The Building Blocks of an Effective Data Strategy

Words like AI, machine learning, and “storytelling” have whirled past our heads so many times during the past decade or so that some of us have grown tired of seeing them. While buzzwords like these no doubt have the ability to increase the profitability of a business and the endless use cases to prove it, there are overlooked foundational building blocks that are an integral part of any data strategy which is how you acquire, store, manage, share, and use your data.

To effectively take advantage of your data and reach the realm of advanced analytics, you need to have the following:

Your data strategy should be aligned with your business objectives and be seen as an expansion of your business strategy. Aligning planned data initiatives with strategic business goals allows for the ability to prioritize profitable initiatives and helps create a focus on the data, technology, people, and processes that are necessary to make them a success. This prevents organizations from becoming side-tracked and working on projects that may be interesting and incorporate advanced modeling techniques, yet are not worth the time and effort needed to implement. 

Data is not a magical commodity. Just because you have an abundant amount of it does not mean that you will be able to wave a wand and extract all of the key insights that will drive your business. Just like relying solely on gut-instinct referring to poor-quality data that is inaccurate or has been used in the wrong context as this can lead to flawed assumptions and missed opportunities. This can be quite costly.

Below are six dimensions that you can take into consideration while assessing the quality of your data. An extra dimension that you need to consider is compliance – an increasingly important requirement for those who operate in a highly regulated country like China with new data laws.

The definition of data management can be seen as quite broad and feature several components, but for the purposes of this article, we will focus on the following two.

Metadata Management

Data needs context. The word ‘red’ has no inherent meaning except for being a color. However, given context like “the traffic light’s color is red” or “our consumers’ favorite color for product X is red”, you can use that information to plan your next actions.

Metadata is simply data that describes data. Especially when companies have thousands of data elements spread across different sources, metadata makes it much easier to find relevant data and ensure that it is being used in the right context. Without it, it’s easy to ignore or misuse some of the most precious data assets.

Master Data Management

Master data is a single source of “truth” for quality business data that can be used across all departments. It is the small portion of your data that is the most crucial which is cleaned, standardized, and made ready to go for all those who need it. Master Data Management (MDM) can provide significant value to an organization by creating key data assets such as Customer, Product, and Supplier. Not only does MDM improve data quality, but it also reduces workload and streamlines workflows since the data is collected and cleaned only once.

MDM cannot exist without a procedure or framework set in place. With proper data governance, your organization can deliver high-quality data quickly to those who are authorized to use it. It also knows exactly who these people are and who is in charge of each crucial data asset. There are many parts to data governance as it is a series of processes, roles, policies, standards, and metrics that guarantee the effective and efficient use of information. Simply put, it looks at who should be given access to work with what data in which situations using what methods or technologies. Without it, it’s unlikely that the aforementioned building blocks would come together to deliver an enterprise-level solution.

Just because employees have access to self-service tools doesn’t mean that they are self-sufficient to read, understand and work with data. That’s why having a data literate workforce is also crucial. Simply put, data literacy is the ability to read, work with, analyze, and communicate with data and most employees still find this intimidating. 

Organizations often focus solely on hiring external data experts. However, not only is this process expensive, but all of the analysis must funnel through these few people. By equipping your workforce with the data literacy skills they need to thrive, not only will you prevent these bottlenecks from happening, but you will also be able to leverage the domain knowledge of the professionals in your organization and add an extra competency that is likely to bring each department’s performance to the next level. However, in order to do so you must ensure that the training is particular to the context of your business — and that actually engages your employees.

All in all these are the essentials that you need to build any type of data strategy. Those who have profited from advanced data use cases were able to do so because they realized the importance of securing a solid foundation using these building blocks and made sure to play an active role in creating it.

This piece is written by Mariam Ammar, Data Analyst Manager, Fabernovel China

Mariam, Data Analyst Manager at Fabernovel China, is passionate about technology and quantitative analysis, she supports companies in their innovation. She is an expert in creating machine learning models to fit structured data and also in designing deep learning models for unstructured data which focus on image and natural language processing.

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