Greater visibility and insights from data are no longer merely options for business competitiveness. They’re table stakes. Data allows companies to understand and deliver exactly what their customers want when they want it. Monitoring data streams can also ensure product quality, alert operators to anomalies, and minimize production downtime. Businesses that don’t collect, analyze, and use data in their operations will be defeated by those that do.
However, not all of your clients have data analysts on staff or the computing power to perform extensive data analysis in-house. So, to unlock the value of their data, your clients will need you to help them select the right data analytics tools.
Data Analytics as a Service provides subscription-based data analytics tools through the cloud. These solutions are often designed to democratize data analysis, allowing people in any role in the company, regardless of their technical expertise, to have the ability to access and use insights from data.
There is a wide range of data analytics tools to choose from, which makes it possible for you to tailor a solution to your client’s needs. However, you must carefully select to avoid the following pitfalls.
Complexity
People in all roles with different skill sets need to make data-based decisions. Therefore, data analytics tools must be intuitive so everyone who needs them can use them. The data analytics solution also needs to be easy to launch. You can eliminate complexity by choosing an end-to-end solution that includes all business intelligence (BI) stack components, such as ETL (extract, transform, load), analytics and visualization.
Limited Scalability
Before vetting data analytics tools, narrow your choices to those that can accommodate the maximum number of users the company will have at any time and the volume of data they need to process. The solution should also be able to provide the answers your client needs within their expected timeframe.
Limited Data Sources
Some solutions support only one data source — others will analyze data from multiple sources. Ensure you thoroughly understand where your client’s data is coming from, the format the information is in, and the data analytics tools you choose can handle it. If your client uses multiple data sources, ensure your solution integrates disparate data streams as efficiently as possible.
Poor Visualization
Ensure the data analytics tool you choose can visualize data effectively for your client. You may want to select a tool that lets you customize reports and graphics to present the most critical information to the user in a way that’s easy for them to understand quickly. Some data analytics tool vendors offer industry-specific dashboards with their solutions configured for the metrics, key performance indicators (KPIs), and cost analysis that businesses in those verticals commonly need.
Subpar security
Businesses need to protect their data. When evaluating a security solution, ensure it keeps data safe from hackers and includes features that limit access to only employees who need the data to do their jobs. Your clients may also require an audit log showing who has accessed information. Some solutions can also allow administrators to hide specific data from shared dashboards based on the user’s role.
Inadequate support
It’s also vital to ensure that your data analytics tool is backed by reliable support. Research how and when users – and managed services providers – can contact support or submit a ticket and if there’s an additional cost for premium service with shorter response times. Also, investigate whether the vendor provides training and if you will have a dedicated contact that you can reach when you and your clients need them most.
The best data analytics tools for your client will improve, not complicate, their current processes by putting insights from data at their fingertips that they can use to make intelligent decisions to grow their businesses. Ensure you don’t implement a solution for your client that will interfere with the value data can provide to their organization. 
