Special thanks to Elizabeth Wallace (TrackAbout), Eric Wise (Otodata) and Morgan Morris (CO2Meter) for their contributions.
As consumers, many of us enjoy streaming services such as Netflix which seemingly has the widest variety of entertainment choices at an affordable price, “on-demand.” In addition, we also enjoy e-Commerce companies like Amazon which have virtually everything you may need on their online marketplace (over 600 million products and counting) at a competitive price, and delivered to your door in a reasonable timeframe. Whether it is Netflix, Amazon or other consumer industry leaders, one common success trait is their use of advanced data analytics to supplement insights, decision-making, agility and other areas that differentiate their performance from their peers.
Although the first movers may have been operating in consumer-facing and finance-oriented markets, the B2B space where the Industrial Gas industry operates, is also taking advantage of “big data” analytics, and in many respects in a more complex fashion with global customers, unique product and service needs, at much higher dollar values. A recent McKinsey survey of business leaders reveals that about 40 percent of them expect to create new businesses based on data, analytics, and AI within the next five years. So, why should you care? In view of the fact that advanced data analytics has proven it helps you interpret most everything from market trends to optimizing supply chains, it ultimately helps you better understand what your customers really want. Consequently, this article will help you better understand this space by first providing a brief history of data and analytics, latest trends broadly and specific to the Industrial Gases space, then share best practices to leverage data and advanced analytics solutions for your specific business.
Early Innovations
Simply put, advanced data analytics involves collecting, organizing, and analyzing large volumes of data using complex machine learning (ML) techniques, to uncover insights beyond what’s normally obtainable using traditional business analysis techniques. These deeper insights often help in making smarter decisions. Although it appears to be a relatively new space and continues to grow in popularity, it isn’t a new field since many of the early innovations go back well over a hundred years.
Modern data concepts emerged in the mid-1920s, radically changing the way data could be stored, with Fritz Pfleumer devising a way of storing information magnetically on tape in 1928. Soon, relational databases came to the forefront thanks to the work of IBM researcher, Edgar Codd, which organized data into one or more tables or “relations”, based on common fields or attributes. Since the 1970s, relational databases have emerged as one of the most widely used data management tools today, but they do have limitations.
Big Data
In the 1990s, the internet was still in its early days, but it was growing fast. Businesses and organizations were collecting more data than ever before…way more than they could handle with traditional methods. This explosion of information came from various sources: customer transactions, website activity, social media, and much more. By the early 2000s, it became clear that new tools and technologies were needed to manage and make sense of all this data. Companies like Google, Amazon and Yahoo (and its open source solution Hadoop) were pioneers, creating advanced systems to process and analyze large data sets more quickly and efficiently. The term “Big Data” started to be used around this time (largely credited to Roger Mougalas of O’Reilly Media) in reference to large amounts of data that were virtually impossible to manage and process using traditional business analysis tools. As a result, Big Data’s focus was to find ways to process, analyze and ultimately uncover insights hidden in the vast amounts of data being generated regularly…and its journey and focus still continues…
Trends
Today, any business focused on growth and maintaining a competitive edge is staying on top of advanced data analytics trends. These trends are at the forefront of technological innovation, offering essential tools that deepen customer insights, enhance operational efficiency, and enable informed decision-making to drive business outcomes. Some of the biggest trends include:
- Artificial Intelligence (AI) and Machine Learning (ML) – Companies are increasingly leveraging AI and ML technologies to analyze vast amounts of data, automate processes, and make data-driven decisions. In addition, Elizabeth Wallace, VP of Product at TrackAbout states
there is so much activity going on in the advanced data analytics space, and clearly understanding the capabilities and use cases of AI that best aid your business should be a key priority. By leveraging real-time analytics, businesses can better pinpoint customer needs, boost operational efficiency, and make informed decisions. These tools enable businesses to stay ahead of the curve, driving growth and innovation in a competitive landscape.
- Real-Time Analytics – The ability to analyze data in real-time is becoming increasingly important in various industries, enabling organizations to gain insights instantaneously, leading to faster decision-making and improved operational efficiency.
- Unstructured Data Analysis – A significant portion of the data available today is unstructured, including text documents, social media posts, images, and videos. Organizations recognize the value hidden within unstructured data and Natural Language Processing (NLP) and Computer Vision are solutions developed specifically for analyzing textual and visual data.
- Data Visualization – Communicating complex data insights in a visually appealing and easily understandable manner are vital components of effective data analytics. Tools such as Tableau, Power BI, etc., are widely used with many solutions being added regularly.
- Data Privacy and Security – As the amount of data to be analyzed continues to grow, there are increasing concerns around data privacy and security. The larger the data set, the richer a target it becomes for attackers. Consequently, as data breaches become more prevalent, organizations are increasingly prioritizing data governance and compliance solutions.
In addition to the above, other trends include the growing use of prescriptive analytics, integrating augmented reality (AR) and virtual reality (VR) technologies to enhance data visualization, increasing importance of metadata, and so much more.
“Every organization is constantly generating data,” states Eric Wise, Vice-President of Industrial Solutions at Otodata, ”but the key is digitizing and formatting the data in a way that can be processed by analytics platforms.”
Back in 2011, Peter Sondergaard, the former Head of Gartner Research was quoted as saying, “Information (data) is the oil of the 21st century, and analytics is the combustion engine.” Based on what’s occurring (today) across industries, it appears that his quote is quickly becoming reality…
Industrial Gases
Within our industry, these trends are also being observed and consolidated into specific use cases to enhance several business processes including:
- Liquid/Bulk (LB) Logistics Scheduling – this is probably the first use case in the industry initiated over 20 years ago. Initially it utilized wired Telemetry units to capture LB tank consumption as inputs to their logistics models to forecast deliveries. This area has evolved leveraging digital/IoT inputs across the entire LB supply chain from production/distribution asset monitoring, wireless telemetry units, traffic/road information, weather conditions, etc., and capturing all the data into ML forecasting models greatly enhancing distribution efficiency. This approach has also been extended to the Packaged Gases Logistics Scheduling process as well as Eric Wise of Otodata states.
“The combination of accurate, read ready sensors and low-cost wireless monitors on smaller liquid assets is an emerging area of interest,” states Eric. “These solutions, such as Otodata’s dedicated device for wirelessly monitoring capacitance probes, have allowed the benefits of telemetry to reach smaller, mobile assets for the first time.”
- Inventory Management – asset tracking and management of returnable cylinders through their entire cycle has been an ongoing challenge for both producers and customers. However, industry solutions now exist that gather cylinder related data as it progresses from fill plant, shipment to customer, in storage/use at the customer site, pick up from customer, and return to the fill plant, enabling users to get both an inventory “snapshot” and recommendations to better optimize inventory levels.
“In partnering with both major industry players and smaller independents, focusing on identifying process inconsistencies—like gaps in cylinder registration and missing information—and combining this insight with advanced analytics is paramount,” says Elizabeth Wallace, VP of Product at TrackAbout. “This approach significantly increases inventory management accuracy, which provides a clearer inventory snapshot and helps optimize asset utilization throughout the entire supply chain.”
- Gas Detection Monitoring – facilities are increasingly leveraging cloud-based software integrated into their gas detection safety systems to enhance monitoring, reporting, and overall safety management. By utilizing cloud-based platforms, these systems allow for real-time data collection and analysis, enabling facility managers to access critical information from anywhere at any time. Additionally, cloud-based solutions often come with advanced analytics capabilities, which provide deeper insights into gas concentration trends, predictive maintenance needs, and compliance requirements.
“In today’s fast-evolving landscape, the combination of advanced data analytics with cloud-based solutions is not just an innovation – it’s a necessity,” states Morgan Morris, VP of Marketing at CO2Meter. “These technologies empower facilities to safeguard their establishment by monitoring gas levels from anywhere in real-time, anticipating potential issues and responding with the utmost precision. By tying these capabilities into your gas detection systems, you enhance safety, streamline compliance, and ultimately protect both your people, your customers, and your bottom line.”
- Customer Service – this space was an early focus area as well, as each of the Tier one industrial gas players (and many regional companies) proceeded to implement ERP systems. These implementations were traditional projects automating many of the internal business processes and enabling organizations to centralize/consolidate work activities. However, since the pandemic we’ve seen an acceleration across all levels of the industry in the use/integration of digital solutions and web portals with their ERPs, for a variety of use cases. They are capturing, consolidating and analyzing that data to better understand customer patterns, preferences and to better understand, anticipate and predict customer needs.
- Sales & Marketing – Some companies in our industry still tend to manage their sales forces based on intuition and experience, rather than data — an issue that is deeply rooted in commercial organizations across industries. The good news is that many in our industry have transitioned to leveraging advanced data analytics embedded in platforms such as Salesforce. These platforms focus on achieving commercial success by driving profitable sales and optimizing an organization’s sales and marketing resources, utilizing the data you already have or identifying what data you need to start capturing to enhance performance.
Other areas within companies are also leveraging advanced data analytics such as Operations and Manufacturing areas thanks to Industry 4.0 principles. They are leveraging digital technologies (i.e., smart devices, predictive analytics, etc.), capturing more data to operate and optimize on a near real-time basis, resulting in greater efficiencies and delivering an improved customer experience. Functional areas such as HR are also using advanced data analytics to consolidate and analyze data sources both internal and external to their companies to help decrease attrition rates and increase recruiting efficiency. The Procurement function is using advanced data analytics to improve operational efficiency across the entire sourcing and supplier management lifecycle (i.e., spend analytics).
Best Practices
If your company desires to better leverage advanced data analytics in your business, it is crucial to follow proven best practices that enhance the quality of analysis and drive better outcomes. Below are a few recommendations on where to start:
- Strategy – Every “new” business transformation starts with defining how it would support your business strategy. You need to answer some foundational questions such as “what does advanced data analytics success look like for us, and how does it fit into our overall company strategy?” This step may require some education on your part. You don’t need to know the execution level specifics for analytics, but you do need to know the business aspects such as recognizing organizational data gaps, areas of opportunity and measuring success. In addition, it helps to understand (at a high-level) what the best companies are doing in this space, current/future advanced data analytic trends and potential barriers to success. Then establish well-defined objectives that enable your organization to measure the success of their data analytics initiatives. By setting clear, quantifiable targets, it becomes easier to assess the impact of data-driven strategies, and helps in evaluating the return on investment and the effectiveness of data analytics projects. I think for some companies this is a difficult step since they may not have leveraged this technology to be successful in the past. So, there may be a need for external resources to assist and provide a fresh perspective during this step.
- Data Sources & Quality – a company can have the latest data analysis tools, but if their data is not in good shape, they will have a big problem. The accuracy and reliability of data directly impacts the quality of insights and decisions derived from analytics. Assessing your data sources and their integrity is a first step in understanding and defining if there are any gaps that exist and need to be addressed. This step may take the longest especially if data quality is a big issue, or the right data for the analytics is not being captured, or being captured in an unusable format. “One of the best practices I can recommend is just to stick with it,” states Eric from Otodata. “There will be a learning curve, and there likely will be points where digitizing seems like more trouble than it’s worth. If you stay the course though, you’ll set yourself up for benefits for a long time to come.” When data is clean and reliable, it can be more effectively integrated with existing datasets and analyzed over time, enabling organizations to identify trends, patterns, and changes.
- Investment – once the data source/quality gaps are fully addressed, the next step is to invest in both advanced analytics technologies to enable you to start your analytics journey, as well as in the execution resources who will be using and creating the analytics. Some of this may/will occur in parallel during the strategy and data source steps, especially in the identification and the upskilling of internal resources or recruitment of external data-skilled resources. These identified resources can assist with the research/identification of the tools to get started on the journey.
- Launch – After successfully testing/piloting a few key advanced data analytic solutions with a small group (i.e., a few salesreps utilizing and providing feedback on sales forecasting tools), the next step is preparing for the launch to the broader organization. Firstly, ensure that you have a plan for the users of the toolset. Why would they want to use the new system? What is in it for them? How can you leverage testimonials from your pilot users to help along the remaining users more easily? Then engage leadership beyond the sponsor(s) to support their utilization of the solutions and their output. There’s nothing worse than when managers and/or senior leaders discount the insights or output gleaned from the new solutions. And finally, as you prepare to launch, be careful not to move faster than your ability to build capabilities…it is a fine line. Start small (quick wins) and scale gradually demonstrating near flawless execution, rather than the big bang approach.
- Embed – Potentially the most difficult step is to keep improving the platform with regular features, content and functionality updates delivered on a regular basis. This level of support takes significant effort and ongoing engagement with your users. If a solution supports a customer service team, then regular engagement to understand areas for improvement for that team is a necessity. This regular engagement is with both the tool-users and those resources that execute on the insights and analysis received. This is the beginning of building a data-driven culture, one that relies more and more on the data and not solely on intuition. Consider some level of rewards and recognition of those users and teams that have demonstrated both proficiency in the use of the tools but, they also achieved the desired business targets with the assistance of the tools.
It’s a safe assumption that your data is hiding gold nuggets,” states Elizabeth from Trackabout. “All corporate data systems hold insights, curiosities, and patterns that you might not be able to conceive of today, but which could be unearthed and unlocked by someone with the right skillset and tools. The goal is to uncover new key insights that will change your business’s behavior in a positive direction.
Next Steps
Leveraging advanced data analytics continues to grow and expand across many industries and within most functional areas of companies. In many respects, it is fast becoming “table stakes” especially if one desires to remain competitive with their industry peers. This is an area that you can’t afford to be on the sideline and watch. It requires engagement, building your knowledge, doing some benchmarking, and then taking the plunge. Some areas may be a bit uncomfortable such as where/how to use AI, but it is always better to make that decision for yourself proactively, than having to respond to the market that has already done it and is multiple steps ahead of you.
