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Improving Your Business with Big Data Analytics: A Simplified Guide

Big Data

Are you a sales professional or an entrepreneur trying to increase your sales? If yes, then this guide is all you need because, in this guide, we are discussing how using modern-day data analytics in business will help you improve and develop your business.

To put it into plain words, Data Analytics is the study of the collected data to gauge valuable insights and implement actionable changes to improve business. For example, a business owner can gather data about a customer’s shopping habits and display their products where the customer engages the most. This way, the product is well received, and business improves.  Does using data analytics only increase sales? Nope, Data Analytics has grown far beyond just sales tactics. You can use Data Analytics to:

  • Understand your customer behaviour
  • Automate in-house processes within the company, such as filtration of resumes for the HR department
  • Track the productivity of your employees
  • Optimize your websites and generate more leads
  • Health care uses Data Analytics to innovate and find solutions for their patients
  • Improve supply-chain management systems
  • Secure digital footprint to eliminate frauds and thefts online

Every field can use Data Analytics systems to optimize their output by customizing it to their area. But, how can you use this for your business or your team? How can you use analytics for decision-making in your organization?

To effectively use Data Analytics, you need to jot down the problems or challenges you seek to find solutions for in your organization. Collecting raw data isn’t helpful or enough without a practical need. Thus, you need to identify the type of data collection that will work for your team/organization.

Types of Big Data for Organizations: What are the Types of Data Analytics for Decision Making?

To efficiently utilize the big data you are collecting, identify the types of data you can collect and the best way to optimize its usage. They are:

1. Diagnostic Data:

Diagnostic data is automatically recorded by vehicles, machines, software and devices for troubleshooting problems. It tends to be large and is collected to detect issues found in a process or program automatically.

Diagnostic Data answers most of the ‘Why did something happen?’ questions depending on past events based on probability.

2. Descriptive Data:

Descriptive analytics is a statistical method used to search and summarize historical data to identify patterns or meanings. The data analytics tools extract data based on patterns and indicate ‘What exactly happened?’ to identify behaviour.

For instance, if you host an online community, collecting descriptive data allows you to understand its engagement and the type of people who engage the most with the content. Thus, helping you with valuable insights on the kind of content you need to post in the future.

3. Prescriptive Data:

Another best use of data analytics is to interpret the available data to comprehend the available findings and prescribe what else might fulfil your users’ needs. In other words, using data to decide the best course of action is known as prescriptive data. This form of analysis generates recommendations for the following actions by considering all relevant elements. Because of this, prescriptive analytics is a valuable tool for data-driven decision-making.

For example, a course portal can recommend courses to you after assessing the skills you possess and require to progress in your career.

4. Predictive Analytics:

Predictive analytics is a part of advanced analytics that uses historical data, statistical modelling, data mining techniques, and machine learning to predict future events.

Predictive analytics can help you in decision-making for your business by giving you predictive stats of ‘What to do ahead?’ and a plan of action.

5. Cognitive Analytics:

Cognitive analytics is the most advanced type of analytics, using artificial intelligence, machine learning algorithms, deep learning models, and other intelligent technologies to evaluate data and make inferences from existing patterns to arrive at conclusions.

Cognitive analytics is used in functioning smart devices like Apple, Amazon, Microsoft etc.

Data Analytics Tools You Need to Grow Your Business: 7 Data Analytics Tools for Better Decision Making in Business

Decision Making in Business is a crucial factor. Using Data Analytics tools to make informed judgements helps you cater to your users/customers’ immediate needs. By using a helpful platform for your business, you can measure along the four V’s—volume, velocity, variety, and veracity. This data can help you streamline your customer acquisition, onboarding, retention, upsell, cross-sell and other revenue-generating indicators.

Some of the Data Analytics tools that will help your business grow are:

1. Python

Python is a high-level language that gained popularity just a few years back for its general purpose and the use of an object-oriented approach. Python has a set of libraries where you can store information from the data entered and quickly provide solutions to user or company queries. Because of its user-friendly approach that focuses on simplicity and readability, it is easy to learn, and it is widely used by Data Scientists for data mining, data processing and modelling and also data visualization. It is also used for applications like the Internet of Things (IoT), Artificial Intelligence (AI), Web Development, API Development, etc.

2. R

R is an easy-to-learn programming language that helps with analysis and finding trends and patterns in data. It provides software facilities for quantitative applications, data manipulation and graphical display (resulting in high-quality graphics). R programming language is widely used for data visualization and also to do advanced statistical programming. R is better programming for statistical applications because it is an open-source language where changes can be made.

3. SAS

SAS is a programming language used for data access, data transformation, analysis and reporting with advancements in services. Many companies the licensed SAS as It has the ability to store large data with a stable Graphic User Interface (GUI).

4. Excel

Excel is an electronic spreadsheet software program developed by Microsoft which is used to store and organize data with formulas and functions. It is used across businesses and companies for data analysis. It is user-friendly, and it is not mandatory to learn all the excel functions to start working on it. However, once you start working, make sure to upgrade your knowledge about excel functions from time to time.

5. Power BI

Power BI is a Data Visualization and Business Intelligence service that collects and stores apps and connects data. The data is collected from different data sources and, in turn, transforms the data and produces BI reports. Power BI services are based on Saas, and mobile Power BI apps are available for different platforms. In turn, many business and corporate users consume the data and build BI reports. In addition, power BI helps in updating the information and data regularly with the most up-to-date information and connects with SharePoint, Dynamics 365, etc.

6. Tableau

Tableau is a business intelligence tool developed with an aim to improve the flow of data analysis and accessibility to users through visuals. It helps visually transform the data and connect it to files to acquire and process it. Businesses mainly use it, academic researchers, and many organizations for visual data analysis.

7. Apache Spark

Apache Spark is a computing technology that supports in-memory processing, resulting in great data analysis results. It is compatible with big data storing with in-memory cluster computing, and workloads can be dealt with, such as batch applications, interactive queries and streaming. It supports other programming languages and also can be used to build application libraries to analyze the data with the support of lazy evaluation. It waits for the first set of data to analyze and complete and then processes the next data helping it to speed up.

8. SQL

Structured Query Language (SQL) is a programming language used to operate databases for storing, manipulating and retrieving data stored on a relational database management system. Many standard database languages exist, including MySQL, MS Access, Oracle, Sybase, Postgres and SQL Server. You can enquire, update and also reorganize the data stored as well as create and modify.

9. Oracle

Oracle is a relational Database Management system developed by the oracle corporation. It is also called OracleB with a varied number of product editions such as Standard Edition, Enterprise Edition, Express Edition, and Personal Edition, which the user can choose according to their needs. It is popular in the IT market because of its usability and can store data, organize information and retrieve data. In addition, it is scalable and secure with a high-performance data ability keeping it up to the newest technologies developed in the IT world.

Data analytics has substituted the manual survey of market analysis, customer feedback and product review involved in growing a business. Instead, data analytics efficiently collects, structures and analyzes information and data according to the needs and demands of the business structure.
Data analytics helps in developing problem-solving strategies to transform and grow your business.
Here are some steps on how we can grow our business using data analytics:

Business Expansion Plan:

Expanding a business requires more customers that will interact with your service. It will also demand increased human and material resources, infrastructure and finance. Businesses can collect data on customer information, preferences and dislikes from various social media platforms. This can be done through data analytics.

1. Targeting audience/customer:

It has never been this easy to target customers for businesses based on locations, preferences, age, gender, and lifestyle. Big companies like Amazon, Netflix, and Spotify target audiences using data analytics.

2. Developing Business Plan:

In developing a business plan, several strategies and factors are to be considered. Data analytics can help study previous business models and identify the problematic areas to improve your new business. It can further help in retaining existing customers by promising to increase the standards of your service.

3. Marketing Campaigns:

Another area that is aided by data analytics is organizing marketing campaigns. With data analytics, you can develop a strategic campaign that is specifically targeted at your customers. For example, advertisements based on data analytics, such as programmatic advertising, are an excellent choice for businesses wishing to grow.

4. Making Predictions:

Data analytics and business predictions are like bread and butter. Data analytics aims to make predictions through customer research and feedback. Data analytics can predict possible outcomes, both positive and negative, that will enable you to run your business effectively.

By properly learning about Big Data, Data Analytics and its application, you can optimize your business plans and take your sales/reach to greater heights.

Top Courses in Data Analytics in India:

  • MCA Data Analytics – Jain university Bangalore
  • Certificate program in Data Analytics- IIM Amritsar
  • IIM Kozhikode Executive Post Graduate Certificate in Data Analytics for Decision-Making online course
  • The Advanced Certification in Data Analytics for Business Certification Course, IIT Madras
  •  PG Diploma in Data Science and Analytics, National Institute of Electronics and Information Technology, Chennai.

To unleash Data Analytics and hone its expertise to grow your business, the best way to go about it is by investing in learning. Taking a Data Analytics course online to learn the application from the experts helps you cater to your business needs closely. This also gives you a kind of hands-on experience, expert knowledge and tricks of the trade

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Romina Gopalan is a top-notch upskilling advisor & content writer. Her areas of expertise include Digital Marketing, Data Sciences, IoT, RPA, and UX/UI writing. Her sharp research and writing skills allow her to identify futuristic opportunities. Thus, helping you understand how you can leverage expertise in any domain.

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