In modern companies, data is the backbone of business operations, guiding decisions and strategies. They heavily influence processes, decisions and strategies. The transformational power of well-established data collection, processing, and visualization is well recognized, although barely solved in many companies. However, I don't want to write about data crawling, ETL (extract, transform, load), or dashboards. Instead, this article focuses on the consequences of insufficient (data) transparency, their causes, and a potential solution: our "Automation Thinking" approach.
Inadequate Transparency and Its Consequences
Fundamentally, it should be clear that transparency in handling data is crucial for establishing an appropriate corporate culture on processing of digital information. If that openness is missing, not only the ability to collect and utilize data is limited, but also the confidence and trust in the data is compromised. So, creating the required transparency builds credibility and cultivates a culture of accountability (next to regulatory compliance). But, let's stay further on the dark side of the coin and look at the consequences of insufficient transparency:
Eroding Trust
Companies lacking transparency in data processing risk undermining trust in the data. In many cases, this leads to a significant reduction in data handling and sharing (in the sense of "Never trust a statistic you didn't falsify yourself") because stakeholders do not believe in the accuracy of the data. This can lead to competitive disadvantages and, in the worst case, damage to reputation.
Navigating Legal Nightmares
Given strict data protection regulations like GDPR (General Data Protection Regulation) or CCPA (California Consumer Privacy Act), non-disclosure of data can result in severe legal penalties and liability risks. The reason for non-disclosure is irrelevant in this context. It is all the more frustrating to face potential fines due to insufficient transparency in data processing.
Inviting Security Breaches
Hidden vulnerabilities in the data ecosystem can be potential entry points for cybercriminals, possibly leading to data breaches and the exposure of sensitive information. There have been numerous publicized examples in recent years where sensitive customer data (such as contact information or banking details) unintentionally became public.
Distorted Decision-Making
Inaccurate (difficult to interpret or unclear datasets) or missing data can, simply put, lead to poor decision-making. For instance, if global sales or production figures are not consistent or readily available, this could lead to misinterpretation. The consequences can be far-reaching, from slower growth or missed innovations to jeopardizing business operations.
So, Why is Data Processed Intransparently Anyways?
Nobody wants opaque and therefore poor data processing. So, what are the forces that work against this goal? I've attempted to summarize some of the most common issues, but let's be honest, there are various reasons for insufficient transparency:
Companies often have highly complex system landscapes and navigate data ecosystems. As a result, processes often go undocumented, individual data points are missing (and perhaps even intentionally, see "Loss of Trust" above), or legacy systems make integration difficult.
Additionally, there's the matter of governance. Nowadays, it's common practice in many companies. However, what matters is how it's implemented. The looser data governance is perceived, the more likely ambiguous transparency arises in data processing, including inconsistent data structures or unclear process flows.
Another aspect, especially in Europe, is data privacy concerns. Regulations raise concerns about user privacy (both internal and external). This can, in turn, lead to uncertainty in data processing, for example, by withholding certain details or entire datasets.
Furthermore, nearly every company, regardless of its size, has limited resources for data collection, processing, and preparation. Therefore, one of the main reasons for a lack of transparency is simply the insufficient provision of resources and the absence of suitable experts (e.g., data scientists, data analysts).
Last but not least, new technological possibilities often develop so quickly that they are poorly understood or introduced late. The best example of this is the still very common use of manually maintained spreadsheets instead of individual, possibly AI-assisted, algorithms. The result: low process reliability and thus high opacity as well as high manual efforts.
How to Create More Transparent Data Processing
So far, so bad. Time to focus on ideas that increase transparency in data processing. In our experience, company-wide integrated automation is an efficient way to boost data transparency. Why? Here are a few best practices we from our "Automation Thinking" workshop that can help to achieve more transparency:
- Streamlined Data Governance: Leverage process automation tools to establish and enforce consistent data governance policies, ensuring that data flows transparently across the organization.
- Automated Transparency Reports: Generate and share routine transparency reports automatically, detailing data processing procedures, security measures, and compliance protocols.
- User-Centric Notifications: Utilize automation to keep users informed about data collection, processing, and usage, empowering them with knowledge and fostering trust.
- Integrated Technologies: Invest in automation solutions that seamlessly integrate with data processing tools, offering transparent data lineage and real-time audit trails.
- Collaborative Synergy: Automate cross-functional collaboration between departments like IT, legal and compliance, ensuring holistic transparency in data processing practices.
Now What? Ask The Right Questions
Poor knowledge management and limited process know-how present significant challenges to companies striving for efficiency and innovation. The "Automation Thinking" workshop can offer an approach to scrutinize the process landscape and identify potential solutions to address these challenges.
Here is an overview of the technologies in terms of their level of automation and their placement in the value chain:
By automating knowledge distribution, centralizing knowledge repositories, or enhancing collaboration, companies can close existing knowledge gaps and effectively support their employees. We are already in the age of increasing automation. It is evident that companies focusing on improved knowledge management through automation can continuously enhance their competitiveness by optimizing operations, reducing costs, and improving decision-making.
Time to ask some hard questions, like:
- Which automation approach do you want to take?
- What processes to look at?
- Which knowledge sources should be centralized?
- Which processes are appropriate for automation?
- Which departments can support?
Our Automation Thinking Workshop is an exchange on automation best practices in collaboration with ROI-EFESO to provide precise answers to these questions. We offer tailored consultation for company-wide automation. Combined with our extensive expertise in technology and design thinking, we enable a holistic perspective. Motius has years of experience in the fields of data management and analysis, robotics, and drones, as well as modern AI tools like chatbots, LLMs (Large Language Models such as ChatGPT), and knowledge graphs. In our Automation Thinking Workshop, you can access this expertise.
