Engaging Data Explains :
Creating The Gold Standards in Data –
Part I
Achieving an excellent level of data architecture is far from easy. But it is certainly possible if you implement certain guiding principles. Central to this is the implementation of a gold standard benchmark, which can then underpin any effective data architecture operation.
However, a gold standard is not something that comes naturally. In our experience, it requires diligent thought, effort and openness to change.
So in this four-part blog, we’re going to discuss some of the considerations related to this important goal for many organisations.
Resource Considerations
When we think about creating engaging analytics or data platforms to shape the growth of an organisation, the focus is often on the finding tool capable of developing the solution and not the surrounding aspects. But all of the ingredients that go into the mix are critically important.
Imagine you’re the owner of a cake shop providing bespoke cakes for your customers. Your products have to be good enough to keep customers coming back, but they also have to retail at an attractive price point. This means that there are immediately resource considerations.
You may choose to focus on providing a premium product, creating high-quality goods for a premium price. Alternatively, you may deliver a higher volume product, baking lots of different cakes on a larger scale, which are still of a good standard, but only suitable for a lower price point.
In order to make this decision, you need to understand the following:
- Product – what we are providing, and the value that we create.
- Place – the environment that makes it possible to create the necessary standard of products at a sustainable rate.
- People and Process – the team, the processes and the delivery environment (the bakery, the storage, the front of house, stock control, delivery, billing, etc.) that produce and maintain the consistent quality of product and experience.
If you can put all this together then you have the beginning of a gold standard in cake production. By the same token, in our field Engaging Data helps companies to review all of the data elements supporting such a setup, combining this with their aspirations to form bespoke gold standards. This enables our clients to achieve profitability and success.
Controlling The Input
Controlling input is a critical component of processing gold standard products. These vary, depending on what you are trying to produce, but examples include:
- Requirement gathering.
- Sources of data.
- Quality of data.
Controlling inputs and creating quality is critically important, as if you put terrible into a system then the ultimate outcome will be a terrible product! Thus, you need to understand the requirements of your customers. In the cake shop example, this would mean knowing what type of cakes your customers desire, the toppings needed, the date and time of delivery, any dietary requirements, and so on.
The good news is that controls can be quite straightforward. They can be something as simple as checking data. So in a cake shop, it’s vital to confirm the direct requirements of your customers, noting down all relevant information. This can make the vital difference between providing the ideal products for your customers, or producing something that seems excellent, but is rendered useless or sub-par by one important constituent. For example, you might produce a cake for someone with allergy needs that is simply inedible from their perspective.
Quality control can be achieved by creating a simple order form. For example, a cake shop might include:
- All vital information being distilled into yes/no questions – eg. “should cake contain nuts?”.
- Ensuring that all product types are selected, and that nothing out of the unusual is ordered.
- Product limitations being noted expressly on the form – acting as a reminder and preventing incorrect ordering.
Such a review process ensures that information is gathered correctly, and creates a collective responsibility for discerning the appropriate information. Important questions that you can ask yourself in a data environment to acquire such critical information include the following:
- How will the requirements come into the team?
- How do we need to record them?
- Do we have the right tools to collect the data?
- How will we handle data quality?
Output Consistency
The output is the result of your efforts, so you have an innate interest in ensuring that it’s the best possible product. In common with the input, it is important to understand what you can control to reduce risk, as this can have a big impact on your output.
Central to this process is building systems and controls that enable you to monitor outputs. This in turn makes it possible to assess if they need to be altered in any way. This means that in a cake shop, you may consider the impact that each of the following areas has on the supply chain of cakes:
- Production Team (bakers, shop front, etc.).
- Ensuring similar standards and experience.
- Providing the same customer experience.
- Ensuring knowledgeability about the production processes, industry and competitors.
Each aspect of the order and production process also needs to be assessed and standardised:
- Enjoyable and consistent ordering experience.
- Stock control to manage high-quality ingredients.
- Quality control of all products.
- Meeting all food hygiene regulations with a 5-star rating.
And then the tools of the trade should also be taken into consideration, as part of an ongoing auditing process. Central to this is ensuring that any equipment being used is within acceptable operational parameters, particularly not being overloaded or overstretched in any way.
So when you’re working in a data environment, or any working context, if you want to create gold standards then it’s important to continually monitor and challenge your processes. Ask yourself questions continually, such as:
- Do we have the right team in place?
- Do we understand what standard of products that we need to create?
- Do we have processes in place that enable us to produce quality products?
This is just the beginning of our insight into creating gold standards with data, so in our next blog we will move on to discuss several other important factors.