10 Factors to Consider When Choosing a Data Analytics Tool

We have put together a guide to help begin your data analytics transformation and to guide you through the process of understanding your user requirements, integration considerations, the importance of solid customer support services, and other factors you should consider before buying.

Data that is untagged, file based and unstructured continues to be commonplace. This is particularly clear throughout supply chains where multiple suppliers, service partners and customer systems create a disconnect hiding key data surrounding complexity, cost, and delays from line of sight. It is important to remember that without the right data analytics tools, your data is just big data, not usable data.

While expert opinions vary as to current usage levels there is consensus that less than 10% of all data produced today is useful for analysis and of that less than 1% is analysed. These estimates highlight the enormous potential for data analytics to create value if used effectively.

Without the right data analytics tools, your data is just big data, not usable data.

Many businesses choose to develop their own analytics tools in house, this is not always the smartest use of resources and should only be undertaken after careful consideration of buying alternatives.

 

Read our 10 tips to consider when picking the best data analytics solution for your company’s needs

1. Business Objectives

Your analytics platform should support both your existing and future business requirements. Know your business needs and goals to focus on the right type of data analytics tools that meet your requirements.

 

2. Pricing

Do you have the time, money, and ability to build and maintain your own analytics solution? Before selecting an analytics tool, you must be fully aware of the costs associated with the analytics solutions you are evaluating including subscriptions, growth, and other fees.

 

3. User Interface and Visualisation

Visualisation is the final and often overlooked stage of the big data analytics process before insights are transformed into action. Your analytics tool is what your employees will refer to while making business decisions. Even non-technical users must find it easy to use and be able to create and understand dashboards and reports.

 

4. Advanced Analytics

Your analytics application must be able to recognize patterns in data and predict future trends, events, and outcomes. It must go beyond simple mathematical calculations and generate contextualized insights giving you the ability to build advanced statistical models and future-proof your business.

 

5. Integration

Choose a data analytics tool that integrates seamlessly with your existing technology stack. There are a variety of options with standalone solutions but  integrated solutions allow you to access analytics from applications that your users are already familiar with.

 

6. Agility and Scalability

Analytics platforms that are designed to start small and grow with your business are an excellent choice. You can get instant data access and insights to make fast decisions with analytics that scale according to your business needs. And you can guarantee to be supported when you are experiencing hyper-growth or large sales volumes.

 

7. Multiple Sources of Data

Choose a tool that can combine multiple sources of complex data, and analyse structured, semi-structured, and unstructured data. Having the ability to gather and combine data from different systems onto a single dashboard allows you to have a complete view of your business performance.

 

8. Customisation

Select an analytics tool that allows you to set-up and customise a dashboard that integrates seamlessly into your operations i.e., it displays what you want and need to see.

 

9. Collaboration

Your analytics tool must allow users to share, analyse and interact with data to enable smarter, collaborative decision making. You should be able to quickly distribute insights across your organization whenever you need to make to collaborate and make decisions.

 

10. Security

You must evaluate the security of your analytics provider and vendor to ensure that the necessary precautions are in place to safeguard your information. Set up standard security controls and procedures at all levels.

 

Data Analytics platforms enable businesses to analyse raw data from multiple data sources, find correlations and trends, and even make predictions about the future, data is a key driver of strategic decision-making for companies of all sizes.

Whatever route you choose, remember the analytics tool is a means to an end, not an end in itself. It is important that your analytics platform is resilient, responsive, scalable, and entirely transparent and connects all areas of the supply chain network into a single cohesive and fully integrated supply chain ecosystem.