With abundant data available and businesses searching for insights and ways to innovate, many companies struggle to manage data overload. Leveraging Big Data has become a major focus for research and development, or R&D, departments focusing on the potential of Big Data to create new products and services. However, the challenge lies in collecting and analyzing the vast amount of data available.

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While marketing and IT departments have been using Big Data tools for some time, R&D departments are just realizing the advantages of leveraging Big Data in decision-making. With innovation at the forefront of R&D, Big Data can make it or break it for new product innovation, as it helps teams identify ways to increase their research's success.

Defining Big Data

But what exactly is Big Data? While accessing and storing large swaths of data for analytic purposes is not a new concept, Big Data changed in the early 2000s. Big Data refers to data that’s simply too big, fast, or complex to be processed using traditional management systems or manual methods.

What are the three v's of Big Data?

Former Gartner analyst Doug Laney came up with a definition of Big Data in 2001, encouraging organizations to consider what he called "the three v’s": data volume, velocity, and variety.

• Volume: What types of data are being collected and stored within your organization? While data storage used to be costly, lower-cost solutions like the cloud have eased this task and made it more achievable for businesses of all sizes.
• Velocity: How do you manage vast amounts of data, ensuring they are processed and analyzed in near-real-time? Employing tools like sensors and smart meters can help companies collect vast amounts of data effectively.
• Variety: What are the data formats your organization receives? What mechanisms do you have to classify and manage them? As businesses receive data in structured (e.g., traditional databases) and unstructured (e.g., emails, videos, audio, and social media interactions) formats, classifying and analyzing this data may be challenging.

To leverage Big Data successfully, businesses must organize and visualize data correctly. Shifting from historical data to predictive models based on external information can help companies to understand the entire picture and make informed decisions about future products.

Why is leveraging Big Data important?

The value of big data doesn’t simply reside in how much data you have but 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, a business can:

• Find the root cause of business decisions in real-time.
• Quickly and accurately locate data anomalies that the human eye may miss.
• Improve outcomes by rapidly transforming data into insights.
• Recalculate entire risk portfolios in a fraction of the time.
• Accurately classify and respond to changing variables.
• Detect fraudulent behavior before it causes substantial harm to the company.

R&D data management

There are several many ways in which brands can leverage Big Data and improve their R&D data management to stay relevant in their respective industries.

Leverage AI tools to extract insights from unstructured data

Big Data is just that — a bunch of unstructured information. Data captures many insights, but is meaningless if not organized and visualized correctly.

The value of Big Data doesn’t simply reside in how much data you have. Instead, the value lies in how you use it.

Businesses can use artificial intelligence tools to extract insights from unstructured data and shift to predictive analytics models to stay ahead of the competition. Data integration, contextualization, and interpretation are critical to understanding the whole picture and predicting the road to success.

Shift from historical data to predictive analytics models

Incorporating data into R&D and planning stages enables businesses to forecast future product improvements based on predicted value rather than historical insights alone. While considering historical context remains critical for making business decisions, shifting towards a predictive analytics approach can lead to greater success in the long run, as history is not always a reliable predictor of the future.

Historical data only paints part of the picture of what your consumer might want or need in the next iteration of your product or service. However, predictive modeling offers companies meaningful external information that can influence the company's direction. For example, Mastercard recently acquired Applied Predictive Technologies, APT, to assist in measuring and predicting relevant trends across various markets and industries. By integrating, contextualizing, and interpreting data, businesses can stay relevant and on the cutting edge of their industry, no matter their size or resources.

Understand your customers’ propensity to purchase

Big Data analytics can be tricky, and with so much information, it is easy to draw misleading conclusions from the wrong data set. To measure a customer's likelihood of purchasing multiple products within a portfolio, businesses can use propensity indicators to create customizing experiences to maximize the customer's value to the company.

Businesses need to understand the buying habits of their customers based on past purchases and predict if they are likely to purchase a product. Traditional propensity-to-buy models rank customers based on their similarity to previous purchases, helping businesses determine the most effective cross-selling or up-selling techniques.

Test and learn to find winning ideas

How would your brand compete if every decision was tested before implementation? With the power of Big Data and predictive analytics, companies can leverage a test-learn-adjust-implement process to guide investment decisions; This allows for hypotheses to be tested without wasting time, budget, or resources on physically trying new products or making changes to existing ones.

With business experimentation, R&D teams can differentiate causation from correlation. By leveraging Big Data models to guide decision-making, companies can make more informed decisions that reduce potential failures and variability while improving their results.

The Future of Big Data: What Lies Ahead

In today's data-driven world, relying on traditional visualization-based dashboards won't cut it anymore; The conventional data processing and analysis methods can no longer keep up with the advancements in Big Data and predictive analytics.

R&D teams must leverage Big Data to understand customer behavior better, create attribute-driven social campaigns, track sales and conversions, and test new ideas to stay competitive. By investing in R&D data management, businesses will enhance their brand performance.

Fortunately, Plug and Play’s co-creator program partners companies with startups to harness the power of Big Data utilization. With the right tools and platforms, startups and businesses can unlock unparalleled growth and innovation potential.