• Introduction
  • Data Science and Business Analytics trends
  • Data Security
  • Data Visualisation/ Data Discovery
  • Real-time Data & Analytics
  • Predictive Analytics
  • Comprehensible Models & Transformational Insights 
  • Integration of Analytics with Action and Measurement
  • Takeaway



The past decade has seen a dramatic evolution in the way we collect, process, and analyse data. Data gathering is now easier than ever with the widespread adoption of mobile technology and sensors that measure everything from foot traffic to water quality.

New software tools help us understand vast amounts of information and data in a more efficient manner. The result is an explosion in the number of different analysis techniques available as well as new approaches for interpreting results and deploying them into business operations.

Moreover, automated machine learning algorithms are becoming more powerful. Cognitive computing systems such as IBM Watson are being used by companies like Apple, Google, and Facebook to understand patterns in data.

In this blog post, we will be discussing “Top 8 Trends” that are shaping how businesses leverage business analytics and data science today:

  1. Data Security

Data security will be an essential part of our lives for years to come. The execution of security guidelines like the GDPR (General Data Protection Regulation) in the EU and the CCPA (California Consumer Privacy Act) in the USA have set standard blocks for information security and the executives of clients’ data. While the pandemic has had a massive impact, overall spending on data security items and administrations will rise by 2.4% compared to a year ago (reaching $123.8 billion).

Due to the frequency of data breaches in recent years, businesses are demanding improved data security measures.

  1. Data Visualisation/ Data Discovery

Data visualisation and analytics software allow you to create persuasive data-rich graphics, sales charts, and reports that are not only easy but attractive for your workforce.

Data discovery is a process that encourages decision-holders to disclose insights by utilising visualisations. This enables team members at organisations to recognise the latest trends and significant outliers within a fraction of minutes.

Data Discovery has emerged as a popular trend in business analytics.

A business review that was directed by the Business Application Research Center recorded data analytics being part of progressive systems that have significant benefits for businesses. Current trends in business intelligence (BI) show a steady trend of strengthening clients.

The importance of data discovery tools stems from the need to consider a methodology. A core issue is that data discovery depends on an investigation, and then, the final results will generate business value.

It requires an in-depth understanding of how to prepare data for analysis, how to carry out visual analysis, and the different types of advanced analytics.

According to the Business Application Research Center, “The colossal demand for data discovery tools impacts a huge shift in the BI world towards an increment in data usage and the extraction of insights.”

  1. Real-time Data & Analytics

We have seen an escalating demand for real-time data in recent years, necessitating better tools that can process large volumes of data. In order to create a more precise response in the event of an infectious disease outbreak – one such as the current Covid pandemic crisis – there is a need for continual updates and data analysis.

Some countries have used data to make decisions in these uncertain times, and organisations followed suit to guarantee improvements and success. Constant exposure to information has become a regular part of everyone’s daily routine. Organisations, as well as the population at large, can expect to see new data, visualisations, and insights from this two-year-long battle against the disease, which would help us anticipate what’s to come in combating future pandemics.

The concept of data is a relatively new phenomenon. As the world evolves, companies need to equip themselves accordingly. One of which is quick data access. With the rise of big data, analytics is becoming more accessible and, therefore, more popular. It will continue to grow in popularity for the foreseeable future with real-time data as one of the leaders.

  1. Predictive Analytics

Expanding machine learning and artificial intelligence has made innovation more accessible to a broad range of organisations, including smaller ones. As AI-based arrangements become increasingly more monetarily and technologically accessible, companies no longer need to create their own algorithms.

  1. Natural Language Processing

Today, there is a need for analytics solutions to make more accessible interfaces. Many people don’t know SQL, but an organisation needs someone with knowledge of the fundamental syntax to extract data from a large dataset. A large number of people lack the skills to fetch data, even though they may know what they are looking for.  Using Natural language processing to develop queries allows anyone with basic knowledge of analytics and data science to understand the output.

  1. Verticalisation

Software companies are increasingly being asked to provide verticalized analytics toolkits, in order to streamline the process. As a first step in improving your insight into business analytics and data science, you need to understand the skills needed for whichever job you are looking at taking up. For example, such as knowledge on how to analyse clickstream data generated by a website to reduce shopping cart abandonment and improve effectiveness; Information on how to investigate clickstream information created by a site in order to reduce shopping basket abandonment and improve advertisement viability; Information about how banks combine their overall record and deliver different types of estimates; or how insurance agencies break down data in order to provide an ideally evaluated strategy for current clients.

  1. Comprehensible Models & Transformational Insights

Businesses that rely on analytics and data for their decisions are increasingly employing models that are easier and effective to analyse. For example, in the KDD-CUP 2000 contest, a data mining competition in which insight was significant, the decision tree used, by and large, surpassed different techniques by a good ratio. As an entrepreneur, you might not be able to implement advanced statistical concepts or understand the equations and formulae behind them. But as long as it shows useful information about your business in a visual and tangible way, that would suffice.

For instance, evidence shows that salaries increase with age until the last bracket where it drops off. Evidence for better wages increases with years of education, the number of working hours, and particular social positions and occupations. Naive Bayes uses a straightforward method to produce a better forecast of the probability of different classes based on additional attributes.

  1. Integration of Analytics with Action and Measurement

Businesses are demanding more from their analytics, making it important to know what areas should be acted upon and managed to gain an edge over their competitors and drive successful transformations. The increasing use of analytics in business is changing the way that analysts inquire – “How would I transform found data into action?” and “How would I know the impact of each action?”

A while ago, successful stories of data mining ended in novel analytical outcome solutions that must use the analytical outcomes at the beginning moving towards future steps of action and measurement. For example, businesses might want to know which groups of consumers are more interested. The successful analytical solution must make it easier for the user to understand how these clusters impact the business’s plan. “Here are individuals with an ability to buy new designs.” Before these changes are made, it will require significant adjustments to the initial data models. Traditionally, achieving these goals usually required the input of expert analysts.


The advancements and patterns of business analytics crossing new technologies, UI design, and framework incorporation are driven by business value. This business value is measured as an improvement towards decreasing the gap between the needs of the business clients and the availability and usability of analytic tools.

New trends in business analytics and data science are emerging and evolving every day. They will become even more prevalent in the near future. And the demand for professionals with data science and business analytics skills is expected to increase significantly in the coming years.

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