Despite ongoing investments, massive amounts of data and always emerging technologies, companies still struggle to wring value from data.
More than a decade after Tom Davenport published his transitional Harvard Business Review article “Competing on Analytics,” which catapulted sophisticated data analysis into the limelight, the promise of analytics seems not to have fully lived up to the hype. While corporate spending on business analytics has more than doubled since 2006, the competitive advantage many companies say they receive is on the decline. Is there a way to remedy that situation?
To understand how analytically successful organizations differ from the rest, MIT Sloan Management Review and SAS conducted a survey of more than 2,000 business executives, managers and analytics professionals from a wide range of industries and sizes around the world.
Free MIT Sloan Management Review report
The data and analytics research report "Beyond the Hype: The Hard Work Behind Analytics Success," found that analytics success is tied to more than culture, technology and talent. Companies transforming their data into competitive advantage are approaching analytics in unconventional ways.
Here are five ways you can rethink your approach to analytics.
#1. Open your mind
Conventional wisdom is just that. Conventional. In today’s economy, convention can no longer be a mainstay of business decisions. Companies that report a competitive advantage from using analytics are open to ideas that challenge their current practices. Because of that openness, the results of their analytics efforts have led to changes in how they do business. It’s time to let go of business legacy that could be squashing the innovation needed for competitive advantage.
#2. Stop the insanity
Einstein defined insanity as doing the same thing over and over again and expecting different results. Our research shows that many organizations have done little to nothing to improve their information management process but they expect big payoffs from their analytical efforts. Increased amounts of data have actually led to decreased insights. Companies must examine what they are doing at the front end of the analytics lifecycle where real change is needed to see desired results downstream. You must be willing to manage and use data differently to get out of the cycle of sameness.
#3. Use more right brain
These days the talk is all about data scientists and quants who live and breathe algorithms. And true, they can be the superheroes of big data, extracting new and meaningful insights out of volumes of information. But an interesting finding in our research shows that analytically mature companies – those who derive significant benefit from analytics – approach data analysis with a strong spirit of discovery. Open-mindedness and creativity play a key role in the effective use of analytics. Involve right-brained people in the analytical process to ask the outside-the-box questions.
#4. Don’t put all your eggs in…you know (the same basket)
A misconception about companies that are analytically driven is that they are completely analytically driven. That is, they make decisions solely by the numbers. Our survey dispels that. True, companies more analytically mature are much more likely to use analytics to drive organizational strategy. But these organizations rely on a blend of intuition, experience and analytical results. While it’s important not to be driven by what’s “always been done,” it is also important to let experience inform decision-making. Balance the two wisely.
#5. Stop dabbling
Even though analytics has become mainstream over the past 10 years and is often referred to as “table stakes,” many organizations still run analytics initiatives on an ad hoc or decentralized basis. If analytics is to be the golden goose that helps achieve and sustain competitive advantage, it’s time to get serious about it. Our research shows that companies struggling with their analytics initiatives have one major thing in common: they don’t have a formal plan. If you’re not deriving the value you expect from your analytics initiatives, put in the time to lay out a specific analytics plan, even if it’s only short-term. Determine how you will evaluate success and begin making analytics part of your planning process.
You must be willing to manage and use data differently to get out of the cycle of sameness.
A different approach to the spirit of discovery
The hype around analytics over the past few years has led to high expectations. As more companies jump on the bandwagon, it’s more difficult to gain and sustain an advantage with analytics. Companies still need to focus on the basic tenets of analytical maturity: culture, talent and technology. But taking a different approach – approaching analytics with an open mind and a spirit of discovery – could help companies overcome the speedbumps that many have encountered.
About the Author
Pamela Prentice is SAS' chief researcher, with more than 35 years of experience in the market research field. Her background includes consumer and business research for large businesses and 10 years as a college professor, with a focus on teaching market research and consumer behavior. Prentice has been with SAS since 2000, and in her current role, she uses SAS software to derive insights from customer and market data to understand how businesses are using technology.
- From living on the streets to owning a profitable businessZoe Empowers, a ministry for vulnerable children living in life-threatening poverty in Africa and India, finally found a way to measure and reveal its incredible impact. Using SAS, they developed an "empowerment index" that measures improvements in housing, health, education and more for program participants.
- ModelOps: How to operationalize the model life cycleModelOps is where analytical models are cycled from the data science team to the IT production team in a regular cadence of deployment and updates. In the race to realizing value from AI models, it’s a winning ingredient that only a few companies are using.
- Meet the data scientist: Daymond LingDaymond Ling believes the right personal traits are more important than technical skills when it comes to being a successful data scientist.