Data Optimization Strategies for the Manufacturing Industry
by Rackspace Technology Staff
Data is increasingly the driver of innovation in manufacturing. To explore how data is driving innovation today and how it will shape the manufacturing industry of tomorrow, leaders from Rackspace technology® and Amazon Web Services (AWS) recently met with senior IT leaders from the manufacturing industry at a roundtable event, ‘Unlocking the Potential of Data in Manufacturing.’
Together, we looked at how manufacturers integrate IoT, automation and data to drive and optimize the design and production of everything from automobiles to smart medical devices.
Hemanta Banerjee, Vice President of Public Cloud Data Services at Rackspace Technology, recalled that two years ago, digital transformation was viewed as simply a buzzword, a project title or something requested from boards of directors. Banerjee emphasized that transformation is an integral part of how businesses function. He pointed to three factors that affect an organization's success during the transformation process: public cloud, people and data. "Data is foremost among these, as it lets you understand what is happening in your business process and enables you to improve it. The difficulty then becomes how do we leverage data to improve our manufacturing processes or make our supply chain more efficient," he remarked.
While many manufacturers have already leveraged what data has to offer, still there are a few more worries keeping executives awake at night. A common concern is data residency, which involves storing, managing and protecting personal data and communications. There is also the dilemma of centralization versus decentralization, where manufacturing companies choose whether to keep data in one centralized place or distribute it across various decentralized nodes.
“Previously, we focused on consolidation, so that we could take better advantage of data,” said Isaac Tan, APAC Director for IT at medical equipment manufacturer Hologic. “Now we are exploring ways to decentralize while still achieving desired outcomes. We need to be mindful of data residency issues, considering how best to organize teams, or aggregate data.”
"The challenge that we currently face is collating the data from various sources," shared Azuhar Mohammed, Head of Global Solution Centre Expertise & Innovation, Sanofi. "We have a lot of systems in different countries, but we are trying to deploy our solutions to standardize the data. The next step is gathering our data from various sources such as sales, marketing, finance and HR, taking it to one place like a data lake and then using that data with intelligent analytics for the business to make a meaningful decision."
Many manufacturers have already embarked on their digital transformation journey, making the most of data opportunities offered by new technology.
Pharmaceutical manufacturer Sanofi, for example, has already explored robotic process automation (RPA) as early as 2017. “Now, we are using machine learning and AI to build an intelligent decision-making process,” Sanofi’s Mohammed said. "We're applying AI to our modeling processes and implementing pattern recognition built on large pools of data to enahnce R&D productivity."
For Tan, while the scale is a challenge, it has not deterred Hologic from its IoT push.
"We are very focused on getting all the devices that we place in our customer sites connected,” said Isaac. “From our connectivity standpoint, it is critical to have more data points, more sensors in place."
However, the medical device maker didn't always have its data strategy figured out. Tan elaborated: "A couple of years back, our approach was, 'Just get all the data in, then we'll make some sense out of it.' Over time, we realized this needed to be fixed. The IT team will never be able to meet the diverse demands from stakeholders worldwide. How do we enable everyone rather than give what they ask? That's how we handled it. We thought about decentralized or distributed data products. We are treating them as separate, but we are also putting in a framework and certain core technology foundational components that will then allow them to build faster."
Tan also recommended creating a common data model that can serve as a standard for development that can be accessed as needed.
Indeed, digital transformation projects are more than just one-off undertakings. Banerjee observed that these projects usually take place over several years. “It's a long journey,” he said. “It's not a single project, but rather a way of life.”
Data optimization strategy
One seemingly universal truth is that, no matter which stage of data modernization you’re working through, if you’re experiencing headaches, they usually stem from the existence of data silos. Shwetank Sheel, Director of Data Services Sales – APJ at Rackspace Technology, noted that the first step in breaking down silos is identifying and bringing them to the surface.
“Others have spoken about facing the challenges posed by both centralized and decentralized data,” said Sheel. “But in the context of a curated data lake along with decentralized analytics, one of the things that I've seen a lot of customers talk about — from a governance perspective — is the idea of a catalog or a stream of data products. When you're trying to drive innovation, whether across business units or multiple countries, start by indexing the already existing data instead of starting from scratch.”
In determining the ROI, Sheel said that organizations must first confirm that the data they need exists internally. “By focusing on use cases and their outcomes, we typically work with users to help them understand the potential of the data, and then put into place a framework which helps both the individual and the organization identify the value of the use case, the cost of obtaining it, and whether the data even exists in the organization. This process is then followed through to do advanced analytics with AI and machine learning,” Sheel explained.
Banerjee noted that ultimately, the distribution of analytics to business units has proven to be successful thus far.
“We've seen that we're moving away from a central data team doing everything, to instead distributing the data function within the businesses and the central team providing frameworks, guidelines and infrastructure to enable them to succeed.”
This post was created with Amazon Web Services (AWS) sponsorship.
Are You Realizing the Cloud Optimization Benefits of Kubernetes and Containers?
September 22nd, 2023
Google Cloud Next ’23 Highlights— AI and Beyond
September 14th, 2023
Why You Need an MLOps Framework for Standardizing AI and Machine Learning Operations
September 12th, 2023