Although the concept of big data has been around for quite a few years now, the way that this data is measured and utilised has changed a lot, and is continuing to do so. The reason for this is clear; the sheer amount of data available is simply increasing at tremendous speed.
Can technology keep up?
The business world also has to contend with the fact that we now have a huge number of data generators that we never used to have, such as mobile phones and tablet devices, alongside all the additional data being produced by and from social media.
All of this means it is extremely important that companies, of all sizes, try to keep up to date with all the latest advances in data measurement technology. Companies need to make this effort to ensure they have access to all the information they need to make improvements to their business strategies, and can therefore support their company and customers more effectively.
Companies must be on the ball, flexible and open to adapting their way of measuring big data in order to ensure they don’t get left behind. Emerging technologies are making the measurement of analytics a lot more feasible, as well as cost-effective.
So what has changed recently and which technologies should you looking into?
In this age of “big data,” it is vital for organisations to know exactly how to analyse their data, as well as be able to store the ever increasing amounts of information generated.
Hadoop is a standards-based, open-source software framework that was developed in response to Google’s MapReduce. MapReduce is a programming framework that follows a divide-and-conquer structure, where big data is broken down into small units of work and then processed in parallel, enabling speedy analysis. However, MapReduce is not actually suitable for every organisation, which is what led to Hadoop’s development.
In spite of the fact that Hadoop has been around for a while, its open-source characteristic has permitted its development to move along at great speed, particularly of late. The Hadoop platform has now evolved into something of a general-purpose data operating system, which is much more suited to a wider variety of companies and permits organisations to execute many more kinds of data manipulations and analytical operations.
Structured Query Language (SQL) in Hadoop
SQL is a special-purpose programming language that is already extremely familiar with those who spend their time analysing data and interpreting the information gleaned. It subsequently bodes well for many organisations that SQL, and other similar programming languages, are being incorporated into the Hadoop platform.
Whilst there are many other tools and technologies offering analysts a structured SQL-similar query language for the Hadoop platform, these are not always the most viable and affordable option for companies. SQL on Hadoop offers organisations more of a financially savvy option in comparison to many of the other pricier data analytics alternatives.
In recent years the cloud has become an ever more important tool to organisations, especially as companies become all the more aware of the importance of storing their big data but without too great a cost to their organisation.
One of the biggest benefits of using cloud computing is its flexibility, which makes it perfect for big data analytics. Big data analysts want easy, on-demand access to data, data which needs to be crunched quickly in order for patterns to be recognised and business methodologies enhanced.
Up until now, cloud storage performance has not been up to par, especially for organisations running Hadoop clusters. However, cloud computing pioneers have worked hard to unlock the cloud for big data and produce industry-leading solutions that are focused on speed. With these cloud computing companies making such improvements, cloud computing can now be an extremely viable option for any company looking for an on-demand, scalable, compute and storage service.
The cloud is even more appealing to businesses when compared to an in-house data centre. This is because the cloud removes the need for companies to invest large, upfront sums into their IT structures. Utilising cloud computing also allows businesses to scale out their infrastructure, while only having to pay for the capacity they actually use.
Even if companies use a hybrid of the cloud and in-house data centres, it will still be a lot less expensive and therefore it’s unsurprising that the number of companies adopting the cloud is increasing…fast!
Computing resources = more predictive analytics
One of the key differences between data analysis now and a few years ago is that traditionally statistical analysis focused on only a sample of an entire data set. Traditional machine-learning algorithms (i.e. used for spam filtering, search engines) just aren’t able to explore and compute big data in the manner that is now needed.
But now analysts have the processing power to analyse a huge amount of records and an even greater number of attributes per record. This puts companies at an advantage because it greatly increases accuracy and predictability. Modern computing technology has come a long way in the last couple of years and this means analysts are now able to explore additional behavioural data throughout the entire day, such as which websites are being visited and/or from which location.
Computing resources, real-time analysis and predictive modelling have changed and improved incredibly and now, with analysts no longer having to rely only on machine-learning algorithms, it is possible to identify variables through analytics easily, quickly and best of all for many companies, cheaply.
Big data lakes/ hubs
The world of analysis and its enthusiasts have realised that the architecture for acquiring and understanding data has to be updated. Companies simply do not stand a chance of analysing big data properly when using only the traditional mechanisms of business intelligence.
For instance analysts will be very familiar with the traditional database theory which says data sets should be designed before any data is actually entered (i.e. a data warehouse). The problem with this concept, when it comes to big data, is that the value of the data being collected is relatively unknown; how then do we know what to analyse and which questions to ask if we don’t initially understand the value of the big data?
The relatively new concept of data lakes, or hubs, provides the solution. It takes the traditional concept and turns it on its head. This model assumes all data sources are taken and collected into one area (i.e. Hadoop) and the data is analysed from there, with no pre-defined data set and only ever accessed and analysed when specific questions need to be answered.
In this manner Hadoop allows users to sift through its data lakes and extract the data as and when needed, in order to answer specific questions. The Hadoop platform still requires a lot of work in this area but the key thing is that things are moving in the right direction.
Hybrid transaction/analytical processing
Over the past few years companies have become ever more accepting of the idea that they must sit up and take notice of their business data, not least because it allows them to build up a more accurate picture of their customers.
Fortunately analytics options such as in-memory databases or hybrid transaction/analytical processing (HTAP) are now being offered by many database manufacturers and, because of their ability to execute both online transaction processing and online analytical processing, are a very attractive option for organisations.
Analyst firm, Gartner predicts the trend of using in-memory databases will only increase, stating: “Hybrid transaction/analytical processing will empower application leaders to innovate via greater situation awareness and improved business agility. This will entail an upheaval in the established architectures, technologies and skills driven by use of in-memory computing technologies as enablers.”
Not only SQL (NoSQL)
NoSQL databases provide organisations with the ability to store and retrieve data that is modelled in a manner other than the traditional tabular relations that are used in relational databases. Momentum for NoSQL databases has been swiftly growing recently; the reason being because they can often offer quicker and more streamlined ways of analysing the relationships between specific data sets.
Choosing an NoSQL database is becoming the go-to option for many organisations as they start to recognise that it is not only ideal for special-purpose but because it is always an extremely high-performance and lightweight choice. As people become pickier with the types of analyses they need, these NoSQL databases will only gain in popularity.
Deep structural learning / hierarchical learning
Deep learning is the name for a set of algorithms in machine-learning that is based on neural networking (a system of programs and data structures that approximate the operation of the human brain). The concept of deep learning is still relatively new, however, it shows enormous potential and will no doubt be able to solve many a business problem should your organisation make the most of it.
Deep learning enables computers to identify items, within large quantities of data (unstructured and binary), that are of interest to your business. It also enables the deduction of relationships and does so without the need for specific models or programming instructions. The possibilities for deep learning are enormous, and very analytically advanced, for instance it could be used to identify certain shapes or colours or even specific objects within a video.
To stay ahead of the competition, your business needs to be open to giving new technologies a go. Emerging technologies should be evaluated and tested by your data specialists and then, once you have a clear idea about how things work and whether the tool fits your business, integrated into as many different business areas as possible.
To achieve success, every company needs to analyse their big data and ensure they’re incorporating what is learnt from the analysis straight back into the business.