Coinciding with the ongoing growth in data, organizations have begun ramping up their data science and data analytics efforts. Investing in data science and data analytics looks different to each organization, but for most, it comes down to bringing on new tools, new people, or a combination of the two. We’ll talk about tools a bit later in the article; first let’s discuss people.
PwC predicts that by 2020 alone, there will be 2.7 million job postings for data science and analytics roles. Filling those positions, however, is a big concern. In a recent KPMG CIO survey, nearly half (46%) of CIOs said they are already suffering from a skilled shortage in big data and analytics. This mostly comes down to data scientists, one of the most sought-after roles, who are both expensive to find and expensive to hire. But data scientists aren’t the only ones who can tell us what’s in our data. Increasingly, organizations are leveraging a higher number of data and business analysts (often found within the organization) to drive data analytics initiatives and supplement data science efforts.
This new rise in data workers has been buoyed by the growing variety and accessibility of data analytics tools and data analysis software available on the market. With user-friendly analytics tools, organizations have the opportunity to leverage more of their existing workforce, causing a welcome ripple effect of increased data analytics initiatives across the organization.
Data Preparation: The Bedrock of Data Analytics
Before analysts can start analyzing data, however, they must first cleanse and prepare it into the desired state for their analytics project. This can mean anything from deduplicating data, standardizing it, or removing errors and outliers. Data preparation is essential to the success of the analytics project; it is what allows data analysts to have trust in the end result of their data analytics efforts.
Traditionally, IT teams were responsible for maintaining data quality throughout the entirety of the organization, from ingestion through delivering requirements to the business. However, much like data analytics, organizations are now shifting the responsibility of data quality toward business users. For one, this is a more efficient approach—instead of a small task force chasing down issues of data quality, there are more eyes on the data—but it also leads to better curation for the end analysis. IT will still curate the best stuff and make sure it is sanctioned and reused, but, with business context and ownership over the finishing data preparation steps, these users can ultimately decide what’s acceptable, what needs refining, and when to move on to analysis.
In order to shift the responsibility of data quality toward business users, however, organizations need to adopt user-friendly data preparation technologies, much like they have done with data analytics tools and data analysis software. In the same vein as the design of modern analytics tools, the ideal data preparation technology should be visually-driven and intelligent, assisting in the heavy lifting of the data preparation work so that users only have to point, click, and reassess. If data preparation is essential to the data analytics process, it too, must be accounted for when organizations are adopting new, user-friendly tools for business users and data analysts. Analytics tools should be able to integrate with data preparation tools.
The Collaboration Between Data Preparation and Data Analytics
The key, then, to the future success of data analytics projects is the collaboration between data preparation tools and data analytics tools and data analysis software. It does no good if an analyst has a user-friendly way to analyze data at their disposal, but not to clean data, or if their data preparation and analytics tools can’t communicate between each other. There will always be inefficiencies if analysts can’t have ownership over this end-to-end process.
In fact, we argue that data preparation and data analytics should be thought of as part of the same process. Data preparation, properly conducted, gives you insights into the nature of your data that then allows you to ask better questions of it. Data preparation is not something that’s done in one fell swoop, but iteratively. Each step in the data preparation process exposes new potential ways that the data might be “re-wrangled,” all driving towards the goal of generating the most robust final analysis. Because data preparation is crucial, it’s important to find data analysis software and tools that partner with data preparation.
What to Look for When Adopting New Data Analytics Tools
So what should you look for when adopting a new data analytics tool to ensure that it partners with data preparation? Here are a few of our tips:
- Native integration
A true native API integration uses data inputs from one software to enhance the user experience and functionality of another. In other words, look for data analytics tools that seamlessly import the important data preparation work done in another tool in order to power the data analytics project. All data analytics tools should be architected to be open and adaptable so as the technologies around them change, they can accommodate the latest organizational needs. - Best-of-breed vendor
Some data analytics tools have begun incorporating data preparation functionality into their technologies. However, while initially appealing, adopting best-of-breed technologies is widely regarded as best practice since it encourages greater data access across users and applications. In order to manage a range of applications, IT organizations have developed grassroots approaches to data management that encourage secure self-service across every aspect of the analysis process and lean heavily on transparently tracking data lineage across each application. This is a win-win for the business as a whole—data is more accessible (and therefore, more valuable) to business users, while IT is able to maintain security and governance in collaboration with their business counterparts. To optimize data analysis and preparation, choose the top analytics tools and preparation tools and use both. - Consider a SaaS approach
SaaS applications have experienced huge growth in popularity in recent years—and it’s no wonder why. SaaS applications remove the need for up-front software installation, separate licensing costs, or ongoing operational overhead, which makes it easy for an organization to get up and running with their analytic needs right away. A great example of this data analytics and preparation option is the Google Cloud Platform. Say you wanted to use Google Data Studio, one of the platform’s many analytic services, which allows users to build interactive dashboards. First, you could visually explore, clean, and prepare the necessary data in Cloud Dataprep, which seamlessly integrates with Google Data Studio and the many other services on the Google Cloud Platform.
Trifacta: The Perfect Partner for Data Analytics Tools
Data preparation platform Trifacta has been decades perfecting the data preparation experience. With its roots in cutting-edge research at Stanford and UC Berkeley, Trifacta uses machine learning and unique visual representations of data to accelerate the data preparation process for business users by as much as 90%. Perhaps most importantly, Trifacta plays nice with others. It was built with the understanding that one tool couldn’t—and shouldn’t—do everything, but rather organizations should have the right mix of technologies that work best for its users. The platform seamlessly integrates with any number of technologies, including analytics tools, both upstream and downstream, which affords organizations flexibility now and into the future.