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Data analytics can turn hindsight into foresight

Updated: Oct 15

It’s no surprise that recent events have forced many businesses to change their long-term strategies and fast-track into the world of digitization, automation, and artificial intelligence. Now is a good time to leverage data analytics not only to understand the past but also anticipate the future. The first step is to pinpoint what type of analytics your business is currently using.

An analytics maturity model is a straightforward way to evaluate your data analytic proficiencies. Gartner provides a simple, well-known model defining four types of data analytics: descriptive and diagnostic analytics provide hindsight, whereas predictive and prescriptive analytics enable foresight.

Descriptive analytics

Descriptive analytics processes historical data to answer, “What happened?”.

It the most basic form of analytics that takes the form of summary statistics and aggregations. For example, the KPIs and metrics on your company’s custom sales dashboard are descriptive statistics and include measures such as number of goods sold, monthly revenue, net profit, number of closed deals.

Descriptive analytics helps you understand how your business is currently performing compared to how it performed in the past. What it doesn’t answer is why you are trending in a certain direction or why you have experienced unexpected outcomes in your data, like a steady decline in revenue last quarter.

Diagnostic analytics

Diagnostics analytics attempts to answer, “Why did this happen?”.

It looks for root cause through statistical correlations, drill downs, and data discovery.

In diagnostic analytics, you typically go beyond just looking at the existing data and consider external factors like changes in people, processes, or environment to determine causality. Thinking about the sales example, maybe that decline in revenue was caused by a strong competitor who entered the market.

However, determining “why” in diagnostic analytics still involves investigating what happened in the past and doesn’t answer what may happen in the future.

Predictive analytics

Predictive analytics identifies patterns in data to answer, “What is likely to happen next?”. Advanced analytics techniques and machine learning provide insight into what is likely to happen with a level of confidence.

With this type of analytics, you are typically interested in finding patterns in your data using multiple data sources (e.g. manufacturing, financial, CRM, or HR systems) to capture as many factors that may inflleverage data analytics not only to understand the past but also to anticipate the future. The first step is to pinpoint what type of analytics your business is currently using.

By training an algorithm on this historical data, a model can predict what will happen in the future with a certain probability or likelihood. In our sales example, a predictive model that accurately forecasts monthly sales would help you understand the potential impact the new competitor will have on your business.

Predictive analytics gives you a glimpse into the likely future but stops short of providing you enough information to potentially impact the future.

Prescriptive analytics

Prescriptive analytics proposes answers to “What course of action should we take now?”. Prescriptive analytics provides recommended courses of action based on predicted outcomes, allowing your business to choose the best path forward with a level of confidence.

Combining the use of AI techniques, such as machine learning, with structured and unstructured data and business rules, the complex model continuously iterates and adapts to new data which keeps the model current, improving accuracy and optimizing recommendations over time.

Back to the sales example, a prescriptive analytics tool may identify particular customer segments that have remained loyal to your brand based on predicted monthly increase in sales. With this recommendation, you could plan marketing campaigns targeted for these customer segments to gain new clients or upsell services to try and reverse the loss in revenue.

Conclusion

There is value in every stage of the analytics journey. The best value comes in using quality information from the past to positively influence your company’s future; but this path to foresight does increase complexity. However, in today’s world, businesses need to anticipate opportunities and adapt with the changing times. Implementing predictive and/or prescriptive analytics might seem daunting, but the insights gained could be the advantage that gets you ahead of your competition.

Ready to evaluate your current and future analytics goals? Scalesology can help you develop a comprehensive data analytics strategy so you can reach the next level. Contact us today, and we can discuss how to transform your data into meaningful insights.

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