4 Things to Consider When it Comes to Big Data
What is Big Data? Well, the basic principle behind the phrase is that everything we do leaves some sort of digital footprint which we can use to analyze. This could be data that is structured or unstructured, that can inundate a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what an organization does with the data that really matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
Why Big Data is Important
According to Eric Schmidt of Google, we have used five exabytes of data from the dawn of civilization to 2003. Now we produce five exabytes, every two days. And that pace is accelerating! Most companies collect a very large amount of data every day. The term ‘Big Data’ refers to how this information is analyzed to reveal patterns and trends. It provides companies with real-time insights into their operations and allows them to react to changing market conditions quickly. Whether it be sales trends, employee trends, or market trends. Big Data helps businesses understand their customers, employees and everything in between. This helps them to make their business more targeted and efficient.
There are 4 things to consider when it comes to Big Data: Volume, Velocity, Variety and Veracity.
This is obviously the main characteristic that makes Big Data “big”. It relates to the sheer volume of data that is being produced. The total amount of data is growing exponentially every year so this is probably the most important part of Big Data.
As more and more information is digitized, you will start seeing more untraditional or unstructured data sets needing to be analyzed such as a picture, a social media post, a voice, etc. Structured data is easier to analyze as it follows a set of rules to frame a concept or idea. Structured data, however, has a relational database and is seamless and readily searchable by simple, straightforward search engine algorithms or other search operations. Think of a bank statement, time, date, amount, and basically anything with a label.
Veracity refers to the trustworthiness and accuracy of the data. Is the data being stored meaningful to the problem being analyzed? When scoping out a big data strategy, data veracity becomes ones of the most important things to consider as you help to keep the data clean and implement processes to keep “dirty data” from accumulating in your systems.
Velocity is the frequency of incoming data that needs to be processed. Think about how many text messages, social media posts, or credit card swipes are being processed every minute of every day, and you’ll have a good appreciation of velocity. Streaming applications like Microsoft Azure Event Hubs or Amazon Web Services Kinesis are an example of an applications that handles data velocity.
Dealing with this explosion in data can be difficult, but machine learning is the technology that ensures Big Data delivers on its promise. By using artificial intelligence, machine learning can find new patterns and adapt to new situations faster and more accurately than a human-designed analytical algorithm can.
Machine learning is set to grow massively over the next few years. ReadyNetworks is proud to have teamed up with Powerup Cloud. Not only are they our partner when it comes to everything cloud, but they are the creators of IRA an enterprise-based chat bot. In learning more about the technology behind IRA, I’m seeing the massive amounts of data and machine learning that goes into creating a robust chat bot system for companies. The beauty about using AI for customer service, is that it is constantly learning new things, much like a child. All of this, of course, requires big data.
Big Data Concerns
Privacy and data security are the two biggest concerns that businesses face. Ensuring your business is conforming to the latest data protection legislation and making sure your data is encrypted and remains within your organization are also things to consider.
-Big data skills gap
Many organizations are experiencing a big data skills gap. Not enough people who are data experts and data scientists are on staff, making it much more of a challenge to address security gaps. While it has become difficult to find the right people with the appropriate skills to handle the work, we are seeing a shift with more people filling these positions as this need grows.
-Lack of designed security
One of the main draws of using Big Data is that it gives organizations capabilities that they didn’t have access to before. Unfortunately, Big Data and many other platforms that use it were not designed to address security concerns. This means that encryption, compliance, risk management, policy enablement, and other security protocols are not quite native to the environment.
-Lack of anonymity
Many customers may feel uncomfortable with the idea that vendors are able to collect such detailed information about their identities, behaviors, motivations, and other sensitive facts. Some companies respond to these concerns with data masking policies, but those methods aren’t always effective. Those with the right equipment could put data sets back together in order to re-identify individuals.
-Big data complexity
Variety, one of the V’s we discussed above. Variety of data causes complexity and with that comes more difficulty to protect the data. Data sets can be structured or unstructured. The more diverse the data is, the more work is needed to protect it.
-Data breaches are now common
Between Sony, the DNC, and Yahoo, data breaches have become quite common. But it isn’t only large organizations that are getting breached. When reflecting upon the major data breach headlines of last year, it is important to consider the small and medium businesses that did not make the headlines. The reality is that the overwhelming majority of data breaches occur at the nation’s wealth of SMB’s. Many of these breaches go undetected and with very little press revolving around them because there are simply not enough resources to investigate the thousands of smaller breaches.
How are SMB’s being attacked? Cybercriminals are looking for weaknesses in the company’s security. That could mean people, systems or networks. Cybercriminals make initial contact using either a network attack or a social attack. A network attack is when a cybercriminal uses infrastructure system application weakness to penetrate through the organizations network. Social attacks, on the other hand, involve tricking employees into giving access to the company’s network. One way to do that is phishing scams. An employee will be baited into giving their log-in credentials or may click on a malicious attachment. Once the cybercriminal gets into the computer, he can then attack the network and navigate their way towards sensitive and confidential company data. According to the FBI, Ransomware costs organizations close to $1 billion dollars in 2016. The majority of that was spent by SMB’s.
How ReadyNetworks Can Help
Big Data requires:
- Storage capacity and infrastructure to collect and store data
- The security of Hybrid Cloud
- Processing scalability to deliver quality results.
We begin the process by utilizing the CIA security triad. Confidentiality, integrity, and availability. We then discuss the different options that make sense for your business, utilizing BI, analytics, data mining, and Big Data platforms. To learn more about this process, shoot us an email at email@example.com and we’ll be happy learn about your specific needs and see how we can help.