Why Data Analytics Should Start with Self Service Data Prep Background

Why Data Analytics Should Start with Self-Service Data Prep

July 25, 2019
· 5 min read

Data isn’t merely the topic du jour. It’s the engine that drives the future of big business. 

Already, firms that invest heavily (and smartly) in a data analytics framework are seeing massive returns and getting a big head start against the competition. According to a recent study, analytics leaders are 60 percent more profitable than those without the resources to create a coherent data analytics strategy. 

If you’re just wading into the data analytics waters, it can be tricky to know where to start. Below, we will look at the fundamentals of data analytics, the state of the big business industry, and how self-service data prep (SSDP) eliminates common roadblocks.

Data analytics & analysis definition

If the term “data analytics” sounds daunting, rest assured that it doesn’t have to be. In fact, you’ve probably already engaged in a data analysis yourself.

Ever whipped up a graph to explain something? Generated a report to distribute to stakeholders? Looked for trends within a given big data set? If so, you’ve officially dipped your toes into the data analytics science pool. 

Writing for Southern New Hampshire University, Elizabeth Lamport defines data analytics as: 

The field that analyzes data sets and draws conclusions using the information that they provide.

Simply put, data analytics is the process of squeezing meaningful insights out of the data you collect. The first step, then, is collecting that data. The next step—and this is where many strategies falter—is turning that data science into insight that will fuel growth. 

The data analytics takeover

A significant majority of C-level executives are jumping aboard the data analyst train. According to a report from consulting firm NewVantage Partners, 90 percent of those surveyed are chief data, analytics, or information officers—roles suggesting a significant investment in data analytics. “A decade ago,’ NVP notes, “only one of these jobs even existed.”

So the recognition that data presents a golden opportunity is there. How to capitalize on that opportunity remains a mystery to most firms, though. In the same report,  92 percent said they plan to increase investment in data, but only 48 percent said their organization “competes on the data process.” 

This suggests that while most recognize the need to integrate data analytics into an overall strategy, few have a clear picture of what that data-driven experience looks like. 

Tools such as The University of Melbourne’s Analytics Impact Index are popping up to help, offering to illuminate the data-driven path for companies anxious to get started. The first step is always the same, though: turning data into insight. 

Data & analytics to insight: the alchemical process

To transform data and analytics into genuine insights that can drive revenue, increase operational efficiency, or simply unlock new opportunities, it needs to be in the hands of those who are most likely to be empowered by that data.

Typically, those most likely to be empowered by the data you collect are those closest to the revenue side of the business, or those directly implementing strategic company directives. Ideally, these workers would have direct access to the insights extracted from the data—or they would be able to extract those insights themselves. 

In the first case—where data analysts or data scientists clean and transform the data, then present the extracted insights to others—giving those analysts the toolset they need to work more efficiently, and with greater impact, is the aim. (It may also be the case that the aim is simply to scale their workload.)

In the second case, the goal is to give non-technical workers the ability to extract meaning from the data themselves. 

In either case, there’s a bottleneck that chokes productivity. That’s where self-service data prep (SSDP) comes into play. 

Self-service data prep’s role in data analytics

For data professionals, SSDP provides a faster, more efficient interface to find, clean, and shape raw data without coding. 

For firms that already employ dedicated data analysts and scientists, introducing self-service data prep can dramatically increase their efficiency and impact. This gets the insights needed to support business initiatives into the hands of those who need it sooner, loosening the bottleneck that chokes the flow of information. 

Take Polaris, an organization dedicated to eradicating human trafficking networks by building a big data set analysis that’s then used to “learn how the business of human trafficking really works in real time.” 

Polaris collects data from its own hotline, plus various third-party databases. It’s a complex data set, and one that requires significant resources to unearth insights that reveal the underlying nature of human trafficking networks. 

Self-service data prep accelerated the transformation of data to insight by simply making it easier to work with the raw data. Sarah Crowe, Associate Director of Data Systems at Polaris, explains: 

If there’s a process that we need to do repeatedly, it’s quite easy to just import a new dataset, repeat that process and automatically complete all the transformations.

Empowering non-technical workers to perform data analysis

Self-service data preparation also gives non-technical users the ability to find, clean, and shape raw data without coding, in an environment they’re already familiar with. 

Consider that eighty percent of the effort for ad hoc business intelligence (BI) initiatives are spent on finding and preparing data. Only 20 percent is spent on insight development. That presents a significant opportunity.

Unilog is a particularly illustrative example. Unilog builds B2B e-commerce websites, combining the online storefront with the product data needed to run those websites. Providing that data to customer storefronts required analysis from Unilog’s non-technical content team. 

That’s easy enough for low volumes of data; the team simply worked with the data in Microsoft Excel. But once they started delivering 100,000 new product items per month, the process became extremely time-consuming. 

When they crossed 3.5 million records in Excel, the whole process reached a breaking point. “That was becoming quite the headache,” Noah Kays told us. (Noah is Unilog’s Director of Content Subscriptions.) 

Now,Data Prep is embedded into Unilog’s core processes. Noah Kays, Unilog’s Director of Content Subscriptions, describes it as “data democracy between IT, the business, and in ways we’ve never experienced before.”

A symbiotic relationship to fuel growth

A smart data analytics framework works in conjunction with the strategic goals and business operations in which it’s embedded. That symbiotic relationship can supercharge growth, diversify revenue streams, or simply remove barriers to scale. Integrating Data Prep early in the process eliminates many of the roadblocks that hamstring so many well-intentioned data analytics strategies.

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About the author

Value-Driven AI

DataRobot is the leader in Value-Driven AI – a unique and collaborative approach to AI that combines our open AI platform, deep AI expertise and broad use-case implementation to improve how customers run, grow and optimize their business. The DataRobot AI Platform is the only complete AI lifecycle platform that interoperates with your existing investments in data, applications and business processes, and can be deployed on-prem or in any cloud environment. DataRobot and our partners have a decade of world-class AI expertise collaborating with AI teams (data scientists, business and IT), removing common blockers and developing best practices to successfully navigate projects that result in faster time to value, increased revenue and reduced costs. DataRobot customers include 40% of the Fortune 50, 8 of top 10 US banks, 7 of the top 10 pharmaceutical companies, 7 of the top 10 telcos, 5 of top 10 global manufacturers.

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