Data.Depicted Episode 1: Do You Trust Your Data?

Data.Depicted Episode 1: Do You Trust Your Data?

Paulina Kondratowicz

9 min
Data.Depicted Episode 1: Do You Trust Your Data?

What causes the greatest dilemma in companies, which use and work on data? Lack of trust, one would say. In this series, we will cover the most bothering questions related to data and processes occurring in large enterprises. To start with here’s a first step to getting you introduced.

You are a great entrepreneur, you follow the latest trends, draw knowledge from statistics, and constantly improve your processes. So how is it possible that you don’t trust your data? The modern world is literally made of hundreds of thousands of information, which is calculated multiple times every day at any place on the globe. However, it often happens entrepreneurs don’t trust the information they gather. This happens because of several factors. We are talking about the multi-activities phenomenon in companies, which can disrupt data flow. There also is a common occurrence of errors in data management at many levels, especially, when we deal with a huge amount of information processed by many people. It is the human factor that, in many cases, causes problems related to the information credibility and reliability flowing through company structures. So how do you eliminate it and trust your data?

Multi-activity phenomenon – where’s the mistake?

Multi-activity is a hallmark occurring in the companies that, due to development, have had to introduce an extensive organizational structure. According to SAP statistics, 56% of companies’ CEOs don’t trust or aren’t completely convinced of their data reliability. To use the given data properly by teams, managers and department employees, it’s vital to introduce tools that will enable them to work in real-time. It’s primarily about reflecting any changes that occur in processes and applying them in such a way that, everyone using the data reading tool, know how they present themselves today, what changes have taken place, and at what stage a given project is. Besides, an ideal data modeling tool should allow many people to work on one piece of information at the same time, convert values efficiently  ​​and present them in a comprehensible, intuitive and visually attractive way. Bearing the above in mind, it is necessary to support solutions designed intuitively, and at the same time, as stable and multifunctional as possible, allowing access to all historical data. So, the tools for complex data visualizing, which work in real-time, are perfect. If designed in the GoJS library, they allow you to arrange and sort even complicated data into multi-level graphs. Thanks to this, we avoid extensive workspaces, and searching for data is much simpler.

Multi-stage – make it simple again

The procedures that are continuously implemented in development processes are the firm part of any manufacturing company. Usually, these procedures are complex and multi-layered. Simplifying them, therefore, seems to be an issue that affects almost every major structure. Following the example of the production company, which, due to its specificity, implements related business stages. They are subdivided into priority range, co-created for brigades, teams, or individual projects. Then, familiarizing oneself with the specific to each stage data can be the source of issues due to their accumulation, complex dependencies, or changes. Therefore, it is important to maximally simplify and reduce the number of stages. This can be done by combining several into one or exporting the most important of them so that people working on projects can freely use the implemented data. When using the app for data visualization, it is worth familiarizing with the automatic data adding and convert it to work faster and more efficiently.  Let each change at every stage be visualized in a graphic form. Thanks to this, every person working on a given project will be able to check at what stage changes have taken place. Due to their simplification, changes are also becoming significantly more accessible. What’s more, this state of affairs is also supported by the operation of data downloaded from many sources. Then, both processes and operations on information are characterized by significant automation and simplification, elimination of unnecessary and extensive stages of work

Human factor – everyone makes mistakes

The above problems always have one thing in common – the human factor. As a rule, our mind makes mistakes, resulting from various interpretations of data, associating them or simply omitting them. Automation of activities, implementing data visualization, procedures simplification, and free access to all data within the company is the key to success in the case of revising the truthfulness of the information. Good practices speak of three pillars on which activities related to data revision in the company should be based. Therefore, data discovery involves careful content and related information analysis as well as detailing its impact on company structures. Enrichment, in turn, is a process involving the purification, matching, consolidation, constant monitoring and measurement of data quality, as well as their synchronization concerning departments in the enterprise. The last pillar is an improvement that, as part of managing the life cycle of all content, controlling and implementing archive, allows you to maintain or remove some of the regulations or elements of the company management policy. Finally, it is crucial to ensure that all data is connected and works intelligently. Due to the introduction of real-time operations, which work in applications for collecting and reading data, we can count on full integration and intelligent content management, even if transforming them. Let’s note that data aggregation from many sources allows for full analysis at any time during work. The use of spatial data, time series, machine learning, and other technologies, therefore, allows providing intelligent data. But it may be possible only when the above elements are implemented, so we can talk about the elimination of the human factor and reduce error-making when working with data.

Trust in data should be a key element in running any business. Without the certainty of their reliability, it is difficult to talk about proper business management. Gaining this certainty should be supported by using data visualization tools enriched with Real-Time technology. What’s more, thanks to them it is possible to work on huge data volumes. It is also worth mentioning that the transition to advanced technology and intuitive tools will free employees from stress and full responsibility for operating applications that may simply be inefficient in their work. Opting out of macros programmed in Excel, box solutions or incomplete tools versions will eliminate the human factor, flatten multi-stage and simplify multi-activity and, at the same time, authenticate the data with which we work every day. Data, however, is a complex and sophisticated issue that occurs at any stage of company management. Having that in mind, the next episode will cover decoding data and thus, making it readable again.

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