Introduction
Data analytics plays a pivotal role in empowering managers with the tools and insights necessary for effective decision-making within organizations. In today’s data-driven world, managers are dealing with vast amounts of data from various sources. Data analytics enables managers to process these data, identify the trends and patterns in order to extract actionable insights. By harnessing data analytics skills and techniques such as descriptive analytics, managers can gain a comprehensive understanding of historical trends and performance metrics, providing a solid foundation for informed decision-making. Moreover, predictive analytics equips managers with the ability to anticipate future trends and outcomes, enabling proactive strategies and risk mitigation. This foresight allows managers to navigate uncertainties with confidence, aligning their decisions with the organization’s strategic goals. In addition to enhancing decision-making through trend analysis and predictive modelling, data analytics facilitates a more agile and responsive managerial approach. Managers can leverage interactive dashboards and visualizations to gain immediate insights, facilitating a more dynamic decision-making process. As a result, data analytics not only aids in strategic, long-term planning but also supports day-to-day operational decisions, fostering a culture of adaptability and responsiveness among managerial teams.
What is Data Analytics?
Data analytics can simply be seen as the process of examining data in order to find trends, patterns and draw insights from the data. Data analytics is a way of using technology and tools to collect, organize, and analyze large amounts of data to find patterns, insights, and trends. It involves using mathematical and statistical techniques to make sense of data and turn it into actionable insights.
Data analytics is like a giant puzzle. Imagine a big pile of puzzle pieces representing information about something you are interested in. Data analytics is the process of taking all those puzzle pieces and putting them together to see what picture they make.
A use case, a manager at a video game store wants to know what people think about a new video game(Call of Duty). He could collect data by surveying a group of people and asking them to rate the game on a scale from one to ten. Once he has the data, he can use data analytics tools to analyze the responses and find out which features of the game people like and dislike the most. By analyzing the data, he can learn new things and make decisions based on the findings. Therefore, data analytics helps managers put data together to learn something new(insights). The data analytics process is all about studying historical and current data using statistical modeling, algorithms, and data visualization to gain valuable insights, detect trends, and help make informed decisions about your future direction.
It is important to note that in data analytics, requires you have a very good domain knowledge, basic knowledge in mathematics and statistics (minimum, maximum, average, median, count, probabilities, algebra, matrix, correlation, regression) and also a bit of IT skills (basic programming using Excel, SQL, Python or R)
Who Needs Data Analytics?
Data analytics is needed by various individuals, organizations, and industries that deal with large amounts of data and require insights to make informed decisions. Everybody who makes business decisions needs to use data analytics. As a result of development in technology we now have access to data more than ever before. Any manager who formulating strategies and making decisions without considering the data available , could miss the insights on the opportunities or red flags that it communicates. Managers who can benefit from data analytics skills include: Some specific examples of how HR, marketing, sales, finance, and operations can use data analytics:
Human Resources (HR): HR departments can use data analytics to identify patterns in employee data related to performance, retention, and engagement. For example, by analyzing employee data, HR can identify factors that contribute to employee turnover and develop retention strategies to address them. They can also use data analytics to identify skills gaps and develop training programs to improve employee performance. The application of data analytics tools and techniques in the domain of HR is referred to as HR Analytics or Peoples Analytics.
Marketing: Marketing teams can use data analytics to identify customer behaviors and preferences, such as purchase patterns, social media engagement, and website traffic. By analyzing this data, they can create targeted campaigns and optimize marketing strategies to improve customer engagement and increase sales. The application of data analytics tools and techniques in the domain of marketing is referred to as Marketing Analytics.
Sales: Sales teams can use data analytics to identify patterns in customer data, such as purchase history and demographics, to identify potential customers and improve sales forecasting. They can also use data analytics to analyze sales performance and identify areas for improvement. The application of data analytics tools and techniques in the domain of sales is referred to as Sales Analytics.
Finance: Finance departments can use data analytics to identify patterns in financial data related to revenue, expenses, and cash flow. For example, they can use data analytics to identify trends in customer payments, optimize pricing strategies, and reduce costs. The application of data analytics tools and techniques in the domain of finance is referred to as Financial Analytics.
Operations: Operations teams can use data analytics to identify patterns in production data related to efficiency, quality, and safety. For example, they can use data analytics to identify bottlenecks in production, optimize inventory management, and improve safety procedures. The application of data analytics tools and techniques in the domain of operations is referred to as Operations Analytics.
Overall, analytics can be used in many ways across various departments and industries to improve decision-making, optimize processes, and drive business growth.
Benefits of Data Analytics for Managers
Organizations that use data to make decisions are three times more likely to say that these decisions have improved significantly in terms of innovation, growth, and competitive advantage. Organizations in a variety of industries can benefit from data analytics in a number of ways. Some of the main advantages of data analytics are listed below.
Better decision-making: Data analytics can help businesses make informed decisions based on real-time insights and data-driven evidence. This can help companies identify new opportunities, avoid potential risks, and make strategic decisions that drive growth and profitability.
Improved efficiency: Data analytics can help businesses optimize their operations and cut costs by automating data collection and analysis. Businesses may be able to streamline their procedures and utilize their resources more effectively as a result, increasing productivity and efficiency.
Enhanced customer experiences: Data analytics can help businesses better understand their customers’ preferences and behaviors, enabling them to tailor their products, services, and marketing campaigns to better meet their customers’ needs. This can result in higher customer satisfaction and loyalty, and ultimately drive sales growth.
Competitive advantage: Businesses can gain a competitive advantage by using data analytics to find new opportunities, anticipate market trends, and make more informed decisions. Businesses can use this to set themselves apart from the competition and become market leaders.
Improved risk management: Using data analytics to identify possible hazards and weaknesses in their operations enables businesses to take proactive measures to mitigate risk. This can help businesses limit losses, avert reputational damage, and ensure regulatory compliance.
Budgeting and forecasting: Data analytics can be used to determine the budget and investments necessary to achieve a company’s future growth goals by analyzing historical revenue, sales, and cost data alongside those goals.
Risk management: By understanding the likelihood of certain business risks occurring and their associated cost, an analyst can make cost-effective recommendations to help mitigate them.
Marketing and sales: Data analytics can determine how many leads their efforts need to generate in order to fill the sales pipeline by understanding key metrics like lead to customer conversion rate.
Product development (or research and development): Data Analytics can direct future product development, design, and user experience by analyzing how customers reacted to product features in the past.
How Managers Use Data Analytics
Data analytics has become increasingly important in the business world, and managers need to have a basic understanding of the principles of data analytics to make informed decisions. In this case study, we will examine how a manager in a retail company can use data analytics to make decisions related to inventory management.
Case Study
Sarah is the manager of a retail store that sells clothing and accessories. She is responsible for managing the inventory levels of the store and ensuring that the store has the right amount of inventory to meet customer demand. However, she is finding it difficult to make decisions about inventory management based on her intuition and experience alone. She has heard about data analytics and wants to explore how it can help her make better decisions. Sarah took a course on Data Analytics. She started by analyzing the historical sales data of the store to identify trends in customer demand. She looked at the sales data for the last two years and used a data visualization tool to create charts to help her visualize the data. This analysis helped her to identify the products that were selling well and the products that were not selling as well. Next, Sarah used a forecasting model to predict the future demand for each product. She used the historical sales data to train the model and used it to predict the demand for the next three months. This helped her to identify which products were likely to sell well in the future and which products were not.
The analysis revealed that certain products were selling well during specific seasons or events. During the winter clothing and accessories sold well during the winter months, while summer clothing and accessories sold well during the summer months. Sarah was able to use this insights to adjust the inventory levels of the store to meet customer demand. The forecasting model helped Sarah to predict the future demand for each product. This allowed her to order the right amount of inventory to meet customer demand without overstocking or understocking the store.
By using data analytics, Sarah was able to make informed decisions about inventory management. She was able to identify trends in customer demand, predict future demand, and adjust inventory levels accordingly. This helped her to ensure that the store had the right amount of inventory to meet customer demand, while also minimizing the costs associated with overstocking or understocking the store. As a manager, having a basic understanding of data analytics can be extremely valuable in making informed decisions that can lead to improved performance and profitability.
Types of Data Analytics
There are generally four types of data analytics: Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, and Prescriptive Analytics
source: Gartner Analytics Ascendancy Model (GAAM, 2019).
Descriptive Analytics: Descriptive analytics is the simplest type of analytics, allowing you to pull trends from raw data and describe what happened or is currently happening. It involves analyzing historical data to understand what has happened in the past, answering the question “What happened?”
The sales manager could use descriptive analytics to analyze historical sales data and gain insights into sales trends and patterns. For example, they could look at sales by product, by region, by salesperson, and by month or quarter. By doing so, they can identify which products are selling the most, which salespeople are performing the best, and which regions have the highest sales growth.
Diagnostic Analytics: The next logical question, “Why did this happen?” is answered by diagnostic analytics. This type of analytics goes a step further by comparing concurrent trends or movements, finding correlations between variables, and, when possible, establishing causal linkages. Analytical diagnostics are helpful for identifying the underlying causes of organizational problems.
Once the sales manager has identified patterns in the data using descriptive analytics, they can use diagnostic analytics to understand the root causes of those patterns. For example, if they notice that sales are declining in a particular region, they can investigate why. They might find that a competitor has entered the market, or that there’s been a change in consumer preferences. By identifying the cause of the decline, they can take appropriate action to address the issue.
Predictive Analytics: Predictive analytics tries to answer the question of what might happen in the future? You can accurately estimate what the future may hold for your firm by examining historical data along with current industry trends. Forecasting, statistical models and machine learning algorithms are used in predictive analytics to assess historical data and forecast future events. Finding patterns and trends aids in making predictions about the future. Among other things, predictive analytics is used to forecast market trends, sales, and demand. Making forecasts about the future might assist your company in developing plans based on probable outcomes. Using prescriptive analytics, we can determine what to do next.
Using predictive analytics, the sales manager can forecast future sales trends based on historical data and other factors. For example, they could use predictive analytics to forecast sales for the next quarter or year. This would enable them to make better-informed decisions about inventory, staffing, and marketing. They could also use predictive analytics to identify which customers are most likely to make a purchase, so that they can focus their efforts on those customers.
Prescriptive Analytics: Prescriptive analytics is the most advanced form of data analytics. It involves using machine learning algorithms and artificial intelligence to analyze data and provide recommendations for actions to take. Prescriptive analytics proposes actionable insights after accounting for all potential aspects in a scenario.
The sales manager could use prescriptive analytics to make data-driven recommendations for how to improve sales performance. For example, they could use prescriptive analytics to identify which products are most likely to sell well in a particular region, or which sales strategies are most effective with different types of customers. By making these recommendations, they can help their sales team make better-informed decisions and ultimately drive sales growth.
Data Analytics Tools for Managers
Data analytics tools are essential for managers in today’s data-driven business environment. These tools enable managers to make informed decisions by providing insights into customer behavior, market trends, and operational efficiency. Some of the data analytics tools that managers need to be familiar with:
• Microsoft Excel: The first tool you will need to learn is Microsoft Excel. Excel is the number one data analytics tool. Excel can be used to analyze data, from simple data entry to complex data modeling. Excel provides tools to sort, filter, and manipulate data.
Excel is a valuable data analytics tool that allows managers to analyze past data by creating reports and doing analytics, as well as analyze the future by building models and making forecasts/predictions. Managers can use Excel to analyze sales data, customer feedback, and financial statements.
• SQL (MySQL/Microsoft SQL Server): One of the task in data analytics is data extraction. Most times in the organization, the data isn’t stored in excel due to the limitation of the volume of data excel can handle. Managers need to have some basic knowledge of SQL to communicate with the database.
• Power BI — This is a business intelligence tool that supports multiple data sources, helps in asking questions and getting immediate insights.
• Tableau — This helps in creating several kinds of visualizations for presenting insights and trends in a better way.
• Python — This object-oriented open-source programming language is used for manipulating, visualizing, and modelling data.
• R — An open-source programming language used in numerical and statistical analysis.
To begin your journey in data analytics, it is recommended to start with Excel first, followed by SQL(MySQL), Power BI, and then maybe Python if you really want to build your programming skills.
CRISP-DM FRAMEWORK
The CRISP-DM framework provides a structured approach to data analytics, by helping organizations to maximize the value of their data and make informed decisions based on data insights.
Figure 1: Phases of Cross-Industry Standard Process for Data Mining
CRISP-DM stands for Cross-Industry Standard Process for Data Mining, and it is a widely used framework for data mining and analytics. It consists of six phases:
Business Understanding: In this phase, the focus is on understanding the business problem or opportunity that needs to be addressed. The goal is to identify the objectives and requirements of the project and define the scope of the project.
Data Understanding: In this phase, the focus is on gathering and exploring the data that is needed to solve the business problem or address the opportunity identified in the first phase. The goal is to assess the quality of the data and identify any gaps or limitations that need to be addressed.
Data Preparation: In this phase, the focus is on cleaning, transforming, and preparing the data for analysis. The goal is to create a high-quality dataset that can be used for analysis.
Modeling: In this phase, the focus is on building and testing the models that will be used to analyze the data. The goal is to identify the best model that meets the project’s objectives and requirements.
Evaluation: In this phase, the focus is on evaluating the model’s performance and assessing its effectiveness in addressing the business problem or opportunity identified in the first phase. The goal is to ensure that the model meets the project’s objectives and requirements.
Deployment: In this phase, the focus is on deploying the model and integrating it into the organization’s systems and processes. The goal is to ensure that the model is being used effectively and that it continues to meet the project’s objectives and requirements.
Data Analytics vs Reporting
Both data analytics and reporting deal with data, they do so in different ways. The simplest way to understand it is that reporting shows what is happening while data analytics explains why it is happening.
Reporting entails gathering data to gain a better understanding of the performance of a company’s various functions. Analytics is the ability to interpret data at a deeper level to make better decisions. For example, a company can use reporting to evaluate progress on various marketing campaigns, then use analytics to better prioritize them.
Reporting generates facts about the performance of different functions within a company. Examples of these functions can include sales, marketing, production, and so on. Outputs usually follow a structured reporting template and timeline (e.g. daily, weekly, monthly, quarterly or annual reports). Analytics takes it a step further and provides insights, detects patterns, and generates recommendations
Therefore, the major difference is that reporting is about extracting, organizing, and consolidating the data from the various sources. Analytics, on the other hand, is concerned with examining, questioning, comparing, and identifying trends from the data.
Data Analytics System
A data analytics system is a robust tool that allows its users to interpret data to help them make data-driven decisions. The process should be automated, pull data from several sources within your company, and provide a rounded picture of the overall health of the company. It bridges the gap between siloed data in the various departments within your organisation, it also helps simplify the data reporting and analysis process to save employees time.
Data Analytics System vs Reporting System
A data analytics system is not the same as an operating or reporting system. A reporting system is also called an Operating System (OS).Many software vendors sell operational systems (OS) which have some analytics features. But these analytics features are not analytics systems , they are just reporting on siloed data collected by that specific operating system which tells you what is happening. On the other hand, analytics systems combine data from multiple sources. The data from various operating systems and other data sources is the key part of this system, the relationships between these data sources give better insights on why things happen, also giving richer insights to aid the decision making process. The richer and more diverse the data collected about a transaction, the more contextual the information the more powerful your analytic system will be.
Traditionally, organizations data has been guarded by the IT department, serving as the gatekeeper to ensure the security and integrity of company data. This approach is logical and imperative for safeguarding sensitive data. However, there is a growing recognition among companies that fostering alignment across various departments is not only crucial for data security but is also a key driver for overall business growth. In this evolving paradigm, companies understand that a cohesive approach to data management is vital for achieving synergy across different business units. The traditional notion of compartmentalizing data within specific departments is giving way to a more integrated and interconnected model where data becomes a shared resource. This approach facilitates a holistic understanding of the company’s operations, thereby fostering an environment conducive to sustainable business growth.
The ownership of sales reporting can vary depending on the organizational structure, priorities, and the specific needs of the business. In many cases, a collaborative approach involving both the Head of Analytics and the Head of Sales and Marketing is ideal. It can improve customer experience and provide a 360-degree view of the customer journey, Increase decision-making speed, optimize each and every person’s role at the company and also help each person and team understand their role in the eyes of the customer. Ultimately, the key is to create a symbiotic relationship between the analytical capabilities of the Head of Analytics and the practical business acumen of the Head of Sales and Marketing, ensuring that sales reporting serves the organization’s overarching goals.
The IT departments should be able to own the OS where data is generated. Analytics systems, on the other hand, should be owned by the users, the people who collect that data day in and day out and own the process of evaluating that data. Think about departments like HR, finance and accounting, sales, product development, and so on as the C-suite executives requires the bigger picture.
Executives need to have access to the analytic systems in order to see what is happening throughout the entire organisation. When data analytics are done this way, it’s an all-around win for your organization. IT no longer has to waste time downloading and sending out data that’s requested by the individual units. Siloed departments don’t have to keep asking the same questions again and again. Also company executives can have a birds-eye view of the entire business in real-time — ultimately leading to better business decisions. Therefore, the data analytics systems should be accessed by everyone in the organization.
What Should a Data Analytics System Be Able to Tell Managers?
Data analytics system should be able to give you as a manager some insights about your business. This information will be different across your departments, for example:
Your sales analytics system should be able to tell you:
· What customers are buying,
· Sales by Demographics: gender, age, race, etc.
· When and where they are buying that product and on what device.
· Detailed comparisons over time and targets by multiple categorical subdivisions.
Your HR analytics system should be able to tell you:
· Who is performing & who might not be in the coming months.
· The overall level of engagement & retention.
· About time tracking and workforce productivity.
· The effectiveness of training, etc.
Your accounting analytics system should be able to tell you:
· Predictive analytics, like financial projections, based on sales data.
· Where you might be wasting money.
· Core financial ratios and their trend over time.
· Cost-benefit analysis.
Your overall organisational dashboard should be able to:
· Show visually what is working within and outside of the company.
· Show the C-suite the highs and lows by multiple metrics.
· Answer specific and complex business questions.
Challenges and Limitations of Data Analytics
While data analytics offers many benefits, there are also several challenges and limitations that organizations should be aware of. Some of the main challenges and limitations of data analytics:
Data quality: The quality of the data used for analytics can be a challenge, as it may be incomplete, inaccurate, or inconsistent. The data quality can affect the accuracy and reliability of the insights and decisions based on the data.
Data privacy and security: These concerns can be a significant challenge for data analytics, especially in industries that handle sensitive data. Organizations need to ensure that they comply with privacy regulations and protect data from unauthorized access and cyber threats.
Resource requirements: Implementing data analytics can require significant resources, including specialized talent, technology, and infrastructure. Organizations need to ensure that they have the resources and budget necessary to support a successful implementation.
Complexity: Data analytics can be a complex process, involving multiple stakeholders, technologies, and data sources. Organizations need to manage this complexity effectively to ensure that the insights and decisions are based on accurate and meaningful data.
Bias and interpretation: Organizations must be aware of these potential biases and ensure their analytics efforts are transparent and objective.
Legal and ethical considerations: The use of data analytics can raise legal and ethical considerations, such as data privacy, intellectual property, and discrimination. Organizations need to ensure that they comply with relevant laws and ethical standards when collecting, using, and sharing data.
Conclusion
The ability to distill meaningful insights from data empowers managers to make informed decisions, navigate complexities, and align strategies with organizational objectives. From descriptive analytics to prescriptive analytics, data analytics provides a multifaceted lens through which managers can understand and anticipate trends, fostering a proactive and strategic decision-making culture. Managers play a pivotal role in fostering a data-driven culture within an organization, and their influence is instrumental in driving this cultural shift from the top down. By embracing data analytics, managers should cultivate a culture of adaptability and responsiveness among managerial teams, ensuring they are well-equipped to thrive in an environment where data-driven insights are integral to the success of the organisation.
