While the world has romanticized the concept of intuition from the days of Albert Einstein to the time of Steve Jobs, it’s actually only through data that companies can validate, know, and measure information. In fact, according to a PwC survey, data-driven organizations “can outperform their [less data-centric] peers by 5% in productivity and 6% in profitability.”
With over 2.5 quintillion bytes of data being generated every day, it’s undeniable that there’s a wealth of information available to improve our decision-making process. However, having a lot of data is one thing, and making sense of it is another. What’s even more challenging? Making sense of it in real-time.
Process Mining and Digital Transformation
In a landscape where digital transformation has become more than just a buzzword, process mining has become the key to discovering, monitoring, and improving processes. Process mining includes analyzing data in event logs and building up a graphic representation of workflows to help any organization find problems or issues, locate bottlenecks, highlight repetitive procedures, and suggest appropriate improvements.
UiPath, the world’s leading RPA software company, defines Process Mining as “a technique to analyze and track processes.” It allows businesses to determine which process requires transformation, what areas to prioritize, what gaps to fill, and how to fill them. It involves processing large amounts of data to get a full picture of the business.
Process mining provides C-level executives with deep insights into the business — providing visibility into what is happening — not what your gut feel tells you. Take note of the present tense, and we’ll talk more about that later.
If well-executed, process mining enables businesses to make informed decisions by running through scenarios of how a change in the process will impact the business — how it may disrupt the business, what areas of the process may be affected, etc. This allows businesses to monitor key performance indicators, do predictive analyses and scenario testing, visualize processes and business outcomes, and standardize processes and procedures.
Now, going back to the present tense — “providing visibility into what is happening” — this means your company should make sense of data in real-time to drive superior outcomes. If it’s not real-time, even if the data are factual, then this creates a huge margin of error.
Challenges of Non-real-time Data
Storing huge volumes of data and analyzing them in real-time is quite a different ball game. Real-time data processing entails scalability, extensibility, predictability, resiliency against stream imperfections, and fault tolerance. All of these require processing data at high speed.
There are many factors to consider when analyzing the data once it enters the system. Where did the data come from? What’s its source? What type of data is it? Data speed may vary from milliseconds to minutes, and the architecture for analyzing the data should be built dynamically. As data volumes may spike up or go down anytime, the architecture must be able to handle the spike of data volume and be capable of scaling up or scaling down as needed.
When talking about the source of data, it must be noted that there are offline data as well — which require offline analytics. Enterprises that want to implement real-time analytics frequently overlook offline analytics, but it is also significant to achieve useful insights. For example, learning patterns with machine learning is a time-consuming process most suitable for offline processing.
Another challenge when implementing real-time data analytics is data quality. The goal of real-time processing is to enable quick decisions to be made based on current data. This means every millisecond counts — data must be entered correctly and timely. If not, then a domino effect occurs, and wrong or outdated information may spread across the organization. According to a Gartner survey, 75 percent of organizations have been negatively affected by incorrect information in terms of finances, leading to additional costs only to rectify the data.
Accelerating Process Mining and Innovation
To fast-track innovation and ensure you achieve your transformation goals, you need to optimize Process Mining using a comprehensive suite of Data Science solutions. dotSolved empowers businesses from various industries to accelerate Process Mining and innovation by providing them with a complete suite of Data Science solutions that help streamline the entire data-to-outcome process. The suite includes:
- Data Ingestion, Migration, and Validation
- Operational Data Lake
- Real-Time Processing And Analytics
- BI/DW, ML Modeling, and Business Intelligence Hybrids
- Data Visualization & Discovery
dotSolved’s real-time processing and analytics enable continuous innovation by streamlining insights generation. It automates management and monitoring of patterns, trends, and anomalies and optimizes prediction, recommendations, and explorations, empowering the business to constantly adapt to changes as they happen.
To learn more about how dotSolved’s Data Science solutions accelerate the data-to-outcome process, feel free to drop us a line..
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Is your company about to implement process mining to up your data game? Learn about the challenges that you might encounter when trying to make sense of data in real-time and how your company can overcome these challenges through tried and tested intelligent solutions. Check out our latest blog.
