TechBytes with Edward Chu, Director of Software Engineering, Bedrock Analytics

Edward Chu
Director, Software Engineering, Bedrock Analytics

Most CMOs would agree that the power of data intelligence and automation tools helps drive efficiencies in reporting and analytics. Edward Chu, Director, Software Engineering, Bedrock Analytics, shares his predictions about the future of Analytics-as-a-Service, and the key skills needed to succeed in the sales analytics space.

Tell us about your role at Bedrock Analytics and the team and technology that you handle.

I’m the director of software engineering at Bedrock Analytics, an intelligent analytics and insights automation platform for the Consumer Packaged Goods (CPG) industry. We offer a cloud-based analytics platform and insights automation tools that enable sales managers and business analysts to harness the power of data in order to craft powerful sales presentations. My job is to make sure our analytics and data tools are as powerful as possible yet at the same time are intuitive and easy to use. The team consists of several other engineers and a data scientist. We work very closely with the Sales and Product teams to understand what our clients need and deliver innovative solutions to the market. The CPG industry is being transformed by Big Data and advanced analytics right now, so it’s a very exciting time to be involved in it!

What are the core tenets of your data-driven analytics?

There are several core tenets driving our business. The first is the combination of power and simplicity. We distill complex datasets from market syndicators and retail outlets into the simple, powerful storylines that matter most for CPG manufacturers, and we present the data through a simple, easy-to-use dashboard that anyone in the organization can use. Our goal is to put the power of advanced analytics directly in the hands of every salesperson, marketing manager, and business executive, so that they don’t have to rely on data scientists or BI analysts to get the insights they need to drive their business forward.

Another important tenet to our business is efficiency and automation. We automate the data analytics process so that CPG companies can access important insights in less time and with fewer resources. We believe in the power of data intelligence and today’s automation tools to help drive efficiencies in reporting and analytics. A lot of the tools that we build are intended to help people do their jobs faster, smarter and more efficiently, so they can focus less on querying data and more on what they do best.

And one more tenet is the idea of storytelling. After all, data by itself is meaningless. The point of data and analytics is to tell a story so that people can make informed decisions based on certain outcomes. We’re not talking about stories as pieces of fiction, but stories in terms of narrative structures that help give clarity and insight into the true definition of things. Our data and reports are structured in a way that helps people tell convincing narrative stories that support sales and grow their business.

What are your predictions on Analytics-as-a-Service for 2018-2020? What are the key skills needed to succeed in the sales analytics space?

Analytics-as-a-Service will definitely become the primary vehicle for buying and selling analytics services by 2020, if not much sooner. It is simply easier for the end-user to access analytics through the web than have to deal with software maintenance and updates. This means that to succeed in sales analytics there’s a lot less need for hand-holding and training during the setup and integration periods, and a lot more opportunity to serve as a consultant to our clients. The sales role becomes less of a facilitator and more of a strategic consultant who spends less time getting the software integrated and more time demonstrating the true value it brings. That’s a win-win for analytics providers and their customers.

What impact would analytics and reporting tools have on traditional sales automation platforms?

Across just about every industry, from advertising and media to CPG, we are seeing sales become much more of a data-driven effort. This is especially true in CGP. Retailers used to allocate shelf space primarily to big brands or those companies that they had a pre-established relationship with. But now, companies that have the data to show who their products sell to, at which price point, in which market, and so on are winning over retailers and securing shelf space for their products. This has opened the door for a number of CPG companies to compete in areas that they were blocked out of before. And it basically means that any type of sales tool or automation platform has to have analytics and reporting tools at the very heart of what it does.

How do you see trends in Data Management influencing the adoption of analytics tools for non-marketing and non-sales processes?

Most analytics tools are built to support sales and marketing processes, but the third constituent of analytics users include all of the corporate executives who need insight into how their businesses are growing. With data management becoming more accessible, executives have more power than ever to dig into advanced analytics on their own, without the need for a BI analyst to run queries and produce reports for them. It will take them down paths that they might not have even thought of before because they will learn to think more analytically about their businesses and get better answers to their questions. This mentality will also trickle down to the rest of the organization and help the entire business adopt a more data-driven culture.

What role do you see for AI/ML in sales data reporting and visualization? What does Bedrock’s product roadmap look like for 2018-2022?

Machine Learning and Artificial Intelligence already play a huge role in sales data reporting and visualization, especially in the CPG industry. One area that we apply machine learning to, for instance, is to sync together all of the disparate data sources that CPG companies get from retailers, measurement firms, and so on and make them all match up. This alone can save a company countless hours of time having to reconcile the reports manually, by using machine learning to understand things like category names and product descriptions. We don’t want to give our whole product roadmap away, of course, but let’s just say that ML and AI will come in very handy to help extract meaningful insights from the mountains of data that are available and even make predictions in terms of how a product is expected to perform in certain areas or among certain demographics. The industry is just scraping the tip of the iceberg in terms of what AI and machine learning can do, and we aim to be at the cutting edge of innovation in this area.

Thanks for chatting with us, Edward.

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