The Age Of Analytics: Scope And Prospects Of Data Analytics In 2022


The present era that has been replete with innovation and progress has been popularly called the age of analytics. There is hardly a business in the present time that does not use data analytics in one or the other form. Ranging from manufacturing to retail and e-commerce to any communication industry, data analytics finds applications directly or indirectly. Data analytics also has Spatio-temporal applications and is used widely across different regions. A report by allied market research projects notes that the tools and techniques of data analytics will help in catalyzing the various sectors of the economy that have been plagued by the recession. The application of data analytics is supposed to increase the revenues of digital businesses by about 11.56% annually.

Preparing a skill pool of data analysts

Given the growth at which data analytics is being employed by organizations, it becomes necessary to prepare a structural framework that caters to the needs of the upcoming industry. It is in this context that a skill pool of data analysts will have to be prepared by training them in data analytics courses. There are numerous advantages of preparing a skill pool of data analysts. The first and foremost of them is the resolution of the skill mismatch that is emerging between academia and industry. The second important advantage is the preparation of prospective business professionals as well as data analysts who can cater to the demand of the upcoming business sector.

The best of business intelligence 

The best of business intelligence is possible by the adequate application of data analytics. In simple terms, data analytics takes business intelligence to a different level by harnessing the power of data mining and data processing to predict future trends. This is done by relying on historical data to predict the future curve that a business is likely to trace. Data analytics applied to business intelligence also gives an exact picture of the boom and bust cycles that a market is likely to undergo. It needs to be noted at this point in time that business intelligence sees itself as an emerging branch and discipline by borrowing heavily from data analytics and artificial intelligence.

Business analytics can be regarded as a hybrid of business intelligence and data analytics. This powerful communication is not only helpful for existing businesses but also the emerging ones that deal in data-based products.

Another perspective example of data analytics in business intelligence is related to the development of different business risk strategies. Business risk strategies help in assessing the risks that are involved during investment in different ventures.

The surge of edge computing 

There are two important trends that are coming up to make computing more efficient, effective, and less latent. In order to achieve this, cloud computing and edge computing are rapidly influencing various domains of business intelligence. There is hardly any sector in business analytics that does not plan migration to the cloud environments in the times to come. In fact, it is believed that more than 90% of the businesses will shift operations to the cloud environment in the next five years. The only concern that is limiting the full extension of capabilities of cloud computing technology is related to privacy and security. Once this concern is resolve, the expansion of cloud computing will take place at its full potential. 

Meanwhile, the alternative that is being suggest is in the form of Edge computing. Edge computing involves processing data very close to the source where it is generate. This takes care of various concerns related to security and privacy that we encountered in cloud computing. According to a report by Gartner, the present adoption of cloud technologies in various business sectors stands at about 10%. However, this number is slate to reach 75% by the end of 2025.

This also becomes important from the perspective of an ecosystem of the internet of things that is emerging as one of the novel trends. As per the present scenario, various inter-connected devices transfer and process information through the cloud environment and this make the data pipeline vulnerable to threats and attacks. As such, it is highly likely that edge computing would provide an effective solution to cut down the risks of cyber attacks.

The domain of cloud computing and edge computing is evolving further and new sub-sectors like fog computing are making an appearance.

Data as a service 

Analytics that is being carried out with the help of cloud environments is giving a new lease of life to the traditional and orthodox business sector. At a very cost-effective rate, analytics can be carried out that may be both predictive and prescriptive in nature. In the present times, three popular services are being offered by the cloud-based models. The first one involves infrastructure as a service. The second one involves software as a service and the third one involves platform as a service. One of the most recent trends that are supplementing the above services is call data as a service. 

In the age of data analytics, the prominence of data as a service has very bright prospects. Data as a service offers the collection of data, it’s processing as well as its analysis at a very economical rate. Subscription models are one of the important parts of data as a service.

Let us now understand the various features of data as a service in deeper detail. Data as a service operates through a virtual data layer that sources data from different types of data lakes, data warehouses, and other operational data sources. With the help of an application programming interface, this data is pass through subsequent stages, and orchestration and documentation are carry out. The process data is then channelize to mobile applications, web applications, and external users.

The need for data democratization 

Data is emerging as the new currency and oil of the twenty-first century. It becomes important to focus on data democratization while giving leeway to data analytics. To make it much simpler. It has been observe that there has been a constant bias in the data sets. That are being fed to train various models of artificial intelligence. Directly or indirectly, this puts the entire process of data analytics into question.

Successful handling of challenges related to data democratization will serve two important purposes. Firstly, it will make the adoption of data analytics relevant for diverse regions, countries, and cultures. Secondly, it will increase the confidence of all the stakeholders related to ethical and integral data analytics.

Concluding remarks 

As we head towards an era of digital transformation, the relevance of data analytics becomes even more significant. We may witness a constant surge in the applications of data analytics across various business sectors and other industrial domains. The need of the hour is to invest in analytics technologies and synchronize them. With different business processes in the times to come.