A Team TrendMiner Guest Post
7 Advanced Analytics Trends to Power Your Way Through 2021
In another guest blog, TrendMiner team member Nick Van Damme, explores 7 advanced analytics trends we will see this year. Nick is responsible for translating TrendMiner vision into its product roadmap. He is an Experienced Product/Project Manager with a MSc. from Katholieke Universiteit Leuven with a focus in Mathematical Engineering. He is passionate about using Advanced Analytics, Big Data, and Manufacturing Intelligence to support Digital Transformation, Industry 4.0, and IoT initiatives.
The growing impact of data and analytics on improving all aspects of business operations is unstoppable. As Gartner points out, companies must increase their resilience to volatile markets; it’s about doing the important things significantly better than before. However, data and analytics alone are not enough; both must be embedded into each level of the organization in order to make data driven decisions at scale.
Organizations have spent the past years focusing on efficiency, which meant when hit with a major disruption like COVID-19, many business processes were too brittle to quickly adapt and simply broke.
During the rebuilding process, leaders must design an architecture that:
- Enables better access to information
- Augments that information with new insights
- Increases the autonomy for decisions
Today, data is gathered within each business process, in all formats, and sometimes for unknown purposes. With the growing collection of data, cost reduction for storage, and increasing power of software tools and platforms, we will see new emerging trends in advanced analytics for the industrial manufacturing process industries. Let’s take a look at what’s to come so you and your company can jump start the year and power your way through 2021.
Trend #1: More people start using easy-to-use analytics tools
Although analytics maturity still varies greatly across industries and even within departments, modern advanced analytics tools are being rapidly adopted. And as a consequence, there is actually a lot of confusion about what advanced analytics actually is, who can use it, and for what purpose.
Analytics is used for many different application areas: analyzing buyer behavior, supply chain management, business process optimization, and materials. Therefore, it is important to understand what data one wants to analyze and for what purpose, to ensure the right data is gathered and the relevant people have the right tool to improve efficiency in their daily job.
For production process optimization, time-series data is the most commonly available data type. And often, with years of gathered data, it can still be the least utilized source for improving operational excellence and business resilience. By empowering engineers with easy to use advanced analytics, they all can directly contribute to business objectives and accelerate needed changes due to changing market circumstances, even when working from home.
Trend #2: Connecting data silos, historians, and data lakes
With the rise of IoT and cloud computing, we see more data storage in so-called data lakes. Leveraging data lakes requires understanding of the data that is being stored in the lake in order to get meaningful insights from it. If the data formats are clear, applications like TrendMiner can use the time-series data as well as the contextual data for analyzing operational performance. The TrendMiner platform is open to integrate to any kind of solution, whether it is a data lake, on-premises historians, or business applications that might be in data silos. Quick and easy integration will help global collaboration, knowledge sharing, and faster adoption of advanced analytics within the organization.
Trend #3: From integrations to living connections
Integrations between applications to improve business processes cross-departmental is not new. For years, integrations were made through APIs or through scheduled data exchange. Today, integrating applications on data level is emerging, where the data is represented meaningfully for supporting data insights. In case of production processes, the basis is sensor generated time-series data, which can be illuminated through data from other business applications such as your maintenance management system, OEE, or laboratory information management system. This is what we call contextual data.
The emerging trend is to create living connections between systems. Based on process data analysis and contextual data analysis, the bi-directional application integrations will be enriched with the analytics results for more powerful actionable insights to run your production processes.
Trend #4: From advanced analytics to augmented analytics
This next level of analytics builds upon time-series data analysis and contextual data analysis, combining it to get new insights for production process improvement.
For years, TrendMiner has already had some augmentation functionality embedded: the recommendation engine that suggests interesting measurements to support root-cause analysis activities. This augmented analytics will help process experts analyze the data even quicker and on a larger scale, all while using the expertise gathered from the data in other parts of the business.
Augmented analytics is the use of enabling technologies such as machine learning (ML) and artificial intelligence (AI) to assist with insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms.
Trend #5: Increasing collaboration through data analysis
With more and more operational experts using advanced analytics tools such as TrendMiner, the expertise of how to use the analytics capabilities increases. For onboarding new users to data analysis and providing more insights in the production processes, the self-service analytics platform will become the first go-to source of knowledge. Gained knowledge and experience is directly accessible, and new issues can be analyzed by experts in plants anywhere in the world.
Trend #6: Merging worlds – process engineers meet data scientists
Over the recent years, two conflicting analytics approaches emerged: the belief that the data scientist should be in the lead on one hand and the belief that the business user should be in the driver seat on the other hand (often referred to as self-service analytics). Instead of talking about one winning approach, the trend is a combined complementary approach.
The data science and self-service approach differ greatly:
- Classical data science depends on bringing process / asset know-how to the data scientist. The data science work is typically done with interdisciplinary central teams because for data scientists, the physical and chemical reality of a production process is often a black box, is unknown.
- Self-service analytics aims at packaging a subset of the data science modeling capabilities and bringing these to the subject matter expert as a robust set of features (no technical tuning parameters, no data science training needed). For a typical process engineer, traditional machine learning, AI, and data modeling are a black box.
Both approaches are complementary rather than conflicting. They target different personas, make different assumptions, and have their own strengths. So the reality is that you need to invest in both areas to win with analytics.
Trend #7: From control room to boardroom – and back
Using advanced analytics to get actionable insights for production performance is growing from the level of operational experts to any level in the organization. The rapidly developing self-service analytics platforms allow creation of production dashboards at any level of the organization. Alignment of KPIs on each level improves vertical collaboration and focuses operations to meet the organizational goals.
Jump Start Your Organization into 2021 with Advanced Analytics
Many companies have made their first strives on the path of digitalization, using advanced analytics to improve operational performance. Their experiences and expertise allow them to make the next steps on their journeys in line with the 7 trends mentioned above.
If the journey still has to start for you and your company, any time-series data repository will do. You don’t have to be a data scientist to analyze the data. With the help of our Customer Success team and advanced analytics, you can jump start your organization into 2021 and accelerate into the future.