An introduction to data analytics

At Switchboard, we get excited about data analytics. If you’re new to the field, and wondering exactly what data analytics is and how it fits into your business, here’s a brief introduction.

What is data analytics?

Data analytics refers to any technique which processes raw data to draw conclusions and gain insights from them. In business, this usually means revealing information which can be used to increase revenue or reduce costs. With increasing computational power and the advent of new technologies, such as ML (Machine Learning), the field of data analytics has grown dramatically.

Here are some data analytics examples in business:

• Customer acquisition and retention – Data analytics can make sense of customer behavior and preferences to reveal patterns and trends. This may be their budgets or desirable features in a particular product.
• Supply chain efficiency – Supply chain management is the procurement, processing, and distribution of goods. Data analytics has alleviated much of the guesswork of traditional supply chains. High-level collaboration can be used to understand what’s happening, so you can predict future events.
• Risk management – Commercial risks can come in many forms, such as global events or a shift in the economic environment. The statistical techniques used in data analytics enable businesses to quantify and model risks, thus providing strategies to reduce them.

Data analytics vs. data analysis

Although the concepts of ‘data analytics’ and ‘data analysis’ are similar, they are not the same thing. Data analysis refers to detailed examination of the elements or structure of data, whereas data analytics is the systematic computational analysis of data.

Data analysis can be thought of as a part of data analytics, which more widely involves the manipulation of data and the prediction of outcomes for a particular purpose.

Is data analytics different from data science?

The short answer is yes. But it’s not so much a case of ‘data analytics vs. data science’. It’s more about the two fields complementing one another. Data science uses statistics and data analysis processes to find patterns in data, which can then be used to make predictive tools. Data analytics, or ‘big data’ analytics, then takes over. This uses the tools built by data scientists to process and interpret new data, thereby producing actionable insights for the benefit of a business.

The four main types of analytics in big data are:

1. Descriptive analytics – uses the data to describe what has occurred
2. Diagnostic analytics – explains why changes have occurred, and establishes causal relationships where applicable
3. Predictive analytics – builds models to forecast future trends or events, using the correlations found in diagnostic analytics
4. Prescriptive analytics – processes factors and predictions to recommend actions, given the likely scenarios.

What are the top 3 skills for a data analyst?

When hiring, it’s important to construct a data analyst skills matrix, so you can assess talent systematically. Given the data analyst’s responsibilities of producing valuable insights for your business, the top 3 skills should be:

• Programming – Data analysts must be au fait with computer code. They need to be able to manipulate and transform many different types of data, and connect multiple sources with different APIs.
• Statistical analysis – This is the application of statistical methods to gain insights. These models are used to discover patterns and relationships between data points, so data analysts need to be experts at building them.
• Data visualization – While data analytics lives in the world of numbers and computer code, it’s important to illustrate conclusions clearly. Data analysts should use charts to help decision-makers understand their findings.

What are data analytics tools?

Data analytics tools refer to software that’s used to develop or carry out the processes required for data analytics. The tools you need to use will depend on your requirements and the data pipeline being used. Consider the data sources, data types, and data integrations required. Another consideration is whether to carry out data modeling manually, or use a ready-made solution.

Additionally, you need to make sure your solution adheres to data security and regulatory governance. And perhaps most importantly, you need to decide whether to build a solution in-house, or outsource it. There are a range of free data analytics tools, but these usually require more in-depth data engineering skills than a paid solution.