Building the Best Enterprise Data Strategy in 2018: Real-World Examples
Ashley Stirrup joined Talend in 2014 as Chief Marketing Officer. In this role, Ashley is responsible for driving market leadership, global awareness, product management and demand generation. Prior to Talend, Ashley held a number of senior leadership positions in marketing and products at leading cloud and software companies, including ServiceSource, Taleo, Citrix and Siebel Systems.
It’s an exciting time to be working in the Cloud, Big Data, and Machine Learning industry, but it’s even more exciting to hear how Talend customers are building their data strategy to drive business results. Every year we invite representatives from some of our most strategic customers to join us for two days to share their experiences with Talend’s products and provide input into our roadmap.
This year we had an amazing group representing some of the best-known brands in the world, including leaders in financial services, payments, automobile manufacturing, laptops and servers, a restaurant chain, health insurance, pharmaceutical data and sports betting and gaming. The group ranged from mid-sized companies to some of the largest in the world, and together they have a combined market value of over $200 billion. Despite how varied the group is, they share at least one thing in common – data is a game changer for all of them and building a data strategy is imperative. Most of them are in the data business directly or indirectly, but all of them have a core competency in data.
Often customers speak about the technologies fueling their digital transformation. Our Customer Advisory Board is always a great way to hear about how companies are achieving bottom line results by leaning on their data.
One automotive company showcased how it has delivered hundreds of millions to the bottom line by using data and machine learning to support everything from autonomous driving to supply chain optimization programs. They have even gone so far as to track the screws that go into every car, so that they pinpoint cars that need to be recalled, reducing recalls by as much as 50X.
Another company, a restaurant chain, is tracking almost 16 million active customers, tying together the preferences of their families to truly personalize the customer experience and optimize a multi-billion-dollar supply chain of ingredients.
Overall the advisory board has become far more mature in their use of machine learning in the last year, with much deeper experience around how and where IT and data scientists should work together.
