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Effective business use cases for data analytics

Effective business use cases for data analytics

There has never been a better time to achieve impact with your data analytics. More and more data is available both through your organisation’s primary data and data sourced from third parties. Not to mention that computing power is ever increasing, and the mathematical techniques and data science making data more accessible and easier to understand are growing in advancement.

The McKinsey Global Institute estimates data and analytics could create value worth between $9.5 trillion and $15.4 trillion a year if embedded at scale. There is a huge opportunity to fuel business growth with data analytics – if you do it well. 

In this article, we will explain the scope of data’s growing importance and impact potential in business when executed well. We will also review how tools such as VizLib are creating solutions that make data more accessible throughout every level of your organisation.

Examples of data and analytics use cases in business

Data analytics has numerous applications within a business that can impact operations, employees, and the bottom line.

Examples of business use cases include: 

  • Predicting volumes of incoming orders for products which can be combined with optimisation to respond to the changing demand across your supply chain.
  • Dividing your customers into micro-segments based on things such as risk. This can then be combined with optimisation to run through scenarios that could reduce the risk of losing those customers.
  • Review customer feedback with natural language processing and use insights gained to influence your sales team’s communication tactics.
  • Using tools such as VizLib to visualise and simplify your data so it can be used in decision making across all levels of your organisation. 

These use cases all sound interesting, right? But why do they matter? The main reason that businesses are starting to implement more data analytics is because these types of use cases have a direct impact on the bottom line, and deliver a high ROI. 

The ROI of data analytics 

Data analytics has a high ROI when well implemented. In fact, according to McKinsey, companies that are using data-driven B2B sales-growth engines have an  above-market growth and their EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortisation) increases in the range of 15 to 25%. 

Here are some of the ways that companies are seeing an ROI on their data analytics:

  • Using data in day-to-day operations across the business. For example, A/B testing new features.
  • Driving new strategic initiatives, for example, identifying opportunities to develop a new user segment.
  • Discovering new opportunities, for example, finding user behaviours that could lead to new product features or marketing ideas.

Data analytics examples: How Walmart excels through data analytics 

The opportunities to grow your business with data are endless. And the revenue it can generate is sometimes jaw dropping.

According to ProjectPro Walmart is one of the best examples of this. Walmart is the major US retailer that operates in 10,500 stores and clubs in 24 countries, along with having an eCommerce website and managing 2.2 million employees across the globe.

Data science has been fundamental in the continuous improvement and success of Walmart’s products and services. The organisation has invested heavily in digital transformation and data management. It is now home to the world’s largest private Cloud, which can manage 2.5 petabytes of data every hour.

So, what does it do with all that data? Well, Walmart has numerous strategies for its data analytics – as you would expect from a retail giant of its size. One of the most notable, that businesses of all sizes can use as inspiration, is their stocking strategy. Walmart analyses customer preferences and shopping patterns to optimise the stocking and displaying of merchandise in their stores which has resulted in increased sales.

Another great strategy from Walmart looks at on-time delivery. It runs a backend algorithm that estimates delivery time based on the distance between the customer and the fulfilment centre, inventory levels, and shipping methods available. These predictions keep customers informed and result in a better customer experience. 

Data analytics examples: How Qlik + Vizlib streamlined reporting for Tectum

Another excellent example of the real world possibilities of data analytics comes from our own archive. Tetum Group is a VizLib client that operates in the construction industry that was having problems gaining insights from their ​​rapidly growing database.

With VizLib, they were able to move away from spreadsheet chaos to a replicable data analytics model that could be used across all departments. This democratisation of data and the inclusion of visualised elements made data more accessible. In turn, this saved the team a lot of time in their decision making and gave employees greater autonomy. 

Use data analytics to fuel your business growth

Ready to shift your business into the modern era? Click here to get a free trial and see how VizLib can help your business to quickly implement a scalable and easy to understand data analytics solution.


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