of business leaders believe data insights are key to the success of their
business in a digital environment. However, many companies struggle to build a
data-driven culture, with a key reason being the lack of a sound data democratization
But what's data democratization?
Democratizing data in an organization is much more than just giving employees
to data. It's a process that enables everyone in an organization, regardless of their
technical skills, to be comfortable working with data, feel confident talking about it, and
make data-driven decisions that can positively impact various aspects of the business.
It's an ongoing process because it goes hand in hand with data literacy, which is also a
Until recently, data was only used by a few technical specialists in
would set up and operate the systems needed to make data available and accessible to
the rest of the team. But as the data democratization trend grows, new tools are being
developed for non-technical professionals to provide organization-wide access to data
Of the data software available on the market, all-in-one tools that offer a
range of solutions through a single platform are the most popular options for many
organizations looking to extract more value from their data.
There are two main challenges that all-in-one data tools are designed to
namely the gap between data and commercial teams, and the expensive and
time-consuming nature of the process of data maturity—a measurement of how capable
an organization is at making the most of its data.
Data and commercial teams are often siloed functions within a company, and
uncommon for them to fail to communicate with each other and even get in each other's
way. Different training backgrounds, different goals, or lack of context are just some of
This inevitably leads to inefficiencies as each department takes a different
direction on a
given project. In fact, 97% of employees and executives believe lack of communication
and alignment within an organization impacts the outcome of a task or project.
Better collaboration and cross-functional coordination between different
therefore a key success factor, especially in today's world of remote work where a
company's growth and survival depends more than ever on effective teamwork.
Data maturity can be a costly and lengthy process for many organizations. A
survey reveals that 87.5% of companies have low levels of data and analytics maturity.
This means that individual business units are pursuing their own data and analytics
initiatives as stand-alone projects, and there's no common structure that would enable
the enterprise to efficiently leverage advanced analytics.
Gartner, low maturity can be the result of a lack of vision and skills, a lack
of experience in strategic planning and execution, limited budgets, and primitive or
outdated infrastructure. As a result, organizations suffer from incomplete or inconsistent
data sets, poor data quality, duplicate data processes, data security issues, and poor
coordination across the enterprise.
For data democratization to reach its full potential, organizations must first
maturity, but they may not have enough time and financial, technological, and human
resources to do so. It can also be difficult to bring on board skilled data experts—who
are in high demand today—or to get buy-in from leadership and senior management to
advance data initiatives.
This is where all-in-one data tools can shine. They eliminate the need to
multiple external tools or in-house solutions, consolidate and centralize all data, and
save teams the time and effort of searching multiple data sources.
Let's now take a closer look at how commercial teams, and companies in
access and benefit from all-in-one data tools.
1. Improved marketing performance
According to a study,
of marketers say that data silos are their biggest challenge
when it comes to gaining insights from data.
A single data platform allows companies to create a journey map for customers
shows how they interact with the brand over time. This journey map helps marketers
identify key touchpoints that can be improved or better leveraged. By being able to
segment users by their behavior, location, or device, companies can better target them.
2. Improved customer experience
A Forrester report shows that 72% of companies find it challenging to manage
customer relationship management (CRM) tools across geographies and technology
An all-in-one data platform enables the collection of customer feedback from
channels, providing a holistic view of customer sentiment and satisfaction. This
feedback helps commercial teams identify what customers want and find ways to
improve the overall experience for their customers, while data teams get to reclaim their
time and energy to in-depth, impactful work.
This is especially important because
of people are willing to pay at least 5% more
if they know they'll have a good customer experience, and 56% say that the quality of
customer service has a greater impact on how positively they view a brand than any
3. Better customer lifetime value and retention
The ability to identify key metrics in real time using a single data platform
decision making and helps increase customer loyalty and engagement. This also
improves customer retention as companies can tailor their products or services to meet
4. Reduced costs and time allocated to data operations
As all-in-one data tools help companies identify their customers' needs in
real time, they
can help reduce costs, while increasing profits, and time spent on data operations.
That's because they can become data-driven without a full team of specialists, and if
and when they do hire a data expert or team, they’ll be well equipped to dive right into
the work. Additionally, companies can identify when the cost of customer acquisition
outweighs the benefits, allowing for rapid growth or discontinuation of programs that
5. Higher efficiency and revenue
When a company has access to information about customer needs through a
data platform, it can streamline its operations and achieve greater efficiency by
delivering products or services in the shortest possible time. This, in turn, enables faster
All-in-one data solutions can facilitate the seismic shift to a data-driven
can help organizations launch their data operations in a matter of hours instead of
months or even years. They enable businesses to become data-driven without a full
team of specialists, and even if they do hire a data expert or team, they're well-equipped
to get started right away.
Meanwhile, teams in each department can focus on important tasks instead of
through and pulling data from multiple data sources. Easy access to data-driven insights
would lead to greater efficiency, productivity, and profits as well as higher data security.
Customers will also have a much better experience. So it's a win-win situation.
In today's borderless digital world where data is the lingua franca, data
is no longer a nice-to-have, but a necessity for organizations to achieve their goals and
gain a competitive advantage.
To succeed, executives should invest in all-in-one data platforms and take a
and holistic approach to their efforts toward data democratization, which has triggered a
seismic shift in the business world.
You’ve likely heard about ELT — Extract Load and Transform… the Modern Data Stack’s evolution on ETL. This is a game changer by nature in that it enables organizations to ingest raw data into the data warehouse and transform it later. ELT gives end-users access to the entirety of the datasets they need by circumventing downstream issues of missing data that could prevent a specific business question from being answered.
Just like data mesh or the metrics layer, active metadata is the latest hot topic in the data world. As with every other new concept that gains popularity in the data stack, there’s been a sudden explosion of vendors rebranding to “active metadata”, ads following you everywhere and… confusion.
As the amount of data rapidly increases, so does the importance of data wrangling and data cleansing. Both processes play a key role in ensuring raw data can be used for operations, analytics, insights, and inform business decisions.
Do you know the current status — quality, reliability, and uptime — of your data and data systems? Not last month or last week, but where they stand at this moment. As businesses grow, being able to confidently answer this question becomes more important. That’s because data needs to be clean, accurate, and up-to-date to be considered reliable for analysis and decision-making. This confidence comes through what’s known as data observability.
In the past years, organizations have been investing heavily to convert themselves into data-driven organizations with the objective to personalize customer experiences, optimize business processes, drive strategic business decisions, etc. As a result, modern data environments are constantly evolving and becoming more and more complex. In general, more data means more business insights that can lead to better decision-making. However, more data also means more complex data infrastructure, which can cause decreased data quality, a higher chance of data breaking, and consequently erosion of data trust within organizations and risk of not being compliant with regulations. The data observability category — which has quickly been developing during the past couple of years — aims to solve these challenges by enabling organizations to trust their data at all times. Although the category is relatively young, there are already a wide variety of players with different offerings and applying various technologies to solve data quality problems.
Data governance is more than just having a strategy – it is about establishing a culture where quality data is achieved, maintained, valued, and used to drive the business. Modern-day businesses are supported by data and information in many ways and forms. In recent years, data has become the foundation for competition, productivity, growth, and innovation. We are seeing successful organizations shift their focus from producing data to consuming it, and data governance strategies becoming increasingly important to support their crucial business initiatives. Executives and shareholders are starting to realize that data is a strategic asset and data governance is a must if they want to get value from data.
I started my career as a first-generation analyst focusing on writing SQL scripts, learning R, and publishing dashboards. As things progressed, I graduated into Data Science and Data Engineering where my focus shifted to managing the life-cycle of ML models and data pipelines. 2022 is my 16th year in the data industry and I am still learning new ways to be productive and impactful. Today, I am now the head of a data science & data engineering function in one of the unicorns and I would like to share my findings and where I am heading next.