Future of Data and Data Driven Decision Making (DDDM)

Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science is one of the most rapidly growing and evolving fields in the world, as it has applications in almost every industry and sector. Some of the current and future trends in data science are:

1 - The rise of artificial intelligence and machine learning, which enable data scientists to automate complex tasks, create predictive models, and generate new insights from large and diverse datasets.

2 - The increasing demand for data literacy and data-driven decision making, which require data scientists to communicate effectively with stakeholders, visualize data in compelling ways, and translate technical findings into actionable recommendations.

3 - The emergence of new data sources and types, such as social media, sensors, images, videos, audio, text, and geospatial data, which pose new challenges and opportunities for data analysis and integration.

4 - The development of new tools and platforms, such as cloud computing, big data frameworks, open source software, and online learning resources, which enable data scientists to access, process, store, and share data more efficiently and collaboratively.

Data science is a dynamic and interdisciplinary field that offers many opportunities for innovation and impact. Data scientists need to constantly update their skills and knowledge to keep up with the changing landscape of data and technology. Data science is not only a technical discipline, but also a creative and ethical one, as it involves solving real-world problems with data.

Data is becoming one of the most valuable assets for any business. It can help you understand your customers, optimize your processes, improve your products, and increase your profits. But how can you make the most of your data?

In this short article, I will explore the types of data and how to use data to get maximum results for your business. We will also highlight data-based decision making and how to benefit from it.

Types of data

There are many ways to classify data, but one of the most common is to distinguish between quantitative and qualitative data. Quantitative data is numerical and can be measured, counted, or expressed in units. Qualitative data is descriptive and can be observed, recorded, or expressed in words.

Quantitative data can be further divided into discrete and continuous data. Discrete data is finite and has a fixed number of possible values, such as the number of customers, orders, or products. Continuous data is infinite and has an unlimited number of possible values, such as the temperature, speed, or weight.

Qualitative data can be further divided into nominal and ordinal data. Nominal data is categorical and has no inherent order, such as the colour, gender, or type of product. Ordinal data is also categorical but has a meaningful order, such as the rating, satisfaction, or preference.

How to use data to get maximum results for your business

The first step to use data effectively is to collect it. You need to identify what kind of data you need, where to get it from, how to store it, and how to ensure its quality and security. You can use various sources of data, such as surveys, interviews, observations, experiments, web analytics, social media, sensors, or transactions.

The second step is to analyze it. You need to transform your raw data into meaningful insights that can help you answer your business questions and solve your problems. You can use various methods of analysis, such as descriptive, exploratory, inferential, predictive, or prescriptive.

Descriptive analysis summarizes what has happened in the past using metrics such as mean, median, mode, standard deviation, frequency, or percentage.

Exploratory analysis investigates what is happening in the present using techniques such as visualization, clustering, correlation, or anomaly detection.

Inferential analysis tests what could happen in the future using methods such as hypothesis testing, confidence intervals, or significance tests.

Predictive analysis forecasts what will happen in the future using models such as regression, classification, or time series.

Prescriptive analysis recommends what should happen in the future using optimization, simulation, or decision analysis.

The third step is to communicate it. You need to present your findings and recommendations in a clear and compelling way that can persuade your audience to take action. You can use various formats of communication, such as reports, dashboards, charts, graphs, tables, or stories.

Data-based decision making and how to benefit from it

Data-driven decision-making (DDDM) is the process of using data and statistics to make and guide business decisions. DDDM can include everything from understanding what has happened in the past, what is currently happening, and what might happen in the future by using machine-learned models to analyze data.

DDDM is becoming an important tool for businesses because it can help them gain a competitive edge, improve customer satisfaction, optimize operations, and increase profitability. According to a survey by PwC, highly data-driven organizations are three times more likely to report significant improvements in decision-making compared to those who rely less on data.

The Process of Data-Driven Decision-Making

The process of data-driven decision-making can be divided into several steps:

- Data collection

- Data pre-processing

- Exploratory data analysis

- Extracting valuable insights and making decisions

- Automation and democratization

Let's look at each step in detail.

Data Collection

The first step in DDDM is to collect relevant and reliable data that can help answer the business question or problem. Data can come from various sources, such as internal databases, external sources, surveys, web analytics, social media, sensors, etc.

The quality and quantity of data are important factors that affect the accuracy and validity of the analysis and decisions. Therefore, it is essential to define the data requirements and criteria before collecting the data. For example, what kind of data is needed? How much data is needed? How often should the data be updated? How should the data be stored and accessed?

Data Pre-processing

The second step in DDDM is to pre-process the data to make it ready for analysis. Data pre-processing involves cleaning, transforming, integrating, and reducing the data to remove errors, inconsistencies, outliers, duplicates, missing values, etc.

Data pre-processing is a crucial step because it can improve the quality and usability of the data. It can also reduce the complexity and dimensionality of the data, which can speed up the analysis and reduce computational costs.

Exploratory Data Analysis

The third step in DDDM is to explore the data to understand its characteristics, patterns, trends, correlations, distributions, etc. Exploratory data analysis (EDA) involves using descriptive statistics, visualizations, and hypothesis testing to summarize and display the data.

EDA can help reveal insights that are not obvious or expected from the data. It can also help identify potential problems or opportunities that can be further investigated or exploited. EDA can also help generate hypotheses or questions that can be answered by more advanced analytical techniques.

Extracting Valuable Insights and Making Decisions

The fourth step in DDDM is to extract valuable insights from the data and use them to make informed decisions. This step involves using various analytical techniques, such as regression, classification, clustering, association rules, sentiment analysis, text mining, etc., to discover patterns, relationships, predictions, recommendations, etc., from the data.

The choice of analytical technique depends on the type and purpose of the data, as well as the business objective and question. The results of the analysis should be interpreted and validated with domain knowledge and common sense. The results should also be communicated effectively to stakeholders using clear and concise reports, dashboards, charts, etc.

The ultimate goal of this step is to use the insights derived from the data to support or guide decision-making. The decisions should be aligned with the business goals and objectives and should be evaluated based on their outcomes and impacts.

Automation and Democratization

The fifth step in DDDM is to automate and democratize the process so that it can be repeated and scaled across different scenarios and domains. Automation involves using tools and platforms that can streamline and simplify the process of collecting, pre-processing, analyzing, and presenting the data. Automation can also enable real-time or near-real-time analysis and decision-making based on dynamic and changing data.

Democratization involves empowering more people within the organization to access and use the data and analytics for their own purposes. Democratization requires creating a culture that encourages critical thinking and curiosity among employees. It also requires providing training and development opportunities for employees to learn data skills. Finally, it requires having executive advocacy and a community that supports and makes data-driven decisions.

An Example of Data-Driven Decision-Making

To illustrate how DDDM works in practice, let's look at an example of a company that wants to launch a new product in a test market.

Data collection: The company collects data from various sources, such as customer surveys, competitor analysis, market research, industry reports, etc., to understand the customer needs, preferences, pain points, expectations, etc., as well as the market size, demand, trends, opportunities, threats, etc.

Data pre-processing: The Company cleans and integrates the data from different sources and formats. It also reduces the data by selecting the most relevant and important features and variables for the analysis.

Exploratory data analysis: The Company uses descriptive statistics and visualizations to explore the data and discover insights. For example, it finds out that the target customers are mostly young, urban, tech-savvy, and environmentally conscious. It also finds out that the market is growing and competitive, but there is a gap for a product that is innovative, affordable, and sustainable.

Extracting valuable insights and making decisions: The Company uses various analytical techniques to extract insights from the data and make decisions. For example, it uses regression to estimate the potential sales and revenue of the new product based on different price points and features. It also uses classification to segment the customers based on their characteristics and preferences. It also uses association rules to identify the most popular and profitable product bundles and cross-selling opportunities. Based on these insights, the company decides to launch the new product in a test market with a specific price point, feature set, and marketing strategy.

Automation and democratization: The Company uses tools and platforms that can automate and democratize the process of data-driven decision-making. For example, it uses a cloud-based data warehouse that can store and access the data from different sources. It also uses a business intelligence tool that can create and share interactive dashboards and reports with different stakeholders. It also uses a machine learning platform that can create and deploy predictive models that can update and improve over time based on new data.

Data-driven decision-making is a process that can help businesses make better decisions based on data and analytics. It involves several steps: data collection, data pre-processing, exploratory data analysis, extracting valuable insights and making decisions, and automation and democratization.

By following this process, businesses can gain a competitive edge, improve customer satisfaction, optimize operations, and increase profitability. However, this process requires a dedicated approach to developing and refining the data and analytics program. It also requires creating a culture that encourages critical thinking and curiosity among employees.

Conclusion

Data is a powerful tool that can help you achieve maximum results for your business. By understanding the types of data and how to use them effectively, you can gain valuable insights that can inform and improve your decisions. By adopting a data-based decision making approach, you can benefit from reduced uncertainty and risk increased efficiency and effectiveness enhanced innovation and creativity improved customer satisfaction and loyalty boosted competitive advantage growth