Data Strategy: the practical guide to steering your business without drowning in data

Vision and objectives: how do you define them, and what are they for?

A strategy is above all a vision and objectives. At the foundation of any strategy are the answers to seemingly simple questions: where are we right now? Where do we want to be in 1 month? In 6 months? In 2 years?

Now that you have a direction, you can define the vision that will get you to your destination: what is the ideal scenario that gets us where we want to be in 1 month? In 6 months?

Bit by bit, the vision emerges and naturally, your objectives will become clearer. Do you want to become a reference in your market within 5 years? To achieve that, will you need to double your customer portfolio over the next 5 years? If you currently have 100 customers, you still need to win 20 each year over the next 5 years. That means 3 customers every 2 months or 1 new customer every 18 days. In a single step, you have now defined both the objective and a first draft of the indicators to track in order to measure your progress toward that objective.

Too many teams make the opposite bet by focusing on problems before defining their direction. The result: they feel like they are standing still!

To better frame the vision and objectives of your "data" strategy, here are a few examples of questions to ask the leaders of your organization:

A strategic "data" guide in 4 parts

With our strategic guide to transforming your business into a "data-driven business", you cover the following areas:

Here is how we build our "data" strategy for immediate impact:

Do you want to move from strategy to execution in 10 days?

If you do not have the time to build a data infrastructure yourself, we created the Data Starter Pack.

Data governance: why and how?

It is a trendy term in 2026, and "data governance" is a recurring concept in any respectable "data" strategy. Be careful, however: the concept of "data governance" is inherently vague and leaves a lot of room for improvisation and impractical theories. At Datavizin, we adopt a pragmatic approach based on operations and action. For us, there is not just one "data governance" but many "data governances" that apply differently depending on sectors, professions, maturity levels, etc.

In our view, the principle of data governance is simple: it consists of putting in place every possible means to apply the company's strategy while making maximum use of data. Put even more simply: how do we ensure that our data delivers value to our organization?

At first, forget the technical terms, Data Products, Data Architecture, Master Data, etc., and focus on what matters most: what do we need so that our data brings value to our company?

Data Governance will in particular help define who in the organization is responsible for which data, and who can serve as support, relay, or backing for your organization's "data" strategy. This central element applies whatever your size and whatever your profession. In other words, Data Governance defines the who and the why before looking at the how.

Here again, we have too often seen organizations rush into execution before asking the main questions. The result: a state-of-the-art architecture, but tools that are too expensive and unsuited to the company's real needs.

Architecture: central foundations, but do not base your entire strategy on a single pillar

Data architecture is the central point of the entire data strategy, and it is one of the key elements of our Data Strategy Toolkit. With a clear vision and a broad definition of the who and the why, you know what you need to build, for which audience, and for what purpose. And that changes everything.

You can base your data architecture on Excel files. If the strategy is clearly defined upstream, it will work. Of course, we can still suggest alternatives that are a bit more powerful and automated than Excel! 

The question of the data stack will of course arise! You can choose to build your own infrastructure, dedicate part of your existing datacenter resources (if you have one) to Business Intelligence, or trust an external provider such as Datavizin to offer you a managed, turnkey infrastructure.

Unpopular opinion: the choice at a given moment is not the most important point. What we recommend that you put in place from the very beginning is:

• A fallback strategy: which architecture should be adopted if Plan A fails?
• A flexible architecture: do not put all your eggs in one basket.
• Understand how your infrastructure is built. Ask questions of your IT department, your suppliers, etc., but make sure you know how your data works.

The foundation of a data architecture is:

• A database
• A tool for writing data
• A tool for reading data

That is all. The rest (medallion architecture, ETL, ELT, etc.) is detail at this stage of operations. If you need to start with the simplest approach, focus on the concept of a database.

Data quality: an ongoing effort for every team.

Goal: build a "data culture" and a "data driven business"

Data quality is a topic as recurring as it is vague in the world of "data." Beyond the big concepts and complex theories on the subject, keep in mind that the main objective is for you to be able to interpret the data available to you. For optimal reading and analysis, we generally prefer a calm environment.

The main activity here will therefore consist of eliminating all the "background noise" from your datasets, tables, and charts. The important point is that you redefine these notions of "noise" and "waste." Before moving on to cleaning proper, you will therefore need to clearly define what "clean" or "usable" data is. In short, you will define a number of rules for each domain, and your data team will ensure that these rules are applied as effectively as possible to your data.

Be careful: this is where major biases can be created! Above all, do not fall into the trap of over-sanitization: some data is not "bad data," but weak signals that absolutely must be integrated into your analysis.

For example, for a CRM: if 80% of your prospects' email addresses are missing from your database, that is a sign that you need to thoroughly rethink your email acquisition method!!

Data catalog and data products: for whom, by whom, and how?

The notions of "data catalog" and "data products" have one thing in common: they rely on a logic of centralization and standardization of data.

To take an example, if you manufacture bicycles, you will buy some parts, produce others, and assemble these parts together to obtain a finished product.

Before you get to a fully operational bicycle, you will set up several intermediate steps, and for each of these steps you will use parts. These parts each have their own characteristics, and every stakeholder—from the buyer to the workshop manager to the handler—will mainly focus on the characteristics they need to do their job. The buyer will mainly be interested in price, availability, suppliers, etc., while the workshop manager will want to know more about size, weight, the color of the part, etc.

Thus, each stakeholder will tend to use the system or tool that gives them the information they need most as a priority, even if that means ignoring all the other aspects of the part. This is a known and documented phenomenon, which ultimately leads to fragmented information across the company's different systems and applications. This situation quickly causes frustration and a lack of trust in the various systems being used, but it also has more serious consequences: stock errors, problematic sales caused by duplicate customers, decisions made on incomplete data, hours lost searching for the "right" technical information, etc.

To avoid these very business-specific issues, data professionals use "master data," "data catalogs," or "data products": to guarantee a single source of truth for all company stakeholders. It is often foundational work whose virtues and benefits are not very visible, but are essential for a high-impact "data" strategy.

A good data catalog is like a shared technical specification sheet for the entire company. Everyone finds what they need there, without confusion or wasted time. And it is this foundation that then makes it possible to produce reliable dashboards, launch AI projects, or simply... manage better.

Get started with the industrial Data Starter Pack

In 10 business days, we set up a first simple but robust BI cockpit, based on your real data. The pack includes: