Working with data requires trust

Working with data requires trust

Giving and gaining trust is for data nothing different than for a customer to trust you with his assets. Your customers give you something that you know you will handle with care. 

But how will a customer trust on your service if you struggle with the making of insights to base a good conversation upon? What will it be if your customer also has access to daily available insight regarding carbon, logistic spent, and region analysis?

The insights you dare to share, because you know they are correct. There is a lot that goes into that!

the reality

the BI lifecycle

In reality, a lot of insights are made in the heat of the moment. Capacity is used on the gathering, combining, visualizing and checking of the information. A time-consuming task of which the outcome is doubtful due to poor data quality. How often is a conversation with a customer prepared like this?
 
The made report must also be maintained, which is done by the Power BI specialist doing this "on the side". The number of requests increases and thus also the proliferation and the time spend on maintenance. 

To prevent performance issues, the source is often completely copied and uploaded in a Data warehouse. However, the data quality remains an issue and the combining of multiple sources into one report continues to be a challenge. A lot is "solved" in the report and in case of a change in systems, all interfaces must be adapted between de systems and all the coded measurements in the reports. 

Meanwhile, the number of data-related issues increases but because of this the flexibility of obtaining insights decreases. Also, the poor data quality and the managing of this becomes an issue (of time). In working with data, this growth curve is often noticed. It's a very logical, and useful, curve. You know exactly which data you have and you're better able to name what you need. 

This is what we call, the BI Lifecycle. It often stops at the Data warehouse and the logistical supplier now stands at the crossroad of trusting external help or doing it theirselves with an expansion of the team. 

Giving and gaining trust 

trust availability and quality 

Trusting the data starts with the continuous availability of data and verifiable good data quality. You want to have your own team managing the data quality, without it taking up too much time. In addition, in case of abnormalities in the quality, you'd want your team to get a signal so that they can immediately adjust. 

The base to work data-driven is to trust in this flow of availability, quality, and knowledge of your team. After all, your insights and measurements in the company, lean on this data availability and -quality.   

Additionally, you want flexibility in available and reliable analyses. You don't need everything, all the time. This depends on trends during the year, such as tariff/rate negotiations with your customer, or cost price changes due to CBA (Collective Bargaining Agreement). Also any ad hoc changes, such as the profit or loss of a customer, could be a reason to temporarily turn on a customer analyses per region. 

trust through doing it yourself

To make this happen as a logistics supplier, you need a team of specialists who understand the field of data integration. Think of, data engineers, data analysts, and front- and back end specialists. 

Quite unknown territory for a logistics supplier, but the wish to have that in-house is very logical. Instinctively this contributes to flexibility and the trust in data, since it has been passed through "one's own hands", but I dare to sat that the opposite is true. 

How much time is put into the setting up of such a data team, hoping that the quality of said specialists is so aligned that you actually achieve what you wanted to achieve. And who takes the next step in to the path of change?

vision Bricklog: trust on techniques so you can do even more yourself

Basically we say that you should do as much as possible yourself. That's also what a logistics supplier wants. But to what extend? Do you really want to set up all the insights yourself or do you want to use the insights so that you can provide even better service?

We choose to do both. Data (quality) should be available on a daily basis, as well as a library of insights in finance, operations, commerce, customer, and carbon, that you can turn on and off when you see fit. 

Furthermore, you want to be able to continuously improve your IT landscape without it having impact on the intended path of working data-driven. On the contrary, it should contribute to this new way of working. 

Moreover, it's important to have the in-house capacity to build these reports and that the knowledge about this should always be available online. That way the Power BI specialist, who often works alone - can always fall back on the explanation of Power BI techniques without the development of this coming to a standstill.   

For distribution of all those insights, you want one secure Data portal in your own branding for the entire company, in which the logistics provider decides who has access to which reports with its own user management.

This way - and with the feature of own user management -, also the customer, charter and other external stakeholders can profit of these daily available insights of which you know are correct. This transparency distinguishes you as a service provider and lets you pass on the trust in data and in your company. 

For a logistics supplier it is essential to have the in-house capacity to build these reports. That way you stay agile and you can answer quick questions. However, you should not want to do everything yourself. That is to say that data integration and the safely sharing of data is a field of expertise itself. 

Analyses that you can turn on and off per month contribute to flexibility. By enabling your team to work independently in choosing the reports they need at any given moment and leaning on verifiable good data quality, also gives your team the autonomy. This way you, the entrepreneur, give trust and confidence to your team. 

Examples or trusting on techniques so you can do even more yourself

You're not sure which customer really earns you money. In that case, the commerce department want to turn on the customer analysis report, take possible measurements, and turn the customer analysis off again. 

What this brings is that since the information is quickly available, you don't have to wait for the Power BI specialist to have time to this ánd you know for a fact that the information is correct. You enable your team to quickly and independently adjust on crucial reports and situations. 

Another example could be a change in the cost price. You want to know immediately what the impact of this change will be and this can be a gamechanger for decision-making. 
In this case, you want to look ahead and fill in the cost price change "somewhere", so that they are immediately taken into account in the reports. But this change cannot have an impact on the result previous to the cost price change. 

Enriching your reports yourself via a data feed application in your Data portal, such as a cost price change, is a good example of leaning in the techniques which enables to do more yourself. Due to data engineering and BI techniques, these prices are directly calculated through in your reports so that you can play with the changes coming onto your path. This then enables you to sharpen your decision-making on that time. 

trust in the approach 

In our Academy, we are continuously educating others. For a lot of organizations, a data-driven way of working is very new. Because although everybody talks about data quality and the drive to work with data, it is not that simple what to do with those acquired insights. 

How do you get your organization along in this drive and how do you pace all the optimizations you want to implement as a result of the newly found insights? An overkill lurks around the corner and you need to be protected from that. 

A nice saying of a Samurai teacher goes as follows; "Sword and mind must be united, technique itself is insufficient, ands spirit alone is not enough." It suffices as a nice parallel for the implementation approach that our team chose for. 
 
In the Academy, we take de person with us step by step in the development of the insight (that is made by us) and what you can achieve with it. We work in small teams, which can be expanded depending on the question/demand. 

The logistics supplier determines the scope and the pace of working. With our change approach, we steer on the progress. Because of this combination, both the technique as the people stay in development. 

trust on a solid base

The advantage of this productized work-method is that it's clear from the start what you get, what it costs, when you get it, how you work with it, and what it brings you. This always starts with a solid database 

This base exists out of the Connection to the Data platform and the joint validation of the Data quality. With a good base, you can create the opportunities to higher your revenue and lower your process costs, which ultimately contributes to a better profit. 

trust on the way of working

After the approach has been talked through extensively with the IT department, in order to gain trust in our way of working, the Data platform gets connected. The next step is the connecting of the Data quality reports and the basic insights regarding revenue and costs. 

In order to quickly adapt and adjust, we made all our analyses and reports scalable. Experience with "standard" reports are very different in the sector. A data quality system makes herein the difference and enables you to apply your own business ruling. As a result, it is easy to recognize the data and let the trust in this method grow. 

After the connection to the Data platform, we continue with reviewing and improving the data quality with 1 or 2 people of the company. Important to note is that happens in the source, by adjusting the process of business ruling in the Data platform. In addition to the data quality, we immediately connect basic costs and revenue insights. Despite where you work towards with your company. But why?
 
If we want to enable this small team to bring people along in the organization to work on the data quality, they should present the added value of it themselves. 

In example: It often happens that, according to the data, there are more vehicles in the fleet compared to the real numbers. Are they then also canceled in the sell. We also tend to "find" long lost trailers, that nobody knows about. This is where the money is. 

If the fleet manager can use the fleet data for a proper resource planning, he would be much more capable of scheduling maintenance hours with his planners.

Insights in such a basic report, motivates to keep paying attention to the data quality. This also applies voor resource insights of HR, costs, revenue, Finance, Commerce, CO2 basic, and more. 
 
Small but essential insights to let the organization get used to the function and thus the value of data. If the quality slips, the data quality instrument supports with the signaling of it. 

Once it's all installed, a check on the quality is only needed for a couple of times per week and until it's clear who within the organization is responsible for it. 

Bricklog Power BI Academy

Most of the organizations want to build their own reports. However, more often than not, there is only one Power BI specialist who frequently has too little time and knowledge to really develop this. This is a pity and also the exact reason why we have opened the (Online) Power BI Academy.

With our online Power BI environment, these BI specialists can train themselves in their own pace, and always have new knowledge at hand. 

In the physical Academy all these specialists (from different companies) meet each other and they can share their struggles. With the help they give each other and the guidance they get from our trainers, their trust and confidence in their skills and the quality of the reports increase. As a result, the whole company profits from this. 

Is a ‘Data gatherer’ trustworthy?

That you have to work hard to gain trust as a data specialist is logical. "What are you going to do with all that data?" is a returning question. To which our answer is "Nothing". 

Of course we get the question if we can make analyses and reports based on the data we have, but we don't. Purely because that would break the trust other companies have in us. In other words, making these analyses and reports, would be the end of our company. 

Maybe that in the future, we can talk about this with our customers. It could be possible that they are in need of such analyses. Anonymized and with a clear goal that contributes to the sector. But only in negotiating and with permission who want this for that specific purpose. 

For now, that is not the priority, because also in the Data lifecycle of our customers; everybody works for themselves. And there is nothing wrong with that. 

Gaining insights, keep working, lower the costs, and higher the revenues: that is the current priority. And it's already busy enough as it is. 

trust of It

As an IT specialist, you wan to know how Bricklog operates. You have carefully build an IT landscape and someone else is going to do "something" with it. Above all, you have a stream of other projects on the planning and "this comes in between". 

This is the reason why we immediately involve the IT department as one of the first to join the project. This enables us and your organization to talk about potential specialties about the IT landscape, but also to explore opportunities. 

If you score an ICT manager's project calendar on Data or IT you really see a clear division there. And that while everything is with IT.

When replacing a TMS, all historic data should be preserved. In case of a takeover, the data must be migrated. Countless interfaces between systems, must support the data flow to the reports. This can be done differently. 

A collaboration between IT and Data ensures that the IT project calendar is much more clear. Those newly "cleared" time is needed hard, because a lot of logistics suppliers deal with older environments during transition, which is a challenge at itself.  
 
But there is also a growing need of insights based on data. These two developments compete with each other. Often the vision is: "First the IT landscape and then start working data-driven." 

But this can start at the same time. With the connection of your source systems to a Data platform, you start with the data quality and the basic insights if they are part of it. At the same time, the IT manager researches the opportunities for improving the IT landscape and once decided upon, the (in need of replacement) system gets logged off and the new system gets logged on. 

The historic data remains preserved, the reports remain to exist, and the new data flows effortless to these reports. The road to more in-depth analyses and AI is open, because 1) your historic data is saved, and 2) you have continuously been working on the data quality of this. 

From a business point of view, this also comes in handy during takeovers to gain accelerated insight into the taken over company whilst preserving the systems. 

trust from the operation

The Operations department often worries about the "data-driven way of working" and the time it will costs to do so. Time is scarce and is, logically, preferred to be used to deliver the service. 

However, it is underestimated how much time this in reality saves. Easily turning a trips - or stop time - analysis on or off to test your gut feeling contributes to a quick decision making. 

Gathering and visualization of the data, often coming from more sources such as a TMS and Excel-document, takes up a lot of time. And if there is no data quality system in place, the making and interpretation of the report is a time-consuming job with an uncertain outcome. 

The next worry is the working in standardized products to gain speed and spare scarce capacity. "We have all sort of exceptionalities, you cannot catch them in standardization. We've tried that, it didn't work, so now we're going to do it ourselves."  

That's true, each company has its own business ruling and exceptionalities which should be taken into account in the basic insights. Without that, standardization isn't an option. 

So before you burden your operation with insights, you want to know for sure that it's correct. That trust has to grow, which is why start working on data quality in a small team. 
 
Often this is someone outside and someone inside of the operation or finance. Do you recognize yourself in the data? With a targeted data validation plan, you decide step by step which action is needed to up the data quality. 

What this provides is a data quality system that expresses in percentage what the quality is relative to a few important topics. In the Transport branche, these topics are shipments, resources, and trips. 

In the Warehouse branche, this is naturally different. Given the fact that data quality an essential, but also - to be fair - a bit boring practice is for most people, you also want to directly have insights into basic revenue and costs. With this, your operation can immediately see the impact of the changing data quality and recognize if this system matches their gut feeling. 

The period of setting up the base takes a maximum of three months until your data quality system stands and the game can start. Since the operation has slowly been brought along in the transition to data-driven work, the question will arise from the operation naturally. This is also true for the Commerce, CSRD, and Finance departments. 

trust of decision makers 

Are you a decision maker, then you want to compare your operational numbers with your financial numbers. Monitor unbilled revenue, review the impact on the changing cost prices, prepare well for the tariff negotiations, look at the impact of new customers on your revenue, track the logistical spent of your customers, and have a charter analysis available. 

You also want to comply to laws and regulations, such as proper Carbon and ESG reports you don't want to look back to. And if you're really honest, you want everyone to speak the same business language. 

In short, as a decision maker you strongly depend op data and that this information should not only be ready when you ask for it, but on a daily basis. Although, for now, many are still doing well with the knowledge inside their heads and hearts...  

But do you want to keep trusting that in a time where changes are piling up at high speed and you need to be able to decide quicker than ever?

This dependency on data connected to people who work with this data, and exactly that group of people is structurally overloaded. You actually want to rely on a few colleagues that are familiar with the making and maintaining of insights, and that know their way around in the company if this deviates. 

This requires a certain Database, educations for the whole team, and a common realization of the value of data for this company. That starts with a small team such as described above. 

Interest in this comes often out of a surprising corner of the company. If the seeds are allowed to sprout, combined with good guidance, you're quickly out of the starting blocks 

in short…

The reality of gaining insights is unruly and that is explainable. But it doesn't have to be like this anymore. 

The realization that it can be different, is there. The hurdle that often remains to be taken is the "handing over your data". Lean on technique so that you can work with your data in a focused way and do more yourself is what it delivers.

In data it works both ways. Gain trust and give trust. Once you're in that flow, you'll see that you will also pass on trust. To your team, your customers, your charters, your suppliers. How your customer trusts you with his goods, and you trust your team to handle that. That's how our customer trust us with their data and we trust our team to handle that with care.

In January 2025, we had our 10 year anniversary, without our team we wouldn't have reached this milestone!