Whenever we take any decision in our day-to-day life, it is by thinking about what happened last time or what will happen by choosing that decision. This is nothing but analyzing our past or predicting the future and making decisions based on it. For that, we gather memories of our past or dreams of our future. So that is nothing but data analysis. Now the same thing an analyst does for business purposes is called Data Analysis.

You may ask this question: Why do we need to analyze data and why is data analytics so important? Well, to grow in life and do be better, we always analyze our previous decisions and try to do better next time. Similarly, Data Analysis is required to study the previous decisions made by a business, acknowledge the mistakes, and develop a model to be better in the future

Data Analytics and more importantly, Data Science is called the “New Big Thing” or “The Future of Technology” but the question really to be asked is this: Is Data Analytics relevant in a tech-driven future?

The Present

Before we answer that question, we first must see what real-life applications Data Analytics is being used for in the present time. Currently, Data analytics is used in many domains of the world, particularly:

·      Marketing and Sales: Sales analytics and marketing tend to be more advanced and complicated, at least in B2C commerce. Data like customer segmentation and personalization, social signal mining, pricing, and customer loyalty must be thoroughly analyzed before developing a model for e-commerce and sales to smoothly function.

·      Operations: Advanced analytics in Operations tends to be on the lower maturity side. This is usually because opportunities are harder to spot and cross-business domain knowledge is required to create a step-change. Data and analytics use in operations has traditionally included identification of new oil and gas drilling sites but has now come to include mining sensor data for predictive maintenance, integrated and demand-driven workforce management, and real-time scheduling optimization.

·      Data-Driven Ventures: A few firms have started to explore the merits of big data and advanced analytics, to expand their current business model, by developing analytical insights to offer as a service to its customers. Examples include credit card companies providing data-driven customer targeting, or telecom companies selling location data for traffic monitoring and fraud detection.

So, we can see that data analytics is being widely used in many domains, including technological as well as non-tech related domains. This shows us that big data and analyzing data are very important in this tech-driven world. But what is the future of Data Analytics? Based on the information we saw above, we can predict the future of this technological trend.

The Future

Data analytics is constantly evolving. It started with descriptive analytics, which merely described data. Now, we are at a stage where analytics can predict future outcomes in the form of predictive analytics. Thanks to new technologies, like cloud computing, AI, IoT, and machine learning, analytics is taking on new forms to complete even more complex operations. Some of these new technologies that can have a big impact on data analysis are:

·      Augmented Analytics

When machine learning and natural language processing are integrated into data analytics and business intelligence, it creates augmented analytics. This form of analytics is going to play a huge role in analyzing data in 2020. Augmented analytics is going to be the future of data analytics because it can scrub raw data for valuable parts for analysis, automating certain parts of the process and making the data preparation process easier.

·      Relationship Analytics

The ability to connect different data sources using several analytical techniques can transform data collection and analysis methods because it allows organizations to maximize the value of their data network and infrastructure. For example, relationship analytics allows organizations to optimize several functions at once, like account renewals, account servicing, and pipelines. Salespeople will get a 360-degree view of their customers, allowing them to be smarter and targeted in their marketing campaigns. Relationship analytics is the future of data analytics because it gives organizations that extra dimension to their data analytics procedures.

·      Continuous Analytics

In the past, data analytics platforms could deliver insights in a few days or weeks, and it would be completely acceptable. However, with the proliferation of IoT devices, the future of data analytics will expect platforms to generate even faster insights, to take full advantage of IoT devices. This is where Continuous Analytics comes into play, it allows organizations to continuously analyze streaming data, so analysts can shorten the window for data capture and analysis. The level of analysis may depend on the speed of delivery analytics teams are looking for.

·      Augmented Data Preparation and Discovery

The future of data analytics will see data discovery and preparation change, in a practice known as augmented data preparation and discovery. Traditional methods often involve rule-based approaches to transform data. However, augmented data preparation makes the process more flexible because it automatically adapts fresh data, especially outlier variables.

To summarize, Data Analysis is crucial for the advancement of technology and in general, the future of newer business models. Various advancements in technological trends such as Machine Learning, Artificial Intelligence, and the Internet of Things will have a massive impact on the big data sector and will be very influential in replacing the traditional methods of performing data analysis and its general applications. It will also influence the business and sales sector which heavily relies on Data Analysis to develop its marketing and customer satisfaction models.