Analytics: Was macht eine Big Data Plattform aus?

  • Beitrags-Kategorie:Analytics

Interessanter Artikel über die 5 V's in Big Data Plattformen.

Whether data is structured or unstructured, it’s only as valuable as the business outcomes it makes possible. However, the data itself isn’t the only factor responsible for those outcomes. How you measure that data, from a business point of view, helps you tie the value of the data to its potential and supports decisions that lead to positive business results. To get there, you need a big data analytics platform.

Once you have a platform that can measure along the four V’s—volume, velocity, variety, and veracity—you can then extend the outcomes of the data to impact customer acquisition, onboarding, retention, upsell, cross-sell and other revenue generating indicators. You can also look at this information as a competitive strategy that brings corresponding improvements in operational efficiency and helps you leverage data across the enterprise for other initiatives.

  • Volume-based value: The more comprehensive your integrated view of the customer and the more historical data you have on them, the more insight you can extract from it. In turn, you are making better decisions when it comes to acquiring, retaining, growing and managing those customer relationships.
  • Velocity-based value: The more rapidly you can process information into your data and analytics platform, the more flexibility you get to find answers to your questions via queries, reports, dashboards, etc. A rapid data ingestion and rapid analysis capability provides you with the timely and correct decision achieve your customer relationship management objectives.
  • Variety-based value: The more varied customer data you have – from the CRM system, social media, call-center logs, etc. – the more multifaceted view you develop about your customers, thus enabling you to develop customer journey maps and personalization to engage more with customers.
  • Veracity-based value: Amassing a lot of data does not mean the data becomes clean and accurate. Data on customers must remain consolidated, cleansed, consistent, and current to make the right decisions.

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