The Top 3 Big Data Challenges Facing Startups and How to Solve Them
Content Marketing Manager, Sigma
If you run a startup or small enterprise, you probably already know the following:
- Cloud services are great – Platforms like Salesforce, Hubspot, and G Suite keep your organization running. You love the flexibility of not having to depend on on-premises and internal IT resources.
- You need to leverage data to scale – While you have found success making decisions based on individual app data, you know you’ll need interconnectedness to draw more meaningful insights.
- Team members with interdisciplinary skills are valuable – You save time and money when domain experts are able to analyze data and make sound business decisions based on that information.
With that said, how do you make the leap into being a truly data-driven organization? It seems like the most well-known success stories come out of large enterprises with the resources to manage on-prem data warehouses and hire an army of IT talent and skilled SQL analysts. Can a smaller organizations compete when it only has one data engineer, maybe a SQL analyst or two, and fewer IT resources?
Believe it or not, the answer is yes. It’s an exciting time in the world of analytics. A growing number of providers offer every level of the cloud analytics stack. With these modern solutions, you can compete with the large enterprises in a way that is affordable and scalable from the beginning.
Read on to learn how you can address the top big data challenges with a modern cloud analytics stack.
Challenge #1: Disjointed Data Sources
According to one SaaS management vendor, the average small enterprise uses 20 paid SaaS products. Each of those SaaS applications probably generates data that is critical to at least one department in an organization. If you’re a technology startup, then there’s also the data generated from the product itself.
While SaaS apps are affordable and provide the flexibility that many startups need, they leave much to be desired in the reporting and analytics department. In some cases the only way to view a report is within an app’s own system.
So what do you do when you want to build a model based on data from two, three, four, or 20 applications, in addition to your internal data sources?
The average number of paid SaaS products used by small enterprises.
Solution: Cloud ETL & ELT
To overcome the first challenge, you need a cloud ETL provider that’s connected to a cloud data warehouse (more on cloud data warehouses in a bit). ETL (Extract, Transform, Load) is the process of moving data from a source like a database into a data warehouse where it can be analyzed. Some cloud ETL vendors also support ELT (Extract, Load, Transform) in which data is converted into analyzable form after it is already in the data warehouse.
Before the cloud, ETL meant an engineer manually sending internal sources to the on-prem data warehouse. Now, SaaS application data sources outnumber on-prem data sources. SaaS vendors send data to their customers in real time via APIs. Cloud ETL products handle the constant stream of information coming in from these applications. Cloud vendors also give you the flexibility to support structured and unstructured data.
With cloud ETL you can streamline the process of creating one central source of information and give analysts the ability to more easily conduct complex analyses. Learn more in our online guide to cloud ETL and ELT.
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Challenge #2: Outdated Data
That real-time data coming in from applications and devices is only useful if you can also analyze it in real time. What could be a useful business question one day could be irrelevant or out-of-date the next. Many analytics teams can’t move as quickly as the business because they have to wait for data to be routed into a data warehouse. This lag can create a competitive disadvantage as a company can be constantly behind their competitors when analyzing and processing information.
Solution : Cloud Data Warehouses & Data Lakes
Data warehouses are the centralized hub where you can access and analyze information coming in from different sources. In addition to a data warehouse, some organizations also utilize data lakes as a place to store non-relational data from sources like IoT or mobile devices.
Gartner calls on-premises data warehouses the “new legacy.” This is hardly a revelation as on-prem options require a large upfront financial investment. In addition they require dedicated IT and data engineering resources to maintain software and scale the system up and down depending on usage and company needs. Cloud data warehouses can scale elastically, don’t require an upfront infrastructure investment, and can manage structured and unstructured data—allowing engineering and IT resources to stay focused on more core business projects.
With all of the data in a dynamic warehouse, analysts can quickly develop up-to-date dashboards, reports, and worksheets based on what is happening now, not five days ago.
Learn more in our online guide to choosing a cloud data warehouse.
Challenge #3: Hard-to-Use Analytics Tools
Business teams sometimes find themselves in a difficult situation. They want a simple question answered, or a simple analysis conducted, but the company’s analysts don’t have the bandwidth to deal with it quickly.
So, the business user decides to take matters into their own hands, but they face a couple of obstacles.
First, they simply don’t have access to company’s BI tools because:
- It’s cost prohibitive to grant licenses to employees outside of the core data team
- The company doesn’t feel comfortable granting access to everyone in the organization
- The business user hasn’t gone through the tool’s training program
Or, they simply don’t feel comfortable using the company’s analytics tool especially if it requires some advanced SQL knowledge. So what do they do? Turn to a spreadsheet program like Microsoft Excel or Google Sheets. Not only is this inefficient, it can also present serious security risks.
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Solution: Cloud-Built Analytics Tools
Most analytics solutions on the market already offer some sort of cloud option. The problem with many of these hybrid solutions is that they were not built to analyze cloud data warehouses, or take on the scale of data sources like those generated from SaaS applications. Cloud-first analytics solutions offer unique real-time data access and information sharing within an organization.
Modern cloud analytics tools draw data from a cloud data warehouse, so all relevant information is available to those who need it. And they are designed with the needs of all users in mind, not just those with programming skills.
Modern cloud analytics tools draw data from a cloud data warehouse, so all relevant information is available to those who need it. And they are designed with the needs of all users in mind, not just those with programming skills. Spreadsheet-like interfaces along with drag-and-drop components allow anyone to conduct advanced queries without code—so you can empower people outside the BI team with data access and insights.
Security risks are reduced because these tools connect directly to the cloud data warehouse. There is no additional data store at work that moves it from one place to another.
Finally, because all of this analysis can be done in a browser, there is no need to save data locally. Cloud analytics tools like Sigma eliminate the need to download spreadsheet files to local PCs or email documents. Every worksheet can be accessed and shared in team workspaces.