Some fifteen years ago with the birth of social networks and opening of the internet, businesses had little clue on the ability of the data that was being shared. Besides, the number of inter-connected devices, sensors, and objects is set to reach nearly 50 billion by 2022.
Fast forward today, companies have been forced to reconsider almost all points of contact with their customers, and they have been required to radically improve everything that is around customer service, pre-sales, and after-sales services, marketing, commercial action, the management of reputation and conflicts, the ways to develop the attributes and the positioning of brands. But the Internet continues to evolve and continues to force businesses to review what they do and how they do if they want to remain competitive.
Now almost all new challenges are related to the data. What data do you have? How much more data would you be able to have? Are they offer quality? And the most relevant: what are you capable of doing with data in real time?
5G has to do with the speed and blockchain is nothing more than guaranteeing that the data has not been altered and therefore is reliable. What we are now dealing is how effectively to obtain data and know how to use them. This is where fast data derives to the rescue.
From Big Data to Fast Data
The opportunities are to know how to manage the data. The television networks no longer ask you what your favorite program, car manufacturers do not want you to tell them how your car is doing, because they find it much more reliable and accurate than the car itself. We have talked a lot about Big Data, but the real key for business is Fast Data: how much time does it take to act once a piece of data arrives?
Big data takes you to a territory of analysis, while fast data takes you to an area of action. Thinking in terms of fast data leaves you very close to the design of services, puts pressure on the redesign of processes to increase efficiency and reduce deadlines. Fast data monetizes much better than big data, allows more concrete action plans, more oriented to the result and more pragmatic.
Big data is a lot of data and often little action, while fast data is so action-oriented and the result that it usually concentrates on a few data to work faster. Big data tends to write a doctoral thesis on the pile of correlations that you have identified, while fast data leaves you much closer to the action, from the design of services thought in terms of time to market. Big data seems like a long-term conversation, while fast data tells you about immediacy.
Fast Data: Potential transformation
Like cloud computing, big data is gradually installed in the mores of the company. Today, its place in IT budgets is almost systematic. Judging by the increasing significance of big data, this democratization is reflected in the supply than in demand.
Business departments are said to have become “data-driven” – they collect and analyze more and more their data, optimize their decisions, personalize their applications. In short, the history of big data is on the move, and the trend is moving towards acceleration.
Let us look at a few use cases of how fast data can be potentially useful.
Retail: If we take the example of management of the sales performance radius by radius in a large area, predicting the next day sales goal would be a tedious job. In the case of fast data architecture, it will be possible to observe in real time the sales curve, store by store, radius by radius and therefore react in time by activating the right levers (management, promotions, marketing, etc.).
It can also be used not to miss a sale related to a stock-out thanks to direct knowledge of the disruptions on the carrier side or to detect an attempted fraud through a careful and instantaneous analysis of the purchase procedure.
Healthcare: While big data is predominantly useful for historical analysis of big data sets that institute medical records, fast data offers doctors with comprehensive insights enabling specific care recommendations. Fast data empowers acquiring in-the-moment sitting patient data based on predictive models of analytics.
Payment systems and financial applications: Since financial dealings tend to be batch-oriented, end of trading day processing is a must, with reports directed to be done on an hourly basis as well. With the necessity for faster turnaround, fast data allows real-time processing that further lets for more rapid feedback and quicker analytics. For payment systems, choices must be made in split seconds.
Therefore, it is significant for real-time processing of data. It’s essential to keep in mind that fast data is streaming into the organization at wire speed, and just pushing information directly into a long-term analytics or storage engine delays acting on the data in real-time. Shaping in real time whether your portfolio is losing money, or if there is a fraud in your structure means that you can forecast disasters and avert them before any damage is done. For a more precise view of the market along with more accurate actions for maximized profit, it is vital to correlate multiple sources from the market in real time.
Fast Data is not for everyone
On the cost side, it is essentially development time. In general, for those concerned, a maximum of 20% of company data requires a fast data approach. Developments are twice as expensive because more complex to set up – more fine tuning – that brings us to 40% of the initial cost. It is possible to start with big data in batch mode and then move on to fast data. It is also recommended.
It is better to master the big data technologies before launching; this helps to understand the needs better and to avoid the disappointments caused by the lack of hindsight. Finally, remember that in terms of data processing, the time criterion must be reserved for the uses that justify it.