Too often, IT leaders park their data science initiatives until they can build a robust data engineering layer. They wait for a data warehouse to be available before planning data analytics projects, assuming that advanced analytics is essential for transformational business value and that large volumes of neatly organized data are a prerequisite for it.
Nothing is farther from the truth.
Here are four things to keep in mind if you don’t have big data but would like to pursue data science initiatives.
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1. Business problems should determine the kind of analytics you need
About 80 percent of data science projects fail to deliver business outcomes, Gartner estimates. A key reason for this is that leaders do not pick the right business problems to be solved. Most data analytics projects are chosen based on available data, available skills, or available toolsets. These are recipes for failure; a data analytics project should never begin with either data or analytics.
The best way to start the data science journey is by introspecting on the organizational strategy. Find out the most important problems your target users want to be solved and validate whether addressing them will deliver the desired business impact. The chosen business challenges will dictate the analytics approach you should take and hence the data you need.
Not having data to begin with can even be an advantage: When you start with a clean slate, you’re not burdened by legacy baggage. On the other hand, organizations with a much longer footprint often struggle with expensive digital transformations.
Consider Moderna, which built a digital-first culture from its inception in 2010. It built a data and analytics platform in service of its business priorities that revolved around developing mRNA-based drugs. This targeted approach was instrumental in enabling Moderna to create the blueprint for the COVID-19 vaccine in just two days.
2. Your analytics approach dictates the data you source
Organizations can spend months building data warehouses only to find that the data they’ve collected isn’t good enough to perform the analytics they need. Machine-learning algorithms often need data of a particular type, volume, or granularity. Attempting to build a perfect data engineering layer without clarity on how it will be used is a wasted effort.
When you have visibility on the organizational strategy and the business problems to be solved, the next step is to finalize your analytics approach. Find out whether you need descriptive, diagnostic, or predictive analytics and how the insights will be used. This will clarify the data you should collect. If sourcing data is a challenge, phase out the collection process to allow for iterative progress with the analytics solution.
For example, executives at a large computer manufacturer we worked with wanted to understand what drove customer satisfaction, so they set up a customer experience analytics program that started with direct feedback from the customer through voice-of-customer surveys. Descriptive insights presented as data stories helped improve the net promoter scores during the next survey.
Over the next few quarters, they expanded their analytics to include social media feedback and competitor performance using sources such as Twitter, discussion forums, and double-blind market surveys. To analyze this data, they used advanced machine learning techniques. This solution helped deliver $50 million in incremental customer revenue annually.
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Source: https://enterprisersproject.com/article/2021/12/data-science-without-big-data