Before we start talking about industries that use data analytics, let us first look at what data analytics is.
The discipline that studies the process of examining databases to find indications through information is called data analytics. Data analytics helps to discover patterns from raw data to extract valuable insights from it.
Data analytics is extensively used by data analysts and data scientists in their research. Today, a large number of businesses use data analytics to stay ahead of the competition. Data analytics helps companies to understand their customers and assess their internal and promotional campaigns.
It also aids in personalizing their content, creating and developing content strategies accordingly. The primary fact is that businesses are supposed to use data analytics in today’s world, or else they might lose their footing in the industry. According to a recent study, 79% of administrators feel that businesses that are not using data analytics will very soon fail to keep up with the competition.
Data analysis is the future of businesses, and there are numerous institutions out there that offer a suitable data analysis course to prepare the aspirants.
Now, as we know something about data analytics, let us look at three prominent industries that use data analytics significantly to make their businesses flourish even more.
The Agriculture Industry
It may sound surprising, but the agriculture industry does use data analytics to a great extent. Developing new and competent techniques for agricultural purposes is fundamental to the success of any economy. To support that cause and to make the industry grow, the contribution of data analytics is irreplaceable.
Let’s take India, for instance. In India, agriculture employs the largest chunk of the population. But there are lands in the country that are underused, which makes it detrimental to the farmers and the industry itself. A large number of Indian farmers, therefore, often face a shortage of financial resources to invest in proper machinery and fertilizers. They also lack amenities, including but not limited to appropriate technological equipment that could help optimize crop yields.
Data analytics creates several avenues of growth for the agricultural industry. It helps understand the market and provides farmers with data-driven suggestions for the ideal farming metrics. It increases the productivity of agriculture in terms of both profit and yield.
Data analytics helps comprehend environmental challenges too. It predicts environmental movements and helps farmers prepare for the hazards and exploit all the opportunities without wasting any time.
The Retail Industry
Retail is one of the industries that is constantly focused on providing their customers the right product. In this industry, the usage of data analytics is matchless. Data analytics enables the retailers to offer customers new recommendations on what to buy next and set offers and discounts to capture their interest.
Data analytics customizes a purchaser’s personal shopping experience. For example, suppose someone is buying a blue bucket online. In that case, data analytics will provide information about several other blue buckets so that the customer gets more options to choose from than just one.
A study by Adobe revealed that establishments who consider customer experience to be their priority and personalize the entire shopping experience of the latter accordingly are more likely to be surpassing their highest business target.
Optimization of pricing is also done with the help of data analytics. As a PWC study reveals, 60% of consumers believe that the most significant factor about purchasing a product is the pricing.
The Banking Industry
Data analytics has a significant impact on the banking industry. Banks, nowadays, are merging both internal and external customer data to build a predictive profile for each of them. Commercial organizations are also using the insights they are gathering to provide customers with value-driven assistance customized for each party. This practice is way better than pushing out marketing programs that treat all the customers similarly. A recent McKinsey study showed that a top-notch Asian bank used big data for analyzing client information like transaction data, payment inclinations, demographics, etc. It created 15,000 very small sections by discerning patterns in the data. It helped the bank target customers with more precision and heightened the probability of purchasing by three times.
Likewise, the public sector also uses data analytics, machine learning, NLP, and speech and image recognition, to present and maintain law and order and fight crime. The usage of these technologies in the health sector is also very significant. But we will discuss that another time.