Delving into data analytics

We often talk about the mechanics of data analytics; the principles of moving data, connecting sources, and unification. But what about real-world use? How can you use data analytics to improve your business? Here, we’ll explore some examples. But first, let’s see what skills are needed to make data analytics work for you.

What are the top 3 skills for a data analyst?

Data analysts are the lifeblood of data-driven businesses, and their skills drive successful outcomes in almost every arena. So, what are the data analyst abilities that you need to cultivate for your company to reap the rewards?

Here the top three technical skills:

• Programming – Naturally, the ability to easily manipulate computer code is essential. Many different types of data must be manipulated and transformed, and multiple sources with different APIs must be connected together. The two most common languages are SQL – the database querying language, and Python – a general purpose language that has specialized AI libraries useful for statistical modeling.
• Statistical analysis – This is the application of statistical methods to data to gain insights. Some techniques include factor analysis, linear regression, resampling, and sentiment analysis. Statistical models are used to discover patterns and relationships between data points, so data analysts need to be experts at building them.
• Data visualization – Although data analytics primarily deals with figures and code, it’s important to illustrate findings in a way which can be easily understood. Company decision-makers may not have the analytical training to interpret complicated numbers or identify patterns, so data analysts must enable them to easily do so with carefully constructed charts and other illustrations.

However, if your business team doesn’t have the above skill sets, or your engineering team wants to focus on other, more exciting projects, then fear not – Switchboard’s turnkey platform can do all the coding and heavy lifting, so you can focus on everything else.

How data analytics helps business: 3 examples

Data analytics is vital to data-driven businesses because it provides insights which help improve commercial and operational performance. The benefits of data analytics are many-fold. Without it, data-driven companies would be severely hampered in their ability to make good decisions. With that in mind, here are some data analytics examples which demonstrate how the field can be used to help businesses thrive.

1. Higher customer acquisition and retention

Businesses need to offer what their customers are seeking. And the faster they can learn what’s desirable in a product or service, the faster they can provide it. This knowledge can be used to acquire new types of customers, as well as instilling loyalty in existing buyers.

Collecting data on customer behavior and their individual preferences is important to understand wider patterns and trends – for example, software buyers’ budgets or the features they’re looking for in a particular product. Data analytics is the key to unlocking these valuable insights into a brand’s target audience. In particular, descriptive analytics – the summarization of statistical data – is essential to transform raw data into actionable improvements.

2. Increase supply chain efficiency

Supply chain management is the procurement, processing, and distribution of goods. However, traditional supply chain management systems don’t make good use of data, leaving decision-makers with a whole load of guesswork that translates into errors and losses. But data automation has changed this dramatically. Modern supply chain systems use high-level collaboration to attain contextual intelligence about what is happening, and what is predicted to happen in the future. This results in fewer losses, and greater efficiency throughout.

3. Better risk management

Recent global events, coupled with an ever-changing economic environment, mean that it’s critical for companies to manage commercial risks, regardless of their sector. The statistical techniques used in data analytics allows businesses to quantify and model risks. This enables them to build strategies to mitigate negative effects.

What is a ‘big data’ analytics example?

The vast majority of statistical models require large amounts of data to be sufficiently accurate. Hence, almost all applications of data analytics use ‘big data’ to achieve the best results. The general rule of thumb is: the more data available, the better.

But it’s not all about commercial success – governments and other public institutions are also using data analytics to improve outcomes. There are many big data examples in healthcare, such as predicting negative health effects in older recipients receiving home care. These insights have led to changes which drastically cut the number of hospitalizations and emergency department visits. With the move toward electronic medical record systems, there are likely to be more opportunities to improve and advance healthcare using the data available.