Are you looking to get more out of your business intelligence, or even offer your clients more from their BI dashboards? Taking your analytics maturity model to the next level can help you go deeper with your business intelligence data, discovering not just the “what,” but the “why” behind your data and revealing actionable insights for faster business growth.
Before you evaluate your opportunities to advance your analytics maturity and gain more out of your BI dashboards, it’s important to look at the six most common maturity models and gauge where your company sits right now.
The following is just one example of an analytics maturity model. You may customize the stages, or even consider other models, to better fit your organization.
Most importantly, you need some way to gauge whether your company is at one of the earlier, descriptive levels of BI, or an advanced stage of maturity more likely to lead to positive change and rapid growth.
Applying the Analytics Maturity Model to Your Business Intelligence
Some experts look at all six stages of the analytics maturity model as a continuum. But the latter three stages can’t take place without the first three, the descriptive analytics which are the very basics of analytics maturity. If your organization is only making use of reporting tools with rudimentary analysis, you are in the very early stages of analytics maturity.
The early stages often take the form of spreadsheets and may involve manual reporting.
The latter three stages cannot be obtained without advanced technologies such as artificial intelligence (AI) and machine learning.
Using Descriptive Analytics in BI
Descriptive analytics involve the questions of “What happened in the past,” “Why did it happen,” and “What’s happening now?” It doesn’t delve any deeper than the surface. KPIs such as sales figures and leads generated are part of descriptive analytics.
Reporting – Reporting focuses on the “what,” of business intelligence — raw data without any insights into how the organization achieved these numbers or why.
Analysis – Analysis uncovers the trends within reported KPIs by comparing historical data with current figures. By looking at a company’s sales growth over time, executives have a snapshot of how well the organization is performing in that single area. By comparing KPIs within a department or across different departments, the organization can uncover patterns, begin to make sense of the trends, and discover the “why” behind the data.
Monitoring – Monitoring provides real-time data of how an organization is performing at that instance. Combined with analytical data, this information can enable executives to make changes on-the-fly to improve performance.
Stepping Up to Predictive Analytics
Once your organization moves beyond the “what,” and the “why,” you can begin to explore the questions that can positively impact your growth, such as, “What happens next?”
This data can be used to drive the company to its next stage of business growth.
Forecasting – Forecasting takes the data revealed in the descriptive stage of business analytics and looks at past trends to discover likely future scenarios. Even here, though, you are only looking at KPIs without much insight into why these trends are likely to continue or what you can do to make course corrections.
Predictive – Predictive statistics enable your organization’s leaders to look at the forecasted KPIs along with historical data, analyze trends, and determine why something is likely to happen next. This positions leaders to make decisions to positively affect KPIs to improve sales, productivity, retention, or any other results that can lead to company growth.
Prescriptive – Where predictive modeling looks at past trends to foresee future behaviors, prescriptive analytics looks at the relationships between trends to create if…then statements. For instance: “If your sales department does this, then, sales will increase by 50 percent.”
Prescriptive analysis can determine, with accuracy, when changes will occur and exactly how they will happen, diving deeper than the numbers and statistics. This is the most advanced of the maturity models, and if your company is in this level of analytics maturity, you are likely to have embraced digital disruption. The organization may be poised to leapfrog ahead of competitors with the business tools needed for true innovation to occur.
How Machine Learning Is Changing Business Analytics
The final stage in the analytics maturity model, prescriptive analytics, is not possible without AI and machine learning. Because there are so many data points and factors needed to predict and analyze when and why something will happen, it is simply not possible for human beings to do it alone.
AI computers excel at rapidly analyzing massive amounts of data, and even learning from their past predictions to refine their results for greater accuracy through a process called machine learning.
Of course, the first step of such deep analytics begins with user-friendly, intuitive embedded BI dashboards, enabling human workers to make sense of the data and insights the computers share at this level of the analytics maturity model.