Category: Learn

The Gold Standard – Part 2

Dec 15, 2021 by Tyler Bodys

Engaging Data Explains :

Creating The Gold Standards in Data –

Part II : Assessing the Gold


In this second part of our four-part series on gold standards in data, we’re going to examine the importance of instilling a review process.

Reviewing is never the most exciting nor relished procedure! It often feels like an unnecessary grind that slows down your whole operation. But it’s actually a critical part of any good business, enabling you to identify major faults before they become ingrained.


Reviewing Processes

We’ve already seen in the first part of our gold standards blog that the cake shop reviewed their existing processes before making a decision to change their order form. Often it’s only from self-assessing in this way that you can uncover new ways of working that enhance your productivity and efficiency.

It’s also important to emphasise that this needs to happen across the business. It’s normal to have a review as part of project governance for new projects, but it’s always worthwhile to reassess existing projects as well. They may be ‘good enough’, but they also might not be reaching the gold standard.

So in our cake shop scenario, how will the business assess the standard of the cakes being produced? Well, when the business reviewed the kitchen they found that each cook bakes one order, while also being responsible for checking their own cakes before they move to the decoration stage. This all seemed fine, but then the retailer received a few complaints about burnt edges and sub-par ingredients. Consequently, the cake shop reflected that its existing process of ‘marking your own homework’ was not sufficiently robust to identify problems.

In order to address this issue, several ways of reviewing the production of cakes were decided upon:

  • A simple visual inspection – does the cake look uncooked or overcooked?
  • A thorough test, such as breaking the centre of the cake to see if it is cooked. 
  • Pressing the centre of the cake to see if it springs back.
  • Hiring Paul Hollywood as a tester!

All of these checks are designed to see if the cake has been cooked satisfactorily. The shop decided to opt for all options, with the exception of hiring Paul Hollywood! Instead, each baker will review one another’s baking, with the hope that the business will grow to support a head baker who will review all cakes.

Just as the cake shop reviewed its processes to put a more stringent review structure in place, the same can also be implemented in a data-driven environment. The following questions are examples of some that you can ask yourself as part of this process:

  • Does the team need training?
  • Do you need to recruit new people with different skill sets?
  • Do the products need to change?
  • Is the supply line quick enough? 
  • Would more people or different processes help with efficiency?
  • Is the product still worth the effort that is invested in producing it?

Producing Gold

Once you’ve baked some quality cakes, you then need to take steps to market the product. It’s not enough to just produce the cakes and leave your customers to eat them if you want to maximise your marketing efforts. Building advocacy and influence via your customers is a great way of marketing your cakes to the right people. Word-of-mouth feedback from advocates is trusted by other potential customers, and is therefore far more effective than other forms of marketing.

However, there are two sides to the coin here. Negative feedback can be dangerous if it’s not managed effectively. Negative comments about the burnt edges of cakes will spread like wildfire to existing and prospective customers. But there are ways of recovering from this. Making courtesy calls to customers can provide you valuable insight into the process of ordering and consumption. 

Getting the right team in place, tailoring products for your target market, and taking feedback onboard in an active process are all important facets of contemporary marketing. 

What are the Considerations?

Gold standards always begin with what you are looking to deliver and who this will benefit. Gold standards should be designed to support outcomes, having considered both the internal and external factors that will influence design. Creating steps in the process to continually challenge the functionality of the end product and ensure that standards are still relevant to the end user should therefore be considered essential.

Sponsors and influencers can also play an extremely important role. Both can become prime advocates of your product, with the added benefit with sponsors that they pay you to advertise your goods or service!

Internal Considerations

Data

Data can be compared to ingredients within a cake. Naturally, good quality ingredients are critical to producing the best cake possible. The same applies to data. As we mentioned in part one, if you put rubbish data into your systems, you can expect rubbish outcomes!

At some point, the cake shop company realised that the bakers are not periodically reviewing the ingredients within their cupboards. To address this, the manager inspects everything that they have on hand, ensuring that any poor quality ingredients are replaced, and that anything out of data is thrown away. Labelling is updated, while processes are put in place to ensure that there is no repetition of these mistakes.

The key point here is that while complaints were registered about burnt edges, it may have been the ingredients that contributed to the final product that were the problem. Going forward, the team at the bakery put in place a series of key questions that would inform their processes in future baking:

  • Do we have enough data to make the size of cake required?
  • Are we getting our ingredients from the right suppliers? 
  • Does this product contain nuts?
  • Have I mixed sugar up with salt?
  • The milk smells as if it’s on the turn, should I use it?
  • Do we store the ingredients in the right place, in the correct containers? 

Resources and Teams

When baking your cake, you can select from many different types of bakers or specialist chefs to assist with the process. Or you may decide that you wish to train yourself, or an existing employee, so that they can handle the most challenging baking tasks.

In some cases, if the cake shop utilises industrial equipment, people who have been trained to use this equipment can be deployed, as opposed to bakers or specialist cake makers. Having the right team with the right skills, and/or the aptitude to learn them, can be critical to successfully achieving gold standards. Instilling this in your team culturally is critically important in providing direction to your whole operation.

Achieving this can be as simple as asking yourself the following questions:

  • Is the team right to build the end product? If not, what needs to change?
  • Is the team open to changing or evolving in order to improve the product or efficiency?
  • Do we have the right skills? If not, do we need to second or buy them in?

Company Culture

Finally, failing to understand the company culture will lead to failure. Gold standards must fit into the existing culture, or the direction the company is moving towards.

Understanding how your customers think, managing their expectations and developing a standard to consistently perform to those expectations is simple to conceive. However, the human element of this could result in you developing hundreds of different gold standards for multiple different customers. 

Important questions to ask yourself here:

  • Are the customers knowledgeable about your products? If not, can you educate them?
  • Do you share your practice? Would it help your customers to know what you do and how you do it?
  • Are there any expectations that you can manage? 
  • Are they any difficult expectations you have to work towards?

Implementing a gold standard for data may seem like an all-encompassing and intimidating goal. But it instead should be seen as a granular process. Breaking down the ingredients and individual components that collectively create gold standards is the best way to achieve this aim.


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Data Warehousing Concepts Explained

Aug 3, 2021 by Carl Richards

Engaging Data Explains:

Data Warehousing Concepts


Modern commerce is an environment in which companies are increasingly being required to make complex, data-backed decisions. Dealing with vast amounts of information has become an essential feature of a business, which can often lead to siloed data. This is difficult enough to store, let alone analyse or understand.

In many cases, business use demands require a more sophisticated system, improving data management and providing a holistic overview of essential aspects of the company. One of the best ways to achieve this is to invest in a data warehouse. Yet, many companies are still unaware of what this entails or how it can help their business.


What is Data Warehousing?

In simple terms, a data warehouse is a system that helps an organisation aggregate data from multiple sources. Instead of experiencing the sort of separation and siloing discussed previously, data warehousing makes it possible to draw together information from disparate sources. It’s almost akin to a universal translator of languages. Typically, data warehouses store vast amounts of historical data, and this can then be utilised by engineers and business analysts as required.

Data warehousing is particularly valuable as it essentially provides joined-up information to a company or organisation. This was quite impossible until relatively recently, as data has always been based on separate sources of information. Transactional systems, relational databases, and operational databases are often held entirely separately, and it was almost unthinkable until recently that the data from the systems could be effectively combined.

But in this Information Age, companies are seeking a competitive advantage via the leveraging of information. By combining the vast amount of data generated together into one source, businesses can better understand and analyse key customer indicators, giving them a real insight into the determining factors of the company. Data warehousing can build more robust information systems from which businesses can make superior predictions and better business decisions.

In recent years, the escalation and popularisation of the cloud has changed the potential of data warehousing. Historically, it was more usual to have an on-premise solution, which would be designed and maintained by a company at its own physical location. But this is no longer necessary. Cloud data architecture makes it possible to data warehouse without hardware, while the cloud structure also makes implementation and scaling more feasible.

Data Lakes

However, those who are uninitiated in deep data topics may encounter terminology that can be somewhat baffling! The concept of a data lake seems rather surreal and tends to conjure up imagery that is, ultimately, completely useless! Inevitably, people who have never encountered the concept of data lakes before find themselves imagining an expanse of azure water glittering in the sunlight. Well, data lakes aren’t quite like that.

A data lake is used for storing any raw data that does not currently have an intended use case. It really can be seen as similar to the wine lakes that used to be in the news quite regularly, but it doesn’t seem to be a talking point any longer! You can equally view a data lake as a surplus of information; it is data that may become useful in the future but does not have an immediate usage at this point in time. Thus, it is stored away in a lake until it can be consumed adequately.

This differs from data warehousing, which is used to deal with information that is known to be useful more efficiently. Data warehousing may deal with data stored in an impenetrable format. Still, there is a clear use case for understanding this information, or it needs to be stored for a particular reason.

When to use a Data Warehouse

There are a variety of reasons that a company or organisation would choose to utilise a data warehouse. The most obvious would be as follows:

  • If you need to start a large amount of historical data in a central location.
  • If you require to analyse your web, mobile, CRM, and other applications together in a single place.
  • If you need more profound business analysis than it has been possible to deliver with traditional analytic tools, by querying and analysing data directly with SQL, for example.
  • In order to allow simultaneous access to a dataset for multiple people or groups.

Data warehousing makes it possible to implement a set of analytical questions that would be impossible to address with traditional data analytics tools. Collecting all of your data into one location and source makes it possible to run queries that would otherwise be completely unfeasible. Instead of asking an analytical program to continually run back and forth, back and forth between several locations, the software can get to grips with one data source and deliver efficient and more holistic results.

Data Warehouse Factors

Many businesses now require data warehousing services to deal with the vast amount of data that is now generated. And that ‘many businesses’ will rapidly become ‘most businesses’, and then ‘virtually all businesses in the near future. But those that are inexperienced in this field are often confused about what factors to take into consideration.

Thus, we would recommend looking at these six key elements when considering warehousing:

  • The sheer scale of data that you wish to store.
  • The type of information that you need to store in the warehouse.
  • The dynamic nature of your scaling requirements.
  • How fast you require any queries to be carried out.
  • Whether manual or automatic maintenance is required.
  • Any compatibility issues with your existing system.

Concerning the first of these factors, data can be somewhat different in terms of its basic structure. Some data may be highly complex, but it can still be quantifiable, easily organised. However, in the era of Big Data there is a vast amount of unstructured data, which cannot be easily managed and analysed. Companies that generate a vast amount of unstructured data and need to collate and understand it are certainly excellent candidates for a data warehousing solution.

There is a lot to learn when it comes to the subject of data. And it can frankly be a little daunting at times. But what is certain is that this topic isn’t going anywhere. Big Data is here to stay. That’s why we have created our Data Vault 2.0 solution. Data Vault can ideally serve your organisations’ data needs when this is becoming an issue of paramount importance.