How Big Data Is Changing the Financial Industry

How Big Data is changing the Financial Industry
Big Data is the talk of the town these days; not only has it ushered in the next generation of technology, but it has also modified the way businesses and financial institutions are performing their day to day activities.

Financial institutions are always on the lookout to enhance their day to day operations while keeping their competitiveness intact. Let’s have a quick look at analyzing the top 5 financial trends which are quickly taking over the financial industry and paving the path for modernizations.

Strengthening Financial Models: Data is prevalent in every industry. Financial institutions such as banks, lending institutions, trading firms, etc., produce tons of data on a regular basis. To manage such voluminous data, there is an imminent need to bring into operation a data handling language which is equipped to handle, manipulate and analyze massive volumes of information – this is where the role of Big Data comes into the picture. Financial institutions often work on different business and financial models, especially with respect to approving loans, trading stocks, etc. To make efficient working models past data trends need to be taken into consideration. The better the data relativity, the stronger the model and lesser would the risks involved. All such strategies can be derived from the use of Big Data, which in turn becomes an effective method to drive data-driven models through different financial services.

Enhanced Data Processing and Storage: Technology will never stop growing. Since the aforementioned has become an inseparable part of every organization’s life cycle, the data generated by daily operations gives way to the need of the hour storage and data processing. If one talks about the use of Big Data, the name is a clear giveaway in itself; it encompasses the use of the language, which means storing data in the Cloud or on other shared servers becomes a cinch. Thus distribution and processing come as a byproduct of storage capabilities. Cloud management, data storage, and data processing have become the words to reckon with, as more and more organizations are considering opportunities within the technical world.

Machine Learning Generates Better Returns: Financial Institutions deal with customer data on a day to day basis. Not only is such information critical, but very valuable, since it gives insights into the daily functioning of the bank. Considering the sensitivity of the data, there is a pressing need to evaluate the stored data, and protect it from fraudulent activities, while ensuring the risk is reduced drastically. Machine Learning has become an integral part of modern fraud prevention systems, which help to enhance risk management and prevent fraudsters from entering into protected domains.

Blockchain Technology: When customer data is at the fore, and financial transactions are at risk, Anti-Money Laundering (AML) practices become a topic of deliberation. Many people are beginning to give considerable importance to Blockchain technology within the financial industry forum. Blockchain possesses the ability to decentralize databases, and further link separate transaction information through code. This way, it can secure the transactions and offer an extra layer of security to the organizations dealing with sensitive data.

Customer Segmentation: Banks are always under pressure to convert their business models from business-centric to customer-centric models; this means that there is a lot of pressure to understand customer needs and place them before business needs to maximize the efficacy of banking. To facilitate the shift banks need to perform customer segmentation to be able to provide better financial solutions to their customers. Big Data helps perform such tasks with simplicity, thereby enhancing groups and data analysis.

There is no denying the fact that Big Data has increasingly taken over various industries in a short matter of time. The higher the opportunities being exploited, the better the results being displayed by banks and other financial institutions. The idea is to expand efficiency, provide better solutions, and become more customers centric. All the while decreasing the tangent of fraud and risks within the financial domain.

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Why is Big Data Analytics Technology so Important

Why is Big Data Analytics Technology so Important

Big Data Analytics Technology

Yes! Big Data Analytics, as well as Artificial Intelligence, has truly shown its importance in today’s business activities. Corporations & Business sectors are coming up with their procedures to data analytics as the aggressive landscape modifications. Records analytics is slowly becoming entrenched in the enterprise. Today, it’s a well-known behavior and desired practice for companies to apply analytics to optimize something, whether or not it’s operational performance, false detection or purchaser reaction time.

To this point, usage has been pretty easy. Maximum agencies are still doing descriptive analytics (historic reporting) and their use of analytics is characteristic-unique. But in upcoming years more business areas will follow the leaders and boom their levels of class, the use of predictive and prescriptive analytics to optimize their operations. Moreover, extra groups will begin coupling feature-specific analytics to get new intuition & observation into client journeys, risk profiles, and marketplace opportunities.

The “leading” companies were also much more likely to have some sort of cross-purposeful analytics in vicinity enabled via a common framework that enables collaboration and statistics sharing. These pass-practical views allow agencies to recognize the effect of cross-useful dynamics consisting of supply chain effects.

Predictive and Prescriptive Analytics

Whilst descriptive analytics continues to be the maximum popular shape of analytics today, it is no longer the satisfactory manner to advantage a competitive side. Businesses that want to move beyond “doing business through the rear-view mirror” are the use of predictive and prescriptive analytics to decide what is going to possibly arise. Prescriptive analytics has the delivered advantage of recommending movement, which has been the number one gripe approximately descriptive and predictive analytics. The forward-searching abilities enabled through predictive and prescriptive analytics allow groups to plan for possible outcomes, excellent and bad.

Armed with the in all likelihood styles predictive and prescriptive analytics screen; agencies can identify fraud faster or intrude sooner when it seems that a consumer is set to churn. The mixed foresight and timelier action help corporations force extra sales, reduce risks, and improve consumer delight.

Artificial Intelligence (AI)

Artificial intelligence (AI) and gadget learning culture take analytics to new ranges, figuring out previously undiscovered patterns which can have profound outcomes on a commercial enterprise, consisting of identifying new product opportunities or hidden dangers.

Machine intelligence is already constructed into predictive and prescriptive analytics equipment, dashing insights and enabling the analysis of well-sized probabilities to determine the greatest route of movement or the first-rate set of alternatives. Over the years, extra state-of-the-art forms of AI will find their way into analytics systems, similarly enhancing the rate and accuracy of selection-making.

Governance and Security

Groups are supplementing their information with third-celebration records to optimize their operations, comprehensive of adapting useful resource degrees primarily based at the expected level of consumption. They are also sharing statistics with users and companions who necessitate robust governance and a focal point of safety to reduce information misuse and abuse. However, protection is turning into an increasing number of the complex as more ecosystems of records, analytics, and algorithms interact with every other.

Given latest excessive-profile breach instances, it has emerged as clean that governance and safety have to be applied to information at some point in its lifecycle to reduce facts-associated risks.

Developing Statistics

Facts volumes are developing exponentially as agencies connect to statistics outside their internal structures and weave IoT devices into their product lines and operations. Because the records volumes continue to grow, many groups are adopting a hybrid records warehouse/cloud strategy out of necessity. The businesses maximum in all likelihood to have all their records on-premises keep it there due to the fact they’re involved in security.

Groups incorporating not gadgets into their enterprise strategies are both adding an informational element to the bodily products they produce or including sensor-based total information to their existing corpus of statistics. Depending on what is being monitored and the use case, it could be that every piece of information does no longer have value and no longer each issue calls for human intervention. While one or each of those things are authentic, aspect analytics can help identify and remedy as a minimum some common issues routinely, routing the exceptions to human decision-makers.

10 Hot Data Analytics Trends — and 5 Going Cold

Big data, machine learning, data science — the data analytics revolution is evolving rapidly. Keep your BA/BI pros and data scientists ahead of the curve with the latest technologies and strategies for data analysis.

Data analytics are fast becoming the lifeblood of IT. Big data, machine learning, deep learning, data science — the range of technologies and techniques for analyzing vast volumes of data is expanding at a rapid pace. To gain deep insights into customer behavior, systems performance, and new revenue opportunities, your data analytics strategy will benefit greatly from being on top of the latest data analytics trends.

Here is a look at the data analytics technologies, techniques and strategies that are heating up and the once-hot data analytics trends that are beginning to cool. From business analysts to data scientists, everyone who works with data is being impacted by the data analytics revolution. If your organization is looking to leverage data analytics for actionable intelligence, the following heat index of data analytics trends should be your guide.
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Big data, marketing and decision-making – what is it all about?

Last week I got asked if I know about big data and how you use it in digital marketing. Yes, of course, I do. I’ve been using big data for years when analysing numbers from websites and social media.

I’ve also been fortunate to speak at many conferences where some of the speakers are fully trained ‘big data ninjas’, and I’m lucky to know some of them personally.

Big data is complex information, and it feels as overwhelming as a huge waterfall. It’s only if you present big data in a meaningful way it helps you to make better decisions. Continue reading

Big Data’s Relationship with Business Intelligence and Data Warehousing – Big Data Roundup

1. Big Data’s Relationship with Business Intelligence and Data Warehousing

It seems like you can’t pick up a technical magazine without reading about how big data is changing the world—and the untold implications of this technology. But what the heck is big data? And didn’t we already solve this thing with business intelligence and data warehousing?

Big data, or BD, is the collection of transaction-level detail for analysis. The data is kept close to the transactional detail so it can be examined for hidden trends only seen when you analyze the individual transactions. The data can come from different sources but is analyzed in a common pool. This is most often a feed (or copy) of the transactions as they occur; they are streamed to the BD solution. Often, the value of the data is very time-dependent; the sooner the information is available, the more valuable it is.

There are four key terms used when talking about BD:
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2. Visualizing Big Data with augmented and virtual reality: challenges and research agenda

This paper provides a multi-disciplinary overview of the research issues and achievements in the field of Big Data and its visualization techniques and tools. The main aim is to summarize challenges in visualization methods for existing Big Data, as well as to offer novel solutions for issues related to the current state of Big Data Visualization. This paper provides a classification of existing data types, analytical methods, visualization techniques and tools, with a particular emphasis placed on surveying the evolution of visualization methodology over the past years. Based on the results, we reveal disadvantages of existing visualization methods. Despite the technological development of the modern world, human involvement (interaction), judgment and logical thinking are necessary while working with Big Data. Therefore, the role of human perceptional limitations involving large amounts of information is evaluated. Based on the results, a non-traditional approach is proposed: we discuss how the capabilities of Augmented Reality and Virtual Reality could be applied to the field of Big Data Visualization. We discuss the promising utility of Mixed Reality technology integration with applications in Big Data Visualization. Placing the most essential data in the central area of the human visual field in Mixed Reality would allow one to obtain the presented information in a short period of time without significant data losses due to human perceptual issues. Furthermore, we discuss the impacts of new technologies, such as Virtual Reality displays and Augmented Reality helmets on the Big Data visualization as well as to the classification of the main challenges of integrating the technology.
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3. A Successful Approach to the Big Data Adoption Journey

Randy Bean recently wrote in the Wall Street Journal, “Big Data represents a business adoption paradox: It promises speed, but successful business adoption takes time. When I advise executives or speak to business groups, I encourage organizations to view business transforming initiatives like Big Data as a journey. Success ultimately depends upon organizational alignment, process change, and people. Organizations need to develop a long-term plan and destination with many checkpoints along the way. True there are opportunities for “quick wins”– to ensure credibility, build organizational support, establish momentum, and secure funding—but for the most part, patience and persistence are essential.”
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4. Why your next big database decision may be a graph

NoSQL databases are clearly on the rise, but not all NoSQL is created equal.

After all, 451 Research recently discontinued its longstanding tracking of NoSQL database popularity, arguing that since “none of the top 10 look like changing places any time soon, and none of the players outside stand any chance of breaking into the top 10, the time has come to retire the NoSQL LinkedIn Skills Index.”
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Data analytics isn’t about Insights. Idexcel Big Data Roundup

1. How To Use Data To Outsmart Your Competitors

The pressure’s on to use data to outsmart your competitors. Here are six ways companies can use data to imagine and even re-imagine what’s possible.

“Business as usual” can be a risky business practice, especially when there’s cultural resistance to change. While some companies are embracing agile practices, there are a number of data-related barriers that keep companies from reaching their potential, most of which have to do with people, processes, and technology. Read more…

2. Ten Ways Big Data Is Revolutionizing Supply Chain Management

Bottom line: Big data is providing supplier networks with greater data accuracy, clarity, and insights, leading to more contextual intelligence shared across supply chains.

Forward-thinking manufacturers are orchestrating 80% or more of their supplier network activity outside their four walls, using big data and cloud-based technologies to get beyond the constraints of legacy Enterprise Resource Planning (ERP) and Supply Chain Management (SCM) systems. For manufacturers whose business models are based on rapid product lifecycles and speed, legacy ERP systems are a bottleneck. Designed for delivering order, shipment and transactional data, these systems aren’t capable of scaling to meet the challenges supply chains face today. Read more…

3. How Data Projects Drive Revenue Goals

The vast majority of organizations have either already implemented a big data project or plan to do so, according to a recent survey from CA Technologies. The report, titled “The State of Big Data Infrastructure: Benchmarking Global Big Data Users to Drive Future Performance,” indicates that a great deal of these projects are integrated throughout the entire organization. Companies are pursuing big data and analytics primarily to improve the consumer experience while adding to their customer base. However, there are formidable challenges, including a lack of trained staffing to make data projects succeed, as well as the inherent complications of such implementations. Read more…

4. Importance of Big Data Analytics for Business Growth

Until recent years companies have always evaded the question of using data analytics for business execution, leave alone big data. Most of the time it was due to cost of analysis that the organisations kept in mind while keeping away from data analytics. With everything going digital, data is pouring in from all kinds of sources imaginable. Organisations are getting inundated with terabytes and petabytes of data in different formats from sources like operational and transactional systems, customer service points, and mobile and web media. The problem of such huge data is storage and with no proper utilisation of the data, collecting and storing is a waste of resource. Earlier it had been difficult to process such data without relevant technology. Read more…

Big Data: The Engine Driving the Next Era of Computing

You are at a conference. Top business honchos are huddled together with their Excel sheets and paraphernalia. The speaker whips out his palmtop and mutters ‘big data’. There follows an impressive hush. Everyone plays along. You feel emboldened to ask, “Can you define it?” Another hush follows. The big daddys of business are momentarily at a loss. Perhaps they can only Google. You get it? Everyone knows, everyone accepts, big data is big, but no one really knows how, or why. At any rate, no one knows enough straight off the bat.

In the Beginning was Data. Then data defined the world. Now big data is now refining the data-driven world. God is in the last-mile detail. Example: In the number-crunching world of accountancy, intangibles are invading the balance sheet values. “Goodwill” is treated as an expense. It morphs into an asset only when it is acquired externally like say, through a market transaction. Data scientists now ask why can’t we classify Amazon’s vast data pool of its customers as an “asset”? Think of it as the latest straw in the wind of how big data is getting bigger.

Big data is getting bigger and bigger because data today is valued as an economic input as well as an output. The time for austerity is past. Now is the time for audacity. Ask how. Answer: Try crowd sourcing your data defining skills.

When you were not watching, big data was changing the way the technology enablers play the game in the next era of computing. Applications are doing a lot more for a lot less.

Big data isn’t about bits or even gigabytes. It’s about talent. Used wisely, it helps you to take decisions you trust. Naysayers of course see the half-full glass as if it is under threat of an overspill. They insinuate that big data leads to relationships that are unreal. But the reality we don’t know is what is behind all that big data. It is after all, a massy and classy potpourri: part math, part data, with some intuition thrown in. It’s ok if you can’t figure out the math in the big data, because it is all wired in the brain, and certainly not fiction or a fictitious figment of imagination.
When you were not watching, big data was changing the way the technology enablers play the game in the next era of computing. Applications are doing a lot more for a lot less. Just to F5 (we mean refresh…):
You and me can flaunt a dirt cheap $50 computer the size of your palm AND use the same search analysis software that is run by obscenely wealthy Google.

Every physical thing is getting connected, somewhere, at some time or the other, in some or the other ways. AT&T claims a staggering 20,000% growth on wireless traffic over the past 5 years. Cisco expects IP traffic to leap frog ahead and grow four-fold by 2016. And Morgan Stanley breezes through an entire gamut of portfolio analysis, sentiment analysis, predictive analysis, et al for all its large scale investments with the help of Hadoop, the top dog for analyzing complex data. Retail giant Amazon uses one million Hadoop clusters to support their affiliate network, risk management, machine learning, website updates and lots more stuff that works for us.

Data critics though are valiantly trying to hoist big data on its own petard by demanding proof of its efficacy. Proof? Why? Do we really need to prove that we have never ever had a greater, better analyzed, more pervasive, or expansively connected computing power and information at a cheaper price in the history of the world? Give the lovable data devil its due!