Are you data rich and insight poor? Otherwise known as D.R.I.P
Gartner’s research confirms that only one-third of C-Suites are measuring return on investment (ROI) as a strategic priority. This sums up the cavernous gap between the vision and reality of data-driven business strategies.
At CE Strategy, we work backwards to answer complex business questions by providing insight developed through advanced analytics such as AI and ML. We’ve assembled a talented, experienced, and diverse team along with strategic partnerships and university level technology resources to deliver unrivaled data science as a service for the purposes of business ROI.
Once solid data and analytics foundations are in place, the most innovative solutions to extract value from data may still be sourced externally allowing us to provide additional value through:
Data Analysis and descriptive analytics provide insight into the past
A wide range of descriptive analytics Descriptive analysis or statistics does exactly what the name implies they “Describe”, or summarize raw data and make it something that is interpretable by humans. They are analytics that describe the past. The past refers to any point of time that an event has occurred, whether it is one minute ago, or one year ago. Descriptive analytics are useful because they allow us to learn from past behaviors, and understand how they might influence future outcomes.
The vast majority of the statistics we use fall into this category (including basic arithmetic like sums, averages, percent changes). Usually, the underlying data is a count, or aggregate of a filtered column of data to which basic math is applied. For all practical purposes, there are an infinite number of these statistics. Descriptive statistics are useful to show things like, total stock in inventory, average dollars spent per customer and Year over year change in sales. Common examples of descriptive analytics are reports that provide historical insights regarding the company’s production, financials, operations, sales, finance, inventory and customers.
Comprehensive Data Science through predictive and prescriptive analytics using advanced algorithms, machine learning, and AI
A wide range of advanced analytics using sophisticated techniques and tools to discover deeper insights, make predictions, or generate recommendations.
Predictive analytics has its roots in the ability to understand the future before it happens. This means providing companies with actionable insights by shedding light on the likelihood of a future outcome. It is important to remember that no statistical algorithm can “predict” the future with 100% certainty and the foundation of predictive analytics is based on probabilities.
These statistics take the data that you have, and fill in the missing data with best guesses. They combine historical data found in ERP, CRM, HR and POS systems to identify patterns in the data and apply statistical models and algorithms to capture relationships between various data sets. Companies use Predictive statistics and analytics anytime they want to look into the future. Predictive analytics can be used throughout the organization, from forecasting customer behavior and purchasing patterns to identifying trends in sales activities. They also help forecast demand for inputs from the supply chain, operations and inventory.
The relatively new field of Prescriptive analytics uses the latest technologies such as machine learning and artificial intelligence to understand what the impact is of future decisions and uses those scenarios to determine the best possible outcomes. In a nut-shell, these analytics are all about providing accurate advice. Prescriptive analytics attempt to quantify the effect of future decisions in order to advise on possible outcomes before the decisions are actually made. At their best, prescriptive analytics predict not only what will happen, but also why it will happen, by providing recommendations regarding actions that will take advantage of the predictions.
These analytics go beyond descriptive and predictive analytics by recommending one or more possible courses of action and projecting the outcome of each with a growing amount of certainty over time. Essentially they predict multiple futures and allow companies to assess a number of possible outcomes based upon their actions. Prescriptive analytics use a combination of techniques and tools such as business rules, algorithms, and computational modelling procedures. These techniques are applied against input from many different data sets including historical and transactional data, real-time data feeds, and big data.
Prescriptive analytics are relatively complex to administer, and impossible without a solid base foundational data collection and analytics. At CESM we work with companies to nurture, grow, and execute these capabilities.
Most companies are not yet using them in their daily course of business, but the number is growing by the day. When implemented correctly, they can have a significant impact on how businesses make decisions, and on the company’s bottom line. Larger companies are successfully using prescriptive analytics to stimulate need, optimize production, scheduling and inventory in the supply chain to make sure that are delivering the right products at the right time and optimizing the customer experience.