Data analytics teams are becoming part of the strategy group in several organizations, and it's critical for companies to have a framework for big data initiatives. Asha Saxena recently spoke at the Enterprise Data World 2019 Conference about the value big data analytics initiatives bring to the organizations.
Saxena proposed a big data framework that consists of seven elements with:
- One core principle: high-value big data.
- Two pillars: organizational maturity and internal competencies).
- Four foundation components: objectives, data governance, engagement, and transformation.
More and more companies are achieving the monetization of data by improving efficiencies, developing new products, growing new markets, and by reducing risks. Saxena talked about Netflix's original series like Orange is the New Black that are a direct result of data-driven innovation.
She elaborated on the big data framework elements. Organization maturity is about hard assets in an organization, like its strategy, data, quality etc. Every organization should have a business strategy, as well as a data strategy. The internal competencies are about people, and focus on soft assets like leadership, engagement, and adaptability.
Health care organizations in the field of precision health like Geisinger are taking advantage of big data and genomic sequencing to transform healthcare practices, in order to prevent people from becoming sick and to treat people more as individuals (customers), rather than just patients.
Data governance initiatives should include aspects of data integration, quality, accessibility and data security. Executive commitment is also critical for the success. Teams should have an executive sponsor to support the initiatives in the data analytics space. Also, having an independent analytics group, which does not sit in the functional area, but in the strategy group, helps develop common data science solutions for all the teams in the organization.
Data analytics is going to become a product in organizations, so the teams should start thinking about analytics like a product in terms of roadmaps, execution and delivery.
Saxena concluded the discussion with the big data value map and value plan, and explained how to use them. Value map is used for identifying potential value in the initiatives, and value plan can be used for targeting and delivering that value.
