How to Stop Data Theft in the Healthcare Industry?

Cybercrime in the healthcare industry is growing rapidly. Nearly 90% of all healthcare organizations have experienced one or more data breach in the past two years. This industry has suffered more breaches than any other industry in the last ten years, losing an estimated total of $6.2 billion. They are failing largely because they haven’t been securing their applications. When it comes to online security, the healthcare industry has been stuck in their ways, but this needs to change. They need to adapt with the times and become unpredictable, or we could see some life-threatening information get into the wrong hands.

When it comes to data theft in the healthcare industry, there are three areas that these thieves look for: medical records, billing and insurance records, and payment information. The hackers go mainly for medical records and are looking for the best way to sell this information to people. They are leveraging the information they have over social media to get more people interested in what they are selling. Medical records are still a little harder to sell than financial records, but experts believe that very soon, these hackers will be able to sell the information much more easily. This will …
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As a globally renowned BI & Big Data Analytics company, we hold extensive experience in a multifaceted blend of the most modern technologies, frameworks and processes. With enriched quality assurance to provide the best of services, we @ BIZDataPro offer a range of Data warehousing, Big Data Consulting, BI Managed and Consulting services.  You can also read some of the case studies on Business intelligence solutions to have a feel of our expertise.

Applications of Predictive Analytics in various industries

They say that those who do not study history are doomed to repeat it. In no form of big data analysis is this phrase more relevant than in ‘predictive analytics’. In simple terms, predictive analytics is the systematic use of data, machine learning techniques and a host of statistical algorithms to identify patterns that forecast the likelihood of future outcomes based on huge chunks of historical data. It is all about keeping a keen eye on what has and is happening to determine the best possible assessment of what might happen in the near future.

Predictive analytics slightly differs from other forms of big data analytics in that it is the only form that gives futuristic forecasts. Others such as prescriptive analytics gives directions on what actions should be taken to remedy various corporate issues; diagnostic analytics determines what happened and shows us why while descriptive analytics tells us what is currently happening.

Why Predictive Analytics is Crucial in the Business World Today?

Where big money is concerned, strategical mistakes can cost companies millions of dollars in revenue and operational costs. To avoid this sort of loss, businesses need to invest in forecasting.

In that entire chain of occurrences, you can already see just how inefficient the system can be. Solving these issues is critical and that’s where learning data science comes in handy. Especially, analytics that can forecast and help in strategic decision-making.Without predictive analytics, how can you tell whether or not the product you come up with will be useful to the masses? Without professional data scientists, how would you know who is most likely to buy your product? Which marketing strategies are most likely to garner you the most market share?

Minimizing inefficiencies through predictive analytics

The truth is, the world has been running on an extremely inefficient system. It only takes you looking at the statistics to see just how true this is to date. Up to 90% of all start-up companies fail: marketing tactics include casting a wide net that more often than not does not yield favorable results (out of 80 cold calls made, you would be lucky to get 3 sales), and the list goes on.

Over the last few years, most companies have taken a look at these numbers and realized that something must change. Companies are taking advantage of Big Data analytics, which is nothing but a culmination of Business Intelligence and Predictive Analytics, to attain an edge over their competition.

Here are a few applications of predictive analytics in industries:

Optimization of marketing campaigns

The Marketing campaigns have now become more optimized and efficient. Long gone are the days of ‘spraying and praying’ all the while wasting valuable resources trying to capture an unsuitable market niche based on a “hunch”. Today, through specialized predictive analytics, companies across the board can formulate effective strategies to identify, attract and capture markets for their products and services. The dependency on “gut feeling” has reduced.

E-commerce websites like Amazon have been making use of predictive analytics to capture usage patterns and past search data of website visitors to recommend products. A quick look around their website, or for that matter any other e-commerce player, will make you realize how predictive analytics is working so well. Amazon offers choices based on your likes and incites you to buy those products. From insurance companies to real estate, and almost every retail company, predictive analytics is now very much part of every operation.

Fraud detection

One of the main reason as to why predictive analytics has come to the forefront of the business world now is because the digital penetration has increased incredibly. Through big data analysis, there are systems in place that can combine multiple analytics methods to detect fraudulent patterns that indicate criminal behavior.

Today, one of the major concerns in the world is cyber security. With everything going virtual, to protect itself and its clients, major industry players such as banks, hospitals, social media companies and even police stations have resorted to using predictive analytics to minimize network breaches that could expose valuable information. Through the use of analytical methods, companies can detect vulnerabilities within their systems as well as abnormalities that indicate fraud.

Big online financial providers like PayPal have long used predictive analytics to determine what kind of precautions they have to take to protect their clients against fraudulent users.

PayPal uses data such as your historical payment data, to the kind of device often used as well as your PayPal user profile and country of origin, all these go into building machine learning algorithms that detect potential signs of fraud with every transaction.

Law enforcement agencies such as police departments on the other hand, feed off a large pool of data to police and protect the general public. From past criminal records/databases, to incident reports, crime tips as well as citizen feedback and CI information, the police can keep an eye on known criminals as well as potential acts of crime.

Reduction of risk

This is probably one of the very first examples of predictive analytics in action that most of us interact with on a regular basis. Every adult knows why credit score matters.

From banking to real estate, insurance, and even telecommunication, to get any form of credit or service nowadays, you need to have good credit. Credit risk analysis is all about big data.

Computer systems take into account all your past financial dealings and history and use that data to determine whether or not you present a high lending risk. It predicts how you will behave should you be lent any money. Some systems also show whether or not you do pay back your debts despite being labelled as a high risk. It is all about historical data and how you manage any financial mishaps.

Apart from money lending, predictive analytics is put to use by big insurance companies such as Liberty Mutual to determine the policy holder’s life expectancy and thus premium values This is all based on survival models created to predict just how long you will most likely live based on your lifestyle choices and pre-existing conditions. That is why they ask you those entire medical and lifestyle based questions.

It improves operations

Today, most companies use predictive analysis to manage resources and forecast inventory. For examples, airlines and big travel industry providers such as Virgin Atlantic and ‘Amadeus’ use predictive analytics to set ticket prices based on the predicted volume of traveling customers.

Hotels use such systems to determine future occupancy rates to adjust accommodation prices. Similarly, most retailers use similar systems to determine what discounts can be given, when should those promotions be conducted and to figure out the expected ROI of the promotions, etc.

Conclusion

From Oil, Gas and Utilities to Retail and the Banking sector as well as manufacturing and health insurance, everyone is relying on various predictive analytics methods to improve how they run their businesses.

The amalgamation of Data science and Analytics has transcended almost every sector.  applications of Predictive analytics are not only limited in innovatively supercharging business processes but also make the system more data-dependent than based on the gut feeling of the top management. Irrespective of whichever industry you belong to, if you look around the existing processes, you’ll find how predictive analytics helps in better decision making and if isn’t, then it’s time to make use of it.

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As a globally renowned Business Analytics Consultant, we hold widespread knowledge in a comprehensive merge of the most contemporary technologies, frameworks and processes. Our skilled and experienced team of Tableau professionals have implemented a diversity of projects in Tableau BI. With enriched quality assurance to provide the best of services, we @ BizDataPro offer a range of Business Intelligence Services, Data warehousing, Big Data Consulting, BI Managed and Consulting services. Visit http://www.bizdatapro.com/ to get a feel of our enriched offerings, request for a free quote or arrange for a meeting with us. You can also read some of the case studies on Business intelligence solutions to have a feel of our expertise

Refining your ecommerce business with big data

For the last few years, it seems like the concept of Big Data is inescapable. Wherever you look – whether it’s Forbes, Tech Republic or even Fortune – everyone has to say something about Big Data. But when you take a closer look, and realize the potential of Big Data, it’s no longer hard to see why so many people are talking about it.

We’re creating more data than ever

Just look at it like this – according the latest data from the Internet Live Statistic (which are updated in real time) there are over 3.5 billion Internet users in the world. This means that the number of users has grown almost 60% in the last five years (back in 2011, there were “only” 2.2 billion users).

Naturally, all of these users create content on a daily basis, for instance, Tubular Insights estimates that every minute, users upload more than 500 hours of footage on YouTube. And you have to admit, that is quite a lot of information. And while Big Data is used in basically in every business sector and niche, in last couple of years, BDA (Big Data Analytics) have started to play a major role in the world of eCommerce.

We're creating more data than ever

Source: Internetlivestats.com

Structured vs Unstructured Data 

So, when it comes to eCommerce, most of the data falls under two categories – it’s either structured or unstructured. Structured data is basically regular data that includes names, addresses, ages and preferences of customers. Naturally, companies are able to collect this data without much effort. Unstructured data – for instance, Facebook likes, retweets and views – is much harder to collect.

However, since it’s so much harder to obtain, unstructured data is also much more valuable, because it can give you valuable customer insights. Walmart, for instance, collect around 2.5 petabytes (or roughly 2,500,000 gigabytes) of data from customer transactions every hour, according to Harvard Business Review. But this data is also very hard to analyze, especially for small business.

Benefits of Big Data

Of course, some volumes of data require strong computers and multiple servers to process but Business Analysis no longer available only to large corporations. Fortunately, SMBs today have enough resources to leverage Big Data and use it to improve their business processes.

  • Knowing how consumers are accessing your website

In 2014, mobile traffic surpassed desktop for the first time in history, and when you look at the latest version of KPCB’s Mobile Tech Report, users today spend even more time on mobile devices (51% of the time) compared to desktop (only 42% of the time). Therefore, you have to consider just how many people are using mobile devices like smartphones and tablets to access your website.

That means that you have to analyze bounce rates and conversion funnels by each device for some the most important conversion pages on your site. So if you have, let’s say some pages have loading problems you might not think it’s a big deal but according to KissMetrics, an eCommerce site that makes $100,000 every day can potentially lose $2.5 million per year because of a 1 second page delay. Big Data can help you find about these problems, and properly optimize your website for different devices.

internet usage(Engagement) growth solidsource: Smartinsights.com

Big Improvements in the Supply Chain

An average eCommerce company is usually dealing with a number of moving parts like vendors, logistics and delivery. But with the help of Big Data, you’ll be able to build more efficient systems and use advanced analytics to manage and improve your supply process. Some companies are even leveraging IoT (Internet of Things) to gather data and completely redefine their supply chain intelligence.

Conversely, you can even find a number of 3PL providers, such as Invenco, that are also using Big Data to improve their operations, process quality and their integration all across the supply chain. As a matter of fact, if you look at the latest 3PL study report, more than 86% of 3PL providers now think that effective use of data will become “a core potency” of their business in the next few years.

big improvements in the supply chainSource: Kissmetrics

Monitoring business better than ever before 

If you want to track the expenses of your company, your complete income and even Human Resources data down to the last detail, you definitely need a solid data processor like the Hadoop-based EMR. This will not only help your company improve its archiving, but it will also allow you to make more fact-based decisions that will help you successfully navigate your business in the future.

Again, let’s look at one of the more successful companies in the US – few years ago, UPS decided to install sensors in around 46,000 of their company’s vehicles, which allowed the company to track everything from their speed to their direction at any given moment. This allowed UPS to cut more than 85 million miles off, and save around 8.4 million gallons of fuel.

Conclusion

When it’s all said and done, if you want improve your company’s operations in the long run, Big Data is more than worth your time. A recent MGI study revealed that retailers that are using Big Data can potentially increase their operating margin by more than 60%. Simply put, Big Data is the future of eCommerce, and it would be smart to adopt it as soon as possible if you want to see success.

The post Refining your ecommerce business with big data appeared first on Big Data Made Simple – One source. Many perspectives..

As a globally renowned BI & Big Data Analytics company, we hold extensive experience in a multifaceted blend of the most modern technologies, frameworks and processes. With enriched quality assurance to provide the best of services, we @ BIZDataPro offer a range of Data warehousing, Big Data Consulting, BI Managed and Consulting services.  You can also read some of the case studies on Business intelligence solutions to have a feel of our expertise.

What makes green data centers the next big thing?

Consumers and businesses rely on a ton of data. Most of this can be attributed to the technology we use in our daily lives. After all, just about everyone carries a smartphone on their person. Then there are our work and home computers, tablets, smart TVs, streaming devices and more.

As of 2016, there are 3.5 billion internet users worldwide, which is an increase from 2.21 billion in 2015. In just a year, the number of internet users increased by nearly one and a half billion, and all of them are consuming data.

The rise in data usage has helped to highlight the importance of data centers. Platforms like Netflix, Amazon or even Facebook, wouldn’t be possible without the data centers that drive them. But the same is true of the business world.

Businesses need data centers to serve their customers and employees. These centers power internal networks, websites, point of sale systems and so much more.

Simply put, without data centers, we just wouldn’t be able to enjoy the technology we have today. However, in order to run, data centers must use a lot of power. In fact, the amount of energy data centers use increases on a daily basis because more and more people need to access them.

An Emerson study found that the average cost of running a data center increased by 38% between 2010 and 2016. That percentage only continues to climb.

This means even though data centers are necessary, they have extremely high operating costs in terms of money and energy usage.

The solution? Going Green

So, we’ve established data centers are expensive and use up a lot of energy. This is also bad for the environment, especially when there are so many in operation. Using more energy means burning more fossil fuels to generate it, and that means more emissions and fewer resources.

The solution — especially if we want to conserve energy and save money — is to go green.

A green data center is one that has been designed to minimize its energy footprint and environmental impact. Just like with any “green” technology, this means using environmentally-friendly materials and equipment in addition to decreasing traditional energy consumption.

Plus, a number of “green” friendly policies may be implemented. For instance, waste may be recycled or reused as applicable. Servers and hardware may be swapped out for more energy-efficient equipment. Hybrid or electric vehicles may be adopted to get around a campus. Operating hours may be minimized or altered to adjust for energy savings. The list goes on.

The Many Benefits of a Green Data Center

If a green data center is able to achieve 53% annual energy savings — which means implementing a solar system capable of generating that much power — they can save anywhere from $125,000 to $170,000 annually in energy bills.

To be honest, though, cost savings are expected when deploying a solar energy system. The most important benefit, at least for data centers, is that green technology allows them to be more sustainable and reliable.

When a data center experiences down time, whatever may be the cause, they hemorrhage money. Since 2010, the costs of experiencing downtime have increased by 81% to an average of $2,409,991 in 2015. Couple that with the fact that energy costs are rising, and you have a recipe for disaster.

Location also plays an important role in efficiency and sustainability. You cannot just pick up an existing data center and move it, of course, but it’s still worth considering — if your data center is already near water or a renewable energy source, you’re in great standing.

But the further away these resources are, the higher the operating costs are going to be, and the lower the sustainability rating is. Data centers in a remote location, for instance, are more susceptible to power issues, especially when they rely on a traditional power source.

Alternatively, a green data center in a remote location is completely viable. It has its own reliable power source and is more energy efficient. Its operating costs are much lower, too.

Green data centers also tend to be healthier and more comfortable for employees to work in as well.

How to Prepare for a Transition to Greener Facilities?

Going green is more of a process than a single action. You’ll need to implement several new policies and procedures to keep a low energy footprint. Not to mention, the current hardware — including servers and existing data — will need to be phased out for more efficient equipment.

In other words, it’s not something that just happens overnight, but that doesn’t mean we can’t prepare beforehand. There are several things we can do that will make the transition much smoother:

  • Lower the energy footprint of your properties and buildings
  • Install sustainable landscaping on the property, such as trees that require less water to thrive
  • Opt for low-emission construction materials when possible
  • Implement a strict recycling policy, and try to create more environmentally-friendly waste
  • Use electric generators or equip backup generators with the proper emissions systems
  • Swap out gas vehicles with hybrid and electric ones to move around a property
  • Deploy more sustainable and energy-efficient air cooling and conditioning systems
  • Install low-power server technology

These are just a few examples of things we can do to prepare for a “green” transition. As long as we activate them when we have the opportunity, we’ll be well on our way.

The post What makes green data centers the next big thing? appeared first on Big Data Made Simple – One source. Many perspectives..

As a globally renowned BI & Big Data Analytics company, we hold extensive experience in a multifaceted blend of the most modern technologies, frameworks and processes. With enriched quality assurance to provide the best of services, we @ BIZDataPro offer a range of Data warehousing, Big Data Consulting, BI Managed and Consulting services.  You can also read some of the case studies on Business intelligence solutions to have a feel of our expertise.

Six challenges of big data project management

Project management always comes with a set of various challenges. In the past couple of years, as businesses have significantly grown, project management has become even more complicated. On the other side of things, a variety of software solutions were created for easier project tracking and data management.

One of the dominating technologies that are currently very popular is big data. However, with new technologies, new challenges have emerged as well. The following are some of the challenges that big data project managers have to deal with.

Ensuring investments and getting the green light

Before you start of every single project, it is necessary to ensure that there is a good enough budget. Investors and board members are the first ones who are involved in the whole process. Only a couple of years ago, these decisions were made based on the managers’ presentations and uncertain predictions. Luckily, with easy project management software, it is possible to provide exact estimates and reports for clients, investors and other important authoritative figures.

To ensure budgeting, every project manager has to have the right data and predictions about the ROI of the whole project.

Project overview

Big corporations are continually working on several projects simultaneously. Ensuring that several projects are worked on within the same time frame is quite a challenging experience. Fortunately, a project management tool provides great insight into the progress of every single project and whether the team members are working on the right tasks that are pushing projects forward.

This challenge is easily bridged with a project management tool such as ProProfs Project, which gives a perfect overview of all of the projects and their progress stages. This allows for better workflow within a company where deadlines for every single task are successfully met.

Task management

Overviewing multiple projects is a complicated task, but deciding who is going to be responsible for every single task is something much more challenging. Projects often require the completion of several tasks within the same time frame, so that there is always constant work flow without pauses.

For example, a majority of projects are split into several stages, where one cannot begin before a certain set of tasks is completed. When you add a deadline to the whole equation, task management becomes a very big challenge that has to be approached with an incredible amount of planning.

This is another situation where project management software tools are priceless. With great ease, project managers are capable of easily distributing tasks and following how each of them is progressing. If there is any delay, reassigning tasks to different people, or adding more people to a particular task is only a couple of clicks away, and people are immediately notified about the changes and what they should do next.

With the help of easy project management software, those responsible for project management can make this challenging task an easy assignment which can be dealt with in a few seconds.

Documentation and instructions

Ever since so many companies have become enormous international corporations, the amount of documentation involved in the whole workflow has drastically increased. Big data makes maintaining this documentation much easier, as everything is shared online. However, the challenge remains to redistribute and link the right resources to people who are involved with specific projects and tasks.

A project management software tool makes this whole process much less daunting, as it is easy to share  documentation with people working on a project. This involves project plans, client’s conditions, rules of the company, the whole flow of the project and details regarding all the small tasks.

Managing all of this information is something that was incredibly hard to accomplish only a couple of years ago, but now that tools such as ProProfs are present, handling documentation in Big Data project management is a much easier.

Saving Time

One of the most important things in the business world is time. The modern market is shifting at a fast pace nowadays, and being late in the business world race is something that can in some cases cost companies millions of dollars.

This is actually the biggest problem that project managers face on every single project. The deadline is always looming in the background and making sure that everything is completed in time is a very serious responsibility.

This is another area where project management tools are very useful. Time tracking is one of the very important features that these software solutions offer. It is possible for a project manager to exactly track how much time was spent on each individual task. On the other hand, saving time is also possible through the creation of task dependencies, where each task can be created based on its importance.

Combining all of these features supported by the ProProfs platforms, a project manager can be enabled to easily save a lot of time and finish projects by the set deadline.

Picking the right people for the job

This is another challenge that every project manager has to deal with. Working with smaller teams of people, it is easy for the manager to memorize the strengths of each team member. However, when teams count a much higher number of people, it is very challenging to keep track of which types of tasks are suitable for specific team members. Assigning the wrong type of task to someone can delay the project, and this makes it very important to pick the right person for every single task.

This is another part of the task management process where project management tools are of great use. Tracking the performance of every employee is made incredibly easy. A team manager can add personal notes and experiences regarding previous tasks, as well as evaluate the behavior of each individual.

Collecting this data is essential for future projects. The challenge of assigning the right task to a particular person becomes much easier and it makes the whole workflow much easier, more productive and more time efficient.

These are the challenges that big data project managers have to face time and time again. Including a software tool to help with these challenges is of key importance for companies who are looking to finish bigger projects without any complications, errors and interruptions.

The post Six challenges of big data project management appeared first on Big Data Made Simple – One source. Many perspectives..

As a globally renowned BI & Big Data Analytics company, we hold extensive experience in a multifaceted blend of the most modern technologies, frameworks and processes. With enriched quality assurance to provide the best of services, we @ BIZDataPro offer a range of Data warehousing, Big Data Consulting, BI Managed and Consulting services.  You can also read some of the case studies on Business intelligence solutions to have a feel of our expertise.

Discover how big data is implemented in business today!

Big data is a collection of very large sets of data that stream into a business every day. These sets can be structured or unstructured. Big data can be analyzed to reveal trends, relations or patterns in the behavior of a company’s customers. The analysis is conducted with the assistance of computer systems.

Big data has a number of characteristics. They include:

Large volumes: The amount of big data that arrives in a business is tremendous. This is because it comes from many sources all at the same time. Examples of these sources are transactions, social media sites, data from one machine to another and the information provided by sensors installed in the organization’s infrastructure. Due to the size of big data, special databases have been created to store it.

  • High velocity: Big data streams into a business at astonishing rates. Due to the high entry speed, special technology is needed to measure it in real-time. Examples of these are RFID tags, smart meters and high-response sensors.
  • Wide variety: This type of data arrives in a business in all types of formats. Some of it is structured while some is not. An example of structured data is numerical information while unstructured data is documented information. Other formats include video footage, audio files, email transcripts, stock data and financial information.
  • Variable nature: The flow of big data into a business normally changes according to conditions in the sources. Sometimes it can be low and at other times it can peak. Events such as viral content in social media can trigger massive peaks of big data. This can be hard to manage, especially when it is unstructured.
  • Highly complex: Thanks to its multiple sources, big data is highly complex. It is difficult to match, link and cleanse before being transformed for various systems. Substantial computing power is required to create relationships in big data, organize it into hierarchies and link data sets. Without the proper technologies in place, big data can become uncontrollable.
  • Databases are used to store big data. They are collections of data that is properly arranged such that it is easy to access, maintain and update. Special databases are required for big data.

Traditionally, we used relational database software to store data. They were built for Structured Query Language (SQL). Examples of these are Microsoft Access, Oracle RAC and MySQL database software. When big data entered the business scene, they were no longer powerful enough to handle the demands of this type of data. Therefore, computer scientists in conjunction with Remote DBA Experts created the NoSQL database software.

What is it?

NoSQL database software supports dynamic schemas. They are flexible, scalable and customizable too. They use four main methods to store big data. These include:

  1. Document storage
  2. Key-value storage
  3. Graph databases
  4. Column family storage

Examples of NoQSL database software include MongoDB, Couchbase Server, MarkLogic Server, RavenDB, Apache Jena and HadoopNoSQL database software.

It is important to note that you cannot simply install a NoSQL database to replace your relational one and store traditional data in it. NoSQL database software is made specifically for big data. This is because it does not have complete compliance with ACID (Atomicity, Consistency, Isolation and Durability). This compliance normally guarantees the integrity of transactions and consistency of data. Due to the nature of big data, ACID compliance was relaxed. This maximizes the ability of NoSQL database software to collect and manage big data.

Why is NoSQL database software used for big data?

This type of database software does not utilize the usual elements that we are used to seeing in relational databases. There are no tables, columns or rows. Moreover, you do not need a schema to design and create a NoSQL database. This type of database software is designed in this way so that it can provide you with rapid access to real-time data. This sort of access empowers you to run real-time programs for your business processes. An example of this is in the stock market. NoSQL database software organizes data using new formats that were not utilized traditionally. The lack of a schema allows you to interact directly with massive amounts of data, saving you time and money.

Impact of big data in business

It has rejuvenated traditional industries

This type of data has transformed various aspects of traditional businesses. By injecting the power of information, it has refreshed them. Big data affects various departments of a business. Examples are the customer service and supply chain departments.

Quite a number of traditional enterprises have gained substantial benefit from implementing big data. An example is the Rolls Royce Corporation.

Well known for their automobiles, Rolls Royce also manufactures aircraft engines. Traditionally, these engines needed to be inspected in hangars. Today, the engines have hundreds of sensors that monitor their performance in real time. The sensors send engine data to Rolls Royce through big data infrastructure. In this way, the company has leveraged big data in its operations and increased revenue by selling engines and engine-monitoring services in one package. These packages account for over 70% of the company’s annual revenues.

It has created a brand new industry

Traditionally, data was collected simply for reference. Managers and accountants referred to it for the purpose of justifying investment or knowing the progress of the company. Today, big data can be collected for the purpose of profit. Whoever is able to collect and cleanse as much big data as possible can sell it to other companies for a fortune. Stakeholders in the IT industry have discovered this and many are establishing startups whose primary objective is the collection and sale of big data.

Conclusion

Big data is here to stay. By investing in the database software indicated above, companies can reap the benefits of using big data in their business processes. It has even created an industry that is attractive and lucrative.

The post Discover how big data is implemented in business today! appeared first on Big Data Made Simple – One source. Many perspectives..

As a globally renowned BI & Big Data Analytics company, we hold extensive experience in a multifaceted blend of the most modern technologies, frameworks and processes. With enriched quality assurance to provide the best of services, we @ BIZDataPro offer a range of Data warehousing, Big Data Consulting, BI Managed and Consulting services.  You can also read some of the case studies on Business intelligence solutions to have a feel of our expertise.

Can big data analysis help to find the bad guys?

Here is an interesting article on Big data “Can Big data catch the bad guys?” shared as it is..

The following is a flight of fantasy, but in the wake of recent events, I believe that we would be foolish not to develop every possible technology to safeguard the lives of our children and their children.

There are many very reasonable arguments about the potential invasiveness of Big Data and the implications that it may have for our privacy. It is already a simple fact that we have no idea who is in possession of what data about us – the Edward Snowden revelations alleged government snooping on an industrial scale, but is this the price of our future security? A price that is worth paying?

After a few more tragic events like the ones in Paris, I think that the debate might finally come into the mainstream. Are our civil liberties worth surrendering (to a certain extent) in order to ensure that potential terrorists are foiled?

I will leave that question to one side as it will create a huge discussion (and rightly so). I would, however, like to consider a world in which Big Data can combat terrorism…. It is not as far-fetched as it sounds.

I am not an expert on extremism, but the path is generally a well-trodden one. Detailed analysis and risk assessments could be made not only on a retrospective basis but even on a “live” basis – if subject A goes to place B, it is highly likely that he is going to do action C.

Law enforcement agencies can easily share their data across borders – now a subject living in Belgium can pop across the border to Paris and cause mayhem. If more data were readily shared in real time, we would have a greater chance of being able to prevent these events. Who knows, this may already be happening to a certain extent – we will never know about the attempts that have been foiled.

If the key to Big Data, in this case, is about analysing behaviours, “Big Brother” surveillance methods must have some place in our future society. There are many arguments about how we should integrate better in our societies, etc, and they are very valid, but if it is so easy for one individual to procure a suicide belt and cause untold carnage, then maybe we do need to collect more data first in order for it to be analysed and the risks assessed? As always, it is a balance between individual liberties and the security of the wider society. I believe that the balance of opinion will, unfortunately, swing towards the latter at some point.

Data Scientists could be on the front line of protecting our world in the decades to come. We need to give them the tools to do their job, ensure that they have the relevant data to do their job, and accept that without them we are pretty much helpless in the face of an ever more disparate threat.

The C.I.A. isn’t called the Central Intelligence Agency for nothing. Intelligence is what will ensure that wars are avoided in the future. Big Data will be at the very heart of this intelligence, and, in actual fact, it already is.

Originally appeared on LinkedIn.

The post Can big data catch the bad guys? appeared first on Big Data Made Simple – One source. Many perspectives..

As a globally renowned BI & Big Data Analytics company, we hold extensive experience in a multifaceted blend of the most modern technologies, frameworks and processes. With enriched quality assurance to provide the best of services, You can also read some of the case studies on Business intelligence solutions to have a feel of our expertise.