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The big data revolution is transforming the business landscape – not least in the form of the benefits, it can deliver for payroll departments. The modern business landscape thrives on information – not least in payroll departments, where employees must handle a variety of specialised information to carry out the pay process each month. But data is only as useful as your business’ ability to exploit and handle it – which is why the big data revolution is such an interesting proposition for payroll.

The application of big data in business is growing. Recent research revealed that, in 2023, the adoption of big data reached 53% – a dramatic rise from 17% in 2024. Big data applications promise to transform payroll, adding efficiency and insight to the process, and helping employers achieve a greater degree of compliance – but, if you’re considering ways to integrate big data into your payroll deployment and want to avoid

common payroll mistakes

, it’s worth understanding the benefits before you make the leap.

Finding Talent

Payroll is a process which succeeds on the of its employees – but finding those recruits represents a formidable challenge for employers. One of the most interesting applications of big data is to help employers build a multi-skilled payroll workforce by scrutinising factors such as employee feedback, customer surveys, sales data and industry trends – and using that information to formulate hiring strategies. Similarly, big data could help recruiters identify the kinds of employee they need to be hiring – and where to find them.  

Record Keeping

Payroll involves the management and storage of varying amounts of data on a daily basis. One of the more practical applications of big data tools, including , is to provide a space for the storage and access of that data – which includes work hours, overtime, sickness and pension benefits, tax codes and so on. The ability to navigate that data efficiently during the pay process represents a valuable benefit for payroll administrators.  

Addressing Mistakes

Big data offers employers a new perspective on the finer details of their payroll process – and the various small errors and problems which might be holding it back. From compliance challenges to missed deadlines, the analytic capability of big data can reveal where those errors are occurring, and how often – helping to establish trends over time, and revealing ways employers can enhance payroll infrastructure. Similarly, analytics tools can be used to tweak payroll performance with increased precision – delivering productivity boosts over the long and short-term, during a weekly or monthly pay process.  

Career Development Options

Payroll employees are amongst the most specialised members of your workforce, so it’s vital they have the opportunity to develop and direct their careers – rather than risking stagnation and brain-drain. Big data analytics can help employers examine the details of the employee experience at both a local and industry-level. That insight can be used to help direct career development – from which training opportunities would work best for members of your workforce, to the salaries which might be offered to help retain talent.  

Global Solutions

Businesses with a global footprint have vastly increased payroll data concerns – but must contend with uneven and unpredictable compliance environments. Big data tools help businesses with international interests manage and harmonise the data they generate across their international territories – and use it to develop and implement a . Big data offers a way to contend with fluctuations in exchange rates, complicated compliance regulations, and even the administrative challenges of distance and time zones.  

Decision Making

While big data tools have plenty of practical, immediate applications, they can also contribute significantly to a business’ decision making strategy. Going beyond the imitations of the human perspective, Big data analysis can reveal trends and patterns which might have been otherwise impossible to predict. With this in mind, big data might be used to challenge conventional approaches to payroll administration, preparing a business for upcoming challenges or changes in legislation, and for making about its future – like whether to transition to monthly or weekly pay, or whether to outsource aspects of the payroll process to a service provider.  

Thinking Outside the Box

The modern business landscape thrives on information – not least in payroll departments, where employees must handle a variety of specialised information to carry out the pay process each month. But data is only as useful as your business’ ability to exploit and handle it – which is why the big data revolution is such an interesting proposition for payroll.Payroll is a process which succeeds on theof its employees – but finding those recruits represents a formidable challenge for employers. One of the most interesting applications of big data is to help employers build a multi-skilled payroll workforce by scrutinising factors such as employee feedback, customer surveys, sales data and industry trends – and using that information to formulate hiring strategies. Similarly, big data could help recruiters identify the kinds of employee they need to be hiring – and where to find them.Payroll involves the management and storage of varying amounts of data on a daily basis. One of the more practical applications of big data tools, including, is to provide a space for the storage and access of that data – which includes work hours, overtime, sickness and pension benefits, tax codes and so on. The ability to navigate that data efficiently during the pay process represents a valuable benefit for payroll chúng tôi data offers employers a new perspective on the finer details of their payroll process – and the various small errors and problems which might be holding it back. From compliance challenges to missed deadlines, the analytic capability of big data can reveal where those errors are occurring, and how often – helping to establish trends over time, and revealing ways employers can enhance payroll infrastructure. Similarly, analytics tools can be used to tweak payroll performance with increased precision – delivering productivity boosts over the long and short-term, during a weekly or monthly pay process.Payroll employees are amongst the most specialised members of your workforce, so it’s vital they have the opportunity to develop and direct their careers – rather than risking stagnation and brain-drain. Big data analytics can help employers examine the details of the employee experience at both a local and industry-level. That insight can be used to help direct career development – from which training opportunities would work best for members of your workforce, to the salaries which might be offered to help retain talent.Businesses with a global footprint have vastly increased payroll data concerns – but must contend with uneven and unpredictable compliance environments. Big data tools help businesses with international interests manage and harmonise the data they generate across their international territories – and use it to develop and implement a. Big data offers a way to contend with fluctuations in exchange rates, complicated compliance regulations, and even the administrative challenges of distance and time zones.While big data tools have plenty of practical, immediate applications, they can also contribute significantly to a business’ decision making strategy. Going beyond the imitations of the human perspective, Big data analysis can reveal trends and patterns which might have been otherwise impossible to predict. With this in mind, big data might be used to challenge conventional approaches to payroll administration, preparing a business for upcoming challenges or changes in legislation, and for makingabout its future – like whether to transition to monthly or weekly pay, or whether to outsource aspects of the payroll process to a service chúng tôi true power of big data lies in its potential to change the way businesses think about payroll and how it should be delivered by their organisation. The innovation inherent in big data technology continues to gather pace, meaning that employers can explore for themselves the ways in which they can use it to make positive changes in their organisations. As payroll software and digital tax tools are integrated further into the business landscape, the data generated by payroll departments will continue to expand and evolve – to continue to enjoy the benefits of big data, employers must learn to evolve with it.

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The Data Transfer Project’s Big

The Data Transfer Project addresses one pain point we all experience on our phones: moving our stuff around. While it’s certainly gotten easier over the years to share individual photos, songs, and files from one app to another, shifting large chunks of data or entire libraries and histories between services is often an exercise in futility, even with hundreds of gigabytes of cloud storage at our disposal.

But while the four founding members are certainly big enough to get the Data Transfer Project off the ground, it’s missing the support of the biggest player of all: Apple. And without the iPhone maker on board, it’s going to be a tougher sell than it should be.

Share and share alike

On the surface, the Data Transfer Project has a very simple goal that all providers and developers should support: portability, privacy, and interoperability. In the announcement, Google, Facebook, Twitter, and Microsoft served up this clear mission statement: Making it easier for individuals to choose among services facilitates competition, empowers individuals to try new services, and enables them to choose the offering that best suits their needs.

IDG

iPhone users should get the same Data Transfer experience as Android users.

The timing of the announcement isn’t accidental. While the group was officially formed last year, 2023 has been a troubling year for data and privacy, particularly with regard to three of the companies here. Facebook, Twitter, and Google have each taken very public lumps over the handling of user data. Most recently, the European Union implemented a stringent set of laws governing privacy rights and adding layers of transparency for users.

If nothing else, the Data Transfer Project is a public commitment to free users’ data from any one service and respect the right to move it between apps. In simple terms, your Facebook photos are just photos, so when the next big social thing comes along, you won’t need to rebuild your entire digital profile.

The benefit applies to non-social situations as well. As the group explains in its white paper: “A user doesn’t agree with the privacy policy of their music service. They want to stop using it immediately, but don’t want to lose the playlists they have created. Using this open-source software, they could use the export functionality of the original provider to save a copy of their playlists to the cloud. This enables them to import the playlists to a new provider, or multiple providers, once they decide on a new service.”

Opening the walled garden

The aim of the Data Transfer Project is something that simultaneously agrees and disagrees with Apple’s core philosophies. On the one hand, Apple promotes ease-of-use and interoperability among all of its products. The company is constantly working to break down barriers so our data can jump seamlessly from one device and app to the next.

Apple

If Apple is truly serious about privacy, it needs to sign on board with the Data Transfer Project.

But if Apple is truly committed to privacy—and not just Apple device privacy—it needs to take a stand here. While the lock-in inherent to Apple’s ecosystem is often derided, the fact of the matter is, a walled garden is a nice place to play. The devices all work well together, and they’re encrypted and secure and receive the latest security patches and updates. That’s why many people would be plenty happy to stay, even if Apple made it easier to leave by supporting the Data Transfer Project.

As it stands, the Data Transfer Project is an ambitious project that won’t see its full potential without the support of Apple. If the ease-of-use and privacy gains it delivers stops at the iPhone, the rest of the industry will be reluctant to join forces, even with the might of Google, Microsoft, and Facebook behind it. And Apple doesn’t need to tear down its walled garden to support it. It merely needs to put a key under the doormat.

5 Benefits Of Team Training In The Workplace

blog / Workforce Development 5 Benefits of Team Training in the Workplace

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Training your employees in new skillsets and new technologies is an excellent investment. But the benefits of team training, where you teach a working group together, extend even further for your business. As noted by Peter Senge of the MIT Sloan School of Management, who authored The Fifth Discipline, “Team learning is vital because teams, not individuals, are the fundamental learning unit in modern organizations.”

With talent shortages making it more challenging to hire new workers, upskilling your in-house teams simply makes sense. More than half (53%) of executives rank building skills within their existing workforce as the number one way to close skills gaps in their organizations, according to a recent survey by McKinsey & Company. 

The Benefits of Team Training

To supercharge the benefits of upskilling your workforce, train your employees in teams where they can collaborate in a learning environment. Here are five benefits of team training for your workforce.

1. Team training helps build employee relationships.

Your in-house team structure dovetails nicely with proven learning strategies that increase learning. Cohort-based learning (a model used in Emeritus’ online courses) places students in a group. They interact during classes, as opposed to each student watching a lecture and completing coursework alone. This type of education, allowing students to progress through the curriculum at a unified pace, became popular in the 1990s after it was realized students could motivate each other, increasing student retention and course completion rates.

Cohort-based learning is inherently hands-on, and a recent Harvard University study has shown that students gain more knowledge from active learning than from lectures. The collaborative aspects help students develop social capital and social networks, which can improve their understanding during class. It also impacts their future professional development by establishing strong social ties within professional networks. 

Fostering these social benefits within a team unit brings the benefits of cohort-based education directly into the workplace and impacts team dynamics.

2. Team training increases employee engagement.

Following the engaged learning in team training, team members can apply their knowledge directly to the work environment. Engaged employees are enthusiastic participants willing to invest their energy in the company’s success. Not only do engaged workers give their best in the workplace, but they are also less likely to leave the company, improving retention rates.

The statistics on the benefits of engaged teams are striking and measurable. A meta-analysis by Gallup found 36% of U.S. employees and 20% of the global workforce are engaged at work. According to Gallup, when compared to the least engaged teams, the most engaged teams had:

23% higher profits 

10% higher customer loyalty 

14-18% in productivity gains

81% less absenteeism

18-43% lower employee turnover

When you train a team together, you hit many of the features that improve engagement. These include opportunities for development and for employee voices to be heard. Though engagement and job satisfaction are not synonymous, engaged employees are usually much happier in their jobs. They find meaning and purpose in what they do and feel fulfilled by investing in their work products.

In addition, training a team offers a venue for professionals to interact differently with their colleagues. And it allows for improved workplace relationships, increasing social cohesion within the team.

3. Training a team improves collaboration.

Even if your team training focuses on a technical subject area (like data analytics or blockchain), your team members will practice applying soft skills such as critical thinking, teamwork, communication, problem-solving, and flexibility.  

Having employees go through the experience together, discuss topics with each other, and learn cognitive frameworks for evaluating and applying techniques and theories to their projects will expand their ability to work well together. Additionally, an instructor guiding team members in working together on problems offers new input on how the team can function efficiently. This improves the outcomes of the work your team produces.

4. It benefits productivity.

As noted above, engaged teams are more productive. In addition, teams learn and adapt more quickly than individuals, according to Deloitte. Basically, when all team members learn in an environment together, they can apply the skills they gain to the group’s tasks. Collectively learning new ideas and methods gives employees common language and understanding to help them envision more successful and worthwhile goals for their business unit.

Productivity improvements accelerate when groups learn together. The whole team can get on board with a new way of completing tasks quickly. Teams that collaborate well are more productive, as the many benefits of team training feed into productivity gains.

5. Training a team can improve company culture.

A healthy company culture embraces change, inquiry, learning, and discussion, and invests in its workforce through learning and development. Offering development opportunities to teams improves engagement, job satisfaction, and overall happiness, contributing to a positive workplace culture. 

There’s no better way to embed the idea of learning into your organization than to offer team training. A learning format where team members interact helps inject a learning culture into everyday processes. Group learning encourages flexible thinking, which can set a course for your company that’s both culturally and technologically resilient. Promoting training within an organization helps employees feel valued and derive meaning from their work. This fosters a culture where the company mission includes individual well-being.

How to Train a Team

for the Future

As Senge once said in The Fifth Discipline, “A learning organization is an organization that is continually expanding its capacity to create its future.” You can add skills to your workplace by training individual workers, but you will gain even more through team training. Individual employees will enjoy the long-term benefits of upskilling. But the immediate application of learning within a collaborative team will help your company realize gains more quickly. 

Engaged employees, learning together, can then apply 21st-century skills training to their planning and ideation, creating a future that will put your company in the best position to survive and thrive in an ever-changing marketplace.

By Julia Tell

You can schedule a meeting with Emeritus Enterprise to learn about employee training options for your workforce. We can help you deliver a curriculum that targets the hard and soft skills your employees need to thrive.

Boon From Big Data Or Loss Of Privacy?

Today’s post is going to be different.

There is no technical subject matter I am going to talk about. But the article is far more thought provoking than any of the article I have written till date.

[stextbox id = “section”] A real life incident:[/stextbox]

Let me start with a real life example to get your thinking process started:

About 6 months back, I bought a top end Android smartphone. After using it for a month or so, I accidently started Google Now on the phone. The interface looked very simple on first look (nothing more than a search bar and weather update). So I moved back from the application and started living my usual life.

I would have almost forgot this instance like multiple other applications which come with the phone and I don’t use. However, Google had something else in store. A week after I opened the application for first time, I got a notification on my home screen, suggesting that I am 15 minutes away from Home and the traffic on route is normal!

The notification took me by surprise. I never told my phone where my home is! Over the next few days, the application identified my Office, commute place, friends place, the websites I visit frequently. It now integrates my searches across devices. So if I search a restaurant on my laptop, my phone shows me the route to same restaurant!

The incident above is like a dream come true for a lot of analysts and a scary incident leading to loss of privacy to a lot of customers.

As an analyst and some one who specializes in predictive modeling, I am usually a proponent of big data and the changes it is bringing to our day to day life. However, I have to admit that Google took me by surprise and has made me think and reflect a lot more on how life is changing. It has ensued a debate between 2 sides of my personality.

[stextbox id = “section”]Two sides of debate:[/stextbox]

My first personality is that of a common man. I want my privacy, specially during some personal moments. These moments could be the time I spend with my family or when I am reading or may be talking to a friend. I don’t want interruptions or suggestions from any third party during this period. I want to relish the moment as it is. After going through the experience mentioned above and many more like that, I am not sure whether these moments will remain as pristine and unadulterated as I would want them. Would my reading experience be marred by suggestions about different things I might like? Would the phones pop up notifications about my friend when I am talking to them? or may be when I am talking about them to my wife? The possibilities are limitless!

The other side of my personality is a big proponent of technology and Analytics. I remain excited about how technology can be used to solve day to day problems. I come out with innovative ways of using data to create value (for customers as well as Organizations). I continuously think how behavioural modeling can help customers in breezing through day to day chores? How can I predict something before it actually happens.

[stextbox id = “section”]How do we resolve this?[/stextbox]

The second personality needs to be cognizant about the presence of first personality and take actions which are in sync with values of first personality. Here are some rules I have come out with, which every analyst needs to keep in mind while designing a product or working on his next big data project:

[stextbox id = “section”]1. Transparency:[/stextbox]

This is the biggest takeaway. The bare minimum an analyst needs to make sure is that the customer is aware about what data is being collected and how can this be used. This needs to come out clearly. This is similar to apps (on smartphones) asking permissions before installing them. If you are collecting data with out asking customer explicitly, you are headed for disaster.

So, instead of using data through a pre-selected tick box (buried somewhere is my phone settings), I would have appreciated if the app reminded me of the data it will use, when I started it for the first time.

[stextbox id = “section”]2. Develop a character of your Organization by keeping customer at the heart:[/stextbox]

Let me try and explain. Years before Google started collecting information about usage from Android phones, Microsoft started this for MS Office. They asked me whether I would want to share my usage patterns with Microsoft, which will help them improve user experience further. I almost always declined. When Google asks me same thing, I am more open to sharing information.

It might be a personal choice. However, the reality is that I am more open to sharing data with Google because I can relate to the benefits they have provided me by using this information. I have benefited by sharing some of this information with Google.

The message is that if you don’t provide the benefit of this information back to the consumer, they will stop sharing this information.

[stextbox id = “section”]3. Make change in subtle manner:[/stextbox]

Big changes in user interface or the way new product gets rolled out can take customer by surprise. You have to build in these changes in subtle manner. In a way such that the customer still feels as much at home as possible. I think Google does a nice job at it. Here are some best practices:

Provide an option to user to switch back to old proposition, if it is not working for him

Try and keep as much user interface unchanged as possible.

[stextbox id = “section”]4. Test and roll-out:[/stextbox]

Irrespective of how good an idea is, you should avoid making complete roll-outs without testing. There are multiple benefits from this:

You actually act based on how customer feels about the product

You can size the benefit / loss you have seen by moving to a new product.

I think until and unless Organizations and analysts adhere to these rules, it might only be a question of time before they face a bunch of disgruntled customers.

If you like what you just read & want to continue your analytics learning, subscribe to our emails or like our facebook page.

Related

What Is Big Data? Why Big Data Analytics Is Important?

What is Big Data? Why Big Data Analytics Is Important? Data is Indispensable. What is Big Data?

Is it a product?

Is it a set of tools?

Is it a data set that is used by big businesses only?

How big businesses deal with big data repositories?

What is the size of this data?

What is big data analytics?

What is the difference between big data and Hadoop?

These and several other questions come to mind when we look for the answer to what is big data? Ok, the last question might not be what you ask, but others are a possibility.

Hence, here we will define what is it, what is its purpose or value and why we use this large volume of data.

Big Data refers to a massive volume of both structured and unstructured data that overpowers businesses on a day to day basis. But it’s not the size of data that matters, what matters is how it is used and processed. It can be analyzed using big data analytics to make better strategic decisions for businesses to move.

According to Gartner:

Importance of Big Data

The best way to understand a thing is to know its history.

Data has been around for years; but the concept gained momentum in the early 2000s and since then businesses started to collect information, run big data analytics to uncover details for future use.  Thereby, giving organizations the ability to work quickly and stay agile.

This was the time when Doug Laney defined this data as the three Vs (volume, velocity, and variety):

Volume: is the amount of data moved from Gigabytes to terabytes and beyond.

Velocity: The speed of data processing is velocity.

Variety: data comes in different types from structured to unstructured. Structured data is usually numeric while unstructured – text, documents, email, video, audio, financial transactions, etc.

Where these three Vs made understanding big data easy, they even made clear that handling this large volume of data using the traditional framework won’t be easy.  This was the time when Hadoop came into existence and certain questions like:

What is Hadoop?

Is Hadoop another name of big data?

Is Hadoop different than big data?

All these came into existence.

So, let’s begin answering them.

Big Data and Hadoop

Let’s take restaurant analogy as an example to understand the relationship between big data and Hadoop

Tom recently opened a restaurant with a chef where he receives 2 orders per day he can easily handle these orders, just like RDBMS. But with time Tom thought of expanding the business and hence to engage more customers he started taking online orders. Because of this change the rate at which he was receiving orders increased and now instead of 2 he started receiving 10 orders per hour. This same thing happened with data. With the introduction of various sources like smartphones, social media, etc data growth became huge but due to a sudden change handling large orders/data isn’t easy. Hence a need for a different kind of strategy to cope up with this problem arise.

Likewise, to tackle the data problem huge datasets, multiple processing units were installed but this wasn’t effective either as the centralized storage unit became the bottleneck. This means if the centralized unit goes down the whole system gets compromised. Hence, there was a need to look for a better solution for both data and restaurant.

Tom came with an efficient solution, he divided the chefs into two hierarchies, i.e. junior and head chef and assigned each junior chef with a food shelf. Say for example the dish is pasta sauce. Now, according to Tom’s plan, one junior chef will prepare pasta and the other junior chef will prepare the sauce. Moving ahead they will hand over both pasta and sauce to the head chef, where the head chef will prepare the pasta sauce after combining both the ingredients, the final order will be delivered. This solution worked perfectly for Tom’s restaurant and for Big Data this is done by Hadoop.

Hadoop is an open-source software framework that is used to store and process data in a distributed manner on large clusters of commodity hardware. Hadoop stores the data in a distributed fashion with replications, to provide fault tolerance and give a final result without facing bottleneck problem. Now, you must have got an idea of how Hadoop solves the problem of Big Data i.e.

Storing huge amount of data.

Storing data in various formats: unstructured, semi-structured and structured.

The processing speed of data.

So does this mean both Big Data and Hadoop are same?

We cannot say that, as there are differences between both.

What is the difference between Big Data and Hadoop?

Big data is nothing more than a concept that represents a large amount of data whereas Apache Hadoop is used to handle this large amount of data.

It is complex with many meanings whereas Apache Hadoop is a program that achieves a set of goals and objectives.

This large volume of data is a collection of various records, with multiple formats while Apache Hadoop handles different formats of data.

Hadoop is a processing machine and big data is the raw material.

Now that we know what this data is, how Hadoop and big data work. It’s time to know how companies are benefiting from this data.

How Companies are Benefiting from Big Data?

A few examples to explain how this large data helps companies gain an extra edge:

Coca Cola and Big Data

Coca-Cola is a company that needs no introduction. For centuries now, this company has been a leader in consumer-packaged goods. All its products are distributed globally. One thing that makes Coca Cola win is data. But how?

Coca Cola and Big data:

Using the collected data and analyzing it via big data analytics Coca Cola is able to decide on the following factors:

Selection of right ingredient mix to produce juice products

Supply of products in restaurants, retail, etc

Social media campaign to understand buyer behavior, loyalty program

Creating digital service centers for procurement and HR process

Netflix and Big Data

To stay ahead of other video streaming services Netflix constantly analyses trends and makes sure people get what they look for on Netflix. They look for data in:

Most viewed programs

Trends, shows customers consume and wait for

Devices used by customers to watch its programs

What viewers like binge-watching, watching in parts, back to back or a complete series.

For many video streaming and entertainment companies, big data analytics is the key to retain subscribers, secure revenues, and understand the type of content viewers like based on geographical locations. This voluminous data not only gives Netflix this ability but even helps other video streaming services to understand what viewers want and how Netflix and others can deliver it.

Alongside there are companies that store following data that helps big data analytics to give accurate results like:

Tweets saved on Twitter’s servers

Information stored from tracking car rides by Google

Local and national election results

Treatments took and the name of the hospital

Types of the credit card used, and purchases made at different places

What, when people watch on Netflix, Amazon Prime, IPTV, etc and for how long

Hmm, so this is how companies know about our behavior and they design services for us.

What is Big Data Analytics?

The process of studying and examining large data sets to understand patterns and get insights is called big data analytics. It involves an algorithmic and mathematical process to derive meaningful correlation. The focus of data analytics is to derive conclusions that are based on what researchers know.

Importance of big data analytics

Ideally, big data handle predictions/forecasts of the vast data collected from various sources. This helps businesses make better decisions. Some of the fields where data is used are machine learning, artificial intelligence, robotics, healthcare, virtual reality, and various other sections. Hence, we need to keep data clutter-free and organized.

This provides organizations with a chance to change and grow. And this is why big data analytics is becoming popular and is of utmost importance. Based on its nature we can divide it into 4 different parts:

In addition to this, large data also play an important role in these following fields:

Identification of new opportunities

Data harnessing in organizations

Earning higher profits & efficient operations

Effective marketing

Better customer service

Now, that we know in what all fields data plays an important role. It’s time to understand how big data and its 4 different parts work.

Big Data Analytics and Data Sciences

Data Sciences, on the other hand, is an umbrella term that includes scientific methods to process data. Data Sciences combine multiple areas like mathematics, data cleansing, etc to prepare and align big data.

Due to the complexities involved data sciences is quite challenging but with the unprecedented growth of information generated globally concept of voluminous data is also evolving.  Hence the field of data sciences that involve big data is inseparable. Data encompasses, structured, unstructured information whereas data sciences is a more focused approach that involves specific scientific areas.

Businesses and Big Data Analytics

Due to the rise in demand use of tools to analyze data is increasing as they help organizations find new opportunities and gain new insights to run their business efficiently.

Real-time Benefits of Big Data Analytics

Data over the years has seen enormous growth due to which data usage has increased in industries ranging from:

Banking

Healthcare

Energy

Technology

Consumer

Manufacturing

All in all, Data analytics has become an essential part of companies today.

Job Opportunities and big data analytics

Data is almost everywhere hence there is an urgent need to collect and preserve whatever data is being generated. This is why big data analytics is in the frontiers of IT and had become crucial in improving businesses and making decisions. Professionals skilled in analyzing data have got an ocean of opportunities. As they are the ones who can bridge the gap between traditional and new business analytics techniques that help businesses grow.

Benefits of Big Data Analytics

Cost Reduction

Better Decision Making

New product and services

Fraud detection

Better sales insights

Understanding market conditions

Data Accuracy

Improved Pricing

How big data analytics work and its key technologies

Here are the biggest players:

Machine Learning: Machine learning, trains a machine to learn and analyze bigger, more complex data to deliver faster and accurate results. Using a machine learning subset of AI organizations can identify profitable opportunities – avoiding unknown risks.

Data management: With data constantly flowing in and out of the organization we need to know if it is of high quality and can be reliably analyzed. Once the data is reliable a master data management program is used to get the organization on the same page and analyze data.

Data mining: Data mining technology helps analyze hidden patterns of data so that it can be used in further analysis to get an answer for complex business questions. Using data mining algorithm businesses can make better decisions and can even pinpoint problem areas to increase revenue by cutting costs. Data mining is also known as data discovery and knowledge discovery.

In-memory analytics: This business intelligence (BI) methodology is used to solve complex business problems. By analyzing data from RAM computer’s system memory query response time can be shortened and faster business decisions can be made. This technology even eliminates the overhead of storing data aggregate tables or indexing data, resulting in faster response time. Not only this in-memory analytics even helps the organization to run iterative and interactive big data analytics.

Predictive analytics: Predictive analytics is the method of extracting information from existing data to determine and predict future outcomes and trends. techniques like data mining, modeling, machine learning, AI are used to analyze current data to make future predictions. Predictive analytics allows organizations to become proactive, foresee future, anticipate the outcome, etc. Moreover, it goes further and suggests actions to benefit from the prediction and also provide a decision to benefit its predictions and implications.

Text mining: Text mining also referred to as text data mining is the process of deriving high-quality information from unstructured text data. With text mining technology, you uncover insights you hadn’t noticed before. Text mining uses machine learning and is more practical for data scientists and other users to develop big data platforms and help analyze data to discover new topics.

Big data analytics challenges and ways they can be solved

A huge amount of data is produced every minute hence it is becoming a challenging job to store, manage, utilize and analyze it.  Even large businesses struggle with data management and storage to make a huge amount of data usage. This problem cannot be solved by simply storing data that is the reason organizations need to identify challenges and work towards resolving them:

Improper understanding and acceptance of big data

Meaningful insights via big data analytics

Data storage and quality

Security and privacy of data

Collection of meaningful data in real-time: Skill shortage

Data synching

Visual representation of data

Confusion in data management

Structuring large data

Information extraction from data

Organizational Benefits of Big Data

Big Data is not useful to organize data, but it even brings a multitude of benefits for the enterprises. The top five are:

Understand market trends: Using large data and  big data analytics, enterprises can easily, forecast market trends, predict customer preferences, evaluate product effectiveness, customer preferences, and gain foresight into customer behavior. These insights in return help understand purchasing patterns, buying patterns, preference and more. Such beforehand information helps in ding planning and managing things.

Understand customer needs:  Big Data analytics helps companies understand and plan better customer satisfaction. Thereby impacting the growth of a business. 24*7 support, complaint resolution, consistent feedback collection, etc.

Improving the company’s reputation: Big data helps deal with false rumors, provides better service customer needs and maintains company image. Using big data analytics tools, you can analyze both negative and positive emotions that help understand customer needs and expectations.

Promotes cost-saving measures: The initial costs of deploying Big Data is high, yet the returns and gainful insights more than you pay. Big Data can be used to store data more effectively.

Makes data available: Modern tools in Big Data can in actual-time presence required portions of data anytime in a structured and easily readable format.

Sectors where Big Data is used:

Retail & E-Commerce

Finance Services

Telecommunications

Conclusion

With this, we can conclude that there is no specific definition of what is big data but still we all will agree that a large voluminous amount of data is big data. Also, with time the importance of big data analytics is increasing as it helps enhance knowledge and come to a profitable conclusion.

If you are keen to benefit from big data, then using Hadoop will surely help. As it is a method that knows how to manage big data and make it comprehensible.

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About the author

Preeti Seth

From Big Data To Smart Data

Fight the Big Data Backlash and use Smart Data help you identify purchase intent

Big data is starting to experience some significant backlash. A ‘case in point’ comes from a recent popular article in VentureBeat: ‘Big data’ is dead. What’s next? The backlash is more to do with the buzz than the data but the reason relates to the difficulty of extracting meaningful insights from big data.

Born from the backlash comes another buzzword; smart data, a means of extracting these meaningful insights from big data.

Looking past the marketing hype, smart data is actually the metamorphosis of big data into something actionable. Here we look at recognizing purchase intent as an example of actionable data extraction.

Big data vs Smart data rundown Big data, strong signals, Smart Insights

The big opportunity for big data is how to extract a ‘strong signal’ from the noise. Collecting big data and mining it mercilessly is not the opportunity. The opportunity is leveraging ‘a strong signal’ data set and integrating it to label big data, thus making it immediately usable. This is where an information rich contextual data set can inform big data and turn it into smart data.

Let’s take a real example: Say you were trying to identify and target website visitors who intend to purchase. If you were to rely only on mining your web analytics data for this information you would have to sort through the entire data set looking for the behavioral traits of purchase intenders. This not only is difficult but could be wildly inaccurate. You would think that focusing on the shopping cart is all you would have to do to get a stronger signal of purchase intent, but there is more to the story. Data shows that for a typical e-commerce site only 44% of visitors that enter the cart actually have the intent to purchase while the remaining 56% represent all other intent types such as researchers.

By labeling your data set with a ‘strong signal’ such as visitors who are actually intending to purchase, you can segment and contextualize the web data illuminating the most important aspects of the data set.

Empowering your Big Data

Collecting visitor stated intent, or in other words the way someone describes their intention for visiting a website, provides a much stronger signal because it is the visitor who describes their intention.

iPerceptions research shows that a visitor who states that they intend to ‘purchase’ is 15 to 20 times more likely to do so than someone who describes their intent to ‘research’.  This powerful qualitative intent data paired with quantitative and descriptive data creates contextualized data sets, transforming your big data into smart data.

Putting it all together – Big and Smart Data

Big data is complex and vast but many of the benefits cannot be truly realized without adding contextual information. If these data sources are combined not only can you transform big data into smart data, but you can also provide enormous windfalls for consumers and companies alike improving the customer experience and the company’s ability to meet the needs of its customers. However having the right type of data is only half the story. To make personalization a reality and directly impact the customer experience, a real-time approach to leveraging this information must be taken so that the quickly eroding opportunities can be recognized and acted upon.

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