Predictive Analytics and Business Intelligence
Improve daily business decisions
Learn how you can leverage your organisation’s historical data to anticipate trends and outcomes better.
Get Insights with Impact
Maximise the value of your business intelligence (BI)
BI gives you a comprehensive view of your organisation’s performance, including answering questions about what happened in the past and what is happening at present. It allows you to visualise and interpret data to better understand the state of your business and optimise operations for current success.
While BI solutions enable you to examine trends, discover issues and identify opportunities, on their own, they don’t give you the capability to predict outcomes. This is where predictive analytics come in.
With predictive analytics working alongside your BI solutions, you do not only get answers to the question “what happened?”, but also answers to “why did things happen the way they did?”.
Predictive analytics looks at patterns in data, especially those not obvious on the surface. It determines if those patterns are likely to reemerge and their correlation to each other, so you can optimise your resources and take more effective actions in the future. This means your business decisions become more fact-based and truly data-driven.
Achieving Your Desired Outcomes
Transform how your organisation operates
Using predictive analytics and BI solutions together can transform how your organisation operates. Many organisations, however, struggle to leverage these two capabilities due to:
- A lack of a clearly defined data strategy;
- Barriers to data analytics adoption; and
- A failure of empowering end-users.
To help you successfully transition to predictive analytics, here’s a list of steps you can take to maximise the value of your historical data from your BI solutions.
Assess your existing business processes
Understand where you currently are and the data analytics capabilities you have that can be built upon. This will allow you to better identify your organisation’s pain points, their causes, and their impact on the business. The assessment will also help you understand if you’re ready to make this transition, and the opportunities this kind of data transformation will help deliver.
Define Your Objectives
And the direction you want to go
Have a clear understanding of your business objectives and the role data will play in achieving them. Defining your objective will also clarify the actions you need to take to get to your target state. Remember that even the most technologically advanced data analytics can produce misleading results if it’s disconnected from the organisation’s purpose and goals.
And understand their needs
Learn who the business stakeholders are in your data project. They may be the senior management, business application owners and/or your frontline staff. Once you have identified them, conduct a consultation like one-on-one meetings and team surveys to understand their needs, and verify the functionality and value of your data project.
Understand and Prepare Your Data
Identify sources and assess their quality
Understand what data you have and need, and ensure there’s a common understanding of what the data means. You also have to identify the data sources and assess the quality of the data to determine whether it’s fit for the purpose or not. Assessing the quality may mean undertaking some data profiling activities to identify gaps, errors or where data needs to be enriched.
Select your modelling technique
Select the actual modelling technique you’ll be using for the proper sourcing of data. This will help you have a starting point based on your requirements, use cases or user stories. Whilst it may not be perfect at the beginning, it will help your team understand the relationships between the data, and how the data fall in line with your business initiatives.
Assess your model
Evaluation allows you to assess if your model meets your business objectives. You may also find new data patterns at this point which can lead to new objectives.
When validating your model, you have to keep in mind that:
- Each modelling technique may have different data requirements.
- Each model output will be different from another model output.
- There can be multiple approaches to solving the same problem. This means more than one model output may be needed for multiple business cases.
Coming Up Next
How other organisations are maximising the value of their historical data
Next week, we will look at how organisations in the government, banking, health and environmental sectors are using predictive analytics alongside their business intelligence solutions.
If you want to learn more about how you can successfully make the jump to predictive analytics from your business intelligence solutions, contact us today for a no-obligation discussion.