5 Embedded Analytics Pitfalls and How to Avoid Them

As the business world shifts towards an information economy, more companies are discovering the advantages of having a business intelligence (BI) solution for analytics.

Embedded analytics software boosts an organization's main functionality by integrating BI tools and analytical capability directly into software applications, rather than as a separate third-party application. With dashboards and reporting available in the same software, operational efficiency, cost optimization and innovation is increased.

For any business, though, adopting an analytics solution for their product comes with its own challenges. Embedded analytics software requires critical considerations depending on the type of business or product it is being integrated into, and the intended end-users, use cases and objectives. These considerations are unique to each business.

Some universal subjects that need attention before implementing analytics include establishing clear strategic goals, thorough evaluation of the internal team's data expertise, the maturity of internal applications for adopting analytics, and pricing.

5 Embedded Analytics Pitfalls and Solutions

The universal challenges associated with big data pose a careful selection of your analytics solution provider. An awareness of criteria and requirements is as important for the buyer as it is for the analytics solution provider. That’s Yellowfin take a great interest in educating our clients on what to expect while integrating an embedded analytics solution.

This article discusses the five most common embedded analytics pitfalls, along with our recommendations on how to avoid them. 

1. Unpredictable pricing
2. Lack of on-boarding efforts from the vendor
3. Lack of flexible data architecture
4. Limited white-label customization
5. Ignore automated and augmented features

1. Unpredictable pricing

For every business, finding an embedded analytics solution that offers the best value for money is primary. Pricing gets tricky when usage isn’t predictable. Per-user and per-query models introduce unpredictability into the pricing and might leave your finance department with a headache. Businesses thus need to be clear internally and with a potential partner about their requirements to make the best choice for their BI software. 

Most of the focus is typically on the immediate acquisition costs of a software license (which is a small percentage of the entire project) because it’s measurable. Be sure about what’s included and what is not part of the initial acquisition. Are there any optional modules besides the standard ones you need that aren’t part of the initial acquisition price you are being quoted?

Another important element of choosing an embedded analytics solution is its integration. Depending on the integration level, pricing is expected to vary. Be clear about whether you want a front-end or full-stack integration. 

Ultimately, every business is different. Thus, choosing an embedded analytics solution with a custom pricing model such as Yellowfin makes sense. One that addresses factors like maintenance, human capital, and scalability. 

2. Lack of on-boarding efforts from the vendor 

Having an embedded analytics provider that offers support is a sigh of relief in the intimidating launch of the solution. Depending on the project needs and the technical expertise of your internal team, businesses require vendor support and consultation to varying degrees. Be deliberate about the extent of technical and business support your chosen analytics solution provider offers. Support expectations and needs should be clearly communicated at the right time.

There may be areas of this technology a business is unfamiliar with and requires support to train the internal team for optimal utility. Ask for any on-boarding programs or resources on leveraging the analytics tool.

Furthermore, having a project representative of the embedded analytics software provider adds another support layer. They can liaison with businesses on updates regarding best practices and trends. A mix of live and self-service support should be available to address the technical queries that arise from time to time. Also, communities run by the analytics solution provider are invaluable for learning and growing from peer experiences.

3. Lack of flexible data architecture

An embedded analytics solution's scalability, performance, and flexibility depend on its data architecture. Businesses with traditional data analytics are faced with problems like lack of functionality, outdated technology, and the cost of maintaining old-legacy setups. 

Moreover, a lack of flexibility in data architecture can become a hindrance to its scalability in the long term. This is why organizations are shifting towards modern data architectures. This way, businesses boost performance and stay competitive in the age of fast technological advancements. 

Modern data architecture stays relevant and useful due to its ability to cater to changing needs and trends. Also, a modern data architecture can deliver self-service capability for analytics. To make the shift, find an analytics solution provider like Yellowfin that supports unique customer requirements by offering a range of modern infrastructures (cloud, hybrid, and on-site). 

4. Limited white-label customization

Making analytics available at the point of use has more adoption than traditional stand-alone analytics software, which requires the user to switch between applications. This central feature of embedded analytics solutions blends analytics seamlessly with the core application. A step further to this functionality is an analytics solution with white-label customization. 

White-label embedded analytics solutions offer extensive customization. It empowers the end-user to visualize data in ways that best suit their needs. Ensure to address concerns like how much customization and editor functionality is possible while selecting the embedded analytics solution. 

Make sure you don’t find yourself in a situation where your solution provider doesn’t offer customizable analytics graphics without the developer's need.  

5. Ignore automated and augmented features

The decision to introduce an embedded analytics solution into your organization is nothing less than an investment and should be treated as such. Most organizations fall prey  shortsightedness and choose an analytics solution that serves their immediate needs with no regard to future-proofing. 

Artificial intelligence (AI) and machine learning (ML) is already upon us. Having an embedded analytics solution that can provide analytics capabilities is imperative. Augmented features of analytics software bring speed, accuracy, and efficiency to the business intelligence platform. Augmented analytics offers a complete picture of data and 24/7 automation of operational activities. 

Most users of embedded analytics in businesses aren’t experts in analytical techniques but are experts in their fields. They can evaluate the analytics results with less effort and move swiftly towards decision-making with the augmented features. Therefore, choosing an analytics technology that can accommodate analytical capabilities pertaining to future needs is critical.

Choose an embedded analytics solution that matches your needs

Businesses always seek ways to gain a competitive advantage and increase productivity and revenue. Implementing a data-driven culture and decision-making leads to accomplishing these business objectives.  

Yellowfin is an embedded analytics solution that ensures smooth analytics technology adoption for enterprise and independent software vendors by providing active support and a seamless transition that saves you time, money, and effort.