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Every thriving business has thousands upon millions of documented processes and workflows. However, even the best organizations face the challenge of shadow processes. In this article we go through what they are and how they can be avoided.
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Shadow processes are business functions and workflows that are typically not formally monitored or monitored by the organization. This may include manual processes, undocumented activities, or informal practices that are not formally recognized or recorded. Shadow processes can occur within the organization’s operations or spread to customer or supplier interactions. They can add complexity to existing systems, create inefficiencies, and pose security and compliance risks.
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Companies have long been trying to see what digital work looks like within the organization. Many different techniques have been used for that purpose: custom mapping processes, software mining, job mining, and other workflow building tools. However, part of the processes would still remain undiscovered.
There is always an interesting way of how the process should work. They are usually copied and classified, such as onboarding a new colleague with HR or the close monthly Accounting process. However, sometimes the “happy” method doesn’t work as it should in real life. Some deviations and adjustments modify the process from what it should be.
Shadow processes are hidden processes that need to be done but are not documented. They take the main process and add non-memory items and ad work. For example, the Accounts Payable department is involved in the processing of invoices. However, you end up doing a lot of small tasks that aren’t specified anywhere so that the work goes further. As a result, it ends up being more difficult than it should have been in the first place.
These hidden mechanisms can be very numerous. It’s those small corrections in the middle of a major process or small deviations from the work that we end up with reluctantly. Shadow processes are often small processes that people create over time without even realizing it. Unfortunately, it can easily add up in a large part of the time when mixing all the differences from the entire team.
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As one can imagine, these shadow processes steal a lot of time. They may seem necessary to work, but that’s because people are used to doing them for a long time. When rebuilding and going through the processes again, they prove unnecessary in most cases.
Shadow processes stop other projects, for example, digital transformation. They are usually undetectable because they cannot be easily identified and are not contained within IT systems. They accumulate and grow quickly, making them more dangerous for general work.
It is easy to ignore these shadow jobs because they are not the main parts of the job and cannot account for a large part of them. However, it’s these hidden little processes that steal time, block flow, and get overlooked.
Shadow processes can occur when well-intentioned people try to eliminate ineffective and ineffective work. Here are a few ways shadow processes can be eliminated from your organization.
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1. Identify and document expected processes. Take the time to document each process and its associated steps, including manual steps and informal practices that may be effective.
2. Automate processes whenever possible. Look for opportunities to replace manual processes with automated solutions, such as using software to streamline customer interactions or automate supplier management.
3. Establish more formal processes. Ensure that all processes are formally documented, communicated to stakeholders, and tracked and monitored.
4. Monitor and review procedures regularly. Review procedures regularly to ensure they are up to date and that any changes are documented.
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5. Invest in training and education. Ensure that all staff are familiar with the procedures and are trained to use them. This can help reduce the need for manual labor.
1. Interview key stakeholders. Talk to stakeholders in the organization to gain an understanding of the processes they use and the guidelines they have in place.
2. Analyzing process data. Look for patterns in the data that may indicate a process is taking place that is not formally monitored or monitored.
3. Follow up procedures. Take the time to observe the processes in place to identify any manual steps or informal practices.
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4. Ask the right questions. Ask questions that can help uncover any hidden processes, such as “how did you do that?” or “where did you get that information from?”.
5. Conduct process discovery audits. A process audit can help uncover any shadow processes, identify areas for improvement, and provide insight into how to streamline operations. Let’s look at the Mesh Mesh phenomenon, which proposes a socio-technical approach to decentralize data ownership across business domains.
The “rapid buy-in” that Data Mesh’s principles get from business executives shows that they’re experiencing serious pain for big businesses. More flexibility and better time to market in data plans is highly demanded.
In order to successfully implement Data Mesh at scale, radical changes in several pillars are required:
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For this reason, it is better to proceed step by step and make a series of improvements to the technical and process-oriented pillars mentioned above before jumping on the journey of Data Mesh adoption. In this way, everything will come more naturally, and it will be possible to focus more energy on the most important opposing elements: people and the organization.
For example, part of the Data Engineering practice is usually under the responsibility of a single department and does not require cross-functional integration to make the change.
On the other hand, general delivery processes are usually more labor-intensive and, without organizational change, difficult to change.
In the practice of data engineering, we can move several steps towards the following goals to improve the transmission of the data net:
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In general, there are four ways to start a data engineering practice, and they depend on the size and maturity of the data ecosystem in the company. The latter is what we think might be the best first step towards data mesh adoption:
The dominant approach is the most common when a company jumps into the world of data. Hiring a Chief Statistician (“done that”) is the first step. He will begin to create processes and choose the technology to create a data platform. The platform contains all business data, which is organized and processed according to all known requirements we have in data management.
In this case, data processing for analysis purposes occurs within a single technology platform managed by a single team, divided into different tasks (ingesting, modeling, administration, etc.). Adopting a single technology platform creates a well-integrated ecosystem of functionality and user experience. Typically the platform also includes many compliance and governance requirements.
The Data team manages the platform as well as the implementation of use cases. For those who define the principles and practices, it is straightforward to align the team because they have direct control over the people and the delivery process. This approach ensures a high level (at least in theory) of compliance and governance. However, it usually does not meet the time-to-market requirements of business stakeholders because the whole exercise turns into a bottleneck when adopted in a large organization. Business stakeholders (usually those paying for data plans) have no alternative but to adapt to the data team’s schedule and delivery concept.
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While “Centralized Data Engineering” is causing a lot of frustration for business users, we typically see what is known as “Shadow-IT” emerging, resulting in:
This action, at first, seems like an added value because, suddenly, business plans begin to be used in unbelievable times, and it also creates the illusion that “in the end, it is not difficult to deal with data.”
This produces a flywheel effect: the budget increases, the company begins to strengthen the structure more and more, and new technologies are introduced.
Since all of these technologies typically don’t connect to each other, it becomes a problem to connect data immediately because all of these platforms and teams need to find data to work.
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Once the data is stored, it needs to deal with all the issues related to Data Management (security, privacy, compliance, etc.). Business Intelligence is usually the first area where this design will take place because businesses operate independently with dedicated teams. BI tools, instead of just allowing you to explore and present data, often turn into complete data management tools because they need to incorporate data replication (data integration) to achieve better performance.
The same happens with Machine Learning platforms, allowing data scientists to analyze, generate and distribute new data within an organization, operating with high flexibility and autonomy. Machine learning platforms are quickly becoming the new way to deliver data to businesses.
At first all these systems seem to provide value to the company. Yet, whenever we introduce practices and technologies that are disconnected from data management and compliance requirements, we incur Data Debt. This debt manifests itself late and to a large extent with the following effects:
When
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