When Data is Your Product GHC16

The role of (information) technology in business is to facilitate business outcomes. If this statement is true, then it solves the chicken or egg causality dilemma – Tech for business or Business for tech, while also making a case for “hybrids i.e. having people in an organization that are able to combine business acumen with tech savvy. Hybrids are the real MVPs saving companies from waste, and analytics solutions are the tools that enable these superhero abilities. The context of data, as in the title is from viewing data systems as a product, and the goal of this session is to provide a guide to empower business users to make better data-driven decisions.

Behind business goals are Key Performance Indicators (KPIs) dressed up as questions that crucial for organization success. Business users are desperate for data and tools that will provide these answers. They are curious to know for example – how well their marketing campaigns are performing, elapsed time between orders, etc. Placing analytics solutions in the hands of product teams that need to be business smart and data savvy will not only maximize effectiveness but will transform how teams work to make products smarter and better decisions.

To empower business users with data, it’s important to start where the organization is. Below are some pointers to consider when planning a data product.

  • Communication and inclusion. See the business users as customers, and make them an active part of requirements, implementation and testing phases. Start the conversation between the tech and non-tech teams, with a clear vision. Seek to understand the problem and goals, and resist trying to dazzle with tech.
  • Consider the options. Having understood the goals, then proceed to pick the tech or use what you have, bearing in mind that there are many possible paths to success. Make the most of your expertise to solve the problem, harnessing data models and structure. To avoid over- or under- engineering a solution, stay flexible and focused on solving problems without getting too attached to a single idea.
  • Be open-minded about choosing software tools. What matters most is getting the job done, and while there is a variety of good answers, paying attention to needs and success criteria will help find the best without interfering with reaching the goal.
  • Be rigorous in ensuring correctness and availability of your data and product. Apply engineering and operational rigor through SDLC processes (testing, validation, quality, anomalies) and SLAs (availability, stability, performance, scalability) respectively.

Bringing it all together, remember which comes first as you combine and bridge business acumen with technology and let these work in tandem to get the job done.

Disclaimer: GHC speaker Denise McInerney is a Data Architect at Intuit. I am a community volunteer for Grace Hopper Celebration of Women In Computing. Opinions are my own.

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