How Experimentation Can Fast Track Big Data Capability

As more businesses explore the vast expanse of big data utilities, it is becoming clear that big data delivers more when managers are ready to be a bit creative. For a real estate business, big data can provide many standard utilities. Standard because soon most of the real estate organizations will gain similar big data advantages—this trend is already underway. So how does a real estate business make itself unique and more competitive?

This is where experimentation comes to the fore. Big data analysis can yield lots of actionable information provided you are ready to take some chances, allow room for creative experimentation, and use data sources that seem irrelevant at first sight. Here we discuss more about how experimentation can drive better ROI for your big data investment.

Big Data is Being Widely Embraced, but Who Will Benefit More?

We understand that big data is still new for most organizations. They are approaching big data with some apprehension along with lots of enthusiasm. It is unfair to expect such businesses to get creative with big data at the outset. This doesn’t make sense, particularly when digitization hasn’t been associated with the real estate industry in a larger perspective. We realize that most businesses in this niche are finding it difficult to let go of their legacy protocols, wanting to hold on to the traditional methods of working. However, the big data voyage has begun, and soon most businesses will be at ease working in a data-driven environment.

There is another aspect to this realization—some organizations have adaptability as a part of their core skills. On the contrary, some are deep rooted in hierarchies and don’t venture beyond the realm of expected outcomes. It seems that the more adaptable businesses have a better chance of extracting better ROI from their big data adoption. The more established businesses will take time as their bigger teams and numerous offices multiply the hierarchy, breeding lesser elasticity.

Big Data can Look Ungainly, But Don’t Fret - Placate Your Team

Big data involves high volumes, mammoth disparities, and real time performance. This means lots of data is collated from various resources, including data that is highly unstructured and processed in real time. During the initial few weeks, you might feel that your big data team is trying to do too much. However, don’t have doubts about this—the typical big data environment is very challenging and to others, it can seem very perplexing.

Data analysts work together, forming strategies on the go, hungry for processing more data. Once you have established a basic level of understanding with your big data team, try to establish an experimentation policy. The idea is to make the team feel that you support a certain degree of experimentation. The team should know that running correlations or comparisons that don’t amount to any conclusive information will not be ridiculed. You can ask the team to allocate certain hours or weekdays for running experimental data studies. This is vital to discover unexplored market service combinations and unearth new consumer zones.

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Big Data & Hadoop: What’s Cooking?

If you have been following big data, it is most likely you have come across repeated mention of Hadoop. Before we delve deeper into this discussion, let us take a step back in history and understand how data management has evolved and what led to the current scenario where Hadoop and big data are the favored technologies.

Understand Hadoop: The Early Days

Earlier, computing was limited in volumes and in terms of expectations from outcomes. Applications were created specifically for different data structures to ensure that a problem was comprehensively solved. Since computing volumes were humble, this approach didn’t present too many problems. Multiple data resources were uncommon, almost unheard of during this period. Using organizational databases or network data, larger applications functioned rather well. However, the relationship between data structure and application saw a major upheaval with the creation of relational databases.

Due to this, data access to SQL was standardized. More businesses started using spreadsheets. Together, these two developments created the way for more complex data management systems to emerge.

During the 90s, it became clear that there was more business value in combining data from different applications but this was challenging. Slowly, better and bigger data structures surfaced. However, this picture was soon challenged with the arrival of data-intensive business mammoths in the form of Yahoo and Google. Things changed and data didn’t have a similar, streamlined structure. These organizations were handling massive amounts of data on a daily basis, hoping to create a solution for faster access to retrieve and save data. This paved the way for the creation of Hadoop—a more dexterous, organized, and distributed file system that makes it easy to handle unstructured data.

As Hadoop Progressed, Big Data Arrived

Slowly, the advantages of Hadoop became known to businesses across the world. Managers realized that they could benefit a lot in terms of condensing their operational cycles if data access was made easier without compromising its security. The basics of free data management framework that Hadoop had initiated were then routed towards data-driven businesses that wanted data capabilities of bigger organizations.

The Hadoop-Big Data Connection—Hadoop is the platform that makes it easier for unstructured data to be fed into the big data ecosystem. Conventionally, real estate businesses were somewhat selective about retaining data. They feared massive amounts of data will not be accessible. Big data makes it easier to store everything that seems important. With big data, businesses can feel assured that their organizational and consumer data is secured and easily shareable when needed. With big data analysis, the information can be extracted in an orderly, indexed format and then assessed.

Did Google create Hadoop?

Yes, Google can be credited for bringing Hadoop to the world of IT. This happened with Google’s requirement to index enormous amounts of web data, collating, and categorizing it for its search results. Since the market at that time didn’t have a readymade platform that Google could use, it had to innovate. The platform was first called Nutch. Yahoo played a more integral role in helping Hadoop evolve for enterprise applications.

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Big Data Strategies in Real Estate

Predictive analysis and data mining are yet to become a big deal in real estate, but big data is slowly and surely penetrating this segment. Some analysts might question the relevancy of big data in a business where the consumer/homebuyer typically ventures into the market every 5 to 10 years. We argue that this is precisely the reason big data should be used in the real estate niche.

Why Remaining Connected with Customers is Necessary?

Property purchase decisions are heavily impacted by the goodwill a business can generate and sustain. Here, consumer preferences don’t undergo overnight changes like those seen among fast-moving consumer goods. Real estate industry has a special space for references where past clients are invaluable. Having happy, if not loyal, clients can translate into feeding other lines of business like refinancing. If you can maintain a connection with your past clients, many more serious queries are likely to flow from friends, family members, office colleagues, and even neighbors.

Some folks argue that typical retail businesses, like apparel, have more to benefit from big data rather than real estate. Let us take an example to argue our point here—a young family of four with working parents is expecting another child. Such information is often announced in the form of social media updates. A retail business can use this data to send emailers and SMS with links to newborn clothing range. Now, how can a real estate business use similar data?

When a baby is on the way, it is most likely that the mother has taken temporary leave of absence. There is going to be lesser money to pay the bills. With the newborn retail niche overburdened with branded products, the expenses are also likely to rise. Limited to a single income and facing more expenditure, the family might seriously consider refinancing to save money. With three children, they might reconsider moving to a bigger property.

A real estate business can make relevant interaction by emailing pictures of bigger accommodations with more study rooms and better schools. This strategy is a combination of tracking social media conversations and hyper-targeted marketing. Here, a single Facebook update can lay the basis for a new sale or at least create a very serious home-buying or refinancing query.

There is more to tracking consumers’ social media behavior. Online experts now offer targeted social media analysis. They identify audiences whose recent shopping behavior suggests a definite buying capacity. Home purchases might be offline, but online consumer behaviors can help real estate businesses identify more, promising consumers.

Big Data Strategies Deliver but Be Patient

The strategies we discussed above take time. Signing up with a social data analysis firm doesn’t mean that you will be overwhelmed with a glut of serious queries. This is a gradual process where hurrying into decisions can mean disaster. Secondly, keeping past customers engaged isn’t easy. This is particularly true for real estate where the consumer-connect is soon lost after the transaction. This is where data-sharing practices can solve the problem.

If you are ready to share data about the payment pattern or the mortgaging history of past clients, you can get valuable information in return. For instance, some of your past customers might be avid environmental enthusiasts. You can start a small blog or a newsletter about green building practices. Here, you reason to seek an association is logical since you are in the housing niche. Among consumers, such information is less likely to be spammed because it is relevant to them. The e-mailer/newsletter can carry a small, hyperlinked logo, directing the browser to your parent website.

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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.