How can you make data-informed decisions even early on?
Staying focused and aligning data strategy to your business plan were key messages delivered in Bang for Your Buck: A Cost-Effective Data Strategy, a session during Startup Boston Week 2020. Panelists reinforced the importance of data collection to drive business decisions as startups develop new ideas, concepts, and products.
Data Strategy is Business Critical
There is much more to data than the collection process itself. “Data is a resource that enables you to get to a solution. It is not the solution by itself.” Explained Venya Amla, CTO at AdRoxx, who also emphasized that startups should focus on 4 areas to build their data plan:
1. Business Model – Define data success and develop measurable goals with key performance metrics.
2. Team – Hire generalists early in development to interpret engineering and business data and bring this data to life.
3. Data Collection – Collect relevant data. More is not always better. Look at competition, industry trends and existing studies. Do not rely on expensive proprietary sources!
4. Technology – Select relevant tools (storage, integration, reporting, dashboards)
Choosing metrics can be difficult, Amla adds. Everyone has a different interpretation. Be clear on meanings and definitions to ensure everyone focuses on the same result.
Gabriela Veloz, Regional Sales Manager for CloudHealth by VMWare, agreed and stated that startups need a data strategy within an organization’s business plan. “It’s a dream if you don’t have an execution plan.” She said. “Clearly define your objectives including the types of information you want to collect and how it will be used. There are many opportunities to leverage data sources early in the process.”
People Are Your Foundation
Part of a good strategy are your people and resources. You can have an awesome strategy but cannot execute without people who can interpret and bring meaning to the data. Most startups need a generalist who can wear many hats. Look for problem solvers who understand business, technology, and engineering. 90% of data costs should go to finding your team. Do what you can to keep top talent as turnover can be a large cost. If needed, outsource.
Data Apps Provide Dashboards for Decision Making Based
A CRM database is a good investment for startups to consider early in their development. Information collected on customers and prospects as well as trials, cases and demos need to be centrally stored. “Most startups start with one customer with the goal of getting more.“ Veloz added. “In sales we use data to understand what it takes to acquire new customers. If we know 400 prospect calls lead to 8 demos and 4 trials, we can better forecast sales and revenue.” The same is true for product development, engineering and other parts of the business.
Understanding data will help enable goal setting and provide a focused cost saving approach. Don’t take any data as too much may not be helpful. One-size does not fit all. Bulent Kiziltan, Co-Founder & Chief Data and Analytics Officer for Stealth Mode Startup recommends designing a strategy plan specifically for your company.
“Customize data strategy rather than coming up with a black box data approach standardized for all needs.” Kiziltan added. “Startups should also decide upon a single cloud-based tool and aggregate data into it. This is one of the most important investments any startup should consider.”
Manage Data Risk and Adapt for Better Products
There are many cost-effective options to find data including competitor analysis, google trends/searches and existing research studies. Reach out to customers who may have data you can use. Be creative! Consider asking competitors who may be willing to provide non proprietary data.
It is also important to consider data risk factors. Startups need to manage market and technology risks of a given product or model. Data can improve models that can lead to a more viable product or service. Hassan Kane, Lead Data Scientist at Emtropy Labs, talked about prioritizing data culture and hygiene into 3 buckets.
1. Sources – Use a variety of data sources and benchmarking to build and improve models.
2. Behaviors – Understand user interactivity to prioritize features and workflows that customer address sincere customer problems.
3. Production – How models perform in production over time and track these results
Hassan adds that “models are powered by assumptions and need to adapt to changing events. For example, there was a pre-COVID-19 way of doing things that changed quickly once the pandemic hit. If assumptions change over time, they need to be tracked in a centralized database.” Be ready to shift design, manufacturing, or marketing/sales.
Do Not Make Decisions Without Good Data
Everyone agreed that data rules for startups. Managing and understanding data complexities as an organization grows are key ingredients to a strong data culture. Amla concluded that “it’s best to work backwards. Figure out the problem and determine the solution. Then, go find data to support conclusions.”