Previously, we explained why digital transformation is key to survive and introduced data-driven companies.
Now, let’s focus on the individual steps of becoming a data-driven company and learn about the importance of Big Data management and AI-based data analysis. Don’t tap into the same mistakes others have made already. Instead, let our systematic approach guide your digital transformation to success.
Table of Contents
- Data-driven companies are well advanced in their digital transformation and base strategic business decisions on Big Data.
- In order to become successfully data-driven, we recommend a strategic and transparent approach that considers both internal stakeholders and external partners, to prevent failure.
- The collection of data is only the first step along the value chain. As data tends to be all over the place, streamlined data management is required to handle data accumulation, processing, and machine-learning-based analysis.
- Making data accessible to the entire organization is one of the cornerstones of data democratization.
What Is a Data-Driven Company?
One of the major objectives of digital transformation is to base strategic business decisions on data analysis and interpretation. A data-driven approach enables organizations to utilize data with the goal of improving their processes, product portfolio and quality, and customer service. As digital technologies are drastically changing industry after industry, the pressure for established companies is intensifying. The emergence of startups disrupting traditional markets only adds to this pressure. Adaption to this new era of Big Data and AI has become an urgent necessity to survive. True digital transformation has a profound impact on a company’s core business. While individual transformation and innovation activities can be grouped into four different dimensions (processes, business model, business domain, and the cultural dimension), it would be short-sighted and a great risk to solely focus on one of them. In order to become a data-driven company, a holistic approach is needed. In fact, there is no area or department that stays unaffected by the commitment to digital technologies, new skill sets, and operative routines. A study by McKinsey illustrates how difficult this process actually is: only 16% of respondents said that their organizations’ digital transformations have improved performance in the long-term. As with all large-scale change processes within organizations, digital transformation is a highly complex endeavor that requires a well-structured approach in order to be successful. Having guided many customers ourselves – ranging from the manufacturing sector to the telecommunications and agriculture industries – we have gained a broad understanding of the specific needs and structural challenges of large corporations. In this article, we will share our own roadmap to successfully initiate the digital transformation and become a data-driven company. It consists of six steps, starting at the core question “Why are we doing this?” and leading to the implementation of fully automated processes. We will also put a special emphasis on the two major company assets in this era: humans and data.
1. Define: Why Are We Doing This?
While the digital transformation process typically has to be initiated by the C-level management, its success strongly depends on the commitment and acceptance by all departments. Unfortunately, many companies fail to acknowledge that a digital transformation is a holistic approach. While each area of the business might have its own understanding and reasons to initially start out, their eventual alignment is key. When building a digital transformation strategy, first, answer the question “Why are we doing this?” – not “How are we doing this?”. It is necessary to focus on the underlying intent to become a truly data-driven company and to align the expectations towards the process. While enthusiasm for the topic is a great starting point, it needs to be followed by a structured approach and quantifiable objectives. That includes getting a clear understanding of the company’s current needs and challenges, strategic goals, market potential, and threats in the industry. Maintenance work in the energy or construction industry, for example, is an indispensable necessity. Corrosion on steel bridges, rusty or loose bolts on construction equipment, or defects on wind turbines are a big threat to the smooth operation and also a threat to public safety. Manual inspection is an enormous burden for companies as it requires highly skilled workers, it is costly and labor-intense, and often dangerous. So, the objectives behind the implementation of digital technology might be: to reduce labor, risks, and costs associated with maintenance work, and to increase efficiency and overall quality of service.
2. Align: Initiating a Cultural Change
Digital transformation also involves a profound cultural change. Once the corporate strategy and goals have been defined, leaders and corporate communication departments play an essential role in sharing this information. With humans at the heart of this process, they not only have to be aware of the fact that change is about to happen. In order to achieve broad acceptance and build a shared mindset, communication has to clearly address the naturally occurring uncertainty. In times of Big Data and AI, what’s the role and expectation towards the individual? Will digital technology replace them or rather make their work easier? The cultural transformation of an organization certainly is not a one-off undertaking. Unfortunately, many companies concentrate most of their attention on the implementation of the actual technologies, as this tends to be the primary cost factor. But just like the implementation of new technology is a complex undertaking that requires commitment and resources, even more so is the cultural dimension. Dedicated and transparent communication as well as advanced training to upskill existing employees need to be established as core activities of a data-driven company. Redesigned communication activities for creating a shared mindset have to manifest throughout the whole transformation process.
3. Team Up: Get Strategic Partners and Resources
Many organizations start out thinking “We can become a data-driven company on our own!” – and end up failing. In fact, most markets these days are so highly competitive that it is natural to consider starting the transformation process alone. However, while an established company is mighty in its daily business and within its core market, it might lack the in-depth knowledge on digital technology trends from other industries (e.g., IoT devices, image analysis tools or data management platforms), or lack the necessary talents skilled in such (e.g., data scientists). In order to jump-start the transformation process, collaborating with external partners has proven to be an effective approach. Partners like digital transformation/AI consulting firms or advisors allow companies to tap cross-industry expertise without worrying about sharing confidential information with competitors. They share a similar vision and have a broad understanding of structural challenges and potential risks. Instead of solely mapping out available infrastructure, skilled partners can demonstrate if and how technologies like IoT, Big Data, AI and machine learning, augmented reality or robotics, can actually solve the organization’s individual needs. Based on our example of burdensome maintenance work in the energy and construction industries, one part of the solution could be the utilization of drones in combination with sensors and AI image analysis technology. So, when it finally comes to the evaluation and implementation of technology, it is key to consult vendors that not only help identify the most economical and scalable tools on the market but also provide customization for maximum efficiency, and perhaps even support the staffing and training process. After all, investing in talent is as important as investing in cutting-edge technology and experienced partners.
4. Kick Off: Small But Strategic
When being overly enthusiastic and impatient, we tend to take higher risks. By staking everything on one card, we risk failing and losing valuable resources. Whether an organization is considering the implementation of IoT devices for data collection, an Edge AI device with image analysis technology, or a holistic data management system – first, a learning phase is required. Starting small allows room to fail – in a secluded space, that is. For a long time, we have been told and internalized that failure should be avoided at all costs. In the wake of startup and innovation culture, this mindset has dramatically changed. Failure is considered to be a necessary step in order to learn and to get closer to the goal. The goal, in this case, could be to deliver an unprecedented solution to solve the needs of the company, employees, or customers. When implementing new technology into an organization, start with a small but strategically profound initiative that can be measured. We call this phase a “proof-of-concept” (PoC). The PoC is an important stage to evaluate whether this new technology is actually suited to cut costs, optimize internal processes, or improve customer experience. At the PoC stage, machine learning models, for example, are trained with immediately available sample data or internally labeled data. So, before utilizing an actual drone for wind turbine or bridge inspection, the AI model could be trained using previously captured image data. The goal is to prove that an AI algorithm can be trained to address the issues defined (for example, defect detection in wind turbines or corrosion detection on steel bridges). Usually, this phase is characterized by trial and error. While the organization itself usually prepares the sample data, external partners like us are in charge of the PoC stage. The responsibility ranges from evaluating the data and developing AI models to providing data architects who design the entire infrastructure (e.g. a data platform) and data engineers who develop data connectors and parsers. Perhaps, the PoC shows that the intended solution does not deliver the expected outcome or that its running costs are too high or too complex to handle. At this stage, it is not uncommon to completely change the way the system operates. Only through iteration and refinement, the PoC stage will eventually be completed. For companies interested in evaluating what can be done with their data, we offer a free PoC for AI-based image analysis.
5. Apply: Making Data Democratization a Reality
At the beginning of this article, we have stressed the importance of data as a business asset. While a large corporation usually has access to a massive amount of data, it usually tends to be all over the place and in various formats. In order to utilize such Big Data, it needs to be collected, accumulated, processed, and analyzed. Naturally, implementing all of this from scratch can seem daunting at first. If the PoC of the previous step has been successfully completed, the project moves towards the more complex production stage. Instead of proving the theoretic feasibility, this phase is about proving that the solution can be integrated within the organization’s infrastructure and that it performs well under real-life conditions. Deriving business value from data begins at its collection. The implementation of an AI platform or data management tool (like the Avinton Data Platform), can introduce unprecedented smooth handling of data. By utilizing the latest yet proven open-source technology, such as Apache NiFi, the data flow between systems can be automated and managed, while keeping costs at a minimum. Automating the collection and accumulation of data makes a huge difference to the quality of your end-to-end flow. Real-time processing and analysis are the next steps for generating value from your data. Apache Spark is quickly emerging as the de facto standard for processing Big Data. Commercial giants and best-practice examples for data-driven companies (such as Spotify and Airbnb, as well as government agencies like the CIA) are already utilizing it for AI-based data analysis. Apache Zeppelin, being fully supported by Apache Spark by default, then provides interactive dashboards, eye-pleasing visualizations, and alert functions to stay up-to-date. In the era of AI and Big Data, “data democratization” is crucial to any data-driven business. This term describes the process of making your data accessible enterprise-wide to every member of your organization – that includes the average non-technical users of IT systems. Data democratization within a company has become a major aspect of business intelligence.
6. Scale Up: A Data-Driven Company Is Born
Once the newly implemented technologies have proven to perform well in their designated areas and in real-life scenarios, the next step is to automate and scale up these processes. After all, digital transformation is a long-term process that might start within one specific field of business, and gradually expand to more locations or other areas within the organization. As the digital transformation of an organization progresses, new opportunities will emerge. Again, these opportunities have to undergo a learning and training phase to evaluate feasibility. But as many valuable insights have already been gathered in previous stages, the scaling and automation step is usually more efficient. Existing AI models are re-trained with new data sets, and new use cases are introduced to the data platform. Looking at our example of maintenance work in the energy and construction industries once again, scaling could mean expanding the drone-based inspection of wind turbines from a single location to all company sites. It also could mean rolling out AI-based image analysis or Big Data analytics across other areas of the business. One quality is essential for organization-wide digital transformation: scalability. Unfortunately, many companies seem to underestimate the importance of all their tools in use (such as IoT devices, AI cameras, or data management platforms) being highly customizable and scalable. However, only by considering scalability from the get-go, it can be assured that these digital technologies can address future needs and increasing amounts of data at reasonable costs.
The digital transformation of businesses is not a choice, but a necessity to survive. It is to be considered a long-term process instead of a one-off implementation of one specific solution. IoT devices, AI data and image analysis, and the utilization of Big Data have become the main drivers for business intelligence. Data-driven companies gain vast insights like never before. They utilize data to make internal processes more efficient, detect new opportunities in the markets, increase the quality of their current products and services, and develop new innovations for their customers. However, all digital transformation endeavors should be initiated with a clear and strong focus on the predefined strategic goals. The consultation of external partners can provide companies a major jump-start. These partners might have broad cross-industry experience and are able to identify suitable solutions that are the most customizable, economical, and scalable.
Our Mission To Support Data-Driven Companies
At Avinton, we focus on equipping customers with the resources needed to become data-driven companies that utilize the great potential of AI, Big Data, and machine vision. It is our business goal to provide strategic knowledge, manpower, and customized tools to stay competitive and innovative in the future. Our headquarters are based in Yokohama, Japan, and our diverse team consists of highly skilled engineers with expertise in various domains of IT. Since all development is done in-house by such experts, we can be confident that delivered solutions are of the highest quality and reliability. At Avinton, we utilize the latest yet proven technologies from around the globe, and incorporate them in solutions that generate actual value for our customers and society (see our Corporate Social Responsibility goals). We don’t believe in a “one-size-fits-all” approach, especially when it comes to IoT devices, AI analytics, and Big Data management. Instead, we strive to tailor our solutions so that they not only meet but exceed the specific requirements defined by our customers. And since data is information assets that should be handled with great care, we ensure that appropriate security measures are implemented within solutions to ensure that data is protected from unauthorized access. We have been handling data-related projects in various domains, from manufacturing to pharmaceutics. Some of our customers are Nokia, Ericsson, Rakuten, and Toyota. Don’t let competitors take the lead. Now is the time to make use of the great potential lying within IoT, AI, and Big Data. And Avinton is here to empower you to become an innovative, data-driven company. Let’s get in touch and discuss the potential of Big Data and AI for your company’s digital transformation journey!
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