Data Democratization: Transforming the Data Culture at SSENSE

Renaud Guilbert

Published in

SSENSE-TECH

6 min readOct 10, 2019

An overview of the SSENSE data strategy and its benefits.

What is data democratization?

Let’s start with a basic, hardly arguable fact:

Fast and effective decision-making depends on easy access to accurate and complete data.

Today, more than ever before, data is obtainable in large quantities and varieties. In an enterprise context, the more an organization grows, the more the need for data becomes large and complex. The faster such data can be obtained, the sooner data-driven strategies can be developed and deployed.

With this in mind, let’s look at the basic tenets of data democratization, at its most theoretical:

1. Everyone should have access to relevant data (not just executives, analysts, or technical users).

2. There should be no unnecessary gatekeepers or bottleneck between the data and its stakeholders.

3. The data needs to be complemented by a solid structure, to ensure that any stakeholder can access, understand, and use the data at their disposition.

When we combine the statements made in the first two paragraphs with the tenets of data democratization, the conclusion seems obvious: to be an effective and competitive company, we must make quick and effective use of the data at our disposition. The best way to achieve this is to remove the barriers between stakeholders and data, to gain speed and flexibility.

But is it that simple?

Pros and cons of data democratization

Pros

The speed of data distribution and utilization creates a competitive advantage. We can more rapidly identify patterns and trends, measure impacts, and take action on business insights.
This allows for more diverse workflows, and encourages new initiatives and proactivity.
It empowers each individual talent in their analysis and decision-making.
It scales much easier than a strict data bottleneck.
No duplication of work when it comes to making data available, simply need to update the existing flow.

Cons

Misinterpretation of used data leading to inaccurate and misguided insights by employees who do not understand the business rules.
The easier the access to data, the higher the risk to data security and integrity.
Looser ownership and tracking of data utilization.
The potential duplication of work between decentralized entities might offset the benefits of data democratization.

These pros and cons have to be balanced when it’s time to develop a company’s data strategy. Are we interested in a centralized, knowledgeable team dedicated to data distribution and analysis, or in a structured and documented data sandbox?

Requirements when implementing data democratization initiatives

Data democratization doesn’t come without risks. Opening the floodgates without proper documentation, preparation, and frameworks would only result in some kind of data dystopia. There would be no consistency in the analyses and interpretations of the different stakeholders, resulting in time-consuming confusion. It would undermine trust in individual insights, and in the data itself. Too much data with too little control paralyzes data-driven systems.

Data democratization needs

Very strong structure, documentation, maintenance, and reliability.
An equally strong data governance policy (which would require an article on its own).
Company-wide awareness of what data is available where, how stakeholders can access the data, and how they should use it.
An effective way of mining the data.
Balancing quantity with quality. The data available must be tailored to business needs.
An individual, a team, or several teams that take ownership over the data distribution and structure. They should be in charge of curating the data, making sure it’s accurate, respects business rules, and is clearly documented.

Data democratization at SSENSE

Let’s apply these concepts to real life examples; the following section describes the current data democratization initiatives at SSENSE.

About a year ago, this is how data distribution functioned within the company:

The Data Operations team was the main provider of data, receiving requests by emails, querying the info, and sending it back to the requester.
Data Operations was also in charge of creating Tableau data sources for all departments, based on specific requirements made by stakeholders.
The data was mostly split into 3 distinct databases: WMS (warehouse operations), BigQuery (website information), and HQ (mostly everything else: sales, returns, purchase orders, product information, etc).
Getting data by other means than by passing through Data Operations was discouraged. Stakeholders who queried their own data — poetically nicknamed “data cowboys” — sometimes caused confusion and disruption due to not being aware of the business rules.
Every department had their own vocabulary when it came to naming metrics.

In the context of SSENSE growing bigger, with departments needing more complex data in larger quantities, having Data Operations being a data bottleneck wasn’t an effective strategy. SSENSE therefore undertook some initiatives to rectify this situation, as listed below:

Currently ongoing initiatives

The Book of Definitions (BOD): our first draft of a data dictionary, where we document every metric, giving it a single name and definition. The BOD is available to everyone in the company, and our aim is for it to become the single reference for standardized nomenclature for metrics.
The Data Lake (DL): there are currently numerous peel-off initiatives that are progressively breaking down the legacy database into smaller, more specialized, and efficient independent units. To make all of this data easily accessible in one location, the Data Engineering team has built, and currently maintains and improves the DL, a repository for all company data. The goal is to funnel all raw data into it and transform it into business data, which is properly governed and readily available for analysis.
Revamping of Tableau: Tableau is a powerful visualization tool, currently under-utilized in the company. We are hoping to not only increase and normalize its use among all departments, but to change the way it is used. Instead of creating data sources based on specific requests, which causes duplication of work, and dispersion of data, we want to create large and exhaustive data sources accessible to all, which would encompass the vast majority of data needs.
Distribution of data through shared documents: to facilitate collaboration, and reduce the number of independent copies of the same report going around, we are looking to start using shared documents (such as google sheets) to distribute data. This would allow everyone to get their data from the same source, and potentially increase collaboration through shared analysis.
Self-service Business Intelligence (BI): the Holy Grail of data democratization. The goal of self-service BI is to standardize a software that is easy to understand and use for all individuals within the company, such that any stakeholder would be able to autonomously retrieve data necessary for their work, without going through a service team like Data Operations. This service would be backed by systems and procedures which would make sure the data is accurate and well understood by all. At the moment, the self-service BI initiative at SSENSE is in its nascency so stay tuned for more updates.

Moreover, our ongoing initiatives are aimed at propagating a culture shift towards treating analytics data as a separate entity, to be accessed from the data lake with properly defined BI tools and standards.

In conclusion, SSENSE will benefit greatly from these data democratization initiatives. As an ever-growing data-driven company, our data needs to be accurate, complete, and easily available if we are looking to develop a solid and consistent competitive advantage. This will also give various domain experts within our company the chance to steer our strategy in new and unexplored directions.

Reference material

Editorial reviews by Deanna Chow, Liela Touré & Prateek Sanyal.

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