Over the past decade, data analytics has evolved from a mere field of study to a catalyst for business growth. Data analysts leverage their expertise to help businesses in all sectors to streamline their operations, make better strategic decisions, personalize the customer experience, and a lot more.
In this article, we will discuss the importance of data analytics, the overall concept and how it is applied in the business world today.
Data analytics analyzes raw data to uncover trends and answer questions. It encompasses a wide range of techniques with various goals. Data analytics can support a wide range of initiatives. The combination of these data analytics components will give you a clear picture of where you are, where you have been, and where you need to go.
Data analytics helps businesses optimize marketing campaigns, boost revenue, improve security and deliver operational efficiency. Data analytics tools and techniques are now widely accessible and vital for businesses of all sizes.
DATAFOREST offers data analytics services to many different industries, including e-commerce, retail, finance, manufacturing and more. If you are looking for data analytics services to enhance your business productivity, check out our data analytics services.
The idea behind data analytics
Data analytics is an umbrella term that encompasses many types of data. Data analytics techniques can be applied to any information to gain useful insight. Amid a sea of information, data analytics can identify trends and metrics that would otherwise go unnoticed. Business and system efficiency can then be improved by optimizing processes based on this information.
This is best illustrated with an example. In manufacturing companies, machine runtime, downtime, and work queue are often recorded so workloads can be planned more effectively and so the machines stay at peak performance.
When DATAFOREST worked with Biomat, a plastic manufacturing company, we developed an ETL solution that combined all required data sources, making it possible to analyze and identify bottlenecks in the manufacturing process.
What are the steps followed during data analysis?
Here are a few general steps of data analysis that are followed by data analysts:
- Data requirements analysis or grouping of data. Separating data by for example age, demographics, income, gender, numerical and categorical forms.
- Data collection. Data can be collected from many places, including computers, online sources, cameras, sensors, environmental sources, and personnel.
- Data organization. Statistical Data may be entered into a spreadsheet or another form of software.
- Cleaning and verification. Data is scrubbed and checked to ensure it does not contain any duplications or errors and is complete.
- Lastly, the data is analyzed using intelligent tools to retrieve valuable information and facts that can be used by businesses to make strategic decisions.
Data analysts at DATAFOREST are highly-trained professionals who have worked in the industry for some time and gained the critical experience required to keep up with the constantly changing trends in the business world. You can always reach out to DATAFOREST experts for a consultation on how a data-driven approach can create tangible value for your business.
Types of Data Analytics
There are four major types of Data Analytics. Read about them in detail below:
- Descriptive analytics: Describes the evolution of a given parameter over time. The purpose of descriptive analysis is to provide a summary of past events or outcomes. Through the analysis of past data on a specific subject, descriptive analysis can be used to portray what the data indicates.
Descriptive analysis can be used to evaluate a variety of business components, including key performance indicators (KPIs), revenue, and sales leads.
- Diagnostic analytics: Focussing on the causes of events, diagnostic analytics helps to identify why a certain situation occurred. For example, a business may experience sales growth in a particular demographic after a descriptive analysis has been performed. Diagnostic analysis can provide insight into the reasons that led to the growth.
Using diagnostic analysis, one can gain a deeper understanding of how or why the growth in sales occurred, as well as potential marketing strategies that worked better. Diagnostic analysis requires more diverse data inputs, and some hypotheses are also required.
Predictive analytics: In this technique, data previously derived from past analyses are analyzed and reported to provide a forecast of future events and outcomes. In addition to qualified leads, risk assessment, and customer satisfaction, predictive analytics can also be applied to more complex predictions.
For example, businesses that are attempting to predict sales for the next year will use past sales data to gain foresight that can be used to make actionable decisions.
- Prescriptive analytics: In prescriptive analysis, collective insights from the other three types are combined to produce actionable data. A business can use this data to formulate or implement its strategies. This analysis uses actions to be taken by the firm. For example - Our brewery should add an evening shift and rent an additional tank if the likelihood of a hot summer is higher than 58% based on these five weather models.
DATAFOREST’s data science services provide a suite of professional data analytics tools, techniques and consulting. Predictive analytics is one of the primary techniques. It helps businesses ascertain their future in terms of estimated sales growth, expansion in user base etc.
How does data analytics work and what are the key technologies it uses?
The importance of data analytics is clear as it helps organizations operationalize their big data by collecting, processing, cleaning, and analyzing large datasets. To get a deeper understanding of how data analytics functions, consider the following:
- Cloud computing: Data analytics in cloud computing refers to the use of cloud-based resources to store, process, and analyze large amounts of data. One of the main benefits of using cloud computing for data analytics is the ability to scale resources up or down as needed, without having to invest in and maintain physical infrastructure.
- Data management: Data management using data analytics can help organizations to better understand their data and use it more effectively, enabling them to make more informed and data-driven decisions.
- Data mining: Uses statistical and machine learning techniques to analyze data, and can be used to extract valuable insights and knowledge from data. Data mining can be applied to a wide range of data types and sources, including structured data in databases, unstructured data such as text and images, and streaming data from sensors and other sources.
- Data storage: The process of storing digital data on a computer or other electronic device. There are many different types of data storage technologies and devices, including hard drives, solid-state drives, and cloud storage.
- Data lake: A data lake is a centralized repository that allows an organization to store and process large amounts of data flexibly and cost-effectively, and to support a wide range of data analytics and machine learning workloads. It generally uses a distributed file system, such as Hadoop Distributed File System (HDFS), to store data in its raw, unstructured form.
- Data warehouse: A data warehouse typically stores data in a structured format, using a relational database (RDBMS) or other types of database. This structured data is extracted from transactional databases, log files, and other sources. It is transformed and loaded into the data warehouse in a process known as ETL (extract, transform, load).
- In-memory analytics: In-memory analytics refers to the use of computer memory (RAM) to store and process data for analysis, rather than storing and processing the data on a hard drive or external server. This can be a faster and more efficient way to perform analytics, as accessing data in memory is generally faster than accessing it from a hard drive or network.
- Machine learning: In the context of machine learning, data analytics can be used to prepare data for use in training machine learning models. This typically involves cleaning and formatting the data before submitting it to a network training process.
- Text mining: Also known as text analytics, this is the process of extracting and analyzing information from unstructured text data to extract useful insights.
These capabilities are all key components of data analytics and are applied by the data analysts at DATAFOREST. Any business wishing to make the most of data analytics is welcome to contact us directly.
Data is collected and analyzed by businesses based on their intended purpose. In addition to collecting data directly from customers, site visitors, and third parties, businesses can purchase data from third parties as well.
It is common for businesses to look for ways to identify demographics, interests, behaviors, and other factors that are relevant to their business. The importance of data analytics is growing across many fields.
Below is a list of five steps that will help businesses grow and bring changes to their current work:
Preparation and strategy
To critically evaluate your business data and to avoid drowning in the bundles of data sheets, you require a robust plan and strategy. Therefore, it is advisable to follow these steps:
- Decide on a goal. To achieve specific goals, you may need certain types of data and to achieve others, you may need different types of data.
- Collect appropriate data to meet your goals. Collecting data can be accomplished in various ways, including via external sources, internal sources, and implementing different methods.
- Plan how to apply analytics to your Data. Analytics can be used to generate valuable insights that can be used to inform decision-making, improve operations, and generate revenue.
Data collection
The collection of data can be divided into two categories – primary and secondary.
Primary Data: Authentic, first-hand data that the data researchers have acquired and prior to any statistical analysis. Results from primary data are typically quite accurate. Primary data collection includes interviews, surveys etc.
Secondary Data: This is data that has already undergone statistical analysis and has been obtained from other sources. This information is either information the researcher has looked up or information the researcher has asked others to gather. It's second-hand information, to put it simply.
Ensuring data is useful
To ensure your data is relevant, you must embrace the garbage-in and garbage-out philosophy! A good researcher must obtain accurate and reliable data. This is because inaccurate and irrelevant data can lead to incorrect conclusions.
The computer will still produce an output if you input erroneous data, but it will not lead to useful results. A single zero in a cell or input into the wrong category can destroy the reliability of the data. Once data has been ingested, it must be reviewed for accuracy.
Utilizing data properly
To unleash the full potential of your data, you must be able to understand it properly. To determine whether a dataset can be practical, you need to find whether it is of high quality. One has to examine its characteristics and determine if it meets the needs of your business or not. A good baseline is to consider the three characteristics of quality data.
- Adequacy: Is the data good for solving your problem or not?
- Reliability: Are the data sources reliable?
- Continuity: Was the data recorded for a relevant period?
Presenting data
Presentation is something that people often forget. Remember, if you lack in the presentation part, then the research wasn't holistic. Why should this be important? Because for the audience to enjoy and respond to the story, they should be able to walk through the graphs and visualizations easily. Here are some quick tips that might help in the data analytics presentation.
- Don't over-complicate things.
- Pick up the right visuals.
- Pick the right color combination.
- Provide multiple graphics to illustrate complex concepts
- Keep the words short and to the point.
Acting on new insights
To act on new insights, first, clearly define what you hope to achieve, this will help you stay focused and ensure that your actions align with your overall objectives. Then, based on your goal, develop a plan of action that outlines the steps you will take to achieve it.
Next, as you implement your plan, it is important to monitor your progress, identify the correlation and evaluate the results.
Lastly, share your findings and the actions you are taking with relevant stakeholders, including management, colleagues, and customers. This will help ensure that everyone is aware of the changes you are making and how they align with the overall goals of the organization.
The following types of analysis are a part of the core understanding of data analytics:
Regression Analysis
According to the Harvard Business School, regression analysis is used for two primary purposes:
- To study the magnitude and structure of the relationship between variables
- To forecast a variable based on its relationship with another variable
Time Series Analysis
As per Tibco.com, time series analysis is a technique in statistics that deals with time series data and trend analysis. Time series data follows periodic time intervals that have been measured in regular time intervals or have been collected in particular time intervals. In other words, a time series is simply a series of data points ordered in time, and time series analysis is the process of making sense of this data.
Factor Analysis
The factor analysis technique extracts the maximum common variance from all the data variables and puts them into a common score. It is a theory that is used in training machine learning models and so it is highly relevant to data mining.
Business has realized the importance of data analytics and is now ready to invest heavily in this capability. This demand is also causing analytics beginners to join this booming industry. For aspiring data analysts knowing the broad range of steps involved in data analytics becomes highly beneficial:
Step 1: Define the problem or question you want to answer. This will help you focus your efforts and ensure that you are collecting and analyzing the right data.
Step 2: Collect and organize the data. This may involve sourcing data from various sources, cleaning and formatting the data, and storing it in a database or spreadsheet.
Step 3: Explore and visualize the data. Once you have collected and organized the data, you can begin exploring and visualizing it to get a better understanding of the underlying patterns and trends. This may involve creating charts, graphs, and other visualizations to help you understand the data more intuitively.
Step 4: Analyze the data. After exploring and visualizing the data, you can begin analyzing it to extract insights and answer your defined question or problem. This may involve using statistical techniques or machine learning algorithms to identify patterns and trends in the data.
Step 5: Communicate your findings. The final step in the data analytics process is to communicate your findings to stakeholders. This may involve creating reports, presenting your results to a group, or sharing your insights with decision-makers.
From bottom to top, everybody relies on data analytics and that's why the importance of data analytics has grown by such magnitude.
The importance of data analytics is best understood by looking at its applications. Numerous data analytics tools are used today to analyze customer trends & determine relevant product recommendations etc. Here are 3 major data analytics applications:
Business intelligence: Data analytics is often used to support business decision-making by providing insights into key performance indicators (KPIs) such as sales, revenue, costs, and customer behavior. By analyzing data from various sources, such as sales data, market research, and customer feedback, businesses can identify trends, forecast future performance, and optimize their operations.
Customer analytics: Data analytics can also be used to better understand customer behavior and preferences. By analyzing data from customer interactions, businesses can identify patterns and trends that can help them personalize their marketing, improve customer experiences, and increase loyalty.
Fraud detection: Data analytics can be used to identify fraudulent activities by analyzing data for patterns or anomalies that might indicate fraudulent behavior. For example, credit card companies might use data analytics to detect unusual spending patterns or transactions that might indicate credit card fraud.
Hence, the importance of data analytics is backed by numerous such real-life applications that are changing the world as we know it.
We live in the 21st century, where data is the most critical asset. If you see a company changing its price and marketing strategy etc. it is probably because they are constantly analyzing past data trends and identifying changes that will improve its position in the market. This will help them compete with rival brands and create a new space for themselves. It is estimated that the global market for data visualization will reach $7.76 billion by 2023, a 9.47% increase since 2017. Here's a list of reasons to help you understand the importance of data analytics:
Helps in boosting revenue
Data analytics is an excellent source for giving direction to a company's sales data. By analyzing this data, businesses can determine which products work for which segments, allowing you to allocate your sales reps' effort more efficiently. If your current sales strategy calls for your sales reps to try to sell all your products and services to everyone, this could be a waste of time and energy.
Historical transaction data (Possibly supplemented by data on search and browsing behaviors) will help you better understand your customers' buying behaviors. It will help you guide your sales representatives to focus on the products or services that your customers value.
Reduce operating costs
When it comes to running a smooth and effective business, data analytics can be of great help. If you have a sound data analytics system in place, you can determine which parts of your business are wasting money and which areas require more funding.
You will be able to identify where you should cut costs and what technology you should use to reduce operational and production costs based on a clear understanding of your goals. Therefore, the importance of data analytics has witnessed a boom in the manufacturing and logistics Odyssey.
Take the case of ThinkDigital for instance. DATAFOREST’s data analysts created a solution that helped the agency to optimize their customer base. The solution notifies the agency about the services their customers are more likely to buy. To put it in perspective, our data-driven marketing solution led ThinkDigital to gain a 200% boost in website traffic.
Increases efficiency
Data analytics is often used by businesses to identify inefficient internal processes and create new, more efficient workflows to enhance operational efficiency.
As a result of using data analytics, businesses can improve their operation by evaluating the efficiency of current workflows, analyzing the outcome of the processes, automating new workflows, and refining them over time as needed.
Business leaders can also use data to determine whether processes are cumbersome, draining budgets, or challenging to use. For this process to work effectively, a business needs a data analyst to help in streamlining big data into small understandable packets.
Enhances marketing efforts by better targeting
Using data analytics, businesses can determine the methods of advertising a product that truly impacts the target audience, and at what scale such advertising should be implemented. By analyzing data, you can also determine how much you will need to spend to make your business or products known based on how much impact they will have and at the lowest price you can get.
By collecting the correct information, you can save a great deal of money. That's one of the biggest reasons why the importance of data analytics has grown in the marketing industry.
The case of Consumer Electronics Retail nicely illustrates this. Data analysts at DATAFOREST identified commonly visited shelves and products and used security cameras to evaluate the movement of individuals within their stores and the products they ultimately bought. As a result, we gathered data that they could use and shortly after, their sales zoomed by 9%.
Improve customer service
Data analytics can be very useful in customer service, as it can help businesses to better understand their customers and improve the customer experience.
- Identifying customer needs and preferences: Data analytics can be used to analyze customer data, such as purchase history, customer feedback, and social media activity, to identify trends and patterns in customer behavior.
- Improving customer satisfaction: Data analytics can be used to track customer satisfaction over time and identify areas where the customer experience can be improved. For example, data analytics can be used to identify common customer complaints or problems, and to develop strategies to address these issues.
- Personalizing the customer experience: Data analytics can be used to create personalized experiences for customers by analyzing their data and identifying their unique needs and preferences.
Respond efficiently to market events and industry trends
Data analytics can be very useful in helping businesses respond to changing market trends. The data can provide insights into customer behavior and market conditions. Data analytics can be used to analyze data from a variety of sources, such as market research, customer feedback, and social media, to identify trends and patterns in customer behavior and market conditions.
For example, data analytics can be used to identify which products or services are most popular with customers or to identify new growth opportunities. Machine learning algorithms and other predictive analytics techniques can be used to analyze data and make predictions about future market trends.
Minimize the risk
For a company to be protected against a wide range of potential risks posed to it by a wide range of potential threats that may pose to its business, a comprehensive risk management strategy can be created with the help of data analytics.
Today, there is more than one approach to analyzing relevant data for valuable insights. Using today's efficient data analytics software may allow you to generate a risk management plan, considering larger amounts of data and providing more representative insights than you could do manually in the past.
Gives competitive advantage
A well-coordinated data analytics module could also increase consumer demand by helping to understand the target audience of one's service. This can be achieved through a better understanding of one's service's target audience.
In addition, greater interactivity is achieved as consumers become content suppliers by making the information available. As a result of this two-way process of data exchange, a successful and dynamic business operation achieves a synergistic effect which is characteristic of a successful and dynamic business.
The world has shifted to a whole new mode where every action and every preference matters. This has resulted in a massive rise in the demand for data analytics in the last decade and has led to a surge in knowledge that can be used to provide valuable insight into any sector.
Data analytics is of paramount importance to every sector, its application delivers enormous quantities of knowledge that are capable of providing valuable insights into these fields. The compilation of data can be supplemented by its analysis to gain insight into decision-making.
The future for data analytics looks even brighter. With advanced tools and technologies emerging and delivering new capabilities in the scale of data collection, the extent to which it can be automated, the capability of the algorithms and the speed at which the data can be processed, this field of innovation can keep on supplementing the growth efforts of businesses across the world.
From anticipating customer behavior, increasing competitiveness, and helping in making fact-based decisions, data analytics has emerged as the go-to service for all businesses, irrespective of their nature and size. .
DATAFOREST believes in data analytics because of the proven results it has allowed us to deliver to our clients, time after time. Our solutions, produced by core experts in the field, have received much praise and guided us on a more data-driven path.
Serving many industries, our data analytics services have matured. And any business willing to engage with us can do it with ease. Just click on this contact us link.
Who uses data analytics?
Many industries use data analytics to enhance their operations, improve their decision-making etc.
- Science: In science, data analytics is used in a number of different ways, including: data mining, machine learning, visualization, modeling, research etc.
- Banking: Banks use data analytics in a variety of ways to improve their operations, risk assessment, fraud detection etc.
- Manufacturing: Manufacturing companies use data analytics for quality control, production planning, supply chain management etc.
- Healthcare: In healthcare, data analytics is used extensively for managing and proving the effectiveness of treatment trials, clinical decision support, patient data management, predictive modeling etc.
- Government: Data analytics is used by governments to inform policy decisions, improve public services, enhance citizens' data etc.
- Social Media: Data analytics in social media is used for targeted advertising, personalized recommendations, sentiment analysis etc.
- Retail: In the retail industry, data analytics is used for inventory management, customer segmentation, price optimization etc.
- Marketing: Data analytics is used by marketing professionals for segmentation, customer behavior analysis, targeting etc.
- Information Technology: The IT industry uses data analytics for multiple reasons, including security analysis, platform performance management etc.
- Human Resources: HRs use data analytics for talent acquisition and retention, employee engagement, performance management etc.
- Accounting: For financial reporting, tax compliance etc, accountants use data analytics.
- Sales: Sales managers and personnel use data analytics for lead scoring, sales forecasting etc.
- Business Development: Data analytics can help business development teams make more informed decisions, target their efforts more effectively, and identify new opportunities for growth and expansion.
What is the role of data analytics?
Data analytics are the masters in the field of Data. Where every day, new trends are coming alive, and many more are being adopted by the industry. So below is a list of some of the activities that the data analytics perform.
- The ability to settle an argument with objective answers.
- Increasing the effectiveness of advertising budgets for companies.
- Identifying areas of business performance where the action is needed and sometimes recommending measures to correct them
- Making better decisions faster and easier by presenting data in a visual manner
- Identifying potential customers' habits
- A strong understanding of the important (and often mission-critical) trends.
- Enhancing the company's competitive advantage.
What are data analytics used for?
A data analyst, who is responsible for creating data pipelines or helping set up models based on the data, must be assisted by data scientists and data engineers during the process of performing data analytics. Data analysts also perform multiple functions:
- Collection of data
- Maintaining data quality
- Implementing the analytic model
- Looking after the presentation of the data
What are the key data analytics roles in a company?
- Data Liaison: Data Liaisons have so much knowledge of your business that they might even be able to identify data-related challenges your company faces.
- Data Architect: An architect helps build the environment(s) for data science and analytics.
- Platform Architect: An organization's platform architect is responsible for ensuring enough "horsepower" for its projects.
- Data Analyst: Using statistical software, the data analyst manipulates databases and produces analytical outcomes.
- Data Scientist: The role of the data scientist is to collaborate with the data analyst to get access to data, and to work with the data liaison to build models that can ultimately drive business value.
In what real-life situations does data analytics come into play?
There are numerous companies engaged in the management of energy who are utilizing data analytics applications to assist them in areas such as smart-grid management, optimizing energy consumption, energy distribution, and automation building for other companies involved in utility services.
What are the five types of data analytics?
There are five types of analytics for business analytics - descriptive, diagnostic, predictive, prescriptive, and cognitive. These are described in detail within this article. Due to emerging trends, the importance of data analytics is increasing every day.
What is the difference between Data Analytics and Data Science?
Data analytics refers to the practice of analyzing data to identify trends, answer questions, and find insights, whereas data science is a component of this process and is the practice of building, cleaning, and structuring datasets for analysis and meaning extraction.
Data analytics is a part of Data science, which is a complete field of study. The growing importance of data analytics has led many to get confused between data analytics and data science.
What is the importance of data analytics?
The importance of data analytics is best described by the following:
- To make informed decisions
- To improve business efficiency
- To identify new business opportunities
- To enhance customer experience
- To be competitive in the changing market trends
