Why All Businesses Should Embrace Big Data

Some industry analysts have made less-than-enthusiastic forecasts about the widespread adoption of big data. According to them, big data will take time to be adopted by businesses—this can be up to five years.

Is this mere speculation?

There is some truth to this viewpoint. It seems that even among businesses using big data, the zone of maximum productivity remains elusive. This is because some companies have adopted big data without the necessary introspection. It should be understood that big data can deliver only when the resources managing it are well equipped. This means having the technical expertise for data collation, warehousing, sharing, and analyzing. Secondly, some businesses have acquired the expertise but lack the right insight necessary for driving business decisions using big data. Overall, it isn’t the ROI of using big data being questioned — the issue lies with handling it effectively.

The Transformation is Underway

You might think that big data hasn’t really taken off. However, the truth remains that it is much more than just another transformational technology. Despite the slightly-pessimistic perspective, it would be foolish to deny the growing interest in big data. Businesses of all sizes are realizing that big data is a definite, mainstream reality. It is just a matter of time when businesses of all sizes will incorporate the skills necessary to use big data. Big data is here to stay and this realization is underlined by the growing urgency to employ data-centric skills. Some businesses are aggressively hiring manpower that specializes in handling big data. Even better, some companies are looking for trustworthy data solution vendors who can empower them with big data capabilities at much lesser costs.

There is no doubt that big data yields better correlations that drive smarter business decisions. When combined with advanced analysis, big data can help you improve customer service and design better products/services. Data analysis can help you resolve problems that seemed exceptional and unsolvable.

Some Big Data Dynamics You Should Keep in Mind

If you have already begun the big data journey or are on the verge of starting it, please read the following:

Data Protocols

Ensure that you have effective data strategies in place. This includes protocols for collecting, saving, indexing, sharing, and analyzing data. Data should be accessible, but not at the cost of compromising data privacy and security. Whether it is the sales department or CEO’s desk, data protocols should be implemented across the organization.

Expertise to Use Big Data

One huge benefit with big data tools is that they can provide immediate results. This refers to simple data sets. Using this, managers can make decisions that affect the current, internal processes, customer service methods, and marketing strategies. Therefore, ensure that you have the required personnel in place to use big data. You should have data experts or managers who can put actionable data in motion.

Don’t be Apprehensive about being Data-centric

Once you start realizing the benefits of big data, it is likely that you will seek a data-centric approach to regular business processes too. This might upset a few employees and challenge authoritativeness of some managers. Accept this unpleasantness as a part of the transformation that will eventually subside.

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Have You Realized the Power of Big Data?

In the real estate niche, big data is still finding its way. Organizational ecosystems are still contemplating whether big data will deliver ROI and enable product innovation. Here, we present some aspects about adopting big data that might have escaped your attention:

The Big Data Acknowledgment

Some key elements about becoming data enabled now seem doable to most lending institutions. For instance, collecting relevant data means coordinating with regional or remotely-located business units. Lenders are prepared to collaborate with their partners and even customers to ensure that the maximum volume of usable data is collected.

Secondly, adopting big data presents a change in technological and management infrastructure which is necessary to derive ROI. The big data strategy is now better understood, helping lending businesses analyze their preparedness for this transformation. Many times, studying about the big data's journey of an equal sized, but differently-industry business can also help.

How can big data contribute towards your lending business?

Some people believe that big data is more applicable to the retail segment. However, its utility in the real estate market has been proven beyond doubt. Big data is extremely useful for segmenting or hyper-segmenting the market. This forms the basis for more penetrative customer targeting. By combining various data resources, data analysts can decode customer behavioral trends. These can be further grouped across various parameters. The finer nuances of real estate borrowing, such as the last-minute reasons to cancel a loan application, can be better realized through big data.

Sometimes, untapped financing or refinancing requirements can be deciphered by analyzing social media conversations or by analyzing recent credit transactions conducted online. This can help you create loan products, better tailored for a certain regional, age-based, or profession-related demographic. Analyzing customer risk profiles can help you explore the lending market. For instance, people with mid-range credit scores might have shown incredible consistency in bill payments along with reduced credit in recent months. Such consumers can be further assessed for smaller loans.

Is big data out to replace organizational management?

Not really. Big data can ease the decision making, making it more informed, always substantiated with confirmed numbers derived from data analysis. Big data helps managers see beyond their established market practices. Using data-driven business models, you might come across better alternatives to increase employee performance and reduce internal wastages. However, putting big data into practice involves human intervention. In fact, big data needs to be incorporated, run, and used with a reasonable amount of human management.

By streamlining your business practices, big data can reduce staffing and operational costs, but it cannot replace the need for management. Even if big data yields information about better ways to package loan products, it will take efficient management to drive new product presentation, marketing, and consumer education.

Can big data help you discover and create new business models?

Absolutely! This is among the most fascinating applications of big data. Big data has spawned across industries, from online retailers to banking giants. The reason for its success lies in the fact that our generation is information-driven. This information is invariably present in the digital form, i.e. data.

Upon analysis, it might turn out that newly-engaged couples are among the most aggressive loan seekers during the Christmas-New Year period. Sensing an opportunity, you can create customer engagement models that can aggressively tap into this newly engaged demographic. Similarly, data from past holidays inspired borrowing can help you forecast the kind of numbers feasible during the forthcoming holiday season. Consumer profiles that have been denied a mortgage by bigger financial institutions, despite reasonable credit scores, can be very useful. When combined with advanced analytics, it can help you identify borrowing households with steady incomes, greater propensity to pay bills on time, and lesser credit spending habits.

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Moving Ahead: Big Data for Credit Lending

Credit lending is a competitive marketplace, relying on everyday transactions where consumer profiles are evaluated with extreme precision. This niche took a drubbing when the entire financial sector suffered due to the economic slowdown. Now progressing towards a complete recovery, credit lending is becoming more accommodating. As a result, customers with poor credit histories able to demonstrate a positive financial performance in the recent past are also being offered short term loans.

Big Data Emerges as the Solution

This doesn’t mean that lending institutions are taking upon a bigger risk pool without worrying for the repayments. It is a matter of using data to identify more households and businesses that aren’t fraudulent and need small to medium value loans. This data is essentially big data that is helping lenders become more competitive. Lending businesses use big data in a very computable way. This usually involves handling massive amounts of legacy and current data. The emphasis is on finding information that can ease credit related decision making.

Big Data Acknowledgment and Deterrents

The credit card segment has quicker data handling at its core. For instance, authorizations are provided instantly when customers swipe their cards at a retail desk. Here, pre-authorization or authorization has to be computed in the blink of an eye. This is just an example. There is much more data that can be used by credit card companies. However, due to server related limitations, only a small amount of data can be retained.

The periodic purging of data means that data retention is maintained at a very basic level. This doesn’t help businesses that are trying to become more data driven. For comprehensive data analytics and business forecasting, more data is needed. The more the data, better is chance to assess risk pools. Businesses vying for sustained growth understand this requirement for more data. This also helps to filter unused data warehouses and retain whatever can be translated into useful information.

Don’t Ignore Utility of Data Automation

Not just data analysis, but automation is also a major consideration. A manual process can be very time consuming. It requires the underwriter to individually analyze every application. Spotting fraud in this way incurs more manpower hours and presents risk of miscalculations. Using automated processes, underwriting becomes more accurate and faster. Here customer data merely needs to be fed to a system. Computing algorithms are put into action using minimal human resources. Within seconds; fraud, credit-worthiness, and overcall credit history are electronically computed.

Don’t Fret if You Don’t Have Big Data Capabilities

Some lending institutions have a monthly schedule for evaluating their current customer base. However, this can be done more regularly. The customer portfolio can be evaluated on a weekly basis too. High-risk customer types can be analyzed on a daily basis. This ensures that current models used for customer categorization are up to date with analytical outputs. Yes, doing this isn’t easy. Speed is the secondary consideration here. Firstly, the business should have the capacity for this kind of data processing. Perhaps, a better solution lies in employing the services of vendors who specialize in collecting, retrieving, and simplifying digitized information.

Buy and work leads smarter, contact only the customers you want to engage, and enable your employees to be productive. Connect with your target audience with Live Connect today!

Is Big Data Scaring Banks?

Borrowing money has always been very challenging for startup businesses. However, the trend is changing these days. Preference among startups to seek loans from smaller lending institutions and not banks is emerging as a nationwide trend.

Alternative Lending

Startups, including those that have just begun, are finding it easy to fund their dream projects. Lending is coming rather easily to this segment, including Subprime and Payday loans. The trend is also being referred to as Alternative Lending. This is essentially small business lending that has confused analysts with its unprecedented growth. It seems that this trend is largely fueled by a changing underwriting approach. Conventionally, underwriting has strictly been a standard-driven niche. Credit scores, like FICO scores, have had unchallenged acceptance across the lending sector. Now big data is challenging this typical landscape.

Why this shift?

We believe that this has something to do with the credit crisis that followed the 2008 slowdown. Banks have suffered a lot at the hands of Subprime mortgages. The pessimism continues with banks being apprehensive about anything that doesn’t guarantee repayment. On the other hand, smaller lending institutions have understood the potential of start-ups. They are ready to offer attractive interest rates. Secondly, smaller lending institutions are increasingly using analytical tools to retrieve actionable information from bigger volumes of data.

This big data shift is not as common in the banking sector. Based upon the big data indicators, new underwriting models have surfaced. Start-ups don’t mind their repayment potential being evaluated on the basis of their social media handles or email communication. These individual pieces of data form a part of a widespread big data structure that thrives on using data derived from unconventional resources. This approach is working for startups that are ready to share even more information, as long as the privacy of their business model is not compromised.

Startup Payday Loans Don’t Stretch for Decades

These loans have another advantage over traditional lending instruments like mortgages. An average mortgage might take 15-30 years to be realized. This is too long a period to derive decision driving data that can be used to curate more home loans. However, a bad loan emerging from the startup sector can be analyzed much sooner. Here, the average length of a loan is a few weeks or months. Credit factors that were under evaluated can be quickly identified and used to improve the current underwriting model.

Startup Payday Loan Underwriting isn’t So Demanding

The documentation and repeated screening by the underwriting team can test the consumers’ patience. However, payday loans aren’t as demanding. With little documentation and proofs to establish the borrower’s profile, a payday loan can be executed much quicker. Using analytics to improve this already simplified loan sanctioning process isn’t very difficult.

Payday Reputations are Not so Vulnerable

There is another risk associated with loans to not-so-worthy, newly launched businesses. In case of a default, the bank’s reputation is at risk. Even a few days of bad press can compromise the faith of customers in the bank. However, smaller lending institutions aren’t so sensitive about this issue.

Big Data Costs can be Challenging but Solutions are Emerging

It would be foolish to assume that creating a functional big data infrastructure is without any casualties. The traditional concept of learning as you proceed applies here as well. Using big data doesn’t guarantee the absolute removal of bad loans. Initially, a lender needs to test its big data parameters and their accuracy to observe patterns that can be used for future loan applications. The default rates at the start of the big data journey can be high.

Yes, a big data led underwriting model can be expensive, but another solution has surfaced recently. This is in the form of different business models that use elaborative data. Now lending institutions have the advantage of comparing their organizational structure or lending processes with similar business environments. They can choose a big data model that seems more appropriate for their business environment.

Can Big Data Rescue People with Bad Debt Cycles?

Big data adoption is sweeping across the financial sector, gaining widespread acceptance among lending institutions as well. While some banks might call themselves pioneers of this movement, it is the medium and small scale lenders who are really driving this trend. The lending marketplace is abuzz with how big data is helping to decode more risk-appropriate lending profiles. Data analytics is being used to increase the quality of customer service and arrest cases of fraud.

Big Data Not Always Associated with Bigger Businesses

The adoption of big data doesn’t have a uniform pattern. Some of the bigger, more established financial institutes might be collecting more data from conventional points of customer interaction only. However, lending agencies handling smaller loans are more likely to analyze poor credit histories to ascertain the overall chances of recovery and the ability to repay another loan. Here, the idea is simple—take a calculated, manageable risk by offering smaller loans to credit-worthy consumers.

Big Data Can be Seemingly Insignificant Data

The indicators used to evaluate borrower profiles are breaking new ground with regularity. Even social media channels that carry employment information or email accounts with a regular history of making payments or receiving funds are being analyzed. The emphasis is on looking beyond the typical buying and payment history of a consumer. Instead, the focus is on the present and near-future ability of the borrower to repay a small loan. Having a stable job over the past few months can be a stronger argument against the non-payment of a small loan years ago. Similarly, small businesses paying taxes on time and interacting with customers on Facebook, Twitter, or LinkedIn are more likely to be approved for a loan.

Yes, demographic information and credit history are still important. However, these conventional parameters took a severe beating in the aftermath of the recession. People with otherwise good credit histories too were caught in this mess. Therefore, credit histories that show positive signs of complete recovery are worthy of being given another chance. When analyzed further with big data analytics, many such deserving borrowers can be identified.

Will big data overturn debt cycles that cripple households?

The answer lies in how you perceive the question:

Do you look at it as technology coming to the aid of people who are conventionally not credit-worthy? (unlikely)

Or,

Do you perceive it as traditional evaluation parameters being improved by using contemporary technologies? (practical and feasible)

The ideal way to look at this argument is accepting that big data provides a better way to identify consumers who can make monthly payments despite a somewhat-flawed credit history. Big data is not the magical cure that some families might expect it to be.

Similarly, big data cannot guarantee loan payments. It is essentially a relief to a market that is still recovering. It cannot alleviate poverty or the healthcare crisis. However, big data will ensure that a larger part of the credit-deserving population is well served. Much of the information collected as a part of the big data strategy is already in the public domain. If the reputation of a neighborhood or money earned during seasonal employment is used to determine credit reliability, is someone being harmed?

Why your Big Data Analysis System Should be Insightful

There is a lot of emphasis on data and data analysis these days. However, data analysis alone won't deliver the desired results; companies should be able to turn the insights acquired by analyzing data into valuable actions.

Using data analysis, a real estate agent should be able to identify potential customers who are likely to move homes in the immediate future. Proper analysis of data involves asking the right questions. This will help realtors detect trends and adjust their business processes.

If you are an agent you could perhaps ask:

Why am I making fewer sales? Or how can I know what my customers want? Remember that the goal of data analysis is to change compelling insights into tangible business actions.

Companies should build a data-first strategy that will help them gather valuable data using their new products. Data analysts attempt this type of exploration to discover critical business insights. They will then present these insights to stakeholders, who after interpreting the insights will use them in making tactical decisions. There is one problem, though. Thanks to its broad nature, open-ended explorations may uncover insights that have little value.

On the other hand, closed loop data explorations are more focused. For example, a real estate company might ask: Are there any real estate investors who bought more than 5 properties last month? This information allows the company to flag that investor as a very important customer eligible for special offers. However, even closed-loop data analysis carries the risk of producing insights of little value.

Why it is important to take quick decisions

One of the biggest challenges of doing business is making great decisions. While professionals in the IT sector are quite comfortable delivering reports, real estate brokers are yet to get into that habit. It is even more difficult to take an action based on available information. And if a company fails to take decisions in spite of having lots of insights, its value will begin to erode.

You may have invested in information management tools and generated all kinds of reports and dashboards, but all of these are useless if you cannot convert your observations into actions. In order to benefit from data analysis, companies should be able to quickly and efficiently transform insights into actions.

This wouldn't be possible without investing in streamlined data collection technologies and decision-making strategies.

The need to adopt a "data first"strategy

When you design or launch a new product, data collection and analysis is probably the last thing you do. However, it needs to be the first. To have a 'data first' strategy is to have the right tools in place to gather insights that will greatly improve user experience.

Partnerships

Many organizations do not have the technical expertise required to gather and analyze data. In this case, they should consider bringing in outside expertise through partnerships. And when you have the right partnerships, you will be able to reach customers with deals, offers, content, and ads across different channels.

Why is data so big?

Big data analysis is a powerful tool that will significantly improve user experience and increase profits. But it’s critical that businesses are aware of the process, have the right tools, and realize the need to move quickly and decisively once they have gathered insights. 

Yes! Data is Relevant Across the Mortgage Industry

Some folks might say that the operational basics of every business are essentially the same, but significant differences can exist. For instance, the manufacturing industry depends upon its performance to gauge its index of quality. Here, quality refers to the processes through which components are assembled and overall quality of these components. However, in the mortgage industry, data is evolving as the most important component. Here, products are the decisions based upon this data.

Understand the Issue: Data Perception among Bankers and Investors

It is generally believed that "Big Data" is beyond the reach of small to medium-level businesses. This notion is driven by the fact that corporations like Google and Facebook have pioneered the cause of being data-driven. Such brands look upon data as their most valuable resource. Many mortgage industry professionals look at cloud-based data and analytics with circumspection. They are not sure whether these data-driven technologies are useful for managing their day-to-day challenges. However, if looked closely, it becomes evident that these technologies are capable enough to find out meaningful processes and facilitate growth.

Is data the cure for all mortgage industry ills?

To be brutally honest, the answer is NO! Typical roadblocks towards using data efficiently are yet to be addressed. For instance, loss of data integrity shows no signs of abating. Many data mistakes might originate from the lender. This is where established dynamics of the industry come to the fore—sales being aggressive and often ignorant of quality or safety standards. To realize the true potential of data, the industry has to think beyond loan and risk performance. They will have to work towards ensuring accuracy of data incorporated into their database.

The Evolving Industry Shift towards Data

Since the economic crisis of 2008, the mortgage marketplace has become more introspective. The industry might not be using data robustly but it is exploring the utility of investing in big data. Investors have traditionally relied on performance of loan-term as the primary benchmark. Data analytics can provide insight beyond this aspect. Data can facilitate access to lesser-recognized factors. This can help industry regulators and investors in better evaluation of securitization or compliance. Data provides useful analytics that helps to achieve better accountability and well-informed decision-making.

The visible progression is towards creating a more comprehensive dataset. This will eventually make governance more effective. However, this is a colossal shift for an industry that has been primarily driven by financial assets. The changing perspective towards looking at data as an asset will take time. However, the change is underway and very palpable. There have been some slow-downs in the way.

 

Several regulations have been introduced that push towards adopting a more data-tolerant attitude. However, a lag is indicated since all organizations cannot jump on to the big data platform immediately. The big change lies in organizations becoming more open towards collaborating data. This is the first step towards creating uniform data that can be used seamlessly across the mortgage industry. This will help to create more effective home-finance regulations.