Modernizing your organization’s data and analytics stack will make you more productive and able to bring more value to functions across the business. But it has the potential to do more than that: It can turn you into a data hero who plays a huge role in transforming your business with better insight and data-driven decisions.

In this 3-part blog series, we’ll look at the benefits of embarking on this hero’s journey and how to get started. First, we’ll walk through common data challenges and how to overcome them by building a modern data stack. In Part 2, you’ll learn to activate your data by building “reverse ETL” data pipelines that copy master data back to operational applications. In Part 3, we’ll explore data apps and how you can use them to power your business.

Let’s get started!

Common challenges of SaaS

If your organization is like many today, it’s running on spreadsheets and/or with automation help from a large suite of SaaS apps in your departmental systems: Salesforce, Zendesk, Hubspot, Workday, Google Ads, Netsuite…the list goes on.

While SaaS technology has revolutionized the way we work by streamlining IT and moving us into the cloud, operating a business this way increases the urgency to create a central dataset that tracks a 360-degree view of the business. This is true for a number of reasons.

1: Data volumes are rapidly increasing.

Yes, there’s more data now than ever before, and it’s exponentially increasing by the day. At this point, that is a constant. But what has changed in the last few years is the way that data is expanding and changing.

Data comes in more formats than ever, from familiar structured data in tables to unstructured data like video and sound files. It comes from dozens or even hundreds of sources, including SaaS applications, databases, and more. And it’s dynamic, changing literally moment by moment. Just keeping track and keeping up with multifaceted data is a full-time job.

2: SaaS applications create data silos.

There’s a reason that SaaS applications are a $172 billion industry1 “Forecast: Public Cloud Services, Worldwide, 2019-2025, 2Q21 Update,” Gartner, August 2021.. There are numerous products in use in a single company. Companies with more than 1,000 employees use an estimated 177 SaaS applications2 “State of SaaS Report,” BetterCloud, 2020. (and those are the ones you know about). BetterCloud forecasts that by 2025, 85 percent of all business applications will be SaaS3Ibid..

There is a potentially unmanageable, un-trackable number of SaaS applications within each business function. While beneficial to the business, each of those SaaS apps becomes a data silo, containing data that is not integrated with any other business data. Each silo of data is in a different format, with different definitions. And each department may have its own instance of the same application with a different version of the truth. That all adds up to different insights and different outcomes from analytics that may or may not be reliable.

3: Analysis by manual data wrangling is nearly impossible.

Business stakeholders often demand rapid analysis of fresh data. But data teams are limited by manual processes and the number of hours in the day. According to a survey of data scientists by Anaconda, they estimate spending 45 percent of their time on data preparation, including data loading and cleaning4“Data Prep Still Dominates Data Scientists’ Time, Survey Finds,” Datanami, July 6, 2020.

Perpetually adding additional labor and hardware infrastructure to speed up analytics doesn’t scale. From a speed and cost perspective, it’s not possible to add enough of either resource to keep up with volume and demand.

4: Governance, security, and compliance becomes very difficult.

Every organization wants its data users to be able to access data and perform their own analytics. But opening up permissions means also opening a Pandora’s Box of governance, security, and compliance issues.

How do you ensure that people have access to the data they need, but not sensitive data? How do you keep personally identifiable information (PII) private? Who’s in charge of security? How do you keep track of the number of SaaS products being spun up? And how do you ensure that those sources are secure?

The solution: A modern data stack.

5 critical steps to modernizing your data stack

When it comes to modernizing your data and analytics stack, there are five critical things that you’ll need to do along the way to drive long-term success.

1. Implement a cloud data warehouse.

This one is a must. The way to save the day and meet requirements for effective analytics, governance and security, and real-time analytics is to keep your data in a cloud data warehouse. If you haven’t moved data warehousing to the cloud, the time is now.

A cloud data warehouse is the anchor to help you solve some of the challenges mentioned previously including managing data volumes, breaking down data silos, efficient data management and improved data governance. It’s the only place with enough power and scale to manage all your data and do so in a cost-effective manner. 

Start with the implementation of a cloud data warehouse. Then choose cloud-native data pipeline, transformation, and business intelligence tools. These are the fundamentals of a cloud-based data stack. These modern tools are built to integrate with a cloud data warehouse and are built to leverage the power and scale of the cloud. 

2. Don’t take shortcuts.

It’s natural that you want to get to the finish line as soon as possible. But don’t rush. And don’t skip steps. You need to have a thorough, trusted system in place in the cloud before you even think about importing your first batch of data.

At Data Clymer, we follow what’s known as the Actionable Analytics Cycle. It’s a critical, repeatable methodology that combines people, technology, best practices, and design patterns to help ensure a significant return on your data and analytics investment. 

3. Get off spreadsheets. For good.

If you’re like many organizations, you do a lot of data storage and analytics in spreadsheets. It’s still very common. But it’s also time to say goodbye to spreadsheets. They have no place in a modern data stack.

If you have more than one data source, as soon as you need to manually bring that data together to answer a question in a spreadsheet, you’ve decreased your productivity by 10x. Plus, a key to moving faster in the cloud is the ability to automate the analysis you’ve built in the cloud data system.

With automation and consistent workflows in the cloud, you can run analytics over and over for continued and regular insights. Moving old processes into the cloud is just moving bad practices from one place to another and doesn’t take advantage of your new platform.

4. Create a single, centralized source of truth.

Remember how each SaaS application is its own data silo? You need to break down every single silo so your data can be integrated, prepared, and accessible across the business. The way to do that is to bring data from each source you rely on into a centralized data warehouse in the cloud.

There are a few reasons to do so. One, you want to use all of your data in analytics to paint the most complete picture possible of your business and your customers. Two, data that’s accessible from one central place speeds up analytics and time to insight. And three, integrated and prepared data helps establish a single source of the truth, where everyone in the business is using the same trusted data to reach conclusions and make decisions.

5. Integrate data into all of your business processes.

What if your product management team could back up every new product idea with validation and support from trusted data? What if your marketing team could make data-driven bets on where to allocate campaign spend and how to surprise and delight customers? Imagine if your sales team could use data to pinpoint exactly which customers to upsell with a better than ever chance of success.

Get started on your data journey

Once you get started on your hero’s journey to create a strong data and analytics practice and culture to your business, you’ll be amazed with what you can do. One Data Clymer customer achieved more than $2 million in operational savings by modernizing its data stack and data culture.

Being able to bring data back into your operational systems and business processes is the next piece of the puzzle that helps you grow and transform your business. We will discuss this in Part 2 of this 3-part series.

Need a guide for your journey?

Modernizing your data stack isn’t easy. The hero’s path is riddled with pitfalls and challenges. There are innumerable design patterns and best practices that will lead to success down the road. One way to accelerate your journey is to get help from a good consulting partner who’s walked the path many times. It can mean the difference between success and failure.

Ready to take the first step? Get in touch with our team.

Aron Clymer

About the Author

Aron Clymer, Founder and CEO

An executive leader passionate about extracting maximum value from data, Aron founded Data Clymer to help organizations implement optimal data strategies and instill a data-driven culture. With over two decades of experience, he more recently spent seven years building and leading the Product Intelligence team at Salesforce, and another two years doing the same at PopSugar.