The Big Data Game-Changer in Sports

Sport to many means many things. To some it is a hobby, to some, a passion, to some, it is a religion and to some, life. No matter what your definition of sport is, it is something that has millions upon millions of followers across the world. Often the distinguishing factor between the nerds and studs, sports truly has evolved beyond the stereotypes into the 21st century. A part of human culture that continues to remain the last testament to our integrity, morality, and spirit, sport, in general, is trying to blend in with the technological jungle we now call home. From the action replay to ball tracking and trajectory prediction, various sports have incorporated a lot of technology. The latest technology to make its own mark on sports is big data. Big data from where we stand today is a technology that has potential to take sports to the next level.

Big Data on the Field

Given the level of technology available to various sports, there is a lot of information that can be gathered from the game which was not possible a couple of decades ago. Information such as the speed of the ball, the speed of the players’ various movements etc. are readily available for teams and players in real time. When big data is introduced into the equation, the whole concept manifests on a whole different plane. Through predictive analytics, teams can now get deeper insights into the game as well as their own abilities and that of their opposition. Information such as weaknesses in a football formation, weakness in bowler’s action, most optimal positions to counter a particular defense and so on. Big data is even used in choosing the right material, weight, size and shape for sports jerseys, shoes, bats, balls and so on.

Strategizing is where big data and related analytics processes are leveraged to the greatest extent today. The analytics capabilities that sports teams today possess will allow them to strategize every single step of a match to a degree which will provide them with predictions on the chances of their success. With the way this technology is evolving all this may very well be the tip of the iceberg.

Big Data as a Recruitment Tool

Every sport has these players who despite their talents and abilities don’t get noticed and even if they do, don’t get their big break till towards the end of their careers. Today this is an issue that is even more menacing given the number of players available. So, big data again comes to the rescue. Coaches, scouts, and selectors can now leverage big data and data analytics to get detailed information on various up and coming players, their stats, their strengths, and weaknesses, playing style and even possibly in the future, detailed analysis of the prospects of hiring them.

The Fan Experience

Big Data, as the name suggests comes into the context of large volumes of data. This technology in its rudiments has been leveraged quite a bit in the field of marketing. This is where big data comes into play with the fan experience. There are a lot of aspects where big data is influencing fan experience. In cohesion with technologies such as mobile, big data has the prospect for a great number of possibilities. As of now, big data to this end is only being implemented on a marketing level. And as with other verticals that have enjoyed the benefits of data analytics, sports too have enjoyed a huge increase in marketing prospects. Beyond these, there are several potential opportunities for fans to engage in their favorite sports beyond their usual spectator positions. Fantasy teams and such could leverage data analytics to offer a more comprehensive experience.

Overtime

There would be a lot of you that might argue that sports need to stick to tradition and that all this technology is making them lose their essence. Well, if you do, you are not alone on that. Applying data to such an extent has the risk of confining the sports to just strategies and winning, thereby amputating the celebration of the human spirit that most fans often associate with them. Today this is quite a reality and the over-commercialization of an already largely commercialized affair is something that needs to be taken note of. Big data is a tool that is most adept at enhancing this. The use of big data also opens up an avenue for manipulation and misuse and manipulation of data as well the insights thus gathered. For now, these are just hypothetical scenarios with far-reaching possibilities of becoming a real threat. The present concern on the matter yet again remains on the fact that sports are slowly beginning to lose their charm, class, and character they were once known for.

On a positive note though, there are certain aspects of the application of big data that have drawn in a new generation of sports fans who despite the downfalls, like to subscribe to this new era of sporting. The opportunities that have opened up therein have also been welcomed by the sporting community where players now have a chance of getting noticed better. The biggest change that big data has brought about in sports is the overnight proliferation into various regions and diverse demographics that we have been witnessing in recent years.

So, how the global sporting community will find a balance with the use of this new technology is something we’ll have to tune in, wait and watch.

How Big Data is Revolutionizing Healthcare

The healthcare industry is by no means a vertical that is to be taken lightly. Ask any medical practitioner and they will tell you that the nerve-wracking moments that they go through every day in the OR and ER are not for the faint-hearted (no pun intended). Beyond the life or death situations that the industry has become so well accustomed to, there are several processes that revolve around the pre and post-treatment phases. Given the ever-growing population today there is a dire need for a system that will help ease these processes and allow some room for medical support staff to work with. This is where Big Data comes in. In an age where data governs most aspects of human activity, there is a lot that big data can offer to the healthcare sector.

Big Data in Healthcare Research

The amount of data collected during healthcare treatments and procedures is a gold mine for researchers. Apart from the data collected in hospitals, there are several other means through which data can be gathered. The basic devices we use today such as the heartbeat monitor, the calorie counter, and so on, most of which are either available or integrated into our smartphones. Such data once analyzed could be transmitted to various medical institutions where professionals could arrive at diagnosis and treatments for various potential and incipient ailments. The vast amount of data from various sources allows medical professionals to perform a comparative analysis of various different symptoms displayed by each of them.This could in turn aid in the creation of a more comprehensive treatment. This also keeps doctors updated on whatever ailments they are dealing with and will not be left in the dark when they stumble upon a new symptom.

Healthcare Records

One of the most difficult tasks after the treatment process is maintaining patients’ records. Again the massive population today make things infinitely difficult for professionals that maintain records. Even countries with well-established healthcare systems face issues related to this more often than one would care to anticipate in such a field. Mistakes in records have ranged from financial irregularities to complications in treatments and medication given. To resolve this big data has proven to be quite an effective tool. However, this is a process that is easier said than done, as integrating Electronic Healthcare Records into any healthcare systems is quite a daunting task with various drawbacks of its own. The benefits of EHRs far outweigh the efforts many institutions have to go through to adopt them.

There are several efforts being made by many countries to make this a mandatory part of the global healthcare community. Doing so would allow healthcare to be delivered more smoothly. Furthermore, such a system would allow doctors to get better insights on patients who were previously receiving treatment in other institutions or countries.

Clinical Trials

Big data plays a huge role in modernizing many of the methods in which clinical trials are carried out and allow researchers to get the most out of the data acquired therein. Big Data is used to pick the most suitable candidates for the trials. The desired traits that researchers are looking for are sorted out using big data. Likewise, the effectiveness of medication and the areas that they are most likely to affect are also determined using big data. Big data has been used a great deal in finding a cure for the so-far unconquerable cancer.

Wearable Healthcare Devices

There is no deficiency of wearable devices today that are capable of doing a great many things that would ‘make our lives easier’. From blood pressure to heartbeat there is a device you can wear in some form or the other that can monitor each and every one of your bodily functions. These devices are capable to transmit data directly to doctors or the institutions they represent. With advancements in IoT and automation, in the near future, it is quite possible we could have these devices connect with other publicly available healthcare automatons.

Data Security

With cybersecurity being the menace which it is today, data vulnerability is an issue that plagues every industry and healthcare, in particular, could incur huge losses on all levels. It is hard to imagine the horrors that could be unleashed if personal medical and insurance records are hacked, not to mention payment details such as credit cards associated with them. Big data is the key here and quite often could play an instigating role in the data breaches rather than dissuade them. So ultimately when it comes to records big data is a technology that the healthcare industry must tread carefully with.

IBM Watson

The application of IBM Watson in the healthcare sector was quite a revolutionary step. The AI which uses big data to derive various solutions. IBM Watson uses natural language processing and machine learning to help healthcare institutions arrive at the most optimal treatment for the symptoms that patients may display. Watson has access to a comprehensive database of medical records pertaining to previous treatments, clinical trials, personal updates from physicians and researchers on various ailments and so on. While there is a certain margin of error with the diagnosis and hypothesis provided by Watson, it predicts the level of success in the treatment based on that data as well. This is quite a useful tool which in the hands of a seasoned physician could be leveraged to a successful degree.

Conclusion

Big Data today brings a lot of promise for the healthcare sector. In the years to come, there are various possibilities that could be brought to life through the use of big data. However, it potential for security threats and so on is something that we must all be cautious of before jumping fully into the technological development bandwagon. This, as a matter of fact, is a risk that most healthcare sector leaders are well aware of and are to a certain degree prepared to address with caution. Only time will tell how much this seemingly life-changing technology could come to influence our lives.

Big Data applications in Cyber Security

As businesses and business transactions are growing so are the threats of cyber crimes. There used to be a time when if someone wanted to steal from a bank, they had to go through an elaborate planning montage featuring eleven people which included a small Chinese gymnast.

But, today the game has changed quite a bit. Today the only bank robbery montage you have to go through is that of a socially awkward guy in a hoodie sitting in a coffee shop with his laptop. Whoever thought that would be a ‘cool’ thing to do? Well, the answer is pretty much anyone with a laptop, an internet connection and a basic knowledge of Kali Linux. The cavalier approach to this very much real threat which is seasoned with the occasional media outburst is quite understated but justified.

The reason is that there is only so much that can be done to stop cyber crimes. Which is also true for real crimes. So, the only thing that can be done is beef up security by using newer and more advanced systems. But, let us not forget the fact that for every new security feature that professionals come up with, there is a hacker somewhere trying to counter it.

One of the most interesting developments and reasons for debate on this topic is Big Data. As we have seen in recent years, Big Data has opened up several opportunities for all industries and it is being used in every which way conceivable. The argument with big data in cybersecurity is that it is capable of hurting security systems just as much as it is helping it. For something as sensitive as cybersecurity this is not a gamble we could afford to take. So, let us explore whether this argument holds water.

The Cyber Security Threats Today

Every year for the past decade we have had this incident where a massive breach of security has the whole world in an uproar. The past two years, in particular, have been filled with such incidents, from the alleged incident of Russians Hacking the U.S. elections to the Worldwide WannaCry malware. The more recent Cambridge Analytica scandal is a good example of how data breaches don’t necessarily have to involve hacking.

This sheds light on a whole new perspective on data security. With big data at the helm of their marketing strategies, many multinational corporations have access to a lot of information. So, let us move from the data we are trying to hide and have a look at data that we are giving away. With the ongoing Facebook scandal, we have learned one thing, that all the data that we are comfortable with sharing is as exploitable as the sensitive stuff we hide. Big Data here plays a huge role as it has become the go-to data analytics tool. On a global scale, this can become a very notorious issue.

The risks of having a corporation tapping into your published data through seemingly legal means have caused such an issue. Imagine the extent of damage that could be caused if such data fell into the hands of real threats like ISIL. Well, you don’t need to imagine that hard to perceive such a scenario as such hacks are a common thing today. Yet again, it is big data that comes to the forefront as a promoting tool for cyber crimes. Before we can go throwing blame around on corporations, we must remember that they have to maintain and secure several exabytes of data. Which means that they have to constantly analyze potential threats as well as breaches. This is by no means an easy task and the introduction of big data-based technologies in this equation only makes things infinitely worse.

How Big Data helps the cause of Cyber Security?

Risk management and intelligence that kicks into action when required are common outcomes of a big data analysis. It would always be good to have tools that can analyze and automate data so that it is available easily and the analyzed data is transferred to the right people at the right time is of the essence.

By doing this, analysts get to categorize threats without investing long hours. If the data comes in after a long delay, the response that comes in would be irrelevant to the attack that the company is under.

Big data comes in handy in the manner that it helps analysts to visualize cyber-attacks by considering the complexity of data from a vast network of data and simplifying the patterns that have been understood into visualizations.

Big data is an excellent method of detecting Trojan horses that come from employee devices. It does so by identifying anomalies in employee and contractor devices also. Where big data is impressively useful is when certain tangible steps are taken towards improving cybersecurity.

Big data allows you to automatically respond to threats noticed in the data that comes in and also enables you to trust the accuracy of the data. This is what is said to be the X-factor behind what makes big data security all the more proficient.

To sign off

In conclusion, it comes as no surprise that now companies are investing heavily in high-end and advanced infrastructure to enhance cybersecurity thereby detecting threats far efficiently and effectively. Some believe that big data will solve the varied issues of the cybernetics industry in a blink of an eye. The truth, however, is that as these attacks keep getting stronger and advanced, the security installed to avoid them are getting stronger too. Considering the many ways in which big data can be leveraged for cybercrime, it is important for corporations to use their resources to this end responsibly.

Applications of Big Data in E-commerce

There was a point in time when lack of data was an issue. Now, times have changed and it is the overabundance of data that seems to be complicating the matter all the more. Especially in the e-commerce sector, where metrics like GMV, CLV, cart abandonment rate, and AOV are tracked diligently by online retail companies to gauge its performance.

Using the traditional methods to organize, store and study data are no longer feasible. Fortunately, Big Data is going mainstream and offers a range of advantages that will aid the data and analytics needs of the e-commerce companies. Here’s how big data is changing the face of e-commerce:

1. Enhanced customer service experience

The larger your customer base, larger is the data generated and the more you’d need to invest in infrastructure for storing data. This being the traditional method has been leading to poor customer service and unsatisfied customers overall. This can be easily avoided by using big data.

Big data holds the potential to track not just customer information but also maintain their experience records. Companies can then backtrack each customers experience and see what it is that is going wrong repetitively and strive to improve it.

2. Secure payments online

While paying online, big data has a significant role to play. Big data makes paying over the net a more secure and faster process. Big data integrates various payment platforms into one centralized platform. It aids not only the customers but also keeps fraudulent activities at bay.

3. Mobile commerce

Smartphones have turned out to be an extended organ of humanity. An organ so essential that imagining anything over an hour without it would seem impossible. Big data is in favor of mobility simply because anything with an IP address and the ability to transfer and receive data is compatible with Big data. Google also is turning partial towards those sites that are mobile responsive, and we don’t see why it wouldn’t be doing so.

4. VR advancements

When you combine another buzzword in the IT field such as VR and merge it with big data, you get something that holds the potential to reconstruct the future. This merger not only changes the way a consumer would go about with his/her day but it would also revolutionize the way sellers sell things.

Virtual reality would materialize things right in front of your eyes without the dependence on anything remotely human and for that, big data would be of help. Are we looking at the insurgence of Artificial intelligence here as VR and big data score so high on compatibility charts? It is for us to wait for not too long and watch.

Parting thoughts

Seemingly the King Midas of the IT sector, big data is changing to gold whatever it comes in contact with. E-commerce companies are increasingly relying on big data to get that much needed competitive edge and enhance the overall customer service experience that they can deliver.

How Big Data can help HR with hiring decisions

The Human resources (HR) function is usually not the first thing that comes to mind when we think of big data. That’s because it is still a developing concept and a majority of the companies are still using traditional methods to perform HR related tasks.

Organizations should make the most of big data to find the right level of adaptability to offer work-life balance to its employees as well as to determine the right benefits and bonuses in order to encourage employee loyalty towards its company. It can also be used to increase the quality and usefulness of regular training programs, thus maximizing the use of their human capital.

Big data has proven itself fruitful in many businesses; be it sales, marketing or accounting. Today we explore how big data will impact the HR function:

1. Amplify the quality of new recruits

Hiring the wrong person for the job is probably the worst mistake an HR team can make. With the help of big data, recruiters can be more analytical and strategic when it comes to finding the ideal candidate for the job.

If they get access to online employee resume databases, employment records, social media profiles, tests and other profiles, it will help the recruiter find the best candidate with the highest potential by sorting this information and narrowing down the talent pool.

Take the case of Royal Dutch Shell; they made their employee play specially designed video games in order to analyze the best idea generators in their team. As a result, the team found it easier to recruit employees that had the 6 main qualities the company needed i.e. mind wandering, social intelligence, goal-orientation fluency, implicit learning, task-switching ability, and conscientiousness.

2. Promote better training and employee success rate

Training can be an expensive affair if the overall employee retention is unsuccessful. Big data allows businesses to measure the effectiveness of the training program so they can make better investments when it comes to training and development of their employees. If regular performance evaluations are conducted, the efficiency can be measured via big data and it will help the HR understand the effectiveness of their employee development programs.

Check out IBM’s strategy in this context. Traditionally an outgoing personality has been seen as a key trait, but IBM compared worker surveys and tests with manager assessments and found that the most important characteristic of sales success was actually emotional courage. Successful salespeople may or may not be outgoing, but they do need to be persistent, and not take no for an answer.

3. Prevent employee attrition

Making strategic workforce decisions without data to back them up is like guessing, and it’s an issue that has prevented the HR from making a bigger impact on business outcomes. Workforce analytics is the art and science of connecting data to discover and share insights about your workforce that will lead to better business decisions. In order to reduce employee turnover, HR needs to become more data-driven, looking past simple descriptive analytics and towards more exploratory analytics, predictive analytics.

To conclude

These are a few ways in which big data can help the HR perform at a much more efficient rate. It is an infant concept but once companies truly start implementing it in their recruitment processes, it will yield great results.

Blockchain Technology in Life Sciences

In the medical sector, new and innovative therapies keep improving life sciences. However, the same innovation challenges the supply chain. Life science is in a desperate need for authenticated and secure drugs that can become available whenever required.

Why is blockchain a solution?

Blockchain technology has gained an immense level of growth in terms of investment. Experts project that this growth can reach up to a $3 billion market level as we all reach 2025.

Blockchain technology is applicable in almost every step of the supply chain of life science. And at each of those steps, this technology offers a unique benefit.

1. Provenance

Verifying product’s origin point becomes immutable with this technology. Tamper-proof blocks allow the use of digital markers. This way, all the chances of counterfeit product get diminished. Plus, life science supply chain meets every regulatory requirement.

2. Record management

The industry faces a need for extensive documentation. Highly complex records are created and managed, which increases the costs and administrative activities. All these processes can become automatic with this technology. Businesses need smart contracts that include regulations and logic of processing. Hence, all business data can get verified without wasting any time or money.

3. Sensitive data security

Access control is also possible with this technology. Networks act according to incorporated rules and restrict access to critical medical information. Hence, authorities know who accesses certain information and when. This can become a great advantage for healthcare consumers as well, who want to keep their health records confidential.

4. Managing internal process

There is a huge list of internal processes that life science companies have to manage. Tracking products, transactions, and factory operations are a few major internal processes. All in all, companies have to concentrate on their products in different steps such as raw material collection, packaging as well as labeling.

Choosing this technology can integrate each and every process spread across systems. Companies can skip multiple reconciliations and track everything with the help of a single ledger. This ledger will be available to every authoritative body. So, no need to hassle.

5. Multi-party collaboration

Collaboration is the most valuable property of this technology. Hence, clinical trial officers, trial sponsors, and multiple regulators can access and share data at the same time. A secure and shareable network brings transparency to the supply chain. Hence, every considerable party receives trustworthy collaborative network to rely on.

With speed and trust in life science, this technology offers the ability to transform the industry for good. Complications can go away and sensitive data can become more secure.

Happier patients

Soon blockchain is going to enable companies to present more relevant data for patients. Accurate information, continuous product availability, and other features can improve patients’ satisfaction. However, it all comes down to the manageability of health care and drug companies.

Life science sector requires a technology boost to attain much-needed goals in terms of security, speed, and quality. Hopefully, companies will understand this requirement and move forward in this direction as soon as possible. Only time will tell!

Advantages of Apache Cassandra Database

As one of the better-known NoSQL database, Apache Cassandra is fast becoming a preferred database of enterprises and SME’s alike. Its robust performance in applications needing heavy write systems traversing massive volumes of data is what makes it stand apart from its contemporaries.

A typical Cassandra database consists of keyspace (similar to a schema in a relational DBMS), column families (consistent with a table in a relational DBMS), and rows/columns. It also utilizes a Cassandra Query Language (just like SQL) to retrieve records, carry out actions, and communicate with the Cassandra database.

Here are five reasons why Cassandra makes for a great database system

1. No single point of failure

Its masterless architecture makes Cassandra highly fault tolerant. Because of this, any downtime affecting a few nodes will not impact the overall performance of the system.  This enhanced fault tolerance level is a great draw for enterprises who wish to provide ‘always-on’ online services to their customers.

If we look beyond a single datacenter then Cassandra can be of great help. It allows seamless replication of the data center, thus facilitating a strong disaster recovery and backup/retrieval system within your organization.

2. Handling massive datasets made easy

Hulu, NetFlix, Instagram, and Apple, the list of enterprise users who benefit from Cassandra speak a lot about its capability to handle humongous volumes and variety of data. If your organization too faces a probability of data volume expanding exponentially and scaling up at a rapid rate then you need not look beyond Cassandra.

You can rely on Cassandra to continue delivering optimized performance without any impact of the huge rapid change in the data it is handling

3. Logging is simplified

In today’s homogenous environment, a typical company has to deal with multiple clients and servers (Android, web, iOS to name a few). In such an environment, logging and analyzing logs become huge challenges to deal with. Cassandra comes across as a viable solution to centralized logging.

This way, your development team need not spend a lot of time on logging and can instead focus on better product development.

4. Fast reads and superfast writes

Workloads like metrics collection and logging need extremely fast writes for optimal performance. This is where Cassandra scores heavily over its peers. It offers a scalable read-write performance. This means that if you know a single server’s write performance, you can accurately assess how many servers are needed in a particular cluster to meet the performance expectations.

5. Active community support

A lot of young minds are focused on expanding the possibilities around Cassandra. They are highly active and provide assistance in case of issues around managing or configuring complex database setups using Cassandra. The monitoring and troubleshooting systems around the software make it a truly high-performance open source NoSQL database system.

Thus, it is clear that Cassandra offers a host of benefits that can add tremendous business value to your tech offerings. It is time that you explore Apache Cassandra for your enterprise database management needs.

Myths about Cognitive Technology Debunked

Cognitive technology is one of the widely discussed concepts in the world of business. These discussions help businesses understand the importance and opportunities of the technology. However, there are several myths associated with cognitive technology that limit the knowledge of enterprises.

According to a survey on cognitive technology, businesses and enterprises feel confident about the future of AI and cognitive technologies. However, it would be important to clear the myths in order to successfully adapt cognitive technologies.

In this article, we are going to clear the air around the common misconceptions around congnitive technology.

1. Cognitive technology is all about automated functions

There is a myth among enterprises that cognitive technologies are only used to bring automation in the workforce. The technology is used to reduce the required human labor. However, this is not the whole truth.

AI and cognitive technologies are much more than automation solutions. The applications of these technologies can be used in multiple processes including insights. For instance, cognitive technologies can be used to create better customer service for the end users. The technology helps in understanding the customer data through insights and provide relevant and satisfactory services to the customers. So, the application is more about the intelligence, rather than just automation.

2. The financial outcomes are very basic with cognitive technologies

There are business owners who feel that AI technologies require a lot of investment and result in very basic financial outcomes. Also, they argue that the time lag between the investment and benefits is too long for general organizations.

However, the above-mentioned survey suggests that about 83% of companies that invested in cognitive technologies have obtained impressive or moderate benefits in terms of finances. So, the improved functions of the business get much better economic results with the application of cognitive technologies.

3. Cognitive technologies increase unemployment

One of the most argued topics in the application of cognitive technologies is the automation that brings unemployment. However, this is all wrong. In fact, cognitive technologies present great opportunities for the human employees to work side-by-side with the artificial intelligence.

The technology definitely enhances the productivity of employees, but it doesn’t reduce their importance in any manner. Plus, the arrival of cognitive technologies has created multiple new jobs for professionals, which is also a positive outcome in terms of the future of employment.

4. Cognitive technology is just a trend that will fade away

Many people suggest that AI and cognitive technologies are just a trend that is getting hyped by the media. But what they don’t know is that AI presents clear signs of acceptance and growth on a global scale. In fact, the AI market is expecting $59.8 billion revenue worldwide by the end of 2025. And that says a lot about cognitive technology’s future.

5. The application of cognitive technologies requires a complete transformation

This is another myth that stops companies and organizations from implementing AI in their business. A few company leaders think that cognitive technology application changes the functionality of the business drastically. However, it is not about the transformation, but the integration of cognitive technologies in business.

Final words:

The studies are presenting clear signals towards the success of the cognitive technology. It is the time that you understand it too. Debunking these myths will help businesses embrace this technology wholeheartedly and gain from it.

Big Data in Retail Industry

The amount of data involved is huge in the retail industry. This is the reason why industry leaders keep on looking for innovative methods to make their brands more successful.

“Big data” is the name for a collection of huge amount of data. However, it is also associated with the management, storage and use of data. With more than 6 billion mobile users all over the world, big data offers retailers the ability to understand consumer behaviour and market their brands like never before.

Here are the ways big data can help retail brands getting success in their market.

1. Getting precise insights about the retail sector

With the ever-changing market, retail brands need high-quality insights regarding the brand categories, customers, technologies and other aspects. And big data technologies prove to be a perfect choice in order to bring precision in that. The brand managers can get multiple combinations of reports that weren’t possible without big data technology.

2. Accelerating the analysis process

The ability to take action at the right moment defines the success rate in the retail sector. The brands need high-performing analytics to predict and take actions. This has become feasible with modern big data technologies.

The big data technology offers the ability to generate daily predictions and analyze the market in no time. As a result, there is no need to wait for days and lose the opportunities to succeed in the market.

3. Better coordination between brand and IT

Before big data, the retail brands always felt that there was a gap between their business and IT strategies. However, this problem can go away for good with big data implements. The IT support can design applicable strategies for retail brands including factors. The exploration, visualization, simplicity of understanding and other benefits make it easier for retail brands to adapt to big data IT solutions.

4. Fast decision-making that improves supply chain

A retail brand has to manage and handle multiple steps of the supply chain from the inventory to the shelf. These steps require immediate decision-making capacities. But that is only possible when the business leaders have enough data available regarding the channels and segments associated with their supply chain.

Knowing which area is beneficial in terms of brand popularity makes it easy to manage the supply chain for high ROI. And that is exactly what big data implementation offers for the business.

5. Understanding and fulfilling customer demands

The whole foundation of the retail industry lies in the demands of customers. Retail brands need to understand those demands in real-time in order to stock the right products and provide them to the customers. The big data presents the opportunity to get accurate data on customer demands, which helps in forecasting the potential sales. As a result, brands can stock the necessary quantity and save themselves from wastage and loss.

Big data technologies offer all those solutions that any retail brand requires in order to succeed in the market. So, it would be a smart strategy to find such solutions and get your brand visible and successful.

R or Python? Which is the better for Data Science

Data Science has gained a lot of popularity in recent years and the amount of people opting for a career in data science has increased significantly.  Data science is basically an amalgam of data interpretation, algorithm design, and use of technology to solve problems.

Data scientists are provided with data which they have to mine in order to gain invaluable insights, to solve complex problems, and to provide direction to stakeholders. A data scientist should be able to dive into the given data to effectively draw helpful analysis for business decision making. This is the chief KRA of this job, thus making it slightly complex when compared to others.

If you are looking to enter the field of data science, it is important that you equip yourself with the right technical skills so that you can add value in real life corporate scenarios and carve out a lucrative career path for yourself.

Once such question that requires considerable thought is ‘which is the best language for data science, R or Python?’

In this post, we have tried our best to answer the question for you…

R or Python?

R is the best-suited choice for data scientists over the past years because the functionality of R was designed keeping data science in mind. Also, it is compatible with languages like C++ and Java. R is used by Google and hence, it is considered a reliable data science language.

When it comes to Python, it is relatively new in the field of data science and has yet to make a lasting mark. However, Python provides many benefits which have led to data scientists to choose Python as their language of choice in data science.

The financial sector switched its data science language to Python after Bank of America started using Python, mainly because it is more versatile and easier to implement. However, some people still prefer R because of its long heritage.

How to Decide

There are a lot of factors which can help you decide which programming language is best suited for you. Data science requires immense programming language proficiency and hence it is important that you choose wisely.

1. Versatility

R is considered the best by many because of all the customized packages it offers especially for data science. Python, however, is easy to learn and has a more refined syntax. In case a person is just starting out as a data scientist, it is better to opt for python.

2. Data visualization support

Data visualization is of utmost importance in data science and it is at this stage that R proves to be a better alternative than Python. Options like ggplot2 and googleVis visualization tools simply expand the utility value of visualization. Python is not as versatile as R in this matter, but it still provides visualization tools like Plot.ly or Matplotlib.

3. Deep Learning support

Data scientists are required to know about deep learning in order to mine data and Python is the more superior language here. Python provides data scientists with various packages like Theano and TensorFlow, which makes it one of the best languages for deep learning. R can also use some of these packages but Python does have an upper hand here

To wrap up, we have provided all the important factors which can help you decide which programming language will suit you better as a data scientist. Do let us know which will be your final choice. We would love to hear from you.