In our data-rich age, the primary driver of success lies in understanding how to analyze and extract true meaning from the digital insights available to a business. Despite the huge volume of data we create every day, a mere 0.5% of data is analyzed and used for data discovery, improvement, and intelligence. While that may not seem like much, considering the amount of digital information we have at our fingertips, half a percent still accounts for a huge amount of data.
Understanding how to analyze and extract true meaning from digital insights is the primary driver of a successful business.
In the past, specialists saw data as nothing more than a static collection of figures used for annual accounts. However, by quickly gaining an ever-evolving insight into relevant data, people can make a strategic contribution at various levels.
- In the past, you would evaluate past data and pass judgement in relation to compliance and reporting.
- In the present, up-to-date data allows you to monitor processes and prevents, or punctually resolves, errors.
- In the future, data modeling will allow you to contribute to the planning process and make strategic decisions.
What Is Data Analysis?
Data analysis is a process that relies on methods and techniques to take raw data and mine the same for insights that are relevant to the business’s primary goals. This is further drilled down into information to transform metrics, facts, and figures into initiatives for improvement.
As the process of analyzing raw data to find trends and answer questions, the definition of the data analytics process captures its broad scope of the field. However, it includes many techniques with many different goals.
The data analytics process has some key components that are needed for any initiative. By combining these components, a successful data analytics initiative will provide a clear picture of where you are, where you have been, and where you should go.
Big data is everywhere these days. What really delivers value from big data is the analytics applied to the data. Without analytics, which involves examining the data to discover patterns, correlations, insights, and trends, the data is just a bunch of ones and zeros with limited business use.
To answer the age-old question, “what’s in it for me?”, analyzing information using big data analysis tools enables organizations to make better-informed business decisions such as when and where to run a marketing campaign or introduce a new product or service.
Types of Data Analytics
Data analytics is a broad field. To break it down, there are four primary types of data analytics: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. Each type has a different goal and a different place in the data analysis process.
Descriptive analytics helps answer questions about what happened. These techniques summarize large data sets to describe outcomes to stakeholders. By developing key performance indicators, these strategies can help track successes or failures. Metrics such as return on investment (ROI) are used in many industries. Specialized metrics are developed to track performance in specific industries. This process requires the collection of relevant data, processing of the data, data analysis, and data visualization.
Diagnostic analytics helps answer questions about why something happened. These techniques supplement more basic descriptive analytics. They take the findings from descriptive analytics and dig deeper to find the cause. The performance indicators are further investigated to discover why the given situation got better or worse.
Predictive analytics helps answer questions about what will happen in the future. These techniques use historical data to identify trends and determine if they are likely to recur. Predictive analytical tools provide valuable insight into what may happen in the future. The techniques of predictive analytical tools include a variety of statistical and machine learning techniques such as neural networks, decision trees, and regression.
Prescriptive analytics helps answer questions about what should be done. By using insights from predictive analytics, data-driven decisions can be made. This allows businesses to make informed decisions in the face of uncertainty. Prescriptive analytics techniques rely on machine learning strategies that can find patterns in large data sets. By analyzing past decisions and events, the likelihood of different outcomes can be estimated via prescriptive analytics.
There are various tools in data analytics that can be successfully deployed in order to parse data and derive valuable insights out of it. The computational and data-handling challenges that are faced at scale mean that the tools need to be specifically able to work with such kinds of data.
Role of Data Analytics
Data analysts exist at the crossroads of information technology, statistics, and business. They combine these fields in order to help businesses and organizations succeed. The primary goal of a data analyst is to increase efficiency and improve performance by discovering patterns in data. The work of a data analyst involves working with data throughout the data analysis pipeline. This means working with data in various ways. The primary steps in the data analytics process are data mining, data management, statistical analysis, and data presentation. The importance and balance of these steps depend on the data being used and the goal of the analysis.
Importance of Data Analytics
The applications of data analytics are broad. Analyzing big data can optimize efficiency in many different industries. Improving performance enables businesses to succeed in an increasingly competitive world. Data analytics is being used with great success in several fields. One of the earliest adopters has been the financial sector. Data analytics has played a huge role in the banking and finance industries. It has been used to predict market trends and evaluate risk. Credit scores are an example of data analytics that affects everyone. These scores use many data points to determine lending risk. Data analytics is also used to detect and prevent fraud to improve efficiency and reduce risk for financial institutions.
The use of data analytics goes beyond maximizing profits and ROI. Data analytics can provide critical information for healthcare, crime prevention, and environmental protection. These applications of data analytics use these techniques to improve the world.
The applications of data analytics are seemingly endless. More and more data are being collected every day, thus presenting new opportunities to apply data analytics to more parts of business, science, and everyday life. These types of data analytics provide the insight that businesses need to make effective and efficient decisions. Used in combination they provide an all-round understanding of a company’s needs and opportunities.