With the collection of mass amounts of data comes the need for data interpretation and understanding. Descriptive analytics is a form of business intelligence that aids businesses in their decision-making processes by answering the question “what happened?” It is part of four categories of data analytics that use statistics and artificial intelligence to glean insights from various sources of data to measure business goals and model the future, which is massively impactful for financial organisations.
Here, we will look at how businesses descriptive analytics work, as well as how companies can use this form of data analysis to see trends and patterns.
Descriptive analytics connects data with key metrics. Using historical data, descriptive analytics paints a picture for businesses to recognise patterns and gives insight into the past. For example, it can show the cause and effect of an action.
To identify patterns and relationships, data mining and data aggregation must occur. As such, data is first gathered and sorted using data aggregation to make the datasets more manageable by analysts. While descriptive analytics then allows for the extraction of meaning from these vast volumes of data. Whether businesses employ automation software or complete this process manually, it is a vital step for descriptive analytics.
After data is sorted, transformed and analysed, it becomes imperative to create a visual representation. Thus, descriptive analytics is used to place critical metrics side by side with business goals to assess a business’ current state-based actions that have occurred. Such analysis trends are used to show business leaders how their organisation is functioning and inform next steps.
To illustrate, descriptive analytics help companies to better understand their customers’ behaviour. As such, they can segment their customers into different audiences and tailor their marketing strategies specifically. In a financial institution, like a bank, for example, they may use descriptive analytics to find out certain things. For instance, when interest rates are high, the audience who are least likely to pay back or struggle with their loans are those that single compared to those who are married. Therefore, if interest rates rise in future, the bank can work out its likely risk and increase the application threshold making it harder for this audience to get a loan during these times. i. Therefore, the bank is using descriptive analytics to understand its exposure to risk. This type of information takes a cause and leads to its effect, all the while informing the best business actions to take.
Descriptive analytics includes different statistical functions, like regression analysis, summary statistics and suppression. To make descriptive analytics work, these are its vital functions:
1. Business Metrics and KPIs: Identify the key performance indicators (KPIs) you want to measure to achieve business goals. For example, business goals could be to reduce costs, increase revenue or better understand productivity. Therefore, a KPI to measure revenue would be units sold. Data can answer this question, but it could come from different sources throughout your organisation, which leads to the next step.
2. Data Collection and Aggregation: Once you have the business goals and relevant KPIs outlined, you must identify the data sources for such information. Businesses hold data in multiple locations, such as databases, desktops and more, so organisations should catalogue data. It must all come together for the accuracy of the information and descriptive analytics to do its job.
3. Data Extraction: The most tedious of all tasks during the data analytics process is data extraction. It spans data transformation, data duplication, data cleansing and more. Data automation is a useful tool to use to do this kind of work for you to save your employees time.
4. Data Analysis: Once data is organised correctly, it should then be analysed. Otherwise, there’s no point in having the data in the first place. With either data scientists at the helm or automation software, data analysis can link the numbers with business metrics to provide insight.
5. Data Presentation: Once the picture is painted, it must be shared with stakeholders, both internally and externally, to inform decisions. This is done through data visualisation and presentation, such as charts and graphs, for example.
Using statistics and summarisation to explain data has clear benefits for businesses. It provides the ability to assess better how processes are working to see if business goals are being met most efficiently.
1. Provides Historical Context: Descriptive analytics allow businesses to look at the past and understand how customers and products relate to one another. It leads to predictive analytics, which can help companies choose how to move forward.
2. Assess Business Goals: By outlining KPIs, descriptive analytics can show how current processes are working to achieve business goals. It is used to recognise patterns for revenue growth, operational effectiveness, and more.
3. Holistic Approach: Since businesses are dynamic and always changing, it helps to be able to see what happens when you edit variables, such as instituting a new supplier or changing product prices. With descriptive analytics, you can understand trends and then have an easy way to visualise patterns. This helps to identify an organisation’s strengths and weaknesses and provide a historical overview to help function more optimally in the future.
Descriptive analytics is just a piece of the data analytics puzzle. Not only can analytics look at the past to identify trends, but it can also provide forecasting models and more to move forward most beneficially.
Here are the different categories of analytics that are generally paired with descriptive to provide a business with a holistic and proven way to use data for good.
1. Descriptive Analytics - The backbone of business intelligence, descriptive analytics answers the fundamental questions of when, where, what and how many. It provides information around your customers and your business to help measure KPIs.
2. Diagnostic Analytics - Diagnostic analytics look at what already happened to find out why something occurred. It digs into the data to better explain anomalies and find hidden causal relationships.
3. Predictive Analytics - Perhaps the most common of the four, predictive analytics understands correlations, trends and causation. It finds out what will happen in the future.
4. Prescriptive Analytics - Taking predictive analysis, a step further is prescriptive analytics, which informs a business about what actions to take. With the help of artificial intelligence (AI), it tests variables to provide the best course of action before a company commits to making a decision.
The growth of information that is available for businesses to utilise compounds exponentially daily. With the use of data analytics like descriptive analytics, business is changing for the better. Once you grasp the power and understand how to use data analytics to your benefit, you will see how your organisation will transform. Descriptive analytics uses past information, but that information can be used to inform a more profitable future.
Software automation tools and business intelligence technology will do the heavy lifting for you!