Building the Best Enterprise Data Strategy in 2018: How Our Customers Are Getting There
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.
4 Common Themes Across our Customer Base:
- Using the Right Data to Drive Business Results: Every one of our customers is seeing a staggering growth in their data volumes. The customers earlier in their data strategy journies are still learning how to scale their data platforms. The more mature customers tended to have more significant challenges with the variety of data and finding the “right data” to improve business results.
- Increasing Customer Centricity: Regardless of the size of the business or their data maturity, every customer has a customer-centricity program designed to create a better customer experience.
- Unlocking the power of Machine Learning: It’s simply amazing to hear how quickly our customers have increased their machine learning maturity. Last year, most were trying to understand the working relationships between IT and data scientists. Today, these same customers have a clear model for creating machine learning models and later deploying them at scale within in their enterprise data strategy.
- “We’re a data company that just happens to be in the X business”. It’s clear just how powerful data can be when you hear almost the same statement from a health insurance company and a restaurant chain.
Delivering Big-time Results to the Bottom Line
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.
Wrestling with Machine Learning
dOverall 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.
They’ve also found machine learning projects are the most data hungry of them all as the models require combing through millions of data points to identify the variables that have the biggest impact on their business. In the case of our financial services customer, it’s not uncommon for their data scientists to stretch the limits of their on-premise storage and compute capabilities. But the benefits are worth it. This customer estimates they’ve delivered over $1B in operational improvements from data projects.
Betting on your Data in Real-time
One of the largest sports betting companies in the world employs over 5,000 people to help run their business in real-time 24x7x365. Their data team has over 50 developers that have built a data platform on AWS using S3, Redshift and Aurora allowing them to track betting and games results down to the second, creating new betting opportunities for their customers. Using Talend and the cloud, they have built their data strategy around an advanced analytics platform with the governed delivery of distributed machine learning models. It is truly one of the largest deployments of real-time data tracking in the world.
Growing Through Acquisitions
Several of the customers at the Customer Advisory Board have grown through acquisitions. These acquisitions have created opportunities for synergies and cost efficiencies. A key theme across these companies was the need to link together distributed business units so they could act as a single company with joint customers and improve the overall customer experience.
Validating our Roadmap
Every one of our customers is deeply committed to using Talend to help expand their core competency in data management. Much of our time was spent talking about opportunities to invest in Talend’s platform so that customers could continue building a unified and modern data management capability that spans all types of workloads, clouds, and on-premise locations.
Customers were especially excited about new self-service data management capabilities that were delivered last year and several new products that we plan to introduce this year. Customers were excited about new self-service capabilities planned for data analysts and data scientists, the fastest growing segments of their teams.
The board’s needs for data quality and data governance are also expanding rapidly as their data needs expand. These areas have been top priorities for Talend over the last few years and will continue to be in the future as reflected in our 2018 roadmap that includes many exciting new capabilities to help customers increase collaboration and the finding and sharing of data sets. However, based on how quickly our customers’ needs are evolving to meet market shifts, our greatest strength will remain our ability to rapidly adapt to put them at the forefront of data innovation.