29 August 2019
We all know what the terms big and data mean. But when you put the two together, it can be a different story. So, what does ‘big data’ entail and how can it be used to investigate patterns, correlations and other insights? A business receives data across all manner of formats. All well and good, but it’s only valuable to an organisation if it can be analysed accurately and efficiently.
For those unfamiliar with the concept of big data, we’ll explore it more fully, delving into its definitions, benefits and impacts to show how important it is in the modern business world.
Even if the term is leaving you scratching your head, big data has been around for a lot longer than you first might realise. Even in the 1950s, long before the term was codified, it was a thing that businesses employed. Put simply, it refers to a vast, diverse data set which can be analysed to shed light on trends and patterns that relate to human behaviour and their associated interactions.
Where big data differs from its old-school origins is the present-day use of speed and efficiency. While analytics could be used to produce solutions for future decisions even a few years ago, they can now be used to provide insights for immediate decisions today. This faster, more agile ability to work means that businesses have a competitive edge they may have lacked ten or so years ago.
Big data combines structured and unstructured data at speed and scale. Structured data encompasses compartmentalised fields like age, height or gender in a relational database. Unstructured data, meanwhile, relates to data that can’t be analysed, such as videos or tweets. Since it can’t be grouped in a relational database, it’s analysed in terms of accuracy, reliability and legibility.
Consequently, big data typically has four dimensions:
e.g. mobile subscriptions, Google searches per day, social media users and IP traffic.
e.g. amount of emails sent daily, stock exchange trading, internet TV streaming.
e.g. social media, email, online payment transactions, voice & video.
e.g. accuracy of data, interpretation of data, reliability of data, language translation.
As this illustrates, there’s a lot of data that a given business contends with. So, what kinds of technology and tools can be leveraged to marshal this data and derive insights from it?
The cloud is pretty much mandatory for businesses nowadays, as a way to share workloads and data sets across premises and online. For data and analytics offerings, it’s a must-have at this point.
Edge computing entails processing data at the edge of the network, i.e. nearer to the data source. This reduces latency and allows actions to be triggered in real-time.
An emerging trend in the realm of big data technology; quantum computing carries out calculations at an incredibly fast rate, far quicker than that of a standard computer. Things like AI and machine learning should benefit from quantum computers since they can complete complex calculations that involve very large data sets in a fraction of the time. The possibilities it will open make it a very exciting proposition.
Perhaps the example that you may be familiar with is the much-feted alchemy of Netflix’s algorithms. The accurate predictions of what its subscribers would like to watch next show how the collation of large data volumes and powerful analytics can glean essential information and solutions. This makes it perhaps the big data-in-action prima facie, a benchmark that others wish to replicate, and it looks a little like this:
The slow and expensive process of clinical research benefits from big data. Trials can fail for a number of reasons, but things like AI and advanced analytics help to improve the speed and efficiency of every stage of clinical research thanks to more intelligent, automated solutions.
Manufacturing issues are numerous, from complex supply chains and motion applications to labour constraints and equipment breakdowns. Big data provides competitive organisations with the chance to identify new cost-saving and revenue opportunities.
Consider the patient records, health plans and insurance information associated with healthcare – there’s a lot to manage within. Applying analytics unearths key insights, including essential diagnoses and treatment options much quicker than what could be achieved without the technology in place.
How does a government tighten the budget without compromising elsewhere? With regards to law enforcement, which is charged with keeping crime rates down with minimal resources, big data can help to streamline operations while providing a more holistic view of criminal activity.
Customers expect businesses to know what they need and when. These demands can be met with the help of big data. With the data from customer loyalty programmes, buying habits and other sources, retailers can understand their customers, predict customer spending habits and recommend products – boosting profitability in the process.
Simply, where can the data be stored and analysed, and will it be big enough to hold this data? Software-defined storage can make it much easier for businesses to store and scale their hardware, while things like compression, deduplication and tiering can reduce the amount of space needed.
Storing data is one thing, but using it to achieve business goals is another. To do this, organisations are looking towards software and analytics tools that reduce the time taken to generate reports. These real-time capabilities allow them to respond to developments in the marketplace instantly.
How can experienced, skilled talent be employed and retained? There is undoubtedly a demand for experts, and businesses have to adapt and react in terms of budget, salaries, training opportunities and new tech.
The vastness of these data sources means there can be difficulties in reconciling it to create reports. Vendors offer a variety of data integration tools, but many say these are still a way off solving the problem of data integration.
If an organisation gets similar data from different systems and the data doesn’t always agree (such as a patient’s address), then how can they tell which piece of data is valid?
Maintaining data security is another big concern. Big data stores are a huge target for hackers, so it’s essential that organisations have these data banks safeguarded and protected as best as they can.
Sometimes, the problem can simply be people within the business. There can be a lack of organisational alignment, misunderstandings amongst middle management and resistance from the top standing in the way of big data integration.
To overcome this, there must be change, which can be difficult for large businesses. Investing in strong leaders can pay off here; those who understand data opportunities and their inherent value is a step in the right direction.
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