3 Strategies To Build And Accelerate Better Data Products
Being in the Data Science industry for the last decade, I have seen small businesses and enterprises alike change their perspective on the value that data has for their company. It is no longer about ‘why’ your business should focus on data and analytics strategy, but about HOW to integrate data within your company. As a leader concerned about delivering success on data projects, it is now left up to you to define how data strategies and initiatives are executed in the whirlwind of all other business objectives, and ever-changing practices.
1. Structure the data team to build and support a data tools product
The complexity of our customer’s data made it difficult to create scalable data products without first putting an emphasis on data democratization. We needed to get a clearer view. Just to get started, we gave our customers access to all their data by creating report builders, dashboard tools, and interactive data visualization builders. All as components of the software(product), and scalable across our entire customer base. In order to best serve our customer, I found the best approach was to have a team stacked with a product manager, a UX designer, a QA engineer, front-end developers, and back-end developers. Their key focus was making it easier for customers to self-serve data business intelligence and analytics on top of the data they’re creating.
2. Embed data science throughout the product scrum teams
Data is not localized to just a single product team. If you think about it, every product is designed to produce data. The challenge that so many organizations have is realizing that data access and usage are already spread across every organization and skill level at their company. Thus, it seems that every agile scrum team has a need for thought leadership and data strategy best practices. Rather than turn those data needs over to a single downstream team, executives embedded data scientists within each software team. This kept the ownership of the solution on the team that produced the data. It also allowed data scientists the flexibility to research cross-product problems (like machine learning) in a centralized team.
This team is a lightweight product discipline. Each data scientist is responsible for the utilization of data within their product domain. A data scientist can function on multiple product teams, and all data science work is prioritized across the entire new product development strategy. This has ensured our company is focusing on the most important data initiatives, simultaneously keeping product managers focused on other deliverables.
3. Service the product through business intelligence
The data products are ready and available to use, and customers are ready to take advantage of your software because of them, but success is dependent on their own data literacy. SAAS business intelligence is matching customer’s data needs into a product journey that facilitates seamless source integration, data cleansing and readable intelligence. The importance of defining what products will be used before the data starts to trickle in is critical. A BI strategy framework acts as a steward and tutor for their customers’ utilization of the data toolbox.
This process provides valuable feedback to data scientists and the data product team on what problems our customers are facing. They’re also the quickest to adopt the utilization of data analytics products.
Your Analytics Odyssey
Business leaders looking to rethink how data products get built and serviced for your company, I hope my odyssey has given you clarity. My goal is to provide you with a blueprint for implementing a successful data and analytics strategy.
This will be some of the most exciting times your company will ever voyage on! If you need support/guidance, and want to avoid some of the pitfalls that come with complex strategic data initiatives, connect with us. We can offer you a free data discovery with data and analytics consulting services. We’re here to copilot your data odyssey.