A growing number of companies are investing in the maturity of their data to put them in a prime position to make wiser and broader use of their data.

In doing so, many stumble upon “the data divide” – a metaphorical void of untapped potential, impacting companies that have mastered data collection but cannot yet use that data to underpin all decision-making.

A recent survey by Calligo and Fivetran found that more than half of SMEs and enterprises in Europe and North America are now stuck in this data chasm. The benefits of overcoming this stage are significant: a third of respondents who are already on the other side report a 33% increase in productivity, a 68% increase in profitability and 21% employee retention rates.

Yet the data divide doesn’t have to be a long stopping point on organizations’ data maturity journeys. To explain what can be done to get out of the gap, we must first look at how companies can spot when they are in it.

How to spot the data gap?

Data is at the heart of every business decision, whether it’s highlighting areas that need improvement or showing which products are most popular among customers. It therefore stands to reason that when data is not used to its full potential, the impact will affect all aspects of the business, such as speed to market, customer satisfaction, employee retention, safety and profitability.

The telltale signs that an organization is in the data divide also permeate all departments and aspects of business operations, and include:

1. No one “owns” data governance, data management is not aligned with business strategy

2. The company has data ethics and privacy initiatives in place, but no standardized framework and lacks a “privacy by design” culture

3. IT architecture is centralized but planning remains per project or reactive

4. Collaboration tools exist but users still do not have a unified view of information

5. Data analysts can’t yet take advantage of automation, AI and ML

It’s important for business and technology leaders to have meaningful conversations about these areas and use the tools at their disposal to identify inefficiencies in their operations so that they can apply the necessary changes to mitigate them.

How to get out of the data divide

It’s clear that even though most companies have invested in technologies and processes to turn data into a competitive differentiator, they still don’t get enough bang for their buck. The good news is that they are at the last mile, where even small process improvements can catalyze data-driven innovation and increased profitability.

In order to become completely data-driven, they need to focus more on the following key areas:

Data governance

Having data stewards in place who can ensure that data is structured, properly stored, processed and protected – so that everyone in the business can trust the data to make decisions – is the first step to companies that want to increase the maturity of their data. More mature, data-driven organizations have formalized and automated data management processes that are fully aligned to support overall business performance.

Data Ethics

Next, it’s crucial that companies have a detailed understanding of each data source and type, workflow, and purpose. Companies wishing to make progress in this regard must systematically, above all, consider ethics and data privacy in relation to all their data processing initiatives.

IT security

The larger the data sources, the more organizations can be exposed to security threats such as phishing attacks, data leaks, and zero-day vulnerability exploits. Indeed, many companies found working remotely challenging as employees had access to swaths of data at home and on personal devices where it was harder to stay safe. Establishing security committees to ensure the proper processes are implemented and continually reviewed will help organizations stay alert and secure in the face of new threats.

IT architecture

Resilient companies place IT architecture at the heart of their organizational strategy to better plan their objectives. Planning and allocating finance on a project-by-project basis will slow business growth and limit its ability to adapt to long-term change. Proactive planning and provision of technology resources that support ambitious use of data – securely and efficiently – will enable businesses to grow consistently.

Data Information

A common mistake that data-hungry companies make is to hire data analysts to leverage AI and machine learning, only to find that the underlying data processes make it impossible to build effective prediction engines. . To make data insights available to the entire workforce, companies must democratize access to it by breaking down information silos and removing the burden of manual pipeline maintenance. of data that is currently costing businesses more time.$500,000 per year. Automating the data integration process will allow data professionals to focus on value-added tasks and maximize the potential of AI and ML.

And after?

Businesses often see themselves as digitally mature due to their technology investments, but money alone won’t get them out of the data divide – it’s a matter of careful data architecture, privacy and security processes robust and with adequate accessibility in all corners of the organization . Adopting a constant and secure strategy, which invites employees from all departments to maximize the potential of data, has never been more essential – or easier! Now is the time to be proactive and take the first crucial steps towards becoming data-driven.


About the Author/s

Alex James is Vice President of Global Customer Support at Cintran. As a world leader, its
achievements span over 15 years in strategy and execution, customer support and success
management, product development, solution deployment and advanced technical sales.

Tessa Jones is Vice President of Data Science Research and Development at Calligo. She almost uses
20 years of industry experience to apply the scientific method to an exciting range and
difficult problems. She has a wealth of experience in developing useful data science models that are
significant for professional users.

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