The business industry has seen a growing demand for data and analytics. Analytics is becoming a significant driver for innovation and changing business models over the years. Since it is becoming popular, a lot of data analytics tools are emerging. But which is the best among them?
Data analytics has been in the spotlight over the past decade. Even now, the demand for data and analytics is still rising. A lot of organizations are harboring more and more talents in data analytics to improve their business models and strategies further. However, how much do we know about data analytics and data analytics tools?
Every report, predictions, and their outcome are from data. These data have patterns and relationships that can help people and machines alike. Data analytics is the systematic process of analyzing raw data to uncover specific information. The goal of data analytics is to produce useful information to help both people and computer programs in decision making. It also generally helps optimize business performance.
Data Analytics Process
Data analytics has four basic types: descriptive, diagnostic, predictive, and prescriptive. Even with these four types, data analytics has a commonly known process.
Step 1: Question
When you conduct business data analysis, you must first define the right question. Start with identifying the problem within your organization. Without clearly defining the problem, you will not know which data to collect.
Step 2: Data Requirement
Determine the data requirement. Data can be grouped by demographics such as age, income, gender, and geography.
Step 3: Collecting Data
The third step is the gathering of data for analytics. These data can be through the Web or online resources, encoded data in computers, environmental sources, or other data sources provided.
Step 4: Organizing
The data gathered will be assured of quality, which means it is organized according to relevance. Then, these data should be prepared to work with the data analyst.
Step 5: Cleaning
The last step is to prepare the data for analysis. The data goes through a “cleaning” process where duplicate and errors are eliminated. These data are appropriately formatted for the data analyst and software.
Data Analytics Tools
Analytic tools vary in different forms; the same way data analytic types are different from each other. To introduce you to data analytics, here are the top five open-source data analytics tools that we compiled in no particular order of popularity nor efficiency.
The list is based on various online sources, data science forums, and subjective preferences from different data scientists and analysts.
R Analytics or R programming language is an open-source software popularly used for massive statistical computing and data mining. What is good about this program is its inclusivity. It does not only analyze data but could also create software and applications that can perform statistical analysis.
It can produce plots and graphics ready for publication down to mathematical notation and formulae. Remember that it needs proper communication to present data outcomes to stakeholders.
It also includes command-line scripting that allows users to store and reuse sophisticated analytical methods with new data later on. Instead of reconfiguring, users can simply recall, making it more convenient for validation and confirmation purposes.
Companies that use R analytics are Accenture, Cognizant, Google, Facebook, Citibank, Genpact, MuSigma, and Fractal Analytics.
Though known as a complex language, it is still prevalent for data analytics.
Most commonly used for:
- Building custom data collection
- Analytical models
Another data analytics programming tool accessible in the data analytics field is Python. They often refer to Python as one of the most valuable skills when pursuing a data science career.
Python’s common usage includes data crawling, data cleaning, data modeling, data visualization, constructing data analysis algorithms from real-time business scenarios and problems, machine learning, and text mining.
Similar to R, Python is a tool that needs to be programmed. Their difference lies in their core features. R is exclusively popular in the field of data analysis, while computing and data analysis is simply a branch of Python. Other than data analysis, Python can also be for maintenance and development, such as websites, games, and system backends.
If you are not adept at coding, there is no need to panic. You can still do data analysis without writing any code with Orange. This open-source toolkit can do data visualization, machine learning, and data mining easily.
Orange is best for both beginners and professionals. Its best features include interactive data visualization, visual programming, and has add-ons functionality. It also supports hands-on training and visual illustrations.
Orange can also execute simple and complex data analysis and external access functions for advanced analysis.
For visualization and business intelligence, Tableau is a tool that a lot in the data analytics field knows. Why? It helps simplify raw data to an easily digestible format.
This tool can perform data analysis fast and create visualizations through dashboards and worksheets. Tableau’s data output is easily understood by professionals at any level, even those who are non-technical.
- Fast and easy visualization creation
- Good customer support
- Helps combine, shape, and clean the data for analysis
- Can handle a large amount of data
- Use scripting languages such as R and Python
Power BI or also known as Microsoft Power BI (business intelligence) is a business analytics tool used to analyze data and share insights into your organization. What made this software famous is its convenience of pulling data together to process it into intelligible insights. It is often through compelling and easy-to-understand charts and graphs.
Remember how data gathering is one of the first few phases of data analytics? Not all data gathered will automatically make sense. In the end, they end up piled and unused. To make use of this piled up data, Power BI recalls these data from different sources and turns them to something useful.
- Offers built-in dashboards and reports for SaaS Solutions
- Real-time dashboard updates
- Power Query provides options related to wrangling and cleaning data
- Extensive database connectivity capabilities
- Feature dashboard visualization
- Data exploration using natural language queries.
- Secure connection to your data sources may it be in cloud or on-premises
A Data Analyst Should be…
Data analysts are a rare find. For one to a skilled analyst, they should have a strong background in statistics, data mining, and relational and non-relational database design. Keep in mind that data analysts are critical thinkers; they should be aware of which data are relevant or not.
When it comes to more technical skills, your candidate should be knowledgeable with different data analytics tools. Additionally, they must know how to write and execute complex queries in SQL.
However, there is a current shortage of data analytics talent in the United States
As the demand for business data analytics rises, skills gaps also widen. An August 2018 LinkedIn Workforce Report stated that data science skills shortage is present in almost every large city in the U.S. How do you address this? There is always an option to hire offshore.
If you want to hire skilled data scientists and more, you can partner up with Full Scale. Our employees possess both skill and dedication. Once they become a part of your team, they will do their best to help your startup reach the next level.
Give us a quick call and share your thoughts on how to transform data into something that can help your startup.