This article was originally published on Technology.Info.
As part of our continuing strategy for growth, ITProPortal has joined forces with Technology.Info to help us bring you the very best coverage we possibly can.
By now, we have all heard the staggering statistics of how quickly the world’s data is growing. Every single day, data is generated by more devices and flows constantly through more systems than ever before. In the last two years, we have generated more data than in the history of mankind. And yet that pales in significance when you consider that data is expected to double in size every two years through 2020, exceeding 40 ZB (40 trillion GB), which is the equivalent of 5,200GB of data for every man, woman and child on the planet.
So why and how is this important? In the age of data, our ability to quickly connect emerging data, analyse it at scale, and take action in real-time means the difference between winning and losing. For companies to guarantee future growth and success, it is imperative that they implement a technology approach today that will help them gain and sustain a competitive advantage over time.
The main data analytics challenges
To maximise value, analytic processes need to quickly respond to the wave of data that is being created every second - not just by humans, but by machines too. Virtually everything around us is getting more intelligent and generating more data, whether it is our smartphones, cars, refrigerators or even our office photocopiers. They are all gathering data and transmitting it so that the information can be used to improve the way we interact with these items.
The main challenges can be summed up in the following two sections.
Connecting the data
With companies moving more of their data and apps to the cloud, integration becomes a bit of a challenge. Companies need to find a simple way of making their data and apps “talk” to each other, connecting systems that are both on-premises and in the cloud that is invisible to the user. In addition, this invisible integration needs to be dynamic and future-proof so that changes in the application or data format do not break the connection.
Analysing it at scale
Once the data is joined up, it needs to be analysed in a timely and regular manner. The key question is: can their existing analytic platforms scale up and scale out to meet current and future needs? The ability to have systems that grow elastically is as imperative as the ability to run fast analytic queries in mere seconds.
Adopting a solid analytics platform introduces two very important opportunities: the democratisation of analytics and getting to the ‘pot of gold’ of predicting the future. With unconstrained analytics, data scientists are no longer the sole expert on analytic algorithms. Now, companies can empower front-line business users to pull in data, connect it together, analyse it and act upon it.
In some instances, acting upon this data may involve trying to predict future business trends. The Danish philosopher Kierkegaard once said, “Life can only be understood by looking backwards, yet it must be lived forwards.” This rings true for organisations that no longer want to report on what happened yesterday, but get to a point where they can foresee what will happen tomorrow and take the appropriate action ahead of time.
Does that mean that organisations have to throw away all their existing analytics solutions in order to survive and thrive in the age of data?
While the naïve technology vendors would of course endorse this notion, the smart ones say just the opposite. In fact, organisations do not need to, and probably could not afford to uninstall existing analytic technologies that they have internally customised and invested a ton of man-years in on a whim. The better strategy is to continue to adopt modern tools and software that fit within existing infrastructure, allowing them to move forward and meet business goals. If those goals are to run high-performance, fast analytics, and their current setup does not allow them to do this, then using analytic platforms as analytic offloads may be a suitable approach to take. In this example, organisations can continue to use their operational systems for their transactional-based processing requirements, but also use a complementary analytic platform to run their intensive data analytics workloads.
While data has become the asset in the corporate world, there are three main things companies should look for when considering big data analytics solutions. First, can the solution connect various data sources together (whether in the cloud or on-premises), integrate and prepare the data, and check its quality? Secondly, can it analyse ever-increasing volumes of data at fast speeds, easily? Thirdly, and perhaps most importantly, will it help them act on the insights and intelligence gleaned? Will it help the business get ever closer to that goal of predictive analytics, where algorithms and machine-based thinking will help humans make better decisions? Ultimately, will the solution truly let them turn big data into business value?
The growing data volume represents a unique opportunity and it is imperative for companies to have an appropriate solution in place that can help deliver on the promise of big data.