Statistical and descriptive data are available all around us. But what is data analytics in business? Is it worth it for businesses to invest time and money to process data?
The answer is straightforward. Every business, from small start-ups to big companies, needs data analytics.
Stay tuned as we dive deeper into the basis of data analytics, different types of data analytics, methods and tools used to process the data, and much more.
What Is Data Analytics in Business
Data analytics is the science of analyzing raw data by collecting and assessing the information to draw conclusions and identify patterns.
Business data analytics is the process of examining data to find important insights about the business’s performance and mapping out trends. The business can make strategically guided decisions after the data is processed and ready to use.
Why Is Data Analytics in Business Important
Analyzing data is crucial for all businesses, no matter the industry they are working in and their size. So how can a business benefit from data analytics?
Typically, businesses collect data from three sources: their customers, site visitors, and third parties. Through analytics of the data, businesses are trying to gain a deeper understanding of the business’s operations, customers, and market trends.
Businesses can leverage data analytics by identifying growth opportunities, optimizing marketing and sales strategies, reducing costs, and improving operational efficiency.
Big data analytics
One of the reasons why data analytics in business is important is that it gives the company valuable insights that can be easily lost in the mass of information. This leads us to answer the commonly asked question, “What is big data in business analytics?”.
Big data analytics is a complex process of going through a large volume of data to find out hidden trends, correlations, and other important insights. In big data analytics, the data can be as large as zettabytes.
Furthermore, big data analytics is known for its complex sources and high volume, velocity, and variety. With bigger data to analyze, the benefits are more emphasized and include faster and better decision-making, operational efficiency, cost reduction, and risk management.
Legal data analytics
Data analytics is even used in the legal industry. Now you may ask, what is legal data analytics? Just like in every other industry, legal data analytics helps lawyers in their decision-making while they are building their legal strategies.
Interestingly enough, lawyers additionally use eDiscovery, which helps them build their lawsuit cases or investigations. Lawyers use electronic data during litigation, and the data they find can be used as evidence.
Who Needs Business Data Analytics
We already mentioned that every business could benefit from data analytics. But which professionals in particular need processed data?
Although every level within the company should know the insights found through data analytics, the most common ones that use data analytics are the decision-making roles. Those that benefit the most are::
- Marketers who develop marketing strategies.
- Product managers who can use it to improve the products.
- Human resources that change how the organization operates according to employees’ needs.
- Sales representatives that adapt their pitch according to the demands of the customer.
- Finance managers who use the data to estimate the financial trajectory of the company.
- Top management uses data analytics to generate value and reach their strategic goals faster.
Data analytics steps
You can’t fully understand what is data analytics in business if you don’t know how it is done. First, you need to understand the steps involved in data analytics for businesses.
In a nutshell, what does a business data analyst do? A data analyst is responsible for:
- Defining the analyst objective.
- Deciding how the data should be grouped.
- Collecting the data from various sources.
- Organizing the data in spreadsheets or by using other software.
- Cleaning the data, deleting duplicated information, errors, and incomplete data.
- Sending the data to a data analyst.
- Interpretation of the collected data and visualization.
The last steps of data analytics include presenting the findings to those who might benefit from them. After that, the decision-makers decide how to use the data to improve the organization’s work.
What Are the Methods and Techniques of Data Analysis
There are two main methods for data analysis: quantitative and qualitative. Both categories are powerful methods that businesses can use to their advantage.
Quantitative instruments for data analysis
All the quantitative methods for data analysis use numerical data to draw valuable concussions about the business. In other words, quantitative methods use every data that can be turned into numbers.
The qualitative methods for data analysis are also called statistical data methods. There are several types of quantitative techniques, including:
- Cluster analysis – grouping data into clusters according to their similarity
- Cohort analysis – grouping people and analyzing their behavioral patterns
- Regression analysis – shows the correlation between two or more variables
- Neural networks – an AI processes the data, but in a way the human brain would discover the insights and values.
- Factor analysis (dimension reduction) – reducing a large volume of variables and putting them into a common score.
- Data mining – uses exploratory statistical evaluation to identify trends, correlations, and dependencies.
- Time series analysis – analyzing data from a specific interval of time.
- Decision trees – visualizing data through classification and regression to make the decision process easier by listing the possible consequences.
- Conjoint analysis – determining how customers value the products and services.
- Correspondence analysis (reciprocal averaging) – uses a table of frequencies to show the relationship between two nominal variables.
Qualitative instruments for data analysis
Unlike the quantitative data analysis methods, the qualitative collects and analyzes non-numeric data, like words, pictures, observations, and symbols. The qualitative method uses descriptive or textual data.
The techniques used by qualitative methods are:
- Text analysis (text mining) – classifying, sorting, and extracting information from texts to discover business insights.
- Content analysis – analyzing data and determining the frequency of use of certain words, themes and concepts.
- Thematic analysis – analyzing data to look for thematic patterns.
- Narrative analysis – discovering the meaning behind people’s stories.
- Discourse analysis – analyzing the linguistic and sociolinguistic context of text structures
- Grounded theory analysis – using comparative analysis on systematically obtained data.
What are the Types of Data Analytics
There are four main types of data analytics, depending on what the data describes and how it will be used. Data analytics can be descriptive, diagnostic, predictive, and prescriptive.
It is important to note that the different types of data analytics can be combined to get more in-depth statistical data.
The goal of descriptive analytics is to provide an answer to the questions, “What happened” and “What is happening currently”. Typically, descriptive analytics is used for data that is easily interpreted.
The most common outcomes of descriptive analytics are reports and data visualization. With descriptive analytics, the business can clearly see the correlation between past and present organizational events.
Descriptive analytics is used by almost all levels in the business structure.
The focus of diagnostic analytics is to figure out why something happened. Diagnostic analytics has a more hypothetical approach compared to the other methods.
The goal of diagnostic analytics is to compare past trends, find correlations between the variables and see how the events are connected. Diagnostic analytics uses different techniques, including data discovery, data mining, correlations, and data drilling.
Diagnostic analytics can be used by every business that wants to determine the causes behind particular events in the past which have resulted in specific outcomes.
As the name suggests, predictive analytics can give predictions of what will happen in the future by processing the trends from the past. Predictive analysis is used interchangeably with descriptive and diagnostic analysis.
This type of analysis uses historical data and industry trends to discover probable event outcomes. Additionally, it determines the likelihood of something happening.
Predictive analytics uses different types of techniques to get the estimations, including machine learning, game theory, data mining, linear regression, time series analysis and forecasting, and decision trees.
Perspective analytics gives businesses suggestions about courses of action. It generally relies on machine learning, AI, and heuristics, and it is used for risk management.
This type of analytics considers all the possible scenarios. Additionally, predictive analysis goes to step further and explains the possible implications of each decision. A perspective analysis can also be used to mitigate future risks.
Hence, this type of data analytics is especially useful for businesses that are making data-driving decisions.
Other data analytics types
Although those mentioned above are the four main data analytics types, it is also important to know the following:
- Real-time data analytics – analyzing data right after they are collected.
- Augmented data analytics – data is analyzed with machine language and natural language processing.
- Exploratory analysis – exploring possible connections between data and variables.
Data Analytics Tools
In addition to the various statistical and descriptive methods for analyzing data, data analytics in business can benefit from a lot of software tools. There are data analytic tools for every step of turning data into insights, from data gathering to sharing documents and presenting insights.
Business intelligence tools
- What are they used for: collecting data from internal and external systems and processing and analyzing the already collected data
- Examples: Microsoft Power BI, Tableau, QlikSense, Dundas and Sisense
Statistical analysis tools
- What are they used for: statistical analysis via computation techniques and programming languages
- Examples: Posit (R-Studio) and MATLAB
Qualitative data analysis tools
- What are they used for: analyzing data coming from descriptive sources like interviews, customer feedback, social media comments, emails, etc.
- Examples: MAXQDA, NVivo, Cauliflower, Qualtrics, Dovetail and Delve
General-purpose programming languages
- What are they used for: solving a problem by using programming languages from letters, numbers, and symbols
- Examples: PyCharm
- What are they used for: managing and structuring data from relational databases with the help of programming languages.
- Examples: MySQL, Oracle, MS SQL, and PostgreSQL
Standalone predictive analytics tools
- What are they used for: predicting future events and their outcomes by using data mining, machine learning, AI, and predictive modeling
- Examples: Altair, IBM Watson Studio, Microsoft Azure Machine Learning ,and RapidMiner Studio
Data modeling tools
- What are they used for: using diagrams, texts, and symbols to structure data, determine their nature and represent data flows.
- Examples: Erwin Data Modeler, Lucidchart, Oracle SQL Developer, and Toad Data Modeler
- What are they used for: technical data management by extracting, loading, transforming data, and building queries.
- Examples: Talend, Fivetran, Microsoft Azure, Informatica, and IBM InfoSphere DataStage
- What are they used for: fully automated data analysis without human interference.
- Examples: Jenkins, DataRobot, Darwin, and Auto-Weka
Document sharing tools
- What are they used for: sharing interactive documents with others in order to get feedback
- Examples: Google Data Studio, Jupyter Notebook, IBM Cognos, Microsoft Power BI and Sisense
Unified data analytics engines
- What are they used for: big data management and building data pipelines using AI technologies
- Examples: Apache Spark, Vertica, Zoho Corporation, and Cloudera
- What are they used for: data analysis that doesn’t require special technical abilities
- Examples: Microsoft Excel, Google Sheets, Numbers, Quip, and Zoho Sheet
Industry-specific analytic tools
- What are they used for: data analysis for businesses in a specific industry
- Examples: Qualtric
Data science platforms
- What are they used for: preparing and integrating data, making reports, and finding trends by simplifying the data analytics process
- Examples: Saturn Cloud, RapidMiner, KNIME Analytics Platform, TIBCO, and Anaconda
Data cleansing platforms
- What are they used for: eliminating errors, removing duplicate data, and finding inconsistencies to bring more valuable and precise conclusions
- Examples: OpernRefine, Trifacta, Informatica and Cloudlingo
Data mining tools
- What are they used for: determining meaningful business trends by processing large volumes of data from different sources
- Examples: RapidMiner, Orange, SAS, Rattle, KNIME, and Oracle Data Mining
Data visualization platforms
- What are they used for: visualizing the insights found with data analytics so the decision-makers can understand them better
- Examples: Google Charts, Infogram, CHartBlocks, Grafana and Cartist
Ultimately, what is data analytics in business? Here, not only did we learn what data analytics is, but we learned about the different methods, instruments, and importance of data analytics. Investing in a team of good data analytics professionals or hiring a third party is definitely worth it in the long run.
Data analytics in business allows better risk management and making informed decisions about products, employees, finances, partners, and planning future steps. With the growing data volume, data analytics is important in the fast-evolving marketplace.
Date: 2023-07-20 07:44:07