With today’s proliferated data environment, creating useful and valuable insights is becoming increasingly challenging. There’s more data, more detail and more sources of information than ever before. On top of this, data is often stored on disconnected platforms, and data delivery options continue to evolve, adding additional complexity. So how do you make sense of all this data? That’s where data science comes in.
Data science is the field of study that focuses on obtaining insights and information (anything of value) from data. The goal of data science is to uncover findings from data. To discover those findings, we dive in at a granular level to mine and understand complex behaviors, trends and inferences. It’s about surfacing hidden insight that can help enable your business to make smarter business decisions.
Here’s an example:
A large retailer uses data science to identify major customer segments within its total shopper base and understand the unique shopping behaviors within those segments, which helps guide messaging to different market audiences.
The bottom line, data science, when applied to different fields, can lead to incredible new insights for all business units.
But data science isn’t all computers, technology, and artificial intelligence. In most organizations, data science involves investing in both technology and people.
Here’s the typical scenario…
You start with data access, buying or creating new data sources. Next, you pull together the data, but centralizing data comes with challenges like implementing metadata management and privacy and/or security concerns that can often derail progress. But that’s just the start. Once you’ve acquired, stored and managed the data, you need to make it useful. But how do you do that?
You hire and build data science teams to conduct advanced analytics. But it’s not easy to develop and build a data science team, let alone design, build and operationalize data science and machine learning projects. According to Gartner, by 2021, 80% of data science labs that have failed to establish measurable operationalization practices will be outsourced.
Investment doesn’t always equal value, and the wrong approach can cause delays. It’s flawed thinking to transition existing resources or hiring data science teams to fulfill your data integration and analysis needs. That’s because most user communities (on average, 70% of data users within an organization) are not capable of absorbing new data and data types. And, while you may find data scientists with the right skills and software experience, there’s one crucial element that dictates success: consumer packaged goods (CPG) and retail knowledge.
Adding data science teams internally does not create value; instead, it puts you at risk for lost opportunities, wasted money, missed deadlines and suboptimal solutions.
What you may not know is that data science, once known as measurement science has been around in one form or another at Nielsen for most of our existence—we’re pioneers in our industry. From early on we recognized the need to build out data science teams to provide complete and honest data and projections to our clients. Along the way our data scientists gained expert knowledge of measurement data and now armed with the right technology, we’re able to offer that service directly to clients.
What is the result of a long appreciation for data science? We’ve repurposed our data science resources to create data-driven solution built by data scientists and powered by robust reference (product label) data on a flexible, secure platform. It allows you to create repeatable workflows that stitch together diverse data sets and enable custom analytics to discover and answer your most challenging business questions. Now, you can feel confident with our experienced data scientists who develop, execute and leave behind models enabling your business to grow.
With data science and our Innovation teams, you can turn actions into insights and expand the potential of your data by bringing together disparate data to create actionable insights.
With our data science capabilities you can:
- Have a holistic approach. We explore and evaluate your data, third-party and Nielsen data to identify creative combinations to unleash new insights. How? We bring disparate data together with the power of label data.
- Have technology built by data scientists. Our data scientists test and develop modular solutions by pulling from a robust, global category of components to move quickly and confidently.
- Have flexible delivery and implementation. Flexible delivery options enable best-fit solutions for your data strategy.
As our Senior Vice President of Data Science at Nielsen Connect, Richard Cook would say, “Technology is transforming every aspect of our lives, and as it does so, creates an abundance of new data. Data Scientists unlock the value from that data to help organizations make trusted, empirical decisions. We combine a deep contextual understanding of the business, with our knowledge of the strengths and weaknesses of the data-set, to employ the best data science solutions; before applying our expertise and judgment to interpret the results.”
Data science: A non-negotiable to turn data into action for your business.