The cloud can help you process and analyze your big data faster, leading to insights that can improve your products and business.
This is the first article in our “big data in the cloud” blog post series. Click here to sign up for our email list and get notified of future posts.
The advancement of technology has allowed companies to reap the benefits of streamlined processes and cost-efficient operations. But the one thing that has become a game changer for businesses of all sizes is the availability of data from every source imaginable – social media, sensors, business applications, and many more.
These large stores of data that bombard companies day in and day out is collectively known as big data. Most have heard of it, many aim to maximize its potential to propel their business forward, and yet, only few have truly succeeded in doing so.
At the same time, enterprises have adopted cloud computing to improve their IT operations and develop better software, faster.
Merging big data with cloud computing is a powerful combination that can transform your organization.
In this article, we discuss the primary characteristics of big data and make a case for putting your data in the cloud. We also go over the pros and cons of making such a move to prepare you for your big data migration. Let’s go!
What is big data?
Gartner defines big data as high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.
Whoa, that’s a mouthful.
Building on Gartner’s definition, the concept of big data and what it encompasses can be better understood with four Vs:
- Volume. The amount of data accumulated by private companies, public agencies, and other organizations on a daily basis is extremely large. This makes volume the defining characteristic for big data.
- Velocity. It’s a given that data can and will pile up really fast. But what matters is the speed with which you can process and examine this data so that it becomes useful information.
- Variety. The types of data that get collected can be very diverse. Structured data contained in databases, and unstructured data such as tweets, emails, images, videos, and more, need to be consumed and processed all the same.
- Veracity. Because of its scale and diversity, big data can contain a lot of noise. Veracity thus refers to the the certainty of the data and how your big data tools and analysis strategies can separate the poor quality data from those that really matter to your business.
Technology leaders also name a fifth V – value. But this one isn’t inherent within the huge amounts of raw data. Instead, the true value of big data can only be realized when the right information is captured and analyzed to gain actionable insights.
To get a better idea of how big big data is, let’s review some statistics:
- Over 1 billion Google searches are made and 294 billion emails are sent everyday
- Every minute, 65,972 Instagram photos are posted, 448,800 tweets are composed, and 500 hours worth of YouTube videos are uploaded.
- By 2020, the number of smartphone users could reach 6.1 billion. And taking Internet of Things (IoT) into account, there could be 26 billion connected devices by then.
For sure, big data is really big.
Why should big data matter to you?
Why should big data and its exponential growth matter to your business?
For one, an Accenture study (PDF) reveals that 79 percent of corporate executives surveyed believe that ‘companies that do not embrace big data will lose their competitive position and may even face extinction’. Furthermore, an overwhelming 83 percent have taken on big data projects with the aim of outperforming others in their respective industries.
Big data projects can impact almost any aspect of an organization. But as this survey by New Vantage Partners (PDF) shows, where it delivers most value to enterprises is in reducing costs (49.2%) and driving innovation (44.3%).
If you haven’t hopped onto the big data train yet, your competitors may be leaving you behind. Enterprises that have the ability to successfully implement big data initiatives stand to benefit from the key insights and useful knowledge that can distance themselves from the competition.
Big data and the cloud – a match made in heaven?
Big data projects typically get started with data storage and application of basic analytics modules. However, as you discover ways to extract data at a much larger scale, you will need to find better methods to process and analyze this data, which will likely require infrastructure upgrades.
You may add more capacity to your in-house data warehouse or power up more servers to cater to the rapidly-increasing analytics requirements. But even with the boost of your on-premise systems, your infrastructure eventually may not be able to keep up.
This is where the cloud comes in, or more fittingly, when your big data goes to the cloud.
Why big data in the cloud makes perfect sense
The benefits of moving to the cloud are well documented. But these benefits take on a bigger role when we talk of big data analytics.
Big data involves manipulating petabytes (and perhaps soon, exabytes and zettabytes) of data, and the cloud’s scalable environment makes it possible to deploy data-intensive applications that power business analytics. The cloud also simplifies connectivity and collaboration within an organization, which gives more employees access to relevant analytics and streamlines data sharing.
While it’s easy for IT leaders to recognize the advantages of putting big data in the cloud, it may not be as simple to get C-suite executives and other primary stakeholders on board. But there’s a business case to be made for the big data + cloud pairing because it gives executives a better view of the business and boosts data-driven decision making.
For instance, optimization of the supply chain and efficient tracking of defects – both principal concerns of a COO of a physical product company – is made easier with material data on hand. Data is also key for the CMO looking to increase customer engagement and loyalty, and for the CFO seeking new opportunities for cost reduction, revenue growth, and strategic investments.
And all of these insights can be easily presented to the CEO to inform fast, strategic decision making.
Whatever perspective you may have, big data complemented with an agile cloud platform can affect significant change in the way your organization does business and achieves your objectives.
Many enterprises are already making the move. A Forrester Research survey in 2017 revealed that big data solutions via cloud subscriptions will increase about 7.5 times faster than on-premise options.
Big opportunities, big challenges
Bringing big data to the cloud presents huge opportunities, but there are some challenges that need to be overcome.
Let’s go over the advantages first.
Pros of putting big data in the cloud
The shift to big data in the cloud isn’t surprising considering the many benefits that the powerful combination of big data analytics and cloud computing can bring. Here are the key advantages.
Requires zero CAPEX
The cloud has fundamentally changed IT spending as organizations know it—and in a good way.
As we mentioned earlier, big data projects require immense infrastructure resources, which traditionally would also mean high on-premise capital expenditure (CAPEX) investments. But the cloud’s Infrastructure-as-a-Service models have allowed companies to practically eliminate its biggest CAPEX expenses by shifting these into the operating expenditure (OPEX) column. So when you need to set up your database servers or data warehouses, you won’t need to make massive upfront investments.
This has been one of the most compelling benefits that has convinced businesses to migrate to the cloud.
Enables faster scalability
Large volumes of both structured and unstructured data requires increased processing power, storage, and more. The cloud provides not only readily-available infrastructure, but also the ability to scale this infrastructure really quickly so you can manage large spikes in traffic or usage.
Lowers the cost of analytics
Mining big data in the cloud has made the analytics process less costly. In addition to the reduction of on-premise infrastructure, you can also save on costs related to system maintenance and upgrades, energy consumption, facility management, and more. You can also worry less about the technical aspects of processing big data and focus more on creating insights. Even better, the cloud’s pay-as-you-go model is more cost-efficient, with little waste of resources.
Encourages an agile and innovative culture
The ability to innovate is a mindset that should be cultivated within any enterprise. This type of culture can lead to creative ways of using big data to gain a competitive advantage, and the cloud makes it easier to spin up the necessary infrastructure to do so. When your team focuses on analyzing data instead of managing servers and databases, you can more easily and quickly unearth insights that can help you augment product lines, boost operational efficiency, improve customer service, and more.
Enables better business continuity and disaster recovery
In cases of cyber attacks, power outages or equipment failure, traditional data recovery strategies will no longer do the trick. The task of replicating a data center – with duplicate storage, servers, networking equipment, and other infrastructure – in preparation for a disaster is tedious, difficult, and expensive.
In addition, legacy systems often take very long to back up and restore. This is especially true in the era of big data, when data stores are so immense and expansive.
Having the data stored in cloud infrastructure will allow your organization to recover from disasters faster, thus ensuring continued access to information and vital big data insights.
Potential challenges of big data in the cloud
Migrating big data to the cloud presents various hurdles. Overcoming these require a concerted effort from IT leaders, C-suite executives, and other business stakeholders. Here are some of the major challenges of big data cloud implementations.
Less control over security
These large datasets often contain sensitive information such as individuals’ addresses, credit card details, social security numbers, and other personal information. Ensuring that this data is kept protected is of paramount importance. Data breaches could mean serious penalties under various regulations and a tarnished company brand, which can lead to loss customers and revenue.
While security should not be a hindrance to migrating to the cloud, you will have less direct control over your data, which can be a big organizational change and may cause some discomfort.
To deal with this, be sure to carefully evaluate the security protocols and understand the shared responsibility model of your cloud service provider so you know what your roles and obligations are.
Less control over compliance
Compliance is another concern that you’ll have to think about when moving data to the cloud.
Cloud service providers maintain a certain level of compliance with various regulations such as HIPAA, PCI, and many more. But similar to security, you no longer have full control over your data’s compliance requirements.
Even if your CSP is managing a good chunk of your compliance, you should make sure you know the answers to the following questions:
- Where is the data going to reside?
- Who is going to manage it, and who can access it?
- What local data regulations do I need to comply with?
If your company is in a highly regulated industry like healthcare or finance, these questions become much more important.
Make sure you know exactly what data is stored where, ensure that your CSP has robust compliance policies, understand the shared responsibility model, potentially create Service Level Agreements (SLAs) for compliance.
Network dependency and latency issues
The flipside of having easy connectivity to data in the cloud is that availability of the data is highly reliant on network connection. This dependence on the internet means that the system could be prone to service interruptions.
In addition, the issue of latency in the cloud environment could well come into play given the volume of data that’s being transferred, analyzed, and processed at any given time.
Over to you
Big data doesn’t have to equal big chaos.
Yes, the volume and the speed with which data is growing can be overwhelming, especially for organizations just starting out. But by utilizing the cloud for big data initiatives, your enterprise can transform itself into an efficient, data-driven organization.
Have you moved your big data to the cloud? How has it impacted the speed and quality of your data analysis, and what effect has it had on improving your business? We’d love to hear from you.
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