Plenty of data is out there, and many companies are now experiencing what we call data overload. Leveraging big data has become one of the focal points of R&D departmental innovation. The typical challenge is to both collect the data, as well as analyze it.
While there have been many Big Data tools created to assist marketing and IT departments, R&D departments are just getting on track for leveraging Big Data into decision making. With innovation at the forefront of R&D, big data can make it or break it for new product innovation, helping teams identify key ways to increase the success of their research.
Defining Big Data
While accessing and storing large swaths of data for analytic purposes is not a new concept, the concept of Big Data changed the game in the early 2000s. Big Data refers to data that’s simply too big, fast, or complex to be processed using traditional or manual methods.
Former Gartner analyst, Doug Laney, came up with the now-mainstream definition of big data back in 2001, encouraging organizations to consider the following:
- Volume: What data is being collected across your organization, and where is it stored? Storing data used to be costly, but lower-cost solutions like the cloud have eased this burden.
- Velocity: What tools are being used to handle torrents of data in near-real-time? Sensors, smart meters, and RFID tags, among other solutions, can help.
- Variety: What format is the data coming in as? Today, data comes in both structured (traditional databases) and unstructured (emails, videos, audio, social interactions) formats.
What’s the Big Deal with Big Data?
The value of big data doesn’t simply reside in how much data you have. Instead, the value lies in how you use it. Companies that effectively analyze big data can:
- Streamline resources
- Improve operational efficiencies
- Optimize product development pipelines
- Drive new revenue and overall growth
By combining big data with a predictive analytics engine, your business can:
- Find the root cause of business decisions, including failures, in near real-time
- Pinpointing data exceptions faster and more accurately than the human eye
- Improving outcomes by rapidly converting data into insights
- Recalculating entire risk portfolios in a fraction of the time
- Accurately classifying and reacting to changing variables
- Detecting fraudulent behavior before it makes a significant impact on your company
Here are a few meaningful ways your brand can leverage big data in your R&D processes to stay relevant and on the cutting edge of your industry.
Leverage AI tools to extract actionable insights from massive amounts of unstructured data
The value of big data doesn’t simply reside in how much data you have. Instead, the value lies in how you use it.
Big data is just that — a bunch of unstructured data. Data holds a massive amount of insight, but it’s meaningless if not organized and visualized correctly. Contextualizing data is key to successfully predicting the road to success. A single data point or chart doesn’t tell the whole story. Instead, the integration, contextualization, and interpretation of data are critical to understanding the entire picture.
Shift from historical data to predictive analytics models that generate real-time insights during the research-cycle
Using data in R&D and during the planning process helps businesses plan for future product improvements based on the predicted value, not historical insights. While it’s still critical for your brand to consider the historical context when making business decisions, we believe that shifting towards a predictive analytics model will set you up for greater success moving forward. History isn’t always the greatest prediction of the future.
Historical data only paints part of the picture of what your consumer audience may want or need in a new iteration of your product or services. On the other hand, predictive modeling can give brands insight into meaningful external information, influencing their company’s direction—companies large and small leverage predictive analytics. For example, Mastercard, recently acquired a company called Applied Predictive Technologies (APT) to assist in measuring and predicting relevant trends in various markets and industries.
Understand your customers’ propensity to purchase
With so much data at your disposal, it can be easy to draw misleading conclusions or analyze incorrect data sets. Organizations with more extensive portfolios can evaluate a customer’s propensity to purchase metrics to assess how likely a customer will be to buy multiple products within a portfolio. But, even if you’re a smaller organization seeking methods to maximize a customer’s value to your business, propensity to purchase data modeling is imperative.
Companies want to understand whether or not a customer will be predisposed to purchasing a product based on purchases they’ve already made at some point in time. Traditional propensity-to-buy models score customers based on their similarity to past purchases, requiring data to determine which cross-selling or up-selling techniques would be most effective.
Test and learn to find winning ideas
If you could test every decision your organization makes, would that change the way your brand competes? For brands that can harness the power of big data and predictive analytics, they can leverage a test - learn - adjust - implement a process to test hypotheses and guide investment decisions. Without wasting budget, time, or resources on physically trying new products or changes to existing ones, you can leverage big data models to guide you on your decision-making journey.
Business experimentation helps R&D teams differentiate causation and simple correlation — a significant delineation often confused in business. Doing this right means your organization can reduce potential failures and variability while improving results.
Where to Go Next
Gone are the days when dashboards composed of visualizations were good enough to compete. Answering new business questions would take time (days to weeks) and skills a company can’t afford to wait or rely on. Big data and predictive analytics have far outpaced traditional methods and provide a runway of opportunity for R&D teams.
Big data and predictive analytics have far outpaced traditional methods and provide a runway of opportunity for R&D teams.
Now is the opportunity for your company to leverage big data as we head into 2022 and the world returns to pre-pandemic norms. Leverage AI, shift away from historical analysis, better understand your customer’s reasoning for making purchases, create attribute-driven social campaigns to track sales and conversions, and don’t be afraid to test and learn new ideas. Find meaningful ways to leverage big data in your company’s R&D, and you’ll undoubtedly see an uptick in brand performance.
A variety of Plug and Play startups are leading the charge when it comes to leveraging big data. For example, Quantexa is a data and analytics software company pioneering Contextual Decision Intelligence that empowers organizations to make trusted operational decisions by making data meaningful. Simporter is an A.I. platform that predicts sales for new products before they hit the market, de-risking new product development and automating research at the ideation stage. Pecan’s A.I. platform automates the entire predictive analytics process, reducing “time-to-model” from months to days.