Data Analytics Basics – Introduction to Data Science For Marketeers

Data Analytics Basics – An Introduction to Data Science This part 1 of this series talks about how AI and ML are now changing the work style and methodologies of marketing. Digital marketing cannot be run as just a computerized version of traditional marketing or print media. The main and exciting use cases of data science and big data analytics are exciting and make the marketing job even more exciting. Some may say it’s getting more of a technology job as now it involves predicting, prescribing, planning, and forecasting. The marketing work of today is all about problem-solving and anomaly detection for businesses.

This post focuses on marketing needs on Data Science.

Analytics: The Role Of Data In Digital Marketing

Data Revolution – Performing Analytics at The Edge

Data Science of Digital Payments

Data Analytics Basics

Data analytics basics encompass the fundamental concepts and techniques used to derive meaningful insights and patterns from raw data. This process involves collecting, cleaning, transforming, and analyzing data to make informed business decisions and gain a deeper understanding of trends, correlations, and outcomes. Here are key components of data analytics basics:

1. Data Collection: Data analytics starts with gathering relevant data from various sources, such as databases, spreadsheets, or online platforms. This can be structured data (organized in rows and columns) or unstructured data (text, images, videos) that requires preprocessing.
2. Data Cleaning and Preprocessing: Raw data often contains errors, missing values, or inconsistencies. Data cleaning involves identifying and correcting these issues to ensure the accuracy and reliability of the analysis. Preprocessing may also involve transforming data into a suitable format for analysis.
3. Data Exploration: Exploratory data analysis involves visualizing and summarizing data to uncover patterns, trends, and outliers. Techniques like data visualization, histograms, and summary statistics aid in understanding the data’s distribution and characteristics.
4. Data Analysis Techniques: Data analytics employs various techniques, including descriptive statistics, inferential statistics, machine learning, and data mining. Descriptive statistics summarize and describe the data, while inferential statistics make predictions and draw conclusions based on sample data. Machine learning algorithms enable the development of predictive models, and data mining uncovers patterns and relationships in large datasets.
5. Interpretation and Insights: After performing data analysis, the results need interpretation to derive meaningful insights. These insights help decision-makers understand business performance, customer behavior, market trends, and potential opportunities for improvement.
6. Data Visualization and Reporting: Presenting the findings in a clear and understandable manner is crucial. Data visualization techniques, such as charts, graphs, and dashboards, aid in communicating complex information effectively to stakeholders.
7. Continuous Improvement: Data analytics is an iterative process. Regularly revisiting and updating the analysis allows organizations to adapt to changing business needs and enhance the accuracy and relevance of insights.

By mastering these data analytics basics, businesses and professionals can harness the power of data to make well-informed decisions, gain a competitive advantage, and drive success in a data-driven world.

Big Data Analytics and Data Science for

• Customer Loyalty & Retention
• Relating to Customers
• Online Marketing Avenues
• Predictive, Prescriptive and Descriptive Analytics
• Getting Quality Leads
• Tools used for marketing Analysis

This post will support you on “How to Gain Deeper Marketing Insights Through the Power of Data Science and enable you to become a better digital marketer”. We will focus on all the pointers mentioned above at a high level as part of the scope of this first Post in this series.

Data-Driven Decisions

Understanding the importance of data and making data-driven decisions are two important factors in making a digital marketing analyst’s job a success. Below are points that describe the correct order in which actions need to be taken.

• Making data-driven decisions is the key to successful marketing.
• Choosing the right marketing analytics tools and measuring relevant data correctly
• Optimizing marketing efforts with help from the above two points

The above efforts will make the difference between failing to reach your marketing goals and completely crushing them. Tools like Hotjar, Google Analytics, etc. interpret and track customer behavior. The data from these tools paints a beautiful picture of business insights.

The insights not only help in making decisions but also in predicting and taking actions on behalf of a million-dollar question, Why do digital marketing analytics matter to any business?” has been answered and demonstrated well by Facebook, Twitter, etc. The golden key to success is to understand the role of big data in digital marketing.

Data Science – As A Business Capability

Data Science is making all efforts to change the perception that it is just another business intelligence task. Artificial intelligence, Natural intelligence, and Data Science technologies are making a huge impact on most businesses by working with big data. Demand for data science and AI-based products is rising, knowingly or unknowingly. The forecast, which will be the norm for sure, that “Data science and AI-enabled products will be seeing exponential market growth” will set the next style of doing business.

Current computers, even with GPUs, don’t have the ability to process large amounts of data at once. Quantum computers will, for sure, have the capabilities to process an entire large enterprise database and instantly access all items at once. Deliver analysis and uncover patterns within seconds.

Through data analytics, businesses can identify opportunities for improvement, optimize processes, and enhance customer experiences. The ability to derive meaningful insights from data empowers organizations to stay competitive in today’s data-driven landscape. Furthermore, continuous improvement and adaptation are vital to ensuring that data analytics remains relevant and impactful.

AI and Big Data for FinTech & InsureTech

Data Intelligence as a Service – DataIntelligence

Digital Transformation of Marketing

Marketing has transformed from just print media or radio ads to a complete digital marketing business. Digital marketing without the support of big data analytics is like running a gasoline car on diesel.

The digital transformation of any modern business requires quality data, not just any data. Marketing is no longer just a business or a process of creating relationships between customers and businesses. It’s now reaching the age where marketing is making customers behave like brand advocates. The three key success factors for any good business that help it grow with the correct digital marketing drive are

• Quality of Data collected.
• Data Scientists.
• Tools to visualise, analyse and summarise the data.

So if Data is the new fuel of today, then we must accept data scientists as oil refineries and data tools as important ingredients that help refine and produce desired results. Cognitive Analytics provides a 360-degree view of them to make the correct decision at the right time.

As data continues to grow in volume and complexity, data analytics will remain a powerful tool for businesses seeking to harness the potential of their data. By embracing data analytics basics, organizations can make better-informed decisions, gain a competitive edge, and drive growth and innovation. In this ever-evolving technological age, data analytics serves as a cornerstone for success, enabling organizations to navigate the vast sea of data and turn it into a strategic advantage.

As technology continues to evolve, the potential for unlocking further innovations in payment intelligence with data science is boundless, promising even more advancements and benefits for businesses and consumers alike.

Points to Note:

All credits, if any, remain with the original contributor only. We have covered all the basics around data analytics for digital marketing analytics in Chapter 1. In the next few chapters, we will talk about implementation, usage, and practice experience for markets.

Books + Other readings Referred

• Research through open internet, news portals, white papers and imparted knowledge via live conferences & lectures.
• Lab and hands-on experience of  @AILabPage (Self-taught learners group) members.

Feedback & Further Question

Do you need more details or have any questions on topics such as technology (including conventional architecture, machine learning, and deep learning), advanced data analysis (such as data science or big data), blockchain, theoretical physics, or photography? Please feel free to ask your question either by leaving a comment or by sending us an  via email. I will do my utmost to offer a response that meets your needs and expectations.

Conclusion – Data analytics basics form the foundation for unlocking the potential of data and transforming it into actionable insights. By collecting, cleaning, and analyzing data, businesses can make informed decisions, identify trends, and gain a deeper understanding of their operations and customers. The process of data analytics involves various techniques, such as data exploration, descriptive and inferential statistics, machine learning, and data visualization, to reveal patterns and correlations within the data.

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