Advanced analytics: Predictions to future-proof your business

 

Nobody can see the future. But what if you could predict and plan for business success, especially under erratic conditions?  

 

Let’s look at how the journey from data input to forecasting insights and agile actions can drive business sustainability. 

 

How does data quality impact data-driven decision making? 

 

The route to strategic BI action must begin with quality data. Quality data should be fit for purpose, valid, consistent and accurate. 

 

And though BI leaders understand the importance of data, many still struggle to extract quality data through their data pipeline. According to the Business Application Research Center (BARC), software users ranked data quality as one of their top three issues in the comprehensive BARC BI Survey 2020. 

 

Yet, without quality data, businesses cannot make intelligent, data-driven decisions. The wrong data can even be detrimental to transformation initiatives aimed at improving operational efficiency, for instance. 

To improve the quality and management of data, organisations should focus on developing a broad framework that incorporates people, process and technology. Assigning data roles, like data owner or data manager, and the specified tasks that go with that role, can go a long way to establishing a strong data-culture, improving data quality and the resultant insights needed for strategic stability and growth.

 

As businesses progress beyond the stage of ensuring quality data and sound data management, they’ll be gearing up for the next step towards actionable insights—using quality data to predict business outcomes.

 

What are forecasting- and predictive analytics 

 

Forecasting is the process of analysing historical data to forecast or predict future business outcomes. And in a volatile economic climate, there is no better time to exercise control on the long-term prospects of a business.  

 

To identify opportunities and risks effectively, forecasters need accurate data. In sales, for example, demand forecasting leverages historical sales data to estimate or forecast customer demand. The information from this process drives critical business decisions involving turnover, cash flow and capacity planning, for instance. It is the foundation for strategic and operational planning. 

 

Yet, a recent McKinsey report found that approximately 40% of surveyed companies felt their forecasts were not accurate.  

 

Image credit: McKinsey 

 

To generate effective forecasting, Harvard Business Review suggests keeping these six points in mind.

 

1. Define your Cone of Uncertainty. The Cone of Uncertainty is a graphical representation of project unknowns that diminish over time. Establish the breadth of the cone to enable decision-makers to think strategically.

 

2. Spot the S-curve. The most critical developments should follow an S-curve. Forecasters should try to identify the pattern as it starts to form. 

 

3. Accept the pieces that don’t fit. Don’t ignore any indicators you encounter that don’t fit neatly into your preconceived categories. Remember, if it’s new, it won’t have a category yet. 

 

4. Don’t cling to one finding that supports your theory. Unfortunately, you cannot rely on that one, juicy discovery that perfectly supports your leaning, even if it appears to be a strong point. 

 

5. History is a powerful forecasting tool. Don’t forget to consult your historical data as a key in predicting future trends.

 

6. Forecasting is not always an option. Since the Cone of Uncertainty is not static, certain possibilities will become closed off over time. 

If BI managers can integrate operations data into forecasts, they stand an even better chance of gleaning valuable insights. For businesses that are ahead on the digital transformation curve, the forecast data collection process is more streamlined. But for those with fragmented data across the organisation, the task at hand often becomes more tedious and resource-draining.

 

Successful BI managers are leveraging advanced analytics solutions, like predictive analytics, to fast-track the data journey and ensure accurate insights result from their forecasting efforts. 

 

Predictive analytics, a subset of advanced analytics, is the process of extracting information from existing data sets to spot patterns and predict future outcomes and trends. 

 

Consequently, this integral forecasting tool could help your business ride the waves, or rather S-curve, of change. 

 

Future-proofing your business with advanced analytics

 

The future may look hazy at present, but business intelligence leaders can carve a path to a sustainable outlook and competitive advantage. The solution lies in data, specifically, advanced analytics. 

 

Advanced analytics solutions enable businesses to drive customer value, perform sales and demand forecasting, improve on products and identify opportunities and risk.

Vizlib Line Chart showing trends and forecasting

 

You’ll get a better grip on your data, develop a greater understanding of your business, and derive more meaningful, actionable insights. 

 

If you’re interested in learning more about advanced analytics and are unsure where to begin, discover practical tips in our blog on Vizlib’s advanced analytics capabilities for Qlik Sense. 

 

Choosing a BI solution that adds value

 

Businesses that utilise user-friendly BI interfaces increase the likelihood of forecasting insights that will help them weather uncertain market conditions. 

Vizlib Scatter Chart Clustering capability

 

And with intuitive applications, more data users can analyse data and extract valuable insights. Your business will benefit from improved ROI, forecasting accuracy and competitive agility. 

 

Additional Advanced Analytics capabilities for more accurate planning and predictions in Qlik

• Linear regression forecasting

With Linear regression forecasting leaders can predict future business objectively by analysing variables and forecasting the potential outcomes, or ‘best guess’, to guide decision-making.

 

Predicting a ‘best guess’ case with Linear regression forecasting in Vizlib Line Chart

 

• Scenario Analysis

Scenario analysis enables businesses to evaluate multiple future scenarios to analyse potential risks, and manage and plan for any unexpected change. The best solution to support big decisions using different assumptions, especially post-pandemic when ML provides lesser quality data.

 

Scenario Analysis with Vizlib’s Advanced Analytics for more accurate planning and decision-making

 

To adapt quickly to the shifting business landscape, and ensure a smooth journey from insights to action, watch the recording of our recent webinar on driving future-proof decisions with intuitive analytics. 

 

 

Now, more than ever, is the time to dive into your business data and explore this treasure trove with your team! 

 

Getting Started with Vizlib

 

Need to supercharge your Qlik Sense apps? Try Vizlib. You can sign up for a free trial today – available for up to 5 users!

 

Please note: Vizlib’s forecasting and predictive capabilities are derived from the trends and seasonality of historic data. Given COVID-19 is creating a major disruption to businesses and business processes, we must stress that the quality of predictions will likely suffer as a result. The resultant impact will not become rectified until enough time has passed for forecasting models to “retrain” themselves. Or if the COVID-19 periods become filtered out, assuming business goes back to normal after this period.