Data Analytics is the science of data analysis that aims to draw conclusions through the study of collected information.
In recent years, many organizations and companies have changed their approach and, thanks to business intelligence and data-driven decisions, the percentage of investments with a return has increased significantly.
The task performed by the analysts allowed them to assist managers in solving problems, keep an eye on user interests, and analyze hidden advantages and pitfalls.
In this guide, you will discover the definition and main features, see the difference between raw data and data sets, and understand the complex workings of data management and data analysis through the main data management techniques.
I will give you the information to understand a very complex topic in a simple way and, by following small best practices, you will understand how to plan a successful business analytics strategy for your business.
But let’s see now what are the benefits of a data analytics strategy:
- administers and optimizes data management;
- improves business intelligence through historical data;
- provides information on profiling cookies;
- helps to make the best decisions;
- can identify new opportunities through prescriptive analysis;
- provides clear reports and optimizes marketing costs;
- improve your digital skills in web analytics.
If you want to learn more about data analytics and the process of capturing and managing digital data, read on. If, on the other hand, you are looking for a data analyst course then you can take a look at our Web Analytics Specialist Course.
What is Data Analytics?
Data Analytics is a discipline that deals with extracting, managing, and analyzing information contained in data using predetermined and functional acquisition processes.
Boring? Not for those who have understood its functionality and use it in their organization.
For decades, data science has driven the evolution of big data analytics by achieving astonishing results in data management. To a large extent, this has been made possible by the technological support and enormous computing power of supercomputers in the search and study of large volumes of data.
We often hear about Big Data in connection with giants like Google or Facebook, but these resources are not just a privilege for a few.
Data science and data analysis have helped enterprises and companies achieve amazing results from their business strategy. This is thanks to new business analytics policies, data-driven evolutions in decision-making processes, and advanced business intelligence methods.
Day after day, corporate data analytics becomes an increasingly necessary resource to be able to control and increase one’s business.
Data Analytics: what does it do?
To understand what data analytics does, one only has to look at search results and observe related searches recommended by Google or Facebook Insight. This is enough to understand its functionality and then begin to imagine the enormous potential of big data analytics.
It is thanks to these complex algorithms that continuously probe big data and data trends that the results in search engines are consistent and comprehensive. It is also thanks to them that today we are able to understand exactly which users we attract to our website or social networks.
Where does big data come from?
Every action performed within a tracked system is recorded and written down, anonymously, in the form of raw data in Cloud or Data Centre facilities. All with the user’s consent, of course.
Just imagine the staggering amount of profiling cookies alone collected during the daily browsing of billions of users. In order to analyze these large volumes of information, data scientists have developed advanced forms of machine learning capable of performing big data analytics operations autonomously and generating reports in a short period of time.
If you think this can only happen in the online world, here’s a question: do you ever spend your loyalty cards when you go shopping? Here is how valuable information on physical purchases can be stored and reused in a business analytics and business intelligence process.
If you have a website, a blog, or e-commerce you will be interested to know that, in all likelihood, you are also collecting some data. Indeed, it is unthinkable to grow any kind of online business without tracking the purchasing behavior of customers.
The use of cookies and tracking systems in fact serves precisely this purpose. If we decode the information collected with data management software, such as Google Analytics, for example, we can understand through patterns and reports the nature of our online traffic.
In a short time, you can start building a business intelligence plan, and find out more about visits, which pages are most read, which products are most searched, and how many users do or do not browse the web pages.
In summary, gaining an objective view of reality with data analytics helps to identify new opportunities, catch new data trends and make informed decisions.
What are the 4 types of Data Analytics?
Data Analytics is such a wide world that its processes are used differently depending on the purpose. There are different types of Data Analysis that a Data Analyst uses to find information and respond to specific business objectives. Here are four of them that I will elaborate on in the following paragraphs:
- Descriptive analysis;
- Predictive analysis;
- Prescriptive analysis;
- Machine learning.
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Why develop a Data Analytics process
Data collection in data analytics, data analysis, and data management are key activities for enterprises, companies, and professionals because they enable them to streamline and simplify processes that are crucial for business growth.
It is no coincidence that many organizations are aiming to increase business intelligence with the wise use of big data and machine learning to gain more strategic insights and enhance marketing strength over the competition.
By analyzing data and information one can understand how to intervene in the communication strategy to make it more effective, modify the user experience to facilitate user navigation, and intercept consumer preferences and needs.
Data analytics can provide real-time insights into the health of the business, enable optimization of budgets, and systemic increase in return investments.
Data Analytics: operation and information gathering
Let’s take a quick look at how Data Analytics works and how information is gathered to start creating a business intelligence strategy. Depending on the needs, different data analysis techniques can be used, from the simplest to the most complex.
The main point to work on is the design of the strategy that will be the reference for the development of data governance. First, therefore, it is necessary to establish the business objectives and identify the data to be monitored.
In the second step, we need to establish which metrics and KPIs we need to analyze in order to determine the progress of our actions and the results we want to achieve. These will be the main indicators of data management.
Now that we have established the Data Strategy guidelines, we need to think about how to retrieve the data and where to store it. Except in exceptional cases, one can rely on cloud systems that record raw data in Data Lake storage and then define it in Data Sets.
Thanks to cloud systems, raw data can be easily accessible and immediately usable, ready to be analyzed. There are numerous tools to perform the analysis process, generally, data visualization tools process data and present it in the form of graphs.
Data visualization software is often the best and most practical choice as it offers the user an easy way to explore data, create views and models in data virtualization, and customize the analysis as needed without risking affecting the original master data.
The whole of this strategic and tactical data management apparatus is called data governance and, as you will have realized, is an essential prerogative especially when we talk about big data analytics.
Data categories
Big data or not, in Data Analytics, data are not all the same and have different compositions and characteristics depending on the structure of the category they belong to. There are three types of data and we can classify them into:
- structured data;
- unstructured data;
- semi-structured data.
Structured data is mostly referred to as historical data and constitutes the best-known and most widely-used category, even by those who are new to the world of data science. These are data organized in relational databases that, by means of software or business analytics tools, are able to show the current and past situation of the observed patterns with a simple and intuitive graphic structure (data visualization).
Unstructured data is primarily understood as raw data. This is information that does not have a predefined data model and therefore does not allow direct access. The unprocessed structure appears in the form of text and may also contain numbers, dates, and facts.
Semi-structured data, on the other hand, as the name suggests, are not constituted in relational databases but still possess a structure and certain organizational properties that simplify their analysis.
Types of Data Analysis
Descriptive Analysis
Descriptive analytics (or descriptive analytics) is the simplest and most widely used form of Business Intelligence which, through KPIs (key performance indicators), allows historical and current data to be analyzed and compared in order to better understand what is happening along the way.
If you needed to understand how much a website or e-commerce is growing in terms of interest and traffic in relation to a marketing action, this would certainly be the type of analysis that could easily and quickly give you the answers you seek.
Predictive Analysis
As the name suggests, predictive analytics, or predictive analytics, is useful for predicting future changes based on current data. Thanks to high processing speeds and complex algorithms, it is possible to develop models, anticipate the occurrence of problems, and prepare in time for evolutions in purchasing systems and changes in trends.
Think, for example, that you want to launch a product in the market, in which case you will need specific data analyses to identify new opportunities, meet customer expectations and understand whether the idea can work or not. By determining the advantages and pitfalls in advance, you can optimize your strategy and make the most effective decisions to go to market.
Prescriptive Analysis
Prescriptive analytics or prescriptive analysis is used to formulate an actual prescription and is based on the technical ability to formulate the right questions and propose the best choices.
Here is a simple explanation of how prescriptive analysis works. AI comes into play and thanks to Artificial Intelligence the data analysis process becomes elaborate and complex enough to be able to interpret the information gathered and formulate solutions to optimize a situation, determine the best action to take, and refine the decision-making process.
A practical example can be found in the optimization suggestions made by tools such as Google Analytics, Search Console, or even Google ADS and Facebook sponsorship campaigns. Through prescriptive analysis, the systems are able to provide the necessary indications for performance improvement and budget optimization.
Machine Learning
Machine Learning was born with the aim of facilitating and assisting human intervention and has the ability to automate decision-making through a data-driven learning and evaluation principle. Machine learning uses complex algorithms to manage and process huge amounts of data, build models, determine answers, and make decisions with next-to-zero demand for human intervention.
Its most glaring application might be in the self-driving car. But without having to go too far, this technology is already widely used in everyday life.
Platforms such as Amazon, Netflix, or Spotify, use Machine Learning technology to offer a user experience increasingly focused on personal interests and tastes, just as Meta uses it to understand what content is right for us and propose it within the Facebook or Instagram feed.
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Data Analytics Tools and Software
In the panorama of Data Analytics tools, the choice is wide and varied. If you are here, you will probably also be interested in finding out which are the most important and widespread tools on the web, useful in any Digital Marketing strategy.
You have read the best practices for creating a business intelligence strategy, now here is the list of the 7 most used software in the field of Web Analytics:
- Google Analytics;
- Hotjar;
- Hubspot;
- Adobe Analytics;
- Google Trends;
- SEMRush;
- Facebook Insights.
Google Analytics
In the sphere of the web, Google Analytics is certainly the best known, free of charge, and offers all the functionalities to accurately observe the traffic of a website or app. It allows you to manage structured historical data, organize metrics and dimensions, set goals, learn about user preferences, monitor conversions, and much more.
Hotjar
Through mouse tracking, Hotjar can tell us a lot about the user experience of our website. It is a tool that observes user behavior and, in addition to providing us with actual video recordings, processes the collected data in the form of color maps revealing the points of greatest and least interest.
Hubspot
The father of automation marketing, Hubspot is a powerful CRM analysis tool that can measure the performance of marketing campaigns and track the customer lifecycle. It is the software of excellence for lead nurturing and is also quite expensive, but much cheaper, valid alternatives can be found on the web.
Adobe Analytics
In the list of professional tools, Adobe Analytics cannot be missing. It is cloud-based and is a suitable solution for integrating every active marketing channel into one comprehensive analysis system.
Adobe Analytics scored best for multi-channel attribution, is great for centralizing different data sources, and focuses on machine learning and predictive algorithms for advanced insights. However, apart from a few technical indiscretions, the cost is very high.
Google Trends
Google Trends, is the perfect tool for anyone interested in data trends and who wants to find out in real-time which topics are the most followed and searched for on the web. Easy and intuitive, it is a free and immediate resource for understanding the popularity of a keyword or topic and discovering users’ main interests.
SEMRush
Many know it for its outstanding SEO and SEM capabilities. SEMRush is a very useful tool for keyword research, but among its functionalities, we find not only tools for Search Engine Optimisation analysis; this tool goes much further by allowing you to view important metrics about your online presence, perform market analysis, look at the state of your competitors and discover their major traffic sources.
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Facebook Insights
Anyone with a business profile or creator will certainly have had to deal with Facebook Insights. Meta’s proprietary tool holds all the information on the progress of our social activity.
Dimensions and metrics are very easy to explore, can provide invaluable information to learn more about the audience, understand how to increase user engagement, and by testing different types of content determine what works and what does not.
Data analysis: benefits for companies
As we have already mentioned, implementing a corporate Data Analytics strategy is instrumental to the success of any business. Collecting data related to the product or service offered is useful for examining reality in an objective manner, free from any purely personal convictions.
Acquiring this awareness puts us in a position to examine information, see a problem early and reason out possible solutions, carry out targeted tests and make decisions based on concrete and tangible results.
More and more companies are looking to data-driven processes or business intelligence to gain a significant competitive advantage and increase their business performance.
A not inconsiderable opportunity for those who would like to get involved and secure a place among the digital professions most in demand in the labor market.
Conclusions and new professional opportunities
The technological advancement of Data Analytics systems is reaching more and more state-of-the-art levels every day, but if you are wondering whether automation can currently completely replace a Data Analyst or Data Scientist, the answer is no.
A Web Analyst can still enjoy that component that, however advanced digital development has reached, every Artificial Intelligence algorithm can only dream of achieving: intuition and creativity.
We do not know if and when this technology will be able to stand on its own feet, what we do know is that more and more companies are slowly discovering the potential and new opportunities of Data Analytics.
Becoming an expert in the field of Data Science and Analytics means equipping yourself with a passport to the world of work and leading to totally unexpected personal growth results.
The world of Web Data Analytics is just a step away. Book a Free Consultation and discover the right path for you!
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