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Data analytics

The importance of data analytics

We’ve heard it before: “Half the money I spend on sales and marketing is wasted. The problem is, I just don’t know which half.”

To solve this age-old problem, companies are investing heavily and using valuable resources to harness one of their most strategic assets: data. The amount of data created and analysed continues to grow exponentially every year, allowing for more granular analysis.

The role of data analytics

Companies that harness their data strategy see an immediate competitive advantage. Techniques such as data analytics are no longer sitting in the IT department as an afterthought. Instead, business leaders now use outputs from data analytics to make more fact-based strategic decisions that help them identify opportunities to generate more sales and boost customer satisfaction.

The role of data analytics is to make the rising volume of data work harder. It is designed to extract quantified insights that can lead to better business outcomes, taking the “feeling” or “we’ve always done it this way” out of the decision-making process.

The perfect intervention: Four key categories

Thanks to cloud computing, smaller organisations now have the same access to datasets, software, and techniques as the big players.

First, however, companies must understand which structured or unstructured datasets to dig into. Next, they need to be sure their data teams are spending the right time and the right level of resources in the right areas to answer the right questions to make the most effective changes to their strategy and daily execution. This is what we at Axis call “the perfect intervention”.

We believe there are four key categories to data analytics, all with varying levels of sophistication, along the path to the perfect intervention:

  • Descriptive
  • Diagnostic
  • Predictive
  • Prescriptive

1. Descriptive analytics

Descriptive analytics helps answer the question, “What’s happened?”

It helps ensure the data is accurate and visualised in a dynamic dashboard environment, allowing teams to track important KPIs and gain insight into historical performance. This is also known as business intelligence and data visualisation.

2. Diagnostic analytics

Diagnostic analytics helps answer the question, “Why did it happen?”

At this stage, reporting is automated, freeing up time for analysts to dig into the why behind each KPI. This involves detecting trends, identifying anomalies in the data, and using various statistical techniques and formulas to define trends.

However, descriptive and diagnostic analytics alone are no longer enough to compete in today’s fast-paced, data-rich world.

3. Predictive analytics

Predictive analytics helps answer the question, “What will happen?”

It is one of the most widely used forms of data analytics today. Predictive analytics is designed to transform historical and current data into future insights. It uses a variety of tools and techniques, from statistical modelling and mathematic formulas to automation and machine learning.

Analysts around the world use decision trees, regression techniques, clustering algorithms and segmentation models to detect trends and patterns within very big data environments. The goal: make more informed, more data-driven decisions about the future. The better your predictions, the better your business outcomes. It’s as simple as that.

Predictive analytics has become commonplace in our society, with models and algorithms working constantly in the background of our daily lives. For example, in the retail industry, algorithms work feverishly in near real-time to set prices, product assortment and promotions based on predicted consumer behaviour. Accurate predictive modelling drives more reliable business decisions, which results in better business outcomes and actionable insight.



4. Prescriptive analytics

Prescriptive analytics helps us answer the question, “What should happen?”

It’s a natural extension to predictive analytics as it helps businesses use predicted outcomes to decide what action to take and how best to take it. It prescribes the next best action to optimise a very specific business outcome. It’s one of the most sophisticated forms of analytics available to businesses today aimed at improving the speed and efficiency of decision making. This is the destination on the insight journey to the perfect intervention.

 

In conclusion

It’s no surprise; companies now understand the importance of leveraging data to gain a competitive advantage and drive better business outcomes. However, this comes with a certain number of challenges and roadblocks. The role of a data analytics agency, like Axis, is to simplify the complex. Our core business is to help you source the most relevant data, effectively manage that data, and extract quantified insights that lead to better business outcomes. This allows our clients to focus on their core business instead of being distracted by the inundation of big data.

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