There was a time when data was available to only the select few: There were analysts, and then there was everyone else. These data analysts worked directly with the numbers and statistics that most everyone else preferred to ignore. Charting trends, forecasting possible events, and creating valuable reports, the data scientist alone had the power to turn raw information into actionable insights.
However, times have changed, and data analytics is no longer solely the domain of specially trained data scientists. Our society now creates digital information at a staggering rate — and with that digital transformation comes an influx of data. In fact, more data will be created this year than was created during the previous 5,000 years of human civilization. With so much available information, relying on analysts to fill data analysis needs just isn’t feasible.
In the past, pulling a report meant putting in a request to a data analyst and waiting a week to find out results. Gone are the days when data was inaccessible for the rest of the company outside of the analytics department. Today, data powers real-time business decisions, so top companies can stay agile and leverage insights to connect with their customers. As such, more and more non-data scientists are stepping up and taking these responsibilities onto themselves. New technologies make it all possible.
That said, there are still some things that set these “citizen data scientists” apart from genuine data nerds, particularly where customer data analytics are concerned. Perhaps chief among these differences is that while amateur number crunchers are generally after results, data nerds are just as interested in what lies behind the results. This gives data scientists — and the businesses that employ them — a distinct advantage when it comes to getting the most out of every byte of digital information.
But even if you don’t self-identify as a data nerd, you can still take a page from their book when it comes to understanding customer data analytics. Here are three secrets to boost your company’s analytics power that only data nerds know:
Raw data is just that — raw. It may contain within it valuable keys to business success, but unless you’re prepared to convert that raw data into something more employable, you’ll likely never know its value. In order to properly make use of unstructured data as something more than silo filler, you need effective and reliable data architecture.
Data architecture is a catchall term used to describe any processes, systems, methodologies, rules, standards, or models used to define which data should be collected and how that data should be stored and integrated within an organization’s data systems. Used collectively, these systems govern data of all kinds and integrate them with data-dependent applications and other processes. In essence, data architecture provides a sense of form and order to the flow of data, allowing for a more formal approach that facilitates better data comprehension.
Data architecture needs to be capable of working with information from a variety of sources across the information landscape. Data architecture acts as a sort of data quality control, assessing the value and dependability of every new piece of information. Perhaps most importantly, data architecture acts as a foundation for a company’s entire data governance strategy. Superior data architecture means more complete data comprehension, which in turn means better-informed business strategies.
According to McKinsey & Company, businesses that adopt data-driven strategies experience 5% higher productivity and 6% higher profits than competitors that do not. Architecture gives your data relevance and context, providing actual, actionable information where once there were only numbers. With the right data architecture, those strategies have the secure informational foundation needed to provide positive results. Without it, they may as well be built on sand.
As previously addressed, the significantly increased flow of available data has resulted in a “too much of a good thing” situation for many organizations. Data is captured, stored, and frequently left to stagnate in untouched data silos, with too few data scientists available to effectively manage it. The responsibility of using that data then falls upon those employees without analyst training. But while these employees may not have the same background as a bona fide data scientist, they can still deliver valuable results, assuming they have the right access.
Data touches and influences every part of your organization, so it only makes sense that every member of your organization should have an idea of how to use it. Unfortunately, too many organizations are keeping their data confined to analysts, excluding the bulk of the workforce from all but the most basic data-analysis functions. As a result, we see situations where only the most essential data issues are ever taken to data experts, while the rest are either left untouched or put into the virtual hands of automated programs.
Artificial intelligence (AI) analysis has come a very long way in a short time, and is now capable of providing highly accurate data insights. But it still needs the real-world context that can only come from human expertise. A superior analysis platform can bridge the gap between the data and the non-analyst employees, giving them the resources they need to make sense of the information and allowing them to put it to effective use for the company. See how Arbonne, an international skincare company, drives smarter customer experiences with Salesforce Einstein Analytics.
The right data informs every aspect of your business and is crucial in telling real-world stories, so it’s only logical that every member of your organization has access to it.
For businesses of all sizes, big data has become undeniably fashionable, to the point where non-data-focused organizations might feel as though they’re too far behind the trend to ever be competitive. However, the reality is that proper data analysis among companies seems to be the exception, rather than the rule. Research suggests that fewer than half of businesses are currently satisfied with their strategic planning process, with data analysis being a big part of that gap.
Organizations that learn to effectively manage their data will be doing more than just leveling the playing field — they’ll be giving themselves a substantial advantage over the majority of their competitors. In terms of marketing and sales decisions, companies that heavily rely on data experience a 15%–20% improvement in their ROI, and that’s only the beginning. The data advantage isn’t limited to just these areas. Reliable data insights have the potential to revolutionize every aspect of your business, setting your organization apart from the rest.
Thanks to the proliferation of digital information, data has become undeniably easy to capture, and with it, our definition of “data scientist” needs to change. Data analytics nerds around the world are beginning to see that their own unique talents for understanding and extracting insights from complex datasets are becoming increasingly vital for every level of business, while non-data nerds are starting to find that leaving analysis entirely to the team of analysts means operating in the dark.
Consider these insights from data professionals, and see how your own strategy can be improved by implementing a data-analysis mindset. Focus on your data architecture, empower your employees, and give your organization the kind of data-driven advantage that leads to success. After all, data is everywhere, and when you start thinking like a data nerd, you’ll be able to use it to improve every aspect of your business.
Check out the Einstein Analytics demo to learn more about advanced analytics powered by AI.