What is Data Analytics and what is it for?
Companies constantly collect large amounts of data - on sales, consumer behavior, company processes and so on. The data itself, however, is only a series of numbers and letters of little use. In order to be used, they must be organized and analyzed. This is what Data Analytics is for.
The goal of Data Analytics activities is to draw practical conclusions based on the raw data. Thanks to this, we can make better business decisions and optimize processes.
When analyzing data, patterns are sought to better understand the current situation of the company, past events and predict the future course of events. This applies, for example, to trends in consumer behavior, employee behavior or the course of advertising campaigns.
Companies not only collect data on their own, but they also use research carried out by external organizations or they request such research to be done. They can also buy data on the aftermarket.
If a company processes large amounts of data, we are dealing (as the name suggests) with Big Data. Of course, they are characterized by size - they are petabytes of information that cannot be processed using traditional tools, but only specialized ones (such as Apache Hadoop, ElasticSearch, Cassandra or Spark).
Big Data are also diverse, i.e. they come from different sources. We are dealing here with spreadsheets, e-mails, sales data from the online store, those regarding user behavior on the website or data from social media.
Data must be processed in real time for the analyzes to be up-to-date. Machine learning (ML) and artificial intelligence (AI) are used for the analyzes. They can see patterns in the obtained data and draw conclusions from them.
Types of data analyses
It is used to describe a situation based on previously collected data. In this way, it is possible to assess whether the key performance indicators (KPIs) have been achieved. These include the most general ROI (return on investment) or more detailed indicators for specific types of activities. This can be a conversion rate, for example in advertising campaigns and other online activities, or customer lifetime value (LTV) in e-commerce.
It allows you to determine why a given event or process took place. The diagnostic analysis uses the data collected during the descriptive analysis to find the cause of situations that have arisen. In this way, we will find out, for example, why the campaign conversion suddenly decreased thanks to the detection of anomalies, i.e. unexpected changes. Statistical techniques find relationships and trends that explain anomalies. It is used, for example, in data mining.
Its task is to predict future events and trends. With the help of the data collected in the above mentioned two analyzes, regularities are identified and forecasts can be made. Machine learning (ML) or artificial intelligence (AI) are often used for this purpose, which, when ‘fed’ with large amounts of data collected during descriptive research, can see repetitive patterns in them, on the basis of which draw conclusions are drawn.
It recommends actions to be taken. It is based on predictive analytics and allows managers to make more informed decisions. Machine learning (ML) or business rules are used here.
Why it is worth using Data Analytics?
Data analytics can be used for many different purposes. Companies, institutions and ordinary consumers benefit from it. What can it help with?
- Optimal financial maganement
Data Analytics allows you to precisely analyze the revenues, expenses and profitability of the company. It is easier to optimally plan your budget, create long-term business plans or forecast and manage business risk. The company can also improve management of its resources.
- Precise marketing
By analyzing the data, you can get to know your target group better. We see more clearly the behavior and needs of consumers in order to optimize the marketing campaign. We also have a chance to conduct personalized marketing activities, directing accurate messages to specific users (Marketing Automation tools will be helpful here).
Examples of marketing personalization include recommendations from services such as Netflix or Spotify. Their algorithms, based on large amounts of data, are able to recommend content to a specific user in accordance with their preferences.
- Better customer service
When we get to know our customers' needs better, we will also be able to improve the way we serve them. We can, for example, analyze the questions asked to shape the algorithms of bots used in instant messaging. Consultants will also receive access to structured data, which translates into consistent customer service.
- Better security
Cyberattacks are a serious threat to companies and their data. However, the fight against cyberattacks does not have to be based only on an ongoing response to them. Thanks to data analysis, it is possible to diagnose their causes and predict future incidents. Statistical models are created and anomalies are detected to alert against possible attacks.
- Lower risk
Data Analytics is used, for example, by companies from the banking and insurance sector. It allows you to predict what the risk is when granting a loan or what the price of the policy should be. Trading companies, in turn, can use it to predict market trends to optimize inventory levels.
- Smart offices and other workplaces
A network of sensors operating within the Internet of Things (IoT) at home, office or workplace is able to collect a lot of helpful data. Their analysis makes it possible, for example, to regulate heating and cooling.
This is just one of the possible uses of Data Analytics in IoT. The use of intelligent devices gives a chance for far-reaching automation, also in e.g. urban infrastructure. The behavior of the inhabitants is predicted and the devices adapted to them. In the so-called Smart Cities analysis of data from sensors enables better management of traffic and transport systems, power plants, communal services, water supply networks and facilitates the fight against crime.
Better data-driven performance
Using Data Analytics allows the use of data that each company collects. Thanks to this, you can make better decisions, automate processes or predict trends and events.
However, even when we use advanced AI and ML mechanisms, there are people at the end of most analytical processes. They decide what actions to take based on the information received. Therefore, it is important that the company employs experienced analysts.