Big data, crystal balls, and looking glasses: Reviewing 2017, predicting 2018

As we move to the end of 2017 and step towards 2018, it’s time to take a look at some of the predictions about the data industry. Big Data has made predictive analytics a reality, and turned out to be the crystal ball. Here’s a compilation of the observations of the year gone by, and what to expect in the near future:

1. Climbing Analytics Stack

As data-driven analytics becomes more and more common as a practice, it can hardly be considered as a factor that differentiates different enterprises. There’s a commercialization of diagnostic analytics, as well as descriptive analytics. So, there’s the new tendency to climb the analytics stack, aiming for predictive analytics to make forecasts based on the past events and data to train the algorithm to predict the future using Machine Learning or ML, as well as prescriptive analytics to decide upon the correct steps to take in order to get the right result by using complicated steps that are close to Artificial Intelligence.

2. ML for Feedback Loop

Data accelerates change, which, in itself, is predictive in nature. Products driven by data obviously lead to more data, which results in better insights. This helps gain more profit, which is further invested to lead to better product, till the loop brings further data. So, many are now concerned about the need priorities in big data. The point is to have automation driven by data, and innovation, and with advances in AI and ML, there will be some huge changes. However, the feedback loop driven by data can give rise to unchecked monopolies, as there is a lack of wary players to handle the data concentration to take action.

3. Subscribing to Platforms for Insights

As data will move more and more towards clouds, the possible preference will be given to platforms that can also operate in the cloud, to run the analytics based on the data and do the necessary data management. In order to get insights from data, there needs to be the troublesome procedure that needs the data collection process to be set up, after which it needs to be maintained, stored, and processed, using complex tools of analytics, visualization, algorithm, etc. In such a situation, subscribing to platforms that perform the underlying processes and deliver the final insights is obviously preferable.

4. Amalgamation of Analytical Processes

The common practice is that data analysis platforms and operational databases are separated from each other. This traditional process is considered common. However, there is going to be a hybridization of the transactional analytical processes, even though there is a huge difference in the needs in cases that demand quick conclusions and integrity of transactions, and those that require more complex analysis along with long-term processing. There’s always the possibility of situations that seem non-ideal in this case. But the unification of transactional databases is not easy. Various approaches will come into play.

5. Real Time Data Streaming

It is possible to stream data when it is processed in real time. This process allows the data to be analysed while the processing or generation of data by applications is still in progress. While this is a new development, it is also to be remembered that this process comes with a number of requirements. To solve the problematic situation, data analytics now depends on the two-layer architecture of Lamda, which can deal with historical as well as real time data. However, this is not a cost effective solution, and also requires more effort. The possible choices of platforms in this case are Apache open source tasks.

It is to be remembered that these predictions may vary, based on the observations of the happenings in 2017, and the subjective market opinions of those who monitor the data industry. In fact, it is a matter of concern when someone claims with conviction to be right about the predictions.