big Data is very beneficial. If it is taken up by industries it can be utilized with the right kind of knowledge. It assists the banks in taking steps and coming up with initiatives that provide guaranteed benefit. Big Data not only helps banks attract a larger client base but also helps them markedly reduce all frauds and secure their system.