In 2018, there are more business tools and resources than ever before. From marketing and sales to HR and ChatOps, the cloud revolution has equipped companies to be more efficient while growing like never before.
The most valuable resource for any business, however, is one they already have: their data. Whether it’s sales revenue from last quarter or the number of customers who have installed your latest app update, data can help you analyze the past, understand the present, and predict the future. Choosing to be a data-driven business is perhaps the most important choice your business can make!
But data has traditionally been difficult to work with. In the past, you would have needed to hire someone like a data scientist or analyst to glean information from your data because very specific skills are required to work with and interpret raw numbers. That’s one reason why it’s so under-utilized: at many companies, these experts can be in short supply.
Additionally, many cloud apps boast reporting as a key feature, but there are limits to how they can show data. Without information from all of your other apps that store key information, the reports will not paint a complete picture. Plus, different departments may want to see different sets of data in different ways.
Facing the challenge of doing their own data analysis, many teams opt to not do it at all. With modern tools and automation, however, it’s easy to get actionable insights from your data—no data scientist required. Here are a few ways you can leverage emerging technology to become a truly data-driven business.
Automate your data collection.
If you’re working with internal data, it’s probably housed in different apps and databases throughout your company. In the past, most businesses relied on either manual data entry or simple point-to-point integrations to move this data around.
With intelligent automation, however, you can build workflows that move your data from around in real time. These workflows can also be conditional so you can move only the data you want, when you want it. And there’s a lot of flexibility in where the data goes: it can go into a data warehouse, a spreadsheet, or another app.
More impressively, automation makes it possible to collect data from external sources in different formats—whether it’s spreadsheets, websites, or even images.
For example, the #1 multinational retailer needs a lot of competitor data in order to perform competitive wage analysis. This data comes from a variety of sources like online job postings and physical job board ads.
Instead of using humans to scour the web, they use ParseHub, a web scraping tool, to trawl the internet for job listings from their competitors. Workato then parses this data and enters it into their HR warehouse in real time.
Then comes the really cool part: when an employee texts or emails a picture of a job ad, Workato picks it up and sends it to the machine-learning powered Google Cloud Vision, which is a service that uses image content analysis to translate the image into text.
Now that Workato can read what was on the image, it can apply taxonomy concepts in order to identify who the merchant is, what the hourly rate is, what the position is (like clerk or store manager), the benefits, and the location. Workato then writes all of this data in the HR warehouse.
This is a great example of how technology opens up new avenues to becoming a data-driven business. Whether you want to better aggregate your business’s existing data or mine external sources for new information, automation makes it possible.
There are many data analysis tools to choose from; pick the right one.
A big obstacle for many businesses is that raw data is difficult to understand. You need to manipulate it using formulas and functions in order to get any sort of insight. That’s no small task for someone without a data science degree; even if you’re a whiz at Excel, it can still take a lot of time.
Thankfully, there are lots of new data visualization and analysis tools that can help you parse through your data. These tools range from enterprise-grade programs like Tableau to chart creation websites like ChartBlocks. And with automation, they’re simple to use with your other apps and databases.
When choosing a data analysis tool, it’s key to identify two things: what you’re looking to glean from your data and how much manual work you’re willing to do.
If you’re not afraid to get your hands dirty, a spreadsheet tool can be more than sufficient. For example, the IT team at Coupa—the world’s premier business spend software—uses a combination of automation and cloud apps to enable better DevOps analytics. On an hourly basis, a Workato automation pulls issues from Jira into Google Sheets for analysis; a similar automation aggregates alerts from VictorOps. This way, the IT team can pick up on evolving incident patterns as they happen.
On the other end of the spectrum, some teams require more robust tools that can provide highly visual interpretations of data.
A great case study is HCSS, a leading software provider for the construction industry. Data is crucial to helping the company plan product improvements and understand how they can better serve their customers. It can even provide the company with insights into specific trends, such as why customers stop using a particular product or are slow to adopt a new one.
“We need to get specific insights into how the customer uses the products and how we can continue to make the user experience better,” says Femi Ariyo, Head of IT at the company.
This data comes from customer surveys, product usage reports, and other sources. Once it’s all collected, it’s stored in an on-premise data warehouse. For easier access, the ESS team pushes it into Einstein, Salesforce’s cloud-based analytics platform, using Workato.
The result is easy to read and understand data analytics that are automatically delivered across departments in the organization. Each department can view the data that is most relevant to them without any manual work, enabling HCSS to make strategic, data-driven business decisions.
Predict the future with data, not magic.
Many companies think of data as a way to assess past performance. But it’s also a valuable way of predicting future trends and outcomes.
“It’s important to look for patterns,” comments Hans Gustavson, the Senior Director of Cloud Operations at Coupa. “You need to develop advanced methods of analyzing data, so you can predict problems in advance.”
Technology is making it much easier to make intelligent, data-driven business predictions using AI. And you don’t necessarily have to purchase or learn a brand-new tool, either; increasingly, data analysis tools come with AI and machine learning (ML) features.
Experts like Sanket Naik, the VP of Cloud Infrastructure and Security at Coupa, suggest it’s worth learning to use these features.
“It’s more valuable for companies to retrain employees on AI than to hire outside experts,” he says. “The tools are getting better; products now come with AI and ML built in. You don’t have to be a data scientist to use AI, because automation makes it easy to apply.”
Consider how your company can make better data-driven business decisions. What trends do you want to keep an eye on? Are there potential problems you want to identify early and work to avoid?
HCSS, for example, teams make full use of Einstein’s artificial intelligence features. The platform delivers predictions and recommendations based on the company’s customer data and business processes. With these predictions in hand, the company can make informed decisions about product changes and strategy.
Becoming a data-driven business is well within reach.
Data is key to optimizing your business, and it’s not as difficult to access or interpret as you might think. With automation, the right data analysis tools, and some creativity, you can empower your business with data-based insights. Whether you want to refine your customer service, improve annual sales, or predict potential IT issues, you can do it—today!
