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Five Data Science Solutions You Need To Accelerate The Data To Outcome Process

Forward-thinking businesses are constantly innovating their processes to stay competitive. Online giants like Amazon and Google, digitally native niche players like Airbnb, Expedia and Netflix, and established industry leaders such as Walmart and Tesco and thousands of others have taken established business models and integrated technology in ways that drive innovation and create new types of customer experiences that are focused on convenience, value and efficiency. Seizing these disruptive opportunities requires developing innovation and digital capabilities to pivot to new sources of growth.

In an episode of McKinsey’s Inside the Strategy Room podcast, Venkat Atluri discussed the shift from industry sectors to customer-focused ecosystems. An ecosystem is a community of interconnected digital and physical businesses that come together to create value for customers, anchored on a digital or physical platform. Start-ups are entering and establishing themselves in various industries very quickly and accelerating the pace of transformation. The ecosystem revolution is driving economic growth, serving customers the way they want to be served, and presenting opportunities to create more value and drive innovation. However, it is also concentrating value creation in the hands of fewer and fewer players, thereby potentially posing barriers to innovation.

This HBR article offers a simplifying framework for digital transformation based on four pillars: IT uplift, digitizing operations, digital marketing, and digital businesses. Optimizing, simplifying, and rationalizing current business processes is one of the four pillars. A company could begin its digital transformation journey by digitizing processes and then rearchitect processes to unlock transformational possibilities.

Innovation is key to success, but it can also be a source of frustration among businesses. Understanding where and how to start the innovation project requires analyzing all facets of the business, and this is where Process Mining comes in.

You Need To Accelerate Process Mining To Speed Innovation

UiPath, the 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.

Manual Process Mining techniques, such as workshops and interviews, may no longer be effective in today’s digital landscape because they are heavily manual, cumbersome, and time-consuming. Manual processing of data collected from workshops and interviews can further slow down the data-to-outcome process. The result of the analysis can also be incomprehensive because the data collected is limited. This can negatively impact the outcome of the innovation project.

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.

The Five Key Data Science Solutions You Need

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:

1. Data Ingestion, Migration, and Validation

Advanced Process Mining techniques require large amounts of data. Where to get data is no longer a problem in that virtually all “things” can generate data. The main problem data scientists face is how to easily capture and validate petabytes to terabytes of data of different velocities and varieties.

As raw data moves from one source to another, it can be prone to integrity and security issues, which data scientists also need to mitigate and prevent to ensure high-quality outcomes. This process alone can be time-consuming. But with dotSolved’s data ingestion, migration, and validation solution, the process can be automated and standardized — allowing data scientists to harness sufficient amounts of data and eliminate errors that can influence outcomes.

2. Operational Data Lake

Data scientists need constant access to the latest development in operations, and it is only possible if they can query data anytime.

Operational Data Lakes enables data scientists to query and process data in an automated fashion and ensure its integrity and security are intact as it moves in and out of databases. Operational Data Lakes also allows for storing and processing both relational and non-relational data — such as data generated from IoT devices, social media, mobile applications — empowering data scientists to generate more comprehensive insights. Operational Data Lakes also makes it simple to understand what data is in the storage, data marts, or warehouses through electronic cataloging and indexing of data.

3. Real-Time Processing And Analytics

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.

4. BI/DW, ML Modeling, and Business Intelligence Hybrids

dotSolved leverages algorithmic intelligence to enable smarter Process Mining and accelerate outcomes. It takes advantage of Machine Learning to enable Intelligent Automation, which, unlike traditional automation, consumes real-time or stream data to learn new patterns and trends. It allows data scientists to automate processes such as data modeling and focus on more crucial activities and/or tasks that require a higher level of human intelligence. dotSolved’s solution also enables AI and human intelligence to work harmoniously together using a BI hybrid platform.

5. Data Visualization & Discovery

dotSolved’s Data Visualization and Discovery tool cater to the unique needs of both technical and non-technical users. It makes it easier for decision-makers to understand what data says about their current processes. It uses visual graphics and turns insights into stories to help decision-makers see the full context and pinpoint the right innovation strategy to use. It graphically presents root causes and what-if scenarios in a way that even business or non-technical users can understand.

To learn more about how dotSolved’s Data Science solutions accelerate the data-to-outcome process, feel free to drop us a line.