Data Analytics: How can they make a difference in your business?
The aim of this article is to overview the evolution of data usage and its importance through the progress of information systems in the business sector. We will review some history, which data analytics are the choice of most firms, which are the different types of analysis carried out, how they are applied to the company, and why they are crucial for an organization that aims to grow and make the most out of its resources.
Knowledge is key when decision making tasks arrive, to achieve it you can benefit from Data collection, which is the technique that helps information come together as a piece; data generates information and information generates knowledge. It boils down to recording aspects of a business that one might want to look back to and make sense of later. The typical recordings are related to financial results, performance measured in time or other factors, and many other variations that can be relevant to the business. The use of data analytics dates back to the 19th century, it continued over time and branched in different types of analysis that could be done. Predictive Analytics enables us to forecast what may happen in a given situation based on specific data inputs, which shows the power that analyzing data can have. Cognitive Analytics allows us to analyze not only information but also human behavioural data from everyday life. For example: Nowadays, the medical industry is starting to use Cognitive Analytics to find the best possible treatments for their patients (Orbit Analytics, 2021). An example is Welltok, which offers a cognitive-powered instrument named HarmonixTM that can instantly process large volumes of data to answer questions and give intelligent, personalized recommendations. Welltok provides this service to health insurers and similar organizations to help their subscribers and patients ameliorate their overall health in a really personalized way (Hager, 2021).
At the end of the 1940s digital storage started growing. The first RAM (Random Access Memory) was developed in 1948, which evolved to very small memory modules with great storage capacity and then to Information Silos, Server Farms or Data Warehouses with a lot of data capacity. With the internet’s appearance, these evolved to cloud storage, which is the most modern approach for data storage around the world today.
Technology enhancement led to the most important period of database evolution. The release of Relational Database technology and Non Relational Database technology now allowed information to be crossed achieving statistics that output timelines, graphic representations and dashboards that companies or users need to make a decision. As technology evolved, storage increased its capacity, allowing databases to become more powerful and useful. Relational Databases and Non Relational Databases technologies could now be reliable enough to handle large amounts of data. Data and Business Analysts had access to cross information and deliver rich graphs and dashboards that lead executives can rely to forecast business trends.
Some of the most performed analytics by companies are Business Intelligence, Data Mining, and Big Data due to its highly customizable reporting and visual handling of information. Business Intelligence is focused on informing decision making through the visualization of business metrics such as financial results, performance, forecasts and any other that is useful for the company in graphs, maps, and any kind of infographic appropriate to represent the information in hand. BIs are typically real time dashboards that allow executives to glance and compare the up-to-date data. Data Mining helps obtain information from given data to find trends and patterns. The purpose of using Data Mining is to make decisions supported in data from enormous sets of data (Sharma, 2020). In the times of traditional marketing – i.e. through newspapers, magazines, television, etc – there was a significant challenge for marketers in terms of understanding customers’ needs and preferences. Nowadays, thanks to data and data analytics, marketers can store user’s search queries and behavioral data to make sense of their interests and build a profile. With it, a customer can be presented with the information that better helps their needs at a given time. This is clearly seen when we are presented with customized information when we visit a website. This customized information is built based on a profile created for you from whatever information is available. Hance, depending on your tastes and preferences – which are predicted based on your past behavior online – you can be presented with information that is of your actual interest. Big Data dates back to 2005. As its name implies, it is about using significant amounts of data. Big Data is a practice that small companies still consider a challenge since both the collection of vast amounts of data and its processing requires a lot of resources and equipment. A significant player in Big Data is Google, since it manages millions of queries from around the world, while constantly processing them to make useful sense of profiles, places, etc. Being able to process such vast amounts of information, Google is capable of matching search terms that allow them to display appropriate results to every search through their search engine.
How can an executive who has to make decisions benefit from all of this? It completely increases the performance of the company’s resources by offering analysis and statistics to monitor its execution. As well, it is useful to make informed business decisions, and to analyze external and internal clients better such as a specific area at the company. We would like to share some examples of sectors that are making use of Data Analytics with you. One of them is the travel industry and everything related to tourism: agencies use statistics, comparisons with past years or seasons and make predictions when looking for the best time to buy a plane ticket, lodging prices or flights. This kind of prediction is not only useful for the company but for becoming more of a differentiating value proposition for agencies. Through these kinds of predictions, whoever achieves a high degree of certainty of how prices will behave, can save big money to its customers and achieve higher margins. Another example is the retail industry: supermarkets, department stores, wholesalers, ecommerce companies, etc. are following the tendency of having a person with business knowledge in the role of analyzing data to help other departments such as commercial or strategic areas. As well, to inform the decisions pricing of items, time of purchase for stock purposes, and targeted marketing campaigns, among many others.
References:
Cognitive Analytics – Benefits & Real Life Applications. (2021, 21 septiembre). Orbit Analytics. Recuperado 12 de noviembre de 2021, de https://www.orbitanalytics.com/cognitive-analytics/
Sharma, R. (2020, 30 april). Data Mining Techniques: Types of Data,
Methods, Applications. UpGrad Blog. Recovered on november 3rd 2021, from https://www.upgrad.com/blog/data-mining-techniques/