Big Data has recently made headlines as one of the important tools recruited to combat the corona crisis. Big Data technologies offer predictive capabilities and insights from vast volumes of economic, scientific, and epidemiological information to enable informed decision making, both locally and globally.
This Big Data renaissance has affected many organizations at this time, and not without good reason. While many countries are starting to recover and open their economy, the business world is still in a state of uncertainty. In both local and global markets, the cards have not only been reshuffled – they have been shredded to bits. Many businesses want to make the right decisions that will drive them ahead of the competition –they want to not only survive but to win.
So, what is Big Data?
Big Data means large and complex data sets, mainly drawn from potentially multiple data sources. The volume of data that runs through the processes of Big Data is so huge that the traditional processing programs cannot handle it. Big Data solutions often allow non-linear analysis of unstructured data and utilize a variety of computing tools in parallel.
To see if you actually have Big Data,you have to first ask about the three Vs: Volume, Variety and Velocity. If you have an extremely large quantity of generated and stored data – from petabytes and exabytes to zettabytes (Volume), of many types, such as text, images, audio and video (Variety), with a demand for the data to be collected or input and then processed at high speed (Velocity), you most probably have Big Data.
Assuming your company has Big Data and wants to utilize it to make smart business decisions, you need to be aware of the four aspects of handling big data: Input, Storage, Analytics and Visualization. For each of these, what should you be considering?
Do you have a large variety of types of sources? For instance, are you including information from Internet of Things (IoT) devices? Are you including real-time information from public databases, such as stock exchange or weather information? Do you count clicks on ads and follow the client through their virtual journey?
Any Data & Analytics solution needs to map out the various sources of information, both in terms of technologies and of the rate of input, in order to integrate them into the system.
The key question is no longer ‘how do I push everything into one database?’. Rather, due to the advent of cloud computing, with its scalability and elasticity, the question has become ‘What information needs to be stored at what stage for what purpose, and how will it be accessed?’ In other words, your solution needs to take into account primarily the usage of the data.
This means that raw data which is seldom accessed can be stored in an inexpensive backup archive service, while real-time analysis data, which has been cleaned, aggregated and arranged, can be saved in a tighter fitting storage space, which allows for multiple and complex querying.
Also, you have to ask what type of data you are saving – jsons? Classic data (text and numbers)? object data? Geospatial data? chart data? Each type can be stored in a correctly tailored data base to fit your needs.
And if in the past, each server meant expensive licensing fees and extensive database administration work, these can be placed in managed services which reduce the overhead costs significantly.
The range of analytics available in the market is constantly on the rise. Your business may need materials availability analysis,constant map conditions updates or presentation of stock fluctuations. New computational capabilities have allowed for faster, smoother and deeper Machine Learning (ML) and Artificial Intelligence (AI).
Whether purchasing a suite of analyses or creating your own personalized deep-dive, you need to ask what is key to the promotion of your business. No two businesses are alike, no two business logics are identical, even in the same field. Each looks at it slightly differently,and therefore the key is to apply analytics in a 100% tailored fashion.
The considerations, therefore, are primarily what is your business logic and what advantage can you get from the analyses your Big Data solution generates.
This is the last aspect, but it should not be overlooked. It is not enough to collect, store and analyze your Big Data. Your information consumers – from end clients to top decision makers– need to be able to consume the information quickly and easily. Make sure your visualization platform can access the sources it needs within your Data & Analytics solution rapidly, in terms of data types and data volume. For example, if you are implementing ML within the visualization, make sure that its results can be presented clearly and coherently, and that end users do not have to manipulate the data presented further. And finally, make sure that the visualization aspect can be adapted later in the process, as your business develops. You don’t want to face new demands from the market that your business cannot satisfy without significantly revamping the way it presents data.
In short, Big Data is defined primarily by Volume, Variety and Velocity. Make sure your Data and Analytics solution satisfies your business needs in terms of Input, Storage, Analytics and Visualization.
Written by Joe Brown, BI Expert - Commit