Data is the new precious Oil in today’s business world. Data is a critical input for business decisions and it can impact the growth of organizations. As said by Charlie Berger of Oracle Corporation, “Without proper analysis, data is just text and numbers and not useful to derive actionable information. It is something that you can exploit today and something that your competitor may not have yet discovered.”
With the humongous growth in volumes of Data that is generated, it is important for organizations to understand and analyze this data to derive trends and actionable insights to address business problems. Data has a variety in today’s user context. It is not only generated in the traditional data types that are structured but also unstructured data types as videos, social media content, audios, and images. This mixed Data type gets generated across all possible sources at high velocity. For some organizations, this might be tens of terabytes of data. For others, it may be hundreds of petabytes.
What is Data Analytics?
Data Analytics involves collecting and analyzing raw data and applying qualitative, quantitative techniques and processes to convert it into useful information for decision-making and enhancing productivity and business gain. Raw data is categorized and analyzed to identify user behavioral patterns. The techniques used for analyzing data vary according to organizational / business requirements.
Six Important steps to perform Data Analysis
Step 1: Set your goals
You need to identify your business goals or objectives and define questions around it. This is an important step as the data that you collect depends on the questions. Collecting irrelevant data is a waste of time. Questions should be measurable, clear and concise. For example, If you have a business and you are not satisfied with the Sales, your questions could be “Is my commodity over-priced?”, “Are there similar competitive products in the market?”, “What is the difference in competitive products?” The data you collect could be your selling price for the commodities, production cost, prices of similar goods in the market and so on.
Step 2: Setting up measurement priorities
Now that you know your goals you need to a) Decide what to measure b) How to measure the data
What to measure
It is important to identify which data points you need to measure. Once the data surrounding the primary question is identified, you need to work on getting answers to the secondary questions. In the above question “Is my commodity over-priced”, your data points to production cost. Secondary questions could be “What is the material cost?”, “What is the labor cost?”, “What is the manufacturing overhead cost?” etc.
Once the data is collected for the primary question and the secondary questions, the data can be converted into information that will assist the company in decision making.
How to measure the data
You need to decide how you are going to measure the data.
For example, while collecting data for the material cost would you be considering the expenses of insurance and freight as a part of the material cost? So when you measure the data, you need to be clear on what measures you would be counting in and measures you plan to exclude.
Step 3: Data collection
Now that you know the measuring parameters and criteria, your next step is data gathering. For data collection, you need to consider some important points.
- You may need to decide the time frame for the data collection. Would you need data for the last 3 years or 5 years or would you like to collect data for a particular season as buying habits could change seasonally?
- Identify who will collect data
- Decide where the collected data will be stored
- Check if any data is already available so you need not spend time collecting it again.
- At times data collection may need a survey or interviewing people, questionnaires, etc.
Step 4: Data cleaning
Once you have collected the data, the data needs to be cleaned up. Removing superfluous data, replacing missing values, removing duplicates and so on. This data is gathered due to multiple reasons such as lack of company-wide standards, having many databases, and user errors. This is referred to as “dirty” data, and it can represent a formidable obstacle to companies hoping to use that data. According to The Data Warehouse Institute (TDWI), dirty data ends up costing companies around $600 billion every year. To fully address this problem, businesses need to understand what causes dirty data and how best to fix it. Data cleansing is not a solution for good quality data. It is important to ensure that valid data is stored rather than wasting time in cleaning it up.
Step 5: Data Analysis
Once you have good quality data it is time for analyzing the data. It is important to understand each type of data. Depending on your needs and the type of data you collect, the right data analysis methods should be used. Type of analysis depends on whether the data is quantitative or qualitative. Quantitative data deals with quantities and hard numbers. This data includes sales numbers, marketing data such as click-through rates, payroll data, revenues, and other data that can be counted and measured objectively. Qualitative data is more interpretive and subjective. This includes information taken from customer surveys, interviews with employees, and generally refers to qualities. Hence, the analysis methods used are less structured than quantitative techniques.
Measuring quantitative data
- Regression Analysis: Regression is a data mining technique used to predict a range of numeric values given a particular dataset. Regressions measure the relationship between a dependent variable (what you want to measure) and an independent variable (the data you use to predict the dependent variable). Regression is used across multiple industries for business and marketing planning, financial forecasting, environmental modeling and analysis of trends.
- Hypothesis testing: Statistical analysts test a hypothesis by measuring and examining a random sample of the population being analyzed. Hypothesis testing is used to infer the result of a hypothesis performed on sample data from a larger population. It also helps you understand how random variables could affect your plans and projects.
- Monte Carlo Simulation: Monte Carlo simulation is applied to predict the probability of various outcomes in a process. It is difficult to predict the outcomes due to the presence of random variables. Monte Carlo simulation can be used to address a range of business problems in almost every industry like finance, logistics, and supply chain, engineering and science. To test a hypothesis or scenario, random numbers and data are used to display a variety of possible outcomes in various situations.
Measuring qualitative data
Unlike quantitative data, qualitative information requires more subjective approaches. Yet, you can still extract useful data by employing different data analysis techniques depending on your demands. Here are two techniques that focus on qualitative data:
This method is a research technique to make valid inferences by interpreting textual material. Content analysis can work well when dealing with data such as user feedback, interview data, open-ended surveys, and more. This can help identify the most important areas to focus on for improvement.
This kind of analysis focuses on the way stories and ideas are communicated throughout a company and can help you better understand the organizational culture. This might include interpreting how employees feel about their jobs, how customers perceive an organization, and how operational processes are viewed. It can be useful when contemplating changes to corporate culture or planning new marketing strategies.
Step 6: Interpret Results
As you interpret the results of your data, ask yourself these key questions:
- Does the data answer your original primary question? How?
- Does the data help you defend against any doubts? How?
- Are there any limitations to your conclusions, any areas you have not considered?
Data Visualization refers to information that is presented in visual forms, such as graphs, charts, and tables or pictures. The main reason for this is to communicate the information in an easily understandable manner. Even very complicated data can be simplified and understood by most people when represented visually. It also becomes easier to compare the data when it’s in this format. For example, if you need to see how a business or product is performing compared to competitor’s product, all the information such as price, specification, how many were sold in the last year can be put into graph or picture form so that the data can be easily assessed and decisions made. It is a quick way to view and help identify the source of the problem.
Types of Data Analytics
There are 4 types of Data analytics.
Descriptive Analytics answers the question “what happened?” For instance, a healthcare provider will learn what made patients get hospitalized in the last month; A retailer – what was the average weekly sales volume; A Manufacturer – what triggered product returns for a past month and so on. Descriptive Analytics juggles raw data from multiple data sources to give valuable insights into the past. However, these findings simply signal that something is wrong or right, without explaining why. For this reason, highly data-driven companies are not contented with descriptive analytics only and prefer combining it with other types of data analytics.
Here, historical data can be measured against other data to answer the question “Why” something happened. With Diagnostic Analytics, there is a possibility to drill down, find out dependencies and identify patterns. Companies often go for Diagnostic Analytics as it gives in-depth insights into a particular problem.
As the term suggests, Predictive Analytics tells what is likely to happen. It uses the findings of descriptive and diagnostic analytics to detect tendencies, clusters, and exceptions, and to predict future trends, which makes it a valuable tool for forecasting. Despite numerous advantages that predictive analytics brings, it is essential to understand that forecasting is just an estimate, the accuracy of predictive analytics highly depends on data quality and stability of the situation.
The purpose of prescriptive analytics is to literally ‘prescribe’ what action to take to eliminate a future problem or take full advantage of a promising development. Data analytics requires not only historical data but also external information due to the nature of statistical algorithms. Besides, prescriptive analytics uses advanced tools and technologies, like machine learning, business rules and algorithms, which makes it sophisticated to implement and manage. Before deciding to adopt prescriptive analytics, a company should compare required efforts vs. an expected added value.
Application of Data Analysis
It is important for Digital Marketing professionals to get greater visibility into the buying behavior of customers. Data Analytics helps to get patterns and insights from customer behavior data, both structured and unstructured. It is possible to combine, integrate and analyze all data in one instance to get the desired outcomes. For example, you can apply analytics to design marketing campaigns that improve sales conversion rates. It is also possible to analyze customers’ beyond their basic profile segmentation. This would help re-target advertisements with even more precise communication so as to reduce the risk of customer churn or drop off.
Fraud and compliance
You can use data analytics in fraud risk management processes, including assessment, prevention, detection, investigation, and reporting. Data analytics identifies patterns deep in your data to identify fraud and generates volumes of information to make regulatory reporting much faster. It is essential to use larger data sets to identify fraud patterns and make detection algorithms work more accurately. Banks around the world have started to use Business Intelligence and Data Analytics platforms to enhance their risk and regulatory compliance programs.
The application of big data analytics in healthcare has a lot of positive and also life-saving outcomes. Specific health data of a population (or of a particular individual) can be analyzed to predict outcomes and potentially help to prevent epidemics, cure disease, cut down costs, etc. For years, gathering huge amounts of data for medical use has been costly and time-consuming. With advanced Data Analytics tools and applications, it has become easier not only to collect such data but also to get relevant and critical insights that can be used to provide better healthcare. The objective and purpose of Data Analytics in Health care are to use data-driven findings to predict and solve a problem before it is too late. Besides, it also helps assess diagnostic methods and treatments faster.
Sports Analytics includes the use of data related to sports such as players' statistics, weather conditions, and pitch conditions. Coaches can use data to optimize exercise programs for their players and develop nutrition plans to maximize fitness. You can see some game-changing results by using data analytics in sports.
A study based on a global survey of 900 advertising leaders across North America, Europe, and the Asia-Pacific region states that:
- Advertisers are planning to increase the average number of integrated data sources from 5.4 today to 6.2 in 2019 to gain greater advertising effectiveness insights.
- 94% of advertisers rely on a broad base of CRM data, from transactions and contact information to brand preferences, to track advertising effectiveness.
- 91% of advertisers have or plan to adopt a data management platform (DMP) in the next fiscal year.
Data Analytics Tools
The data analytics tools help businesses to know its data trends, build patterns and analyze the complexities, and present data by converting data into understandable data visualization formats.
6 Most Used Data Analytics Tools
- Tableau is a BI (Business Intelligence) and analytics software. It can connect to any database, drag and drop to create visualizations and share the data with just a click.
- Talend is an open source data integration platform. It provides various software and services for data integration, data management, enterprise application integration, data quality, cloud storage, and Big Data. Talend provides a development environment that enables users to interact with several Big Data sources and targets without having to understand or write complicated code. Talend Big Data Basics is an introduction to Talend components that are shipped with several products that interact with Big Data systems.
- Apache Spark is a unified analytics engine for large-scale data processing. It is a framework that has become one of the key big data distributed processing frameworks in the world. Spark can be deployed in a variety of ways, provides native bindings for Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning, and graph processing.
- R is a popular and powerful open source programming language for statistical computing and graphics. R implements various statistical techniques like linear and non-linear modeling, machine learning algorithms, time series analysis, and classical statistical tests and so on. R consists of a language and a run-time environment with graphics, a debugger, access to certain system functions, and the ability to run programs stored in script files
- MATLAB is a programming language dedicated to mathematical and technical computing and it is designed for engineers and scientists. The desktop environment has a natural way of expressing computational mathematics such as linear algebra, data analytics, signal and image processing. MATLAB features an application specific solution called ‘Toolboxes’. Toolboxes provide a set of MATLAB functions which are called as M-files that solves a specific set of problems. There are various areas where Toolboxes are available such as digital signal processing, control systems, neural network, simulations, Deep Learning, and many other areas
- Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. It is an open-source GUI software that allows easier implementation of machine learning algorithms through a platform. You can understand the functioning of Machine Learning on the data without having to write a line of code. It is ideal for Data Scientists who are beginners in Machine Learning.
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Required Skills For Data Analytics
Data Analysts collect, organize and interpret statistical information to make it useful for a range of businesses and organizations. With so many businesses today relying heavily on data about their customers, products, processes, inputs and the market, these organizations are increasingly in need of talented, skilled people who can extract information and insights from the data.
But what skills are employers looking for? In Data Analytics, there are specific skills and qualities employers require of all applicants, regardless of the position.
Education will enhance some of these skills and abilities. Others can be sharpened with experience and practice.
Let’s look at some of the top skill requirements.
As an analyst it is important to understand the business strategy, how the business works, how different the business is from its competitors, what is the market position and many such questions. You must be able to have the desire to understand business and think beyond.
As a Data Analyst you work with software, data and systems. Hence mathematics and statistics proficiencies are important. You need to understand the data value chain which will help you draw inferences and extract meaningful insights. Knowledge of programming languages such as Python, R, MATLAB are essential.
As a Data Analyst you need to communicate with stakeholders, colleagues, data suppliers, system owners and many others in the process of developing insights for decision-making. Apart from interpreting the data it is important to share this information with the audience.
Critical thinking and problem solving
You need to ask yourself a lot of questions and think beyond. You need to explore different angles and use visual analytics to look and data with different perspectives.
Data Visualization and Presentation skills
No matter which tool you use you should be able to paint a comprehensive picture of your insights and interpretations. Displaying your data with a click of a button may not help. You need to present your insights to the audience effectively, have a logical order, and be prepared with a list of answers to the obvious questions that the stakeholders may raise. Skills to use Data Visualization tools such as Tableau, Spot fire, etc. will add significant value.
How can you learn Data Analytics?
Data Analytics tutorials are all over the net. However, for graduates, the usual entry point is a degree in Statistics, Mathematics or a related subject involving Math, such as Economics or Data Science. Other degrees are also acceptable if they include informal training in Statistics as part of the course, for instance Sociology or Informatics.
You could also expand your skill sets by intense training with certification courses.
Follow these links to help you better:
Career/Job Market in Data Analytics
Data analyst will be one of the most in-demand jobs in the coming years. According to a recent study by Great Learning in India, More than 97,000 analytics positions remain vacant in India due to the shortage of talent. 38% of all jobs posted are from the Banking sector.
The average yearly salary of a Data Analyst is among the highest, with figures ranging from €30,000 to €50,000 for junior profiles, through to €99,000 for senior ones.
Data Analysts who turn data-driven insights into actionable business recommendations are often called Business Analysts. They use tools like Excel, Tableau, and SQL. Business Analyst salaries range from $54,700 to $69,000 at the entry level. Pay scales vary with the industry. Salaries for transportation logistics specialists usually start around $79,000.
Future of Data Analytics
Roughly 2.3 trillion gigabytes of data is generated every day across the world, and this will only rise in the future. This is Big Data and it is everywhere. Other than phones and computers there are smart watches, smart televisions, smart wearable gadgets, and many more in the market that further gather data from consumers, giving the scope for the huge production of data. According to the forecasts of the World Economic Forum, by 2020 data analysts will be in high demand in companies around the world. ...
This is further confirmed by IBM, which claims that the annual demand for data scientists, data developers, and data engineers will lead to 700,000 new recruitments by 2020. By 2020, data science and analytics (DSA) job openings are predicted to grow to 2.7 million, representing a $187 billion market opportunity.
So what are you waiting for?
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