At various points in history, some people were lucky enough to choose careers at the crossroads of utility and popularity. Consider how the rise of the printing press enabled would-be authors around the world, or what the internet did to empower individuals to succeed in e-commerce.
With analytics coming of age, there’s never been a better time for a career as a data analyst. After all, analysts are powerful brain trusts who truly “get” their business, know how to work with data, and have the power to deliver game-changing insights to the key decision-makers who are asking compelling questions. You can find data analysts across departments and industries, including sales, marketing, finance, public sector, and higher education, because much like where there is water there is life, so too, where there is data there is analysis.
You can find data analysts across departments and industries, including sales, marketing, finance, public sector, and higher education.
And while we know just how valuable data analysts can be to the business, it’s helpful to know how analytics evolved over time, in part to see where it’s been, and more importantly, to see where it’s going next.
Way Back in The Day: The Era of Data Dictatorships
Data analysis of the past was limited to the kinds of data available and solutions accessible to process it. As a result, the kinds of roles for people working with data became highly specialized. They went to school to learn how to investigate and manipulate data in an exceedingly controlled environment, quickly rising as resident experts, the very few, skilled experts.
The problem with this approach to data is that it put boundaries all around what information could be pulled and who could do it. With such a limited workforce of people available to perform the intricacies of the work and the tools themselves, time to insight was so painfully slow that in many cases, the insight became irrelevant.
Data Democratization Expands like the Universe
Like any story about evolution, there are catalysts for change. In the world of data, technology was that catalyst, making information accessible to the many instead of the few. Beyond the technology that democratized access, the amount of data grew exponentially. What we knew as data became its own expanding definition. Think big
— Big Data, that is. Data is everywhere, in many forms, as if the Northern Lights shimmered across the skies of every business, organization, and university.
Data is everywhere, in many forms, as if the Northern Lights shimmered across the skies of every business, organization, and university.
New research from IDC’s info brief “The State of Data Science and Analytics” has confirmed that “data is the lifeblood of digital transformation with over 80% of organizations leveraging data across multiple organizational processes.” There’s so much data that the questions we ask of it are limited only by our own imaginations.
Gasp. Take it in. Revel in the possibilities. But quickly snap back into reality. For a time, only trained scientists could provide answers. Those who knew how to speak the language of data access in SQL and Python, those who wrote multifaceted code, sat as the keepers, and as such, centered themselves at the epicenter of analysis.
Now, the democratization of data via the technology of self-service analytics has cleared the path for more data workers to enter the picture. Highly-skilled data scientists can answer the questions that leave us in awe. Meanwhile, on the same platform, non-traditionally trained analysts and citizen data scientists who want to answer their own burning questions of the universe can forge ahead and do it.
Inhabiting the Analytics Space with Everyone
With a modern self-service analytics platform, you can deliver the same insights faster and easier, providing an alternative to waiting on IT and data scientists. Meanwhile, data scientists can use the same platform to do more in less time. Suddenly everything and everyone has its place (queue intense harmony with nebular music).
Data analysts and citizen data scientists rise to meet the demands of everyday business with the power to explore deeper kinds of analysis all on the same analytics platform, while data scientists are freed to tackle tougher questions. And what’s more is that both are not only answering questions about what happened, but how, where, why, and what will happen next.
Blocks to Analytics Evolution: Survival or Extinction
The trouble is, without self-service data science and analytics, these folks are spending weeks to months working with the data while the analysts and citizen data scientists with a self-service analytics platform perform the very same task in hours and often minutes. Talk about a difference between using paper wings to fly and taking off in a rocket ship. There’s just not a lot of comparison between legacy tools and a self-service analytics platform.
Talk about a difference between using paper wings to fly and taking off in a rocket ship. There’s just not a lot of comparison between legacy tools and a self-service analytics platform.
Beyond time constraints is the inevitability of needing deeper business insight to stay competitive and tap into the wind conditions of a changing market environment. Requirements are handed down by business decision-makers who need answers yesterday. The evolution of the data analyst and the business is reliant on evolving data processes to meet growing expectations. It’s survival or extinction — for both parties.
New Discoveries, Unlimited Possibilities
The ability to ask any question of your data can be incredibly awe-inspiring, not unlike staring up at the night sky without the glaring lights of the city. The true first step begins at the base of the analytics process: how an analyst preps and blends their data. How they prep and blend their data not only determines the speed at which they can answer questions, but also the kinds of questions they can answer at all.
Modern analytics platforms grow with the analyst, the team, and the organization by providing the flexibility and ability to move on to more advanced analytics like prescriptive and predictive analytics. Analysts are rising, or evolving, everywhere to propel change, and here’s what they’ve figured out. It’s time to:
Prep and blend the right data
Access data from anywhere
Create the right dataset for analysis or visualization
Make the most of spatial or location data
Reduce time finding and preparing data
Drive business outcomes with critical insight
Leverage advanced analytics
Deploy and manage models
Create a culture of analytics
The dichotomy of survival or extinction is stark. Such is also the case between ennui or inspiration, boredom or motivation, hating your job or anticipating the joy of what comes next for you, for the team, and the business. Which will you choose?
“The Analytics Lifecycle Revolution: Evolution or Extinction” discusses the key to your analytic
organization’s survival and how to break through legacy analytic slowness.
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