Data consultancy: how to create value from data?

24 July 2020 door
Laurent Bakker

Data consultancy is a concept that everyone has an (vague) idea of, but which also comes with a lot of unclarity. In this blog I make an attempt to remove some of that unclarity. Do you wonder if I succeed in doing this? Please call for additional information!

Data-driven management will lead to enormous process acceleration and to increase the quality of business decisions. The condition is that the data is complete, correct and consistent, so that the correct conclusions can be drawn within the applicable laws and regulations. This is necessary, because the increasing demand for transparency in decision-making means that companies and organisations have to demonstrate more and more that they control that process; from data source to reporting and decision making.

This is exactly where our data consultants come into play: creating opportunities to provide insight into the value of data for a specific business application or question.

 

How do you do that, data consultancy? 

In all honesty, I can write up to 10 pages to answer this question and no one would want that (apart from the fact that every project involves the necessary customisation). That is why I try to answer this question by leading you through the five pillars that we distinguish within data consultancy.

  1. Sustainably valuable data

    We strive for data that adds value, but in a sustainable way. By this we mean that the data must be able to be entered in different applications, so that the value of the information is not lost.
    We also want to be able to continuously add new data.

    Translating data into practical value for business is not straightforward. It deserves a good analysis, so that the information needs are identified. This requires a systematic approach, because only with the right data can you organise an efficient process: from data to information, to business application, and ultimately to visualisation for interaction and communication.

    The quality of data must therefore be good. You have to be able to rely on it, as it is the basis for important decisions. Improving data quality starts with clarifying whether the available data is correct, complete and current. To assess this, we draw up data quality criteria together with the client. Based on this, we perform an initial gap analysis. For example, we investigate whether something is missing and whether the data quality is sufficient.

  2. Data potential

    We try to identify and respond to bottlenecks / thresholds for data usage, management, analysis, compliance, etc. By subsequently doing a second gap analysis, we can make a plan to bridge the gap between the client's wishes and the barriers they still see. In this way we work towards the optimal use of the data potential. For example, things like sensor data or data science suddenly become concrete.

  3. "Fit for purpose" analysis

    How can we "query" data? You can compare this with "open" and "closed questions". Open questions can cause unnecessary  bias, closed questions can cause a lot of data not to be used. Not every question is suitable for an application. Think for example of "Machine Learning". This may seem like a miracle cure with which every question can be answered. However, in practice this is not always the case. There will always have to be a match between the question to be answered, the required data qualities and the application with which the problem can be answered.

  4. Leadership and co-creation

    To achieve better and faster decision-making in the data analysis, leadership and co-creation is necessary. Ownership of data, quality and data management, privacy and security, architecture, integration, reporting and analysis must be jointly designed. We don’t ‘do’ data consultancy on our own, it is an interaction with multiple stakeholders within the client.

  5. Governance and strategy

    A one-size-fits-all approach is no longer sufficient to respond to future questions (system-oriented thinking). By using multiple, smart technologies, it becomes easier to take special (future) needs of all possible stakeholders into account. So do not limit yourself to one technology but ensure that the data system can communicate well with other, new technologies.

Potential value 

Many companies and organisations, perhaps without even knowing it, possess data with potentially great value. But how can you unlock that value for your own application? Based on the elements mentioned, our data consultants can help you to unravel the world of data.

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