Descriptive or Predictive? How to Evaluate Your Data Maturity
How are advances such as artificial intelligence, machine learning, and big data shaking up the ways tech leaders do business? Technology disruptions are progressing at a rapid pace and will likely continue in years to come. But what are these advances, really, and how can professionals harness them for the good of their businesses? As they dive deeper into their big data, how can company leaders better position themselves for information and analytics?
Evaluating data maturity
The first step is assessing your analytics environment using a Data Analytics Maturity Model. The Maturity Model is a guide designed to help organizational leaders understand where they stand with respect to achieving intelligence from their data. Using the Data Analytics Maturity Model to assess current and necessary future business tools, tactics, and technologies can help business leaders create paths forward for successfully implementing and utilizing information analytics.
At the core of any Data Analytics Maturity Model is a visual tool for better understanding which types of analytics fall under the information analytics umbrella. Although descriptive and diagnostic analytics can and do provide useful information such as what happened and why, predictive and prescriptive analytics offer insights into what will happen and how business leaders can better plan for the future. By asking the right actionable questions about problem prevention and elimination, business professionals can develop strategies that could make information analytics an invaluable tool for competitive advantages.
Traditionally designed around five dimensions, this analytic model includes five stages of maturity. A Transforming Data With Intelligence (TDWI) assessment describes the five categories.
- Organization — If at all, how much does your business support an information analytics program? Do you have enough organization within your company to successfully employ information analytics, or do you need to do more work?
- Infrastructure — How advanced and coherent is the architecture supporting your information analytics initiatives? What supportive structures do you have in place, and how did you integrate them into the existing environment?
- Data management — How extensive is your data volume, variety, veracity, and velocity, including data quality and processing?
- Analytics — How advanced are you and fellow company leaders in analytical use? Are you taking predictive or prescriptive approaches yet?
- Governance — How coherent is your company data governance strategy?
By assessing company strengths in each of these fields, leaders can determine how prepared their companies are to utilize information analytics.
From descriptive to predictive
Analysts assess company data maturity in stages — described by its organizational leaders’ experience with and dedication to using information analytics. Those in pre-adoption companies have little existing support for big data or are only beginning to invest in or research big data utilization. Early adopters, on the other hand, have dedicated teams for information analytics and even proofs of concept, depending on the team experience. Corporate adopters and leaders in mature companies have fully adopted and invested in big data, using prescriptive analytics for optimal decision-making. Those with full maturity are using big data to transform the ways in which they operate their businesses, using smart data in a variety of disruptive ways.
Business decision-makers who haven’t yet integrated information analytics into their businesses — those “pre-chasm” companies — may take preliminary steps to identify actionable problems and assess data capabilities. However, their maturity will restrict them to descriptive and diagnostic analytical tools. Of course, it would be problematic for those at this stage to attempt a leap to full maturity; a gradual approach is best for most. Fully mature companies such as Facebook and Amazon have arsenals dedicated to optimizing big data and often feature company cultures built around innovation that took years to cultivate.
At mature companies, leaders use information as a strategic asset and data drives continuous innovation. Business leaders implement machine learning and automation to streamline operations and they also embrace cultures of investigative insight that put new ideas into action. This imbibes a scientific decision-making process instead of an artistic one.
The maturing process requires company leaders to align business strategies with technical strategies, action-based problem solving, and customized road map development.