Four Key Components of Any Digital Transformation



Digital transformation is the integration of new technologies that promote a complete assessment of a company's products, services, processes, and strategies on both a cultural and a business level. A digital transformation is also the key that unlocks data and transforms the way it’s used across an organization. The goal of a digital transformation project is to enable scalability while increasing a business’ competitiveness across multiple verticals.


Here are four key components of any digital transformation.


1. Cloud Optimization


With today's IT departments handling highly complex systems that are filled with distributed architectures, microservices, Big Data applications, containers, and real-time data streaming, cloud computing isn't a nice-to-have, it is a must-have. Without the cloud, most businesses wouldn't be able to keep up with all the data flowing through their IT, operations, and social media systems.


The cloud is the backbone of any digital transformation initiative. The only question CTOs face regarding the cloud is whether to go private, public, or create a hybrid of the two. At Grind Analytics, we have created a standardized data model to simplify reporting across all your data systems. Our game-changing proprietary software MODM, automates the creation and management of your data model.

The cloud isn’t just about storage. Platforms like Microsoft's Azure Stream Analytics, AWS Analytics for Amazon Web Services, and IBM Cognos Analytics can process complex analytics while providing real-time data in a way that is easy to manage even when used in extremely complicated applications, like AI and machine learning. Even business intelligence applications like Domo, Tableau and our own BI platform MODM offer solutions in the cloud that can provide analytics on a scale above and beyond on-prem solutions.


2. Data Integration and Optimization


Data is the foundation of all digital transformations and the first thing any organization needs to do to ensure the success of their implementation is to make their data trustworthy. "Junk in, junk out" is the analyst's dictum, and it holds true for data usage throughout an entire organization. Data that isn't trustworthy should not be used. When a company's data is trusted and administered properly, it can be utilized reliably and dependably throughout the organization.


3. Artificial Intelligence


Artificial intelligence (AI) isn't the simplest technology to implement but can have the highest return on investment. It can be broken down into the following five use cases:


1. Text - sentiment analysis, augmented search, and threat detection

2. Sound - voice recognition, voice search, and sentiment analysis

3. Image - image search, facial recognition, and machine vision

4. Video - motion detection, including threat detection

5. Time series - recommendation engines, log analysis, multiple IoT sensor uses


A complete overview of the capabilities of AI is beyond the scope of this article, but suffice it to say - they’re immense. As an analytics company we’ll focus here on what we know best - how to use AI and machine learning (ML) to gain deeper insights into your data. In the simplest terms, AI involves using technology to complete tasks that previously were reserved for humans. But advances in AI go well beyond what humans could ever do - processing millions of pieces of information in seconds - and doing so 24/7/365. So when it comes to analyzing big data - AI has got you beat.


But while many think that AI and machine learning are fancy buzzwords reserved for those with the deepest pockets, what’s important to understand is that today, anyone can access the power of artificial intelligence. Out-of-the-box applications like the Microsoft Azure AI Platform makes artificial intelligence available to businesses of all sizes and budgets. And additional customization can be leveraged to provide deeper and more reliable insights that can streamline operations, increase employee productivity, and deliver significant ROIs at scale.

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4. Automation


As IT systems have increased in complexity, so have the underlying process systems that support them. What started with Business Process Management (BPM) systems that aimed to improve a company's business processes by analyzing and optimizing them has evolved into Artificial Intelligence and operations (AIOps), which added AI and machine learning to the process.

AIOps applications utilize considerably more data types than BPM or ITOA systems. They analyze a system's data, rapidly learning about a company's day-to-day operation. It proactively fixes operational issues, while also providing end-to-end visibility into a company's applications. It reduces system noise and accelerates root cause analysis of problems that arise in a business's daily operation. Its overriding goal is to proactively self-heal an IT system while breaking down data and system silos.

The benefits of automation include:

  • Increased productivity

  • End-to-end visibility into company applications and infrastructure

  • Improved performance monitoring

  • Noise reduction

  • Reduced operating and labor costs

  • Increasing company-wide employee collaboration

  • Breakdown of data silos

  • Simplified root cause analysis

  • Seamless customer experience

  • Predictive and proactive IT self-healing


Besides AIOps, Robotic Process Automation (RPA) tools can also help a company reduce its labor needs as well as increase productivity. RPA tools let a user configure one or more scripts to activate specific keyboard functionality, resulting in software that mimics selected tasks, including data collection, data manipulation, response triggers, analytical model building, as well as a whole host of other IT processes. RPA tools can replace humans in an 'outside-in'' way, turning a computer into a functioning human who responds to a set of repetitive instructions.


Conclusion

Cloud data warehousing and cloud computing provide the backbone upon which analytics can harness the power of data. Analytics built atop reliable data helps with productivity and optimization efforts, and allows a business to remove inefficiencies, reduce vulnerabilities, and prepare for scale. Artificial intelligence breathes deeper insights than ever thought possible.


Early digital transformation adopters are starting to see the fruits of their labor, while those ignoring this digital revolution risk falling short of their goals, and being left behind. If you are interested in taking advantage of the early digital transformation, contact Grind Analytics. We offer proprietary products and professional development services that help companies optimize how they operate and make decisions in real-time.